A superheterodyne receiver is a type of device universally used in a variety of electronics and information systems. Fault detection and diagnosis for superheterodyne receivers are therefore of critical importance, especially in noise environments. A general purpose fault detection and diagnosis scheme based on observers and residual error analysis was proposed in this study. In the scheme, two generalized regression neural networks are utilized for fault detection, with one as an observer and the other as an (...) adaptive threshold generator; faults are detected by comparing the residual error and the threshold. Then, time and frequency domain features are extracted from the residual error for diagnosis. A probabilistic neural network acts as a classifier to realize the fault diagnosis. Finally, to mimic electromagnetic environments with noise interference, simulation model under different fault conditions with noise interferences is established to test the effectiveness and robustness of the proposed fault detection and diagnosis scheme. Results of the simulation experiments proved that the presented method is effective and robust in simulated electromagnetic environments. (shrink)

A new edge detection technique based on the texture feature coding method is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is (...) employed differently to construct an edge detector. In particular, the texture feature number of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such as the Sobel, Prewitt, Canny, and Canny–Deriche edge detectors. The obtained results were evaluated quantitatively with the Figure of Merit criterion. The experimental results demonstrated that our proposed method improved the edge detection performance greatly. We further implemented the proposed edge detector with a hardware system. To this end, a field programmable gate array chip was used. The related simulations were carried out with the MATLAB Simulink tool. Both software and hardware implementations demonstrated the efficiency of the proposed edge detector. (shrink)

Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high-quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from (...) discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets. (shrink)

Since Seligman's 1971 statement that the vast majority of phobias are about objects essential to the survival of a species, a multitude of laboratory studies followed, supporting the finding that humans learn to fear and detect snakes (and other animals) faster than other stimuli. Most of these studies used schematic drawings, images, or pictures of snakes, and only a small amount of fieldwork in naturalistic environments was done. We address fear preparedness theories, and automatic fast detection data from mainstream (...) laboratory data and compares it with ethobehavioural information relative to snakes, predator-prey interaction, and snakes’ defensive kinematics strikes in order to analyse their potential matching. From this analysis four main findings arose, namely that: 1) Snakebites occur when people are very close to the snake and are unaware or unable to escape the bite; 2) Human visual detection and escape response is slow compared to the speed of snake strikes; 3) In natural environments, snake experts are often unable to see snakes existing nearby; 4) animate objects in general capture more attention over other stimuli and dangerous but recent objects in evolutionary terms are also able to be detected fast. The issues mentioned above pose several challenges to evolutionary psychology-based theories expecting to find special-purpose neural modules. The older selective habituation hypothesis (Schleidt, 1961), that prey animals start with a rather general predator image from which specific harmless cues are removed by habituation might deserve reconsideration. (shrink)

Religions “in the wild” are the varied set of religious activities that occurred before the emergence of organized religions with doctrines, or that persist at the margins of those organized traditions. These religious activities mostly focus on misfortune; on how to remedy specific cases of illness, accidents, failures; and on how to prevent them. I present a general model to account for the cross-cultural recurrence of these particular themes. The model is based on features of human psychology—namely, epistemic vigilance, (...) the set of systems whereby we evaluate the quality of information and of sources of information, and threat-detection psychology, the set of evolved systems geared at detecting potential danger in the environment. Given these two sets of systems, the dynamics of communication will favor particular types of messages about misfortune. This makes it possible to predict recurrent features of religious systems, such as the focus on nonphysical agents, the focus on particular cases rather than general aspects of misfortune, and the emergence of specialists. The model could illuminate not just why such representations are culturally successful, but also why people are motivated to formulate them in the first place. (shrink)

Hierarchies occur widely in evolving self‐organizing ecological, biological, technological, and social networks, but detecting and comparing hierarchies is difficult. Here we present a metric and technique to quantitatively assess the extent to which self‐organizing directed networks exhibit a flow hierarchy. Flow hierarchy is a commonly observed but theoretically overlooked form of hierarchy in networks. We show that the ecological, neurobiological, economic, and information processing networks are generally more hierarchical than their comparable random networks. We further discovered that hierarchy degree has (...) increased over the course of the evolution of Linux kernels. Taken together, our results suggest that hierarchy is a central organizing feature of real‐world evolving networks, and the measurement of hierarchy opens the way to understand the structural regimes and evolutionary patterns of self‐organizing networks. Our measurement technique makes it possible to objectively compare hierarchies of different networks and of different evolutionary stages of a single network, and compare evolving patterns of different networks. It can be applied to various complex systems, which can be represented as directed networks. (shrink)

Mismatch negativity is the electroencephalographic waveform obtained by subtracting event-related potential responses evoked by unexpected deviant stimuli from responses evoked by expected standard stimuli. While the MMN is thought to reflect an unexpected change in an ongoing, predictable stimulus, it is unknown whether MMN responses evoked by changes in different stimulus features have different magnitudes, latencies, and topographies. The present study aimed to investigate whether MMN responses differ depending on whether sudden stimulus change occur in pitch, duration, location or vowel (...) identity, respectively. To calculate ERPs to standard and deviant stimuli, EEG signals were recorded in normal-hearing participants who listened to roving oddball sequences of artificial syllables. In the roving paradigm, any given stimulus is repeated several times to form a standard, and then suddenly replaced with a deviant stimulus which differs from the standard. Here, deviants differed from preceding standards along one of four features. The feature levels were individually chosen to match behavioral discrimination performance. We identified neural activity evoked by unexpected violations along all four acoustic dimensions. Evoked responses to deviant stimuli increased in amplitude relative to the responses to standard stimuli. A univariate analysis yielded no significant differences between MMN responses following violations of different features. However, in a multivariate analysis, acoustic features could be decoded from the topography of mismatch responses, although at later latencies than those typical for MMN. These results support the notion that deviant feature detection may be subserved by a different process than general mismatch detection. (shrink)

The proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID-19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients who are tortured by lung disease. Moreover, lung specialists do their best to detect the presence of lung conditions. X rays or CT scan checks are performed for tomