In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors from a significant (...) number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use. (shrink)

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron (...) neural network, decision tree, random forest, and K-nearest neighbors. The principal component analysis and logistic regression methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F-measure, accuracy, and area under the curve were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC-ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC-ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications. (shrink)

To better reflect the precise clustering results of the data samples with different shapes and densities for affinity propagation clustering algorithm, an improved integrated clustering learning strategy based on three-stage affinity propagation algorithm with density peak optimization theory was proposed in this paper. DPKT-AP combined the ideology of integrated clustering with the AP algorithm, by introducing the density peak theory and k-means algorithm to carry on the three-stage clustering process. In the first stage, the clustering center point (...) was selected by density peak clustering. Because the clustering center was surrounded by the nearest neighbor point with lower local density and had a relatively large distance from other points with higher density, it could help the k-means algorithm in the second stage avoiding the local optimal situation. In the second stage, the k-means algorithm was used to cluster the data samples to form several relatively small spherical subgroups, and each of subgroups had a local density maximum point, which is called the center point of the subgroup. In the third stage, DPKT-AP used the AP algorithm to merge and cluster the spherical subgroups. Experiments on UCI data sets and synthetic data sets showed that DPKT-AP improved the clustering performance and accuracy for the algorithm. (shrink)

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography -based brain-computer interface research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature (...) extraction, we employed discrete wavelet transform and power spectral density, which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor, support vector machine, and random forest. We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset. (shrink)

With the development of communication and computer technology, the application of big data technology has become increasingly widespread. Reasonable, effective, and fast retrieval methods for querying information from massive data have become an important content of current research. This article provides an image retrieval method based on the weighted nearest neighbor label prediction for the problem of automatic image annotation and keyword image retrieval. In order to improve the performance of the test method, scientific experimental verification (...) was implemented. The nearest neighbor weights are determined by maximizing the training image annotation, and experiments are carried out from multiple angles based on the Mahalanobis metric learning integration model. The experimental results show that the proposed tag correlation prediction propagation model has obvious improvements in accuracy, recall rate, break-even point, and overall average accuracy performance compared with other widely used algorithm models. (shrink)

Depression has become one of the most common mental illnesses, and the widespread use of social media provides new ideas for detecting various mental illnesses. The purpose of this study is to use machine learning technology to detect users of depressive patients based on user-shared content and posting behaviors in social media. At present, the existing research mostly uses a single detection method, and the unbalanced class distribution often leads to a low recognition rate. In addition, a large number of (...) irrelevant or redundant features in high-dimensional data sets interfere with the accuracy of recognition. To solve this problem, this paper proposes a hybrid feature selection and stacking ensemble strategy for depression user detection. First, recursive elimination method and extremely randomized trees method are used to calculate feature importance and mutual information value, calculate feature weight vector, and select the optimal feature subset according to the feature weight. Second, naive bayes, k-nearest neighbor, regularized logistic regression and support vector machine are used as base learners, and a simple logistic regression algorithm is used as a combination strategy to build a stacking model. Experimental results show that compared with other machine learning algorithms, the proposed hybrid method, which integrates feature selection and ensemble, has a higher accuracy of 90.27% in identifying online patients. We believe this study will help develop new methods to identify depressed people in social networks, providing guidance for future research. (shrink)

Some breeding technology applications are claimed to improve animal welfare: this includes potential applications of genomics and genome editing to improve animals’ resistance to environmental stress, to genetically alter features which in current practice are changed invasively, or to reduce animals’ capacity for suffering. Such applications challenge how breeding technologies are evaluated, which paradigmatically proceeds from a welfare perspective. Whether animal welfare will indeed improve may be unanswerable until proposed applications have been developed and tested sufficiently and until agreement is (...) reached on how to conceptualize animal welfare. Moreover, even if breeding technologies do improve animal welfare, they might be objected to on other ethical grounds. Ethical perspectives on earlier animal biotechnologies are relevant for today’s breeding technologies and their proposed applications, but may need reinterpretation. The current paper applies the concept of telos, which previously figured mainly in debates on classical genetic engineering, to genomic selection and genome editing aimed at improving animal welfare. It critiques current accounts of telos and offers an alternative conceptualization that applies to recently proposed applications of breeding technologies. This account rejects both removing the desire to pursue characteristic activities and altering animal bodies in ways that compromise their ability to perform such activities, but conditionally allows increasing robustness against environmental stress. Our account of telos enriches ethical debate on these breeding technology applications by insisting on the connection between the good life, an animal’s constitution, and its activities, thus countering reductive conceptions of welfare. (shrink)

Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features and leads to negative transfer. According the mechanism of the human brain focusing on effective features while ignoring redundant features in recognition tasks, a brain-like classification method based on adaptive feature matching dual-source domain heterogeneous transfer learning is proposed for the preoperative (...) aided diagnosis of lung granuloma and lung adenocarcinoma for patients with solitary pulmonary solid nodule in the case of small samples. The method includes two parts: (1) feature extraction and (2) feature classification. In the feature extraction part, first, By simulating the feature selection mechanism of the human brain in the process of drawing inferences about other cases from one instance, an adaptive selected-based dual-source domain feature matching network is proposed to determine the matching weight of each pair of feature maps and each pair of convolution layers between the two source networks and the target network, respectively. These two weights can, respectively, adaptive select the features in the source network that are conducive to the learning of the target task, and the destination of feature transfer to improve the robustness of the target network. Meanwhile, a target network based on diverse branch block is proposed, which made the target network have different receptive fields and complex paths to further improve the feature expression ability of the target network. Second, the convolution kernel of the target network is used as the feature extractor to extract features. In the feature classification part, an ensemble classifier based on sparse Bayesian extreme learning machine is proposed that can automatically decide how to combine the output of base classifiers to improve the classification performance. Finally, the experimental results (the AUCs were 0.9542 and 0.9356, respectively) on the data of two center data show that this method can provide a better diagnostic reference for doctors. (shrink)

The key to action control is one’s ability to adequately predict the consequences of one’s actions. Predictive processing theories assume that forward models enable rapid “preplay” to assess the match between predicted and intended action effects. Here we propose the novel hypothesis that “reading” another’s action intentions requires a rich forward model of that agent’s action. Such a forward model can be obtained and enriched through learning by either practice or simulation. Based on this notion, we ran a series of (...) studies on soccer goalkeepers and novices, who predicted the intended direction of penalties being kicked at them in a computerized penalty-reading task. In line with hypotheses, extensive practice in penalty kicking improved performance in penalty reading among goalkeepers who had extensive prior experience in penalty blocking but not in penalty kicking. A robust benefit in penalty reading did not result from practice in kinesthetic motor imagery of penalty kicking in novice participants. To test whether goalkeepers actually use such penalty-kicking imagery in penalty reading, we trained a machine-learning classifier on multivariate fMRI activity patterns to distinguish motor-imagery-related from attention-related strategies during a penalty-imagery training task. We then applied that classifier to fMRI data related to a separate penalty-reading task and showed that 2/3 of all correctly read penalty kicks were classified as engaging the motor-imagery circuit rather than merely the attention circuit. This study provides initial evidence that, in order to read our opponent’s action intention, it helps to observe their action kinematics, and use our own forward model to predict the sensory consequences of “our” penalty kick if we were to produce these action kinematics ourselves. In sum, it takes practice as a penalty kicker to become a penalty killer. (shrink)

The modern world is multicultural and multilingual, it creates difficulties for the mutual contacts of the nations with different languages. The problem of overcoming the language barrier in a multilingual world is urgent. One of the best ways to solve this problem is bilingualism: possession of two languages, a native and a second one, generally intermediate language. The choice of the intermediate language is determined by socio-political and socio-economic conditions of contacting people. In a multinational state official language of (...) the country serves as an intermediate language. In the multi-ethnic Russian Federation, Russian language chosen as the intermediate language, by which representatives of different nationalities come into contact with each other. National-Russian bilingualism in the Russian Federation has become a mass phenomenon: all non-Russian population in varying degrees is fluent in Russian language. Further improvement of bilingualism at the present stage is solved in the new format - as an active bilingualism. In an active and harmonious bilingualism, languages distribute responsibilities among themselves: native language and intermediate language perform different public functions and the number of functions is usually not equal, one language performs more functions than the other does. It depends on the status of the language: the higher the status of the language, the more reason a speaker has for its extensive use in different areas of communication. Harmonious bilingualism helps to keep the national identity of each language and makes it possible to operate each of them on an equal footing in a multicultural space. Among the many politically and legally equal languages of the Russian Federation, a special place belongs to the Russian language: it performs a consolidating function, combines the languages of the nations of Great Russia into one united family. Multilingual Russia can be compared with a starry firmament. The more bright stars we can see, the more beautiful the starry sky is. The star of the Russian language in the sky shines especially bright. (shrink)

This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system using the proposed integrated features vector with Recurrent Neural Network (RNN) based Language Modeling (LM). The proposed system also implements the speaker adaptation using Maximum-Likelihood Linear Regression (MLLR) and Constrained Maximum likelihood Linear Regression (C-MLLR). This system is discriminatively trained by Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) techniques with 256 Gaussian mixture per Hidden Markov Model(HMM) state. The training of the baseline system has been (...) done using a phonetically rich Hindi dataset. The results show that discriminative training enhances the baseline system performance by up to 3%. Further improvement of ~7% has been recorded by applying RNN LM. The proposed Hindi ASR system shows significant performance improvement over other current state-of-the-art techniques. (shrink)

In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor. This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve (...) the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model. (shrink)

Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, choosing the goal or objective function that will maximize performance and a feature-based analysis of the current game board to determine where to place the currently falling zoid so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning models to determine whether different goals (...) result in different feature weights. Two of these optimization strategies quickly rise to performance plateaus, whereas two others continue toward higher but more jagged heights. In Study 2, we compare the zoid placement decisions made by our best CERL models with those made by 67 human players. Across 370,131 human game episodes, two CERL models picked the same zoid placements as our lowest scoring human for 43% of the placements and as our three best scoring experts for 65% of the placements. Our findings suggest that people focus on maximizing points, not number of lines cleared or number of levels reached. They also show that goal choice influences the choice of zoid placements for CERLs and suggest that the same is true of humans. Tetris has a repetitive task structure that makes Tetris more tractable and more like a traditional experimental psychology paradigm than many more complex games or tasks. Hence, although complex, Tetris is not overwhelmingly complex and presents a right-sized challenge to cognitive theories, especially those of integrated cognitive systems. (shrink)

Macphail’s (1985) null hypothesis challenged researchers to demonstrate any differences in intelligence between vertebrate species. Rather than focus on differences, we asked whether rats would show the same unexpected, counterintuitive features of skill learning observed in humans: Factors that degrade performance during acquisition often enhance performance in a subsequent retention/autonomy phase. Providing post-trial “knowledge of results” (KR) on 30%-67% of trials instead of 100% degrades accuracy, yet increases retention in a subsequent phase without KR. We tested this (...) class='Hi'>feature by providing three groups of rats with KR on every trial (100% KR), 67% KR, or 0% KR. We also provided operant feedback in every trial for completing the left-right lever-press skill (food for correct sequences, timeout for all others). In the autonomy phase, we assessed their ability to complete the skill independently—in the absence of differential cues and KR feedback. In agreement with human performance in the autonomy phase, 67% KR yielded higher skill accuracy than providing 100% KR. Also, providing 67% KR improved skill accuracy above that observed with operant feedback alone (0% KR). Rather than degrading performance during acquisition, the 67% KR condition yielded unexpected higher accuracy than the other conditions. Accuracy increased systematically across our extended acquisition phase, which provided each rat with over 3600 trials compared to 20-30 trials for human studies. Providing limited KR promoted skill learning in rats as it does in humans, consistent with the conjecture that both species share common learning processes. Introducing difficulties to rats during training improved their autonomy. (shrink)

Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, choosing the goal or objective function that will maximize performance and a feature-based analysis of the current game board to determine where to place the currently falling zoid so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning models to determine whether different goals (...) result in different feature weights. Two of these optimization strategies quickly rise to performance plateaus, whereas two others continue toward higher but more jagged heights. In Study 2, we compare the zoid placement decisions made by our best CERL models with those made by 67 human players. Across 370,131 human game episodes, two CERL models picked the same zoid placements as our lowest scoring human for 43% of the placements and as our three best scoring experts for 65% of the placements. Our findings suggest that people focus on maximizing points, not number of lines cleared or number of levels reached. They also show that goal choice influences the choice of zoid placements for CERLs and suggest that the same is true of humans. Tetris has a repetitive task structure that makes Tetris more tractable and more like a traditional experimental psychology paradigm than many more complex games or tasks. Hence, although complex, Tetris is not overwhelmingly complex and presents a right-sized challenge to cognitive theories, especially those of integrated cognitive systems. (shrink)

Machine learning, deep learning and neural networks are extensively applied for the development of many fields. Though their technologies are improved greatly, they are often said to be opaque in terms of explainability. Their explainable neural functions will be essential to realization in the networks. In this paper, it is shown that the bio-inspired networks are useful for the explanation of tracking and classification of features. First, the asymmetric network with nonlinear functions is created based on the bio-inspired retinal (...) network. They have orthogonal properties useful for the tracking of features compared with the conventional symmetric networks, which is also proposed on the biological functions. Next, the analysis for the independence of the subspaces between the Fourier bases and the asymmetric network bases is performed. It was that the asymmetric networks have better performances in the classification compared with the symmetric ones. Further, the layered asymmetric networks generate the higher dimensional orthogonal bases that improve the classification accuracies by the replacements of bases. Finally, we classified Reuters collections data applying the explainable processing steps, which consist of the linear discriminations and the sparse coding with nearest neighbor relation for classification. (shrink)

There is much effort nowadays to protect communication networks against different cybersecurity attacks (which are more and more sophisticated) that look for systems’ vulnerabilities they could exploit for malicious purposes. Network Intrusion Detection Systems (NIDSs) are popular tools to detect and classify such attacks, most of them based on ML models. However, ML-based NIDSs cannot be trained by feeding them with network traffic data as it is. Thus, a Feature Engineering (FE) process plays a crucial role transforming network (...) traffic raw data onto derived one suitable for ML models. In this work, we study the effects of applying one such FE technique in different ways on the performance of two ML models (linear and non-linear) and their selected features. This the Feature as a Counter approach. The derived observations are computed from either with the same number of raw samples, (batch-based approaches) or by aggregating them by time intervals (timestamp-based approach). Results show that there is no significant differences between the proposed approaches neither in the performance of the models nor in the selected features that validate our proposal making it feasible to be widely used as a standard FE method. (shrink)

Parkinson’s Disease can be treated with the use of microelectrode recording and stimulation. This paper presents a data stream classifier that analyses raw data from micro-electrodes and decides whether the measurements were taken from the subthalamic nucleus (STN) or not. The novelty of the proposed approach is based on the fact that distances based on raw data are used. Two distances are investigated in this paper, i.e. Normalized Compression Distance (NCD) and Lempel-Ziv Jaccard Distance (LZJD). No new features (...) needed to be extracted due to the fact that in the case of high-dimensional data the process is extremely time-consuming. The k-nearest neighbour (k-NN) was chosen as the classifier due to its simplicity, which is essential in data stream classification. Results obtained from classifiers based on k-NN: k-NN, k-NN were compared with Probabilistic Approximate Window (k-NN with PAW); k-NN with Probabilistic Approximate Window and Adaptive Windowing (k-NN with PAW and ADWIN); and Self Adjusting Memory k-NN (SAM k-NN), which use the proposed distances, with the performance of the same classifiers but using standard Euclidean distance. Prequential accuracy was chosen as the performance measure. The results of the experiments performed with the described approach are in most cases better, i.e. the performance measures for kNN classifiers that use NCD and LZJD distances are better by up to 8.5 per cent and 14 per cent, respectively. Moreover, the proposed approach performs better when compared with other stream classification algorithms, i.e. Hoeffding Tree, Naive Bayes, and Leveraging Bagging. In the discussed case, an improvement of classification rate of up to 17.9 per cent when using Lempel-Ziv Jaccard Distance instead of the Euclidean was noted. (shrink)

The electric activities of cortical pyramidal neurons are supported by structurally stable, morphologically complex axo-dendritic trees. Anatomical differences between axons and dendrites in regard to their length or caliber reflect the underlying functional specializations, for input or output of neural information, respectively. For a proper assessment of the computational capacity of pyramidal neurons, we have analyzed an extensive dataset of three-dimensional digital reconstructions from the NeuroMorphoOrg database, and quantified basic dendritic or axonal morphometric measures in different regions and layers of (...) the mouse, rat or human cerebral cortex. Physical estimates of the total number and type of ions involved in neuronal electric spiking based on the obtained morphometric data, combined with energetics of neurotransmitter release and signaling fueled by glucose consumed by the active brain, support highly efficient cerebral computation performed at the thermodynamically allowed Landauer limit for implementation of irreversible logical operations. Individual proton tunneling events in voltage-sensing S4 protein alpha-helices of Na+, K+ or Ca2+ ion channels are ideally suited to serve as single Landauer elementary logical operations that are then amplified by selective ionic currents traversing the open channel pores. This miniaturization of computational gating allows the execution of over 1.2 zetta logical operations per second in the human cerebral cortex without combusting the brain by the released heat. (shrink)

Visuomotor adaptation to novel environments can occur via non-physical means, such as observation. Observation does not appear to activate the same implicit learning processes as physical practice, rather it appears to be more strategic in nature. However, there is evidence that interspersing observational practice with physical practice can benefit performance and memory consolidation either through the combined benefits of separate processes or through a change in processes activated during observation trials. To test these ideas, we asked people to (...) practice aiming to targets with visually rotated cursor feedback or engage in a combined practice schedule comprising physical practice and observation of projected videos showing successful aiming. Ninety-three participants were randomly assigned to one of five groups: massed physical practice, distributed physical practice, or one of 3 types of combined practice: alternating blocks, or all observation before or after blocked physical practice. Participants received 100 practice trials. All groups improved in adaptation trials and showed savings across the 24-h retention interval relative to initial practice. There was some forgetting for all groups, but the magnitudes were larger for physical practice groups. The Act and Obs_During groups were most accurate in retention and did not differ, suggesting that observation can serve as a replacement for physical practice if supplied intermittently and offers advantages above just resting. However, after-effects associated with combined practice were smaller than those for physical practice control groups, suggesting that beneficial learning effects as a result of observation were not due to activation of implicit learning processes. Reaction time, variable error, and post-test rotation drawings supported this conclusion that adaptation for observation groups was promoted by explicit/strategic processes. (shrink)

Background: Pulsed dye laser is the gold standard for treatment of port-wine stain birthmarks but multiple treatments are required and complete resolution is often not achieved. Posttreatment vessel recurrence is thought to be a factor that limits efficacy of PDL treatment of PWS. Imiquimod 5% cream is an immunomodulator with antiangiogenic effects. Objective: We sought to determine if application of imiquimod 5% cream after PDL improves treatment outcome. Methods: Healthy individuals with PWS were treated with PDL and (...) then randomized to apply posttreatment placebo or imiquimod 5% cream for 8 weeks. Chromameter measurements for 57 PWS sites were taken at baseline and compared with measurements taken 8 weeks posttreatment. The Δa and ΔE were measured to quantify treatment outcome. Results: Two patients developed minor skin irritation. Other adverse effects were not noted. Average a was 0.43 for PDL + placebo sites and 1.27 for PDL + imiquimod sites indicating a greater reduction in erythema with imiquimod. Average E was 2.59 for PDL + placebo and 4.08 for PDL + imiquimod, again indicating a greater color improvement with imiquimod. Limitations: Effects were evaluated after a single treatment and duration of effect is unknown. Conclusion: Combined selective photothermolysis and antiangiogenic therapy may enhance PWS treatment efficacy. © 2011 by the American Academy of Dermatology, Inc. (shrink)

Robust technology infrastructure is needed to enable learning health care systems to improve quality, access, and cost. Such infrastructure relies on the trust and confidence of individuals to share their health data for healthcare and research. Few studies have addressed consumers’ views on electronic data sharing and fewer still have explored the dual purposes of healthcare and research together. The objective of the study is to explore factors that affect consumers’ willingness to share electronic health information for healthcare and research. (...) This study involved a random-digit dial telephone survey of 800 adult Californians conducted in English and Spanish. Logistic regression was performed using backward selection to test for significant associations of each explanatory variable with the outcome variable. The odds of consent for electronic data sharing for healthcare decreased as Likert scale ratings for EHR impact on privacy worsened, odds ratio = 0.74, 95% CI [0.60, 0.90]; security, OR = 0.80, 95% CI [0.66, 0.98]; and quality, OR = 0.59, 95% CI [0.46–0.75]. The odds of consent for sharing for research was greater for those who think EHR will improve research quality, OR = 11.26, 95% CI [4.13, 30.73]; those who value research benefit over privacy OR = 2.72, 95% CI [1.55, 4.78]; and those who value control over research benefit OR = 0.49, 95% CI [0.26, 0.94]. Consumers’ choices about electronically sharing health information are affected by their attitudes toward EHRs as well as beliefs about research benefit and individual control. Design of person-centered interventions utilizing electronically collected health information, and policies regarding data sharing should address these values of importance to people. Understanding of these perspectives is critical for leveraging health data to support learning health care systems. (shrink)

Division of labour is a marked feature of multicellular organisms. Margulis proposed that the ancestors of metazoans had only one microtubule organizing center (MTOC), so they could not move and divide simultaneously. Selection for simultaneous movement and cell division had driven the division of labour between cells. However, no evidence or explanation for this assumption was provided. Why could the unicellular ancetors not have multiple MTOCs? The gain and loss of three possible strategies are discussed. It was found (...) that the advantage of one or two MTOC per cell is environment-dependent. Unicellular organisms with only one MTOC per cell are favored only in resource-limited environments without strong predatory pressure. If division of labour occurring in a bicellular organism just makes simultaneous movement and cell division possible, the possibility of its fixation by natural selection is very low because a somatic cell performing the function of an MTOC is obviously wasting resources. Evolutionary biologists should search for other selective forces for division of labour in cells. (shrink)

In this work, the significance of combining the evidence from multitaper mel-frequency cepstral coefficients, linear prediction residual, and linear prediction residual phase features for multilingual speaker identification with the constraint of limited data condition is demonstrated. The LPR is derived from linear prediction analysis, and LPRP is obtained by dividing the LPR using its Hilbert envelope. The sine-weighted cepstrum estimators with six tapers are considered for multitaper MFCC feature extraction. The Gaussian mixture model–universal background model is (...) used for modeling each speaker for different evidence. The evidence is then combined at scoring level to improve the performance. The monolingual, crosslingual, and multilingual speaker identification studies were conducted using 30 randomly selected speakers from the IITG multivariability speaker recognition database. The experimental results show that the combined evidence improves the performance by nearly 8–10% compared with individual evidence. (shrink)

Cognitive control processes, such as updating task-relevant information while switching between multiple tasks, are substantially impaired in older adults. However, it has also been shown that these cognitive control processes can be improved by training interventions, e.g., by training in task switching. Here, we applied an event-related potential approach to identify whether a cognitive training improves task-preparatory processes such as updating of relevant task goals. To do so, we applied a pretest-training-posttest design with eight training sessions. Two groups of (...) older adults were either trained in task switching or in performing single tasks and we compared their performance to a group of untrained younger adults. To foster cue updating in the treatment group, we applied a cue-based switching task in which the two task cues were randomly selected prior to target presentation so that participants had time to prepare for the upcoming task. In contrast, the control group also received task cues but those were redundant as only one task had to be performed. We also examined whether training in cue updating during task switching can be transferred to a similar cognitive control task measuring updating of context information, namely a modified version of the AX-Continuous Performance Task. The results revealed training-specific improvements in task switching, that is, a larger improvement in blocks requiring switching in comparison to single tasks at the behavioral level. In addition, training specific-effects were also found at the neuronal level. Older adults trained in cue updating while switching showed a reduction in mixing costs in the cue-related P3, indicating an improvement in preparatory updating processes. Additionally, P3 topography changed with training from a very broad to a parietally focused scalp distribution similar to the one found in younger adults. However, we did not obtain training-specific improvements in context updating in the AX-CPT neither at the behavioral level nor at the neuronal level. Results are discussed in the context of the ongoing debate on whether transfer of cognitive training improvements is possible. (shrink)

Aiming to realize rapid and efficient three-dimensional identification of substation equipment, this article proposes a new method in which the 3D identification of substation equipment is based on K-nearest neighbor classification of subspace feature vector. First of all, the article uses octree encoding to reduce and denoise the point cloud data obtained by a 3D laser scanner. Secondly, position calibration and size standardization are used for the point cloud after pretreatment. Then, the normalized point cloud is divided (...) into a number of cubes with same size. The cosine of the angle between the positive direction of z axis and a vector from the global centroid of the point cloud to the centroid of each subspace is regarded as the feature of the subspace. All cosines of subspaces constitute the feature of the point cloud. Finally, we classify the subspace feature vector by using the KNN algorithm and improve classification accuracy by using the particle swarm optimization algorithm. The simulation results show that the identification accuracy of the proposed method for unknown substation equipment is about 90% and the proposed method is applicable to low-degree losses. Apparently, this method can accurately identify 3D substation equipment. At the same time, increasing the number of subspaces will improve the accuracy; however, it will increase the recognition time. (shrink)

In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhance this situation, this paper proposed a method combining feature selection and ensemble extreme learning machine (EELM) to improve the recognition performance based on sEMG signals. First, the input sEMG signals are preprocessed and 16 features are then (...) extracted from each channel. Next, features that mostly contribute to the gesture recognition are selected from the extracted features using the recursive feature elimination (RFE) algorithm. Then, several independent ELM base classifiers are established using the selected features. Finally, the recognition results are determined by integrating the results obtained by ELM base classifiers using the majority voting method. The Ninapro DB5 dataset containing 52 different hand movements captured from 10 able-bodied subjects was used to evaluate the performance of the proposed method. The results showed that the proposed method could perform the best (overall average accuracy 77.9%) compared with decision tree (DT), ELM, and random forest (RF) methods. (shrink)

Dual innovation, which includes exploratory innovation and exploitative innovation, is crucial for firms to obtain a sustainable competitive advantage. The knowledge base of firms greatly influences or even determines the scope, direction, and path of their dual-innovation activities, which drive their innovation process and produce different innovation performances. This study uses data source patents obtained by 285 focal firms in the Chinese new-energy vehicle industry in the period 2015–2020. Five knowledge-base features are selected by analyzing the correlation and multicollinearity, and (...) four different firm clusters are found by using the k-means clustering algorithm. Based on the classification and regression tree algorithm, we mine the potential decision rules governing the dual-innovation performance of firms. The results show that the exploratory innovation performance of firms in different clusters is mainly affected by two different knowledge-base features. Knowledge-base scale is a key factor affecting the exploitative innovation performance of firms. Firms in different clusters can improve their dual-innovation performance by rationally tuning the combination of knowledge-base features. (shrink)

A formal Ethics Consultation Service (ECS) can provide significant help to patients, families and hospital staff. As with any other form of clinical consultation, documentation of the process and the advice rendered is very important. Upon review of the published consult documentation practices of other ECSs, we judged that none of them were sufficiently detailed or structured to meet the needs and purposes of a clinical ethics consultation. Thus, we decided to share our method in order to advance the (...) practice of ethics consultation. Here, we describe a method of ECS documentation practice, including use of a formal consult report template, as well as a log for maintaining a chronological record of the consultations performed. These two documents facilitate order and organisation of the ECS. They also enable the ECS to keep an account of professional time and experience, enable quick consult trend assessments (by consult theme or ward, for example) and establish a potential registry of consults for future research study. This method of documentation, we believe, not only contributes significantly to the primary purpose of the consultation—namely, the evincing and sharing of ethical opinion about a case—but also enables consultants to improve their practice and to pursue research on clinical ethics consultation. (shrink)

Introduction: Screen-based and mobile technology has grown at an unprecedented rate. However, little is understood about whether increased screen-use affects executive functioning, the range of mental processes that aid goal attainment and facilitate the selection of appropriate behaviors. To examine this, a systematic review was conducted.Method: This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. A comprehensive literature search was conducted using Web of Science, MEDLINE, PsycINFO and Scopus databases (...) to identify articles published between 2007 and March 2020, examining the use of mobile technologies on aspects of EF in healthy adults aged 18–35 years. In total 6079 articles were screened by title, and 39 screened by full text. Eight eligible papers were identified for inclusion. Our methods were pre-registered on the PROSPERO international prospective register of systematic reviews.Results: A total of 438 participants were included across the eight studies. Five of the eight studies examined more than one EF. Five studies measured inhibition, and four studies measured decision-making. Smartphone use was negatively associated with inhibition and decision-making. Working memory performance was found to be improved by increased time engaging in video games and by refraining from smartphone use prior to bedtime. Quality assessments indicated high risk of methodological biases across the studies and a low quality of evidence for determining the relationship between technology use and executive functioning.Conclusions: This review highlights the scarcity of the literature in this area. It presents a call for rigorous and objective research to further our understanding of the impact of mobile technology on different aspects of executive function. (shrink)

The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was (...) employed in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%), G-mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time. (shrink)

The combination of breeding for increased production and the intensification of housing conditions have resulted in increased occurrence of behavioral, physiological, and immunological disorders. These disorders affect health and welfare of production animals negatively. For future livestock systems, it is important to consider how to manage and breed production animals. In this paper, we will focus on selective breeding of laying hens. Selective breeding should not only be defined in terms of production, but should also include traits related to animal (...) health and welfare. For this we like to introduce the concept of robustness. The concept of robustness includes individual traits of an animal that are relevant for health and welfare. Improving robustness by selective breeding will increase (or restore) the ability of animals to interact successfully with the environment and thereby to make them more able to adapt to an appropriate husbandry system. Application of robustness into a breeding goal will result in animals with improved health and welfare without affecting their integrity. Therefore, in order to be ethically acceptable, selective breeding in animal production should accept robustness as a breeding goal. (shrink)

BackgroundRecently there have been growing concerns about problematic mobile phone use by adolescent populations. This study aimed to address this concern through a study of severity and correlates of problematic mobile phone use with a sample of Hong Kong adolescents.MethodsData were collected from a sample of adolescents from three local secondary schools using a measuring scale designated for Chinese adolescents. Participants were allocated into groups of “problematic users” and “non-problematic users” based on the number of occurrence of symptoms due (...) to excessive and maladaptive use of mobile phone and possible functional impairments caused by problematic mobile phone use. A group of “at-risk users” was identified. A sample-based examination on distribution of these three groups of users was conducted via frequency counts and percentage calculation. A series of t-test were performed to make comparisons between “problematic” and “non-problematic” groups on selected personality and health related variable. Risk and protective factors were identified via correlational analysis and logistic regression analysis.ResultsUnder a more stringent cut-off criterion of four or more reported symptoms plus one or more reported functional impairments, 22.9% of the adolescents participating in this study could be classified as problematic mobile phone users. However, a more lenient criterion reported a substantially more severe prevalence rate. A new group of “at-risk” adolescents was identified with such a discrepancy of prevalence rate. Gender difference, some risk and protective factors were also identified for developing this technology-related problem.Discussion and ConclusionsAdolescents who are vulnerable to suffer from this technology-related problem deserve more attention from helping professionals. Results of this study throw some insights on how to identify problematic mobile phone user applying a criterion-referenced approach. This study echoes a recent call for adopting a developmental perspective in understanding this problem and conducting research in this area. Anchored on present findings, effective interventions to tackle this rising problem among adolescents are suggested. (shrink)

What are the criteria for reasonable clinical judgements? The reasonableness of macro-level decision-making has been much discussed, but little attention has been paid to the reasonableness of applying guidelines generated at a macro-level to individual cases. This paper considers a framework for reasonable clinical decision-making that will capture cases where relevant guidelines cannot reasonably be followed. There are three main sections. (1) Individual claims on healthcare from the point of view of concerns about equity are analysed. (2) The demands of (...) responsibility and equity on professional clinical performance are discussed, and how the combination of these demands emerges into seven requirements that constitute the framework is explored. Since this framework is developed to assist in reasonable clinical decision-making, practical implications of all these requirements are also suggested. (3) Challenges concerning the framework are discussed. First, a crucial presumption that the framework relies upon is considered—namely, clinicians’ willingness to justify their decisions as requested. Then how public deliberation may influence clinical decision-making is discussed. Next is a consideration of how clinicians’ need to have confidence in their own judgements in order to perform in a manner worthy of trust would be compatible with adherence to the framework supported by public deliberation. It is concluded that fair distribution in the interplay between macro- and micro-level considerations can be secured by legitimising procedures on each level, by ensuring well-organised and continuing public debate and by basing individual clinical judgements upon well-justified and principled normative bases. (shrink)

BackgroundInstrumented gait analysis post-stroke is becoming increasingly more common in research and clinics. Although overall standardized procedures are proposed, an almost infinite number of potential variables for kinematic analysis is generated and there remains a lack of consensus regarding which are the most important for sufficient evaluation. The current aim was to identify a discriminative core set of kinematic variables for gait post-stroke.MethodsWe applied a three-step process of statistical analysis on commonly used kinematic gait variables comprising the whole body, derived (...) from 3D motion data on 31 persons post-stroke and 41 non-disabled controls. The process of identifying relevant core sets involved: exclusion of variables for which there were no significant group differences; systematic investigation of one, or combinations of either two, three, or four significant variables whereby each core set was evaluated using a leave-one-out cross-validation combined with logistic regression to estimate a misclassification rate.ResultsThe best MR for one single variable was shown for the Duration of single-support or Duration of 2nd double-support phase, corresponding to an 89–90% probability of correctly classifying a person as post-stroke/control. Adding Pelvis sagittal ROM to either of the variables Self-selected gait speed or Stride length, alternatively adding Ankle sagittal ROM to the Duration of single-stance phase, increased the probability of correctly classifying individuals to 93–94%. Combining three variables decreased the MR further to 0.04, suggesting a probability of 96% for correct classification. These core sets contained: a spatial or a temporal variable, Pelvis sagittal ROM or Ankle plantarflexion during push-off, and Arm Posture Score or Cadence or a knee/shoulder joint angle variable. Adding a fourth variable did not further improve the MR.ConclusionA core set combining a few crucial kinematic variables may sufficiently evaluate post-stroke gait and should receive more attention in rehabilitation. Our results may contribute toward a consensus on gait evaluation post-stroke, which could substantially facilitate future diagnosis and monitoring of rehabilitation progress. (shrink)

Instruction in peer assessment of complex task performance may cause high cognitive load, impairing learning. A stepwise instructional strategy aimed at reducing cognitive load was investigated by comparing it with a combined instructional strategy in an experiment with 128 secondary school students (mean age 14.0?years; 45.2% male) with the between-subjects factor instruction (stepwise, combined). In the stepwise condition, study tasks in Phase 1 were domain-specific and study tasks in Phase 2 had both domain-specific and peer assessment (...) components. In the combined condition, these two components were present in all tasks in both phases. Final performance (i.e. speed and accuracy in domain-specific skills and peer assessment skills) showed no significant differences, but performance improved more from Phase 1 to Phase 2 in the stepwise condition than in the combined condition. The results suggest that, with complex study tasks, it might be beneficial to teach domain-specific skills before peer assessment skills. (shrink)

With the increasing proportion of the logistics industry in the economy, the study of the vehicle routing problem has practical significance for economic development. Based on the vehicle routing problem, the customer presence probability data are introduced as an uncertain random parameter, and the VRP model of uncertain customers is established. By optimizing the robust uncertainty model, combined with a data-driven kernel density estimation method, the distribution feature set of historical data samples can then be fitted, and (...) finally, a distributed robust vehicle routing model for uncertain customers is established. The Q-learning algorithm in reinforcement learning is introduced into the high-level selection strategy using the hyper-heuristic algorithm, and a hyper-heuristic algorithm based on the Q-learning algorithm is designed to solve the problem. Compared with the certain method, the distributed robust model can effectively reduce the total cost and the robust conservatism while ensuring customer satisfaction. The improved algorithm also has good performance. (shrink)

Electroencephalography and functional near-infrared spectroscopy have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. (...) The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery and mental arithmetic tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS. (shrink)

The aim of this study is to examine the distribution of news related to nanoscience and nanotechnology published in Turkish newspapers between 2011 and 2018. Nine Turkish newspapers selected using criterion sampling were investigated and the document analysis method was used to analyze them. The electronic archives of the newspapers were used to collect data and the word “nano” was used as a keyword. The obtained data were analyzed with the content analysis technique. While analyzing the news stories, categorization (...) was performed according to four main variables: distribution by years, the newspaper section, the approach in the content of the news stories, and the content of the news stories. As a result, it was seen that there has been an increase in the number of nano-news stories in Turkish newspapers over the years, the news stories are mostly included in the “local” section of the newspapers, and the general approach is positive. In the categories formed according to the content of the news stories, the number of news stories related to the production and commercial applications of nanotechnology was higher compared to the others. In an international comparison, it should also be mentioned that the topic of risk plays a subordinate role in the newspapers examined. (shrink)

Mental health research and care in the twenty first century faces a series of conceptual and ethical challenges arising from unprecedented advances in the neurosciences, combined with radical cultural and organisational change. The Oxford Textbook of Philosophy of Psychiatry is aimed at all those responding to these challenges, from professionals in health and social care, managers, lawyers and policy makers; service users, informal carers and others in the voluntary sector; through to philosophers, neuroscientists and clinical researchers. Organised around a (...) series of case studies in five key topic areas - concepts of disorder, the philosophical history of psychopathology, philosophy of science, ethics and philosophical value theory, and philosophy of mind - the book provides a detailed introduction to the field and a framework for study and skill development. Each case study is supported by selected readings from both philosophy and mental health, thinking skills exercises, self-test questions, key learning points and detailed guides to further reading. There is an introduction for philosophers to classification and descriptive psychopathology, and for practitioners to philosophical methods (including logic). The philosophical topics covered include philosophical methods (analytic and Continental); phenomenology, hermeneutics and existentialism, logical empiricism and its successors; idealism and realism; reasons and causes; and modern theories of mind and brain, free will and personal identity. Topics from mental health include psychiatry and 'anti-psychiatry'; Jaspers' psychopathology and the new neurosciences; the future of psychiatric classifications; strengths-based approaches, recovery practice, social inclusion and diversity; and key topics in psychopathology, such as delusion, autism, disorders of volition, thought insertion and other experiences in schizophrenia. The Oxford Textbook of Philosophy of Psychiatry aims to secure the skills-base of the discipline by bringing philosophers closer to the realities of practice in mental health, and mental health practitioners closer to the resources of philosophy as a partner to the sciences in responding to the challenges of twenty-first century mental health and social care. (shrink)

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit (...) was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated. (shrink)

Alcohol use disorder is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies. Recent machine learning algorithms have raised the attention of researchers as a useful tool in studying and detecting AUD. This work uses AUD and controls samples free of any other substance use to assess (...) the performance of a set of commonly used machine learning classifiers detecting AUD from resting state functional network connectivity derived from independent component analysis. The cohort used included 51 alcohol dependent subjects and 51 control subjects. Despite alcohol, none of the 102 subjects reported use of nicotine, cannabis or any other dependence or habit formation substance. Classification features consisted of whole brain rsFNC estimates undergoing a feature selection process using a random forest approach. Features were then fed to 10 different machine learning classifiers to be evaluated based on their classification performance. A neural network classifier showed the highest performance with an area under the curve of 0.79. Other good performers with similar AUC scores were logistic regression, nearest neighbor, and support vector machine classifiers. The worst results were obtained with Gaussian process and quadratic discriminant analysis. The feature selection outcome pointed to functional connections between visual, sensorimotor, executive control, reward, and salience networks as the most relevant for classification. We conclude that AUD can be identified using machine learning classifiers in the absence of nicotine comorbidity. (shrink)

In his account of scientific revolutions, Thomas Kuhn suggests that after a revolutionary change of theory, it is as if scientists are working in a different world. In this paper, we aim to show that the notion of world change is insightful. We contrast the reporting of the discovery of neon in 1898 with the discovery of hafnium in 1923. The one discovery was made when elements were identified by their atomic weight; the other discovery was made after scientists (...) came to classify elements by their atomic number. By considering two instances of the reporting of the discovery of a new chemical element 25 years apart, we argue that it becomes clear how chemists can be said to have been responding to different worlds as a result of the change in the concept of a chemical element. They saw, did, and reported different things as they conducted their research on the new chemical elements. (shrink)

Visual attention evolved as an adaptive mechanism allowing us to cope with a rapidly changing environment. It enables the facilitated processing of relevant information, often automatically and governed by implicit motives. However, despite recent advances in understanding the relationship between consciousness and visual attention, the functional scope of unconscious attentional control is still under debate. Here, we present a novel masking paradigm in which volunteers were to distinguish between varying orientations of a briefly presented, masked grating stimulus. Combining (...) signal detection theory and subjective measures of awareness, we show that performance on unaware trials was consistent with visual selection being weighted towards repeated orientations of Gabor patches and reallocated in response to a novel unconsciously processed orientation. This was particularly present in trials in which the prior feature was strongly weighted and only if the novel feature was invisible. Thus, our results provide evidence that invisible orientation stimuli can trigger the reallocation of history-guided visual selection weights. (shrink)

This paper reports a study of planning processes in the five-disc Tower of London (TOL) task in 20 younger and 20 older adult participants. A concurrent direct ''think-aloud'' method was used to obtain data on planning processes prior to moving discs in the TOL. A check was made of the effects of verbalising by comparing performance data from the experimental groups with data from control groups who did not verbalise during planning or moving. Verbalising slowed down planning and (...) moving but did not appear to distort the participants' approaches to the task. Older and younger participants did not differ in average moves taken to solve the tasks. However, older participants' planning was less complete and more error-prone than that of younger participants. The planning processes were characterised as showing a means-ends ''goal selection'' strategy. In this strategy participants (1) identify a single active goal disc at the start, (2) select moves and move sequences to enable the placing of the current goal disc in its target position, and (3) continue in this way until all discs are in their target positions. Age differences were found in the planning stage, during which there was no stimulus support and hence a substantial working memory load. During the move phase there was stimulus support and hence little loading of working memory. Age differences in moves required were not found in the move phase. As older participants tend to have depleted working memory capacity the present results suggest that working memory is heavily loaded in TOL planning but less so in the move phase of TOL. (shrink)

Self-monitoring theory explains the individual differences in using interpersonal adjustment techniques like self-control, self-regulation, and self-presentation. Self-monitoring plays a key role for understanding the social life. Therefore, it has been one of most popular research topics in social psychology. The aim of this study is to find out if there is a meaningful relationship between religious orientation and self-monitoring, and to determine the direction of the relationship if it exists. Besides, examining the effect of religious group membership on self-monitoring is (...) another aim of this study. In accordance with this purpose, Religious Orientation Scale and Revised Self-Monitoring Scale are applied to sample group which consists of 437 female and 369 male participants aged16 to 65+ living in Bursa. The results has revealed that there is a meaningful positive correlation between intrinsic religious orientation and self-monitoring. In addition, no meaningful relationship between religious group membership and self-monitoring has been confirmed.Summary: The adaptation process of the human being as a social creature starts in a mother's womb and becomes more complicated in consequence of coming into the world which is full of stimulus. In the beginning, this process has only a physical aspect, but with the inclusion of emotional, cognitive, and social factors over time turns into developmental tasks that people will struggle throughout their lives. The social development involves the process of becoming of the individual as an organic part of the society. As the individual realizes the existence of the other, he/she develops similar patterns of behavior that can be approved by the other. Because the meaningful relationships that form the backbone of social development constitute an important part of the self. Belonging is one of the main needs, that the individual shapes his emotions, actions, and decisions more or less in line with this need in order not to encounter the possibility of social exclusion. While belonging is a universal need for every healthy individual, there are differences between individuals in terms of directing the course of the relationship, role sharing, and using situational clues. Individual differences in how people construct interpersonal processes led Mark Snyder to systematize and theorize the concept of self-monitoring. Self-monitoring is a concept that expresses individuals' control of self-presentation, expressive behavior, and non-verbal emotional expression and their regulation. Accordingly, individuals with a high tendency to shape their behavior according to the context are classified as high self-monitors and those with low tendency are classified as low self-monitors. It can be said that adherence to religion, which is one of the determinants of social behavior, is related to self-monitoring. In this context, the main subject of this study is the relationship between self-monitoring and religiosity. The study aims to examine the effect of intrinsic-extrinsic religiosity on individuals' self-monitoring levels. Besides, as a form of religiosity, to reveal whether religious group religiosity affects self-monitoring, the study examines also self-monitoring levels of individuals belonging to the religious group and those who are not.The sample of the study was selected by a simple random sampling method and it consists of 806 participants aged 16 to 65+ living in Bursa in 2017. 437 (54.2%) of the participants in the research are women and 369 (45.8%) are men. In this study, a relational screening model was used for determining the presence, direction, and degree of the relationship between the variables. The questionnaire technique was preferred as the method of gathering information. The questionnaire was arranged in a way that does not require asking the participant any other questions apart from the questions on the form. The questionnaire form used in this research consists of 43 items. At the beginning of the questionnaire, there is a 9-item personal information form that includes demographic items consisting of age, gender, educational status, marital status, occupation, place of residence, socio-economic status, levels of subjective religious involvement, and religious group membership. For the measurement of self-monitoring, a 12-item "Self-Monitoring Scale", which was developed by Mark Snyder, revised by Richard Lennox and Raymond Wolfe and adapted by İlknur Özalp Türetgen and Sevim Cesur to Turkish (α =.80) was used. For the measurement of religious orientation, a 22-item “Religious Orientation Scale” developed by Gordon Allport and Michael Ross and adapted to Turkish by Fatma Gül Cirhinlioğlu was used (α =.90). Data collected through scales were processed by One Way Variance Analysis (ANOVA), t-Test, Pearson Correlation Analysis, and Simple Linear Regression Analysis and turned into research findings.According to the results of the simple linear correlation analysis conducted to reveal whether there is a relationship between the self-monitoring scores of the participants and the intrinsic religious orientation scores, there is a positive and significant relationship between the variables (r = 0.102, p = 0.004 0.05). The result of the correlation analysis between self-monitoring and the level of subjective religious involvement shows that no significant relationship is found (r = 0.051, p = 0.146> 0.05).This research expected that there was a negative relationship between self-monitoring and intrinsic religious orientation. However, the analyses has not reported anticipated results as addressed in the previous research. Therefore, this study opens the door for asking new questions about the subject. Religious groups, by their very nature, can refer the individual to self-presentation control, obedience, to act as deemed appropriate by the group, to adapt to the group, to change behavior without attitude change, and therefore, to attitude-behavior inconsistency. It can be argued that not only religious groups but, by their nature, all social groups create harmony-oriented pressure on the individual. Therefore, it can be predicted that belonging to a group, in general, may have a significant effect on the level of self-monitoring. The high self-monitoring scores of the participants, whether religious or non-religious group members of this study may have reduced the observability of the difference between the two groups. Therefore, repeating the study on a different sample may produce different results. (shrink)

To describe phenomena that occur at different time scales, computational models of the brain must incorporate different levels of abstraction. At time scales of approximately 1/3 of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level embodiment level,” the constraints of the physical system determine the nature of cognitive operations. The key synergy is that at time scales of about 1/3 of a second, the natural sequentiality of body movements can (...) be matched to the natural computational economies of sequential decision systems through a system of implicit reference called deictic in which pointing movements are used to bind objects in the world to cognitive programs. This target article focuses on how deictic bindings make it possible to perform natural tasks. Deictic computation provides a mechanism for representing the essential features that link external sensory data with internal cognitive programs and motor actions. One of the central features of cognition, working memory, can be related to moment-by-moment dispositions of body features such as eye movements and hand movements. (shrink)

Nowadays, online product reviews have been at the heart of the product assessment process for a company and its customers. They give feedback to a company on improving product quality, planning, and monitoring its business schemes in order to increase sale and gain more profit. They are also helpful for customers to select the right products in less effort and time. Most companies make spam reviews of products in order to increase the products sales and gain more profit. Detecting (...) spam product reviews is a challenging issue in NLP. Numerous machine learning approaches have attempted to detect and classify the product reviews as spam or nonspam. However, in order to improve the classification accuracy, this study has introduced an ensemble machine learning model that combines predictions from multilayer perceptron, K-Nearest Neighbour, and Random Forest and predicts the outcome of the review as spam or real, based on the majority vote of the contributing models. In order to accomplish the task of spam review classification, the proposed ensemble and other benchmark boosting approaches are tested with 25 statistical features extracted from mobile application reviews of Yelp Dataset. Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy. Experimental outcomes illustrate that the proposed ensemble model outperformed the individual classifiers and state-of-the-art boosting approaches like Generalized Boost Regression Model, Extreme Gradient Boost, and AdaBoost Regression Model in terms of classification accuracy. (shrink)

Based on the growing problem of heart diseases, their efficient diagnosis is of great importance to the modern world. Statistical inference is the tool that most physicians use for diagnosis, though in many cases it does not appear powerful enough. Clustering of patient instances allows finding out groups for which statistical models can be built more efficiently. However, the performance of such an approach depends on the features used as clustering attributes. In this paper, the methodology that consists of (...) combining unsupervised feature selection and grouping to improve the performance of statistical analysis is considered. We assume that the set of attributes used in clustering and statistical analysis phases should be different and not correlated. Thus, the method consisting of selecting reversed correlated features as attributes of cluster analysis is considered. The proposed methodology has been verified by experiments done on three real datasets of cardiovascular cases. The obtained effects have been evaluated regarding the number of detected dependencies between parameters. Experiment results showed the advantage of the presented approach compared to other feature selection methods and without using clustering to support statistical inference. (shrink)

BackgroundApproximately 7% of the general population is affected by an orphan disease, which, in the United States, is defined as affecting fewer than 1 in 1500 people. Disease awareness is often low and time-to-diagnosis delayed. Different legislations worldwide have created incentives for pharmaceutical companies to develop drugs for orphan diseases. A journalistic article in Bloomberg Businessweek has claimed that pharmaceutical companies have tried marketing orphan drugs by placing a specific disease into the popular television series “House, M.D.” which features diagnostic (...) journeys and was produced between 2004 and 2012. This study aimed to describe the presentation of orphan diseases in the television series “House, M.D.”, to test in an exploratory fashion the hypothesis that treatable orphan conditions are overrepresented in “House, M.D.” and to discuss whether such marketing practices may or may not be ethical.MethodsA list of all medical cases depicted in the television series “House, M.D.” was obtained and classified as orphan or non-orphan according to the Orphanet database. The ratios of orphan diseases among all diseases, such with an orphan drug designation and such with an orphan drug approval by the FDA were then compared with conservative approximations of real world conditions (chi-squared tests for equality of proportions). STROBE criteria were respected.ResultsOut of a total of n = 181 different medical diagnoses, n = 42 (23.2%) were orphan diseases. The difference in percentages in between “House, M.D.” and reality was not statistically significant for orphan diseases overall (p = 0.96), yet was statistically significantly higher for both orphan diseases with one or more orphan drug designations (p = 0.0192) and such with one or more approved orphan drugs (p < 0.0001).ConclusionsOrphan diseases with a designated and/or approved orphan drug were overrepresented in the television series “House, M.D.” with statistical significance while orphan diseases overall were not. This may be explained by (so far) undocumented efforts of pharmaceutical companies to place their orphan drugs in the television series, as described in the article in Bloomberg Businessweek. Further research is needed into marketing practices in popular and emerging media formats. (shrink)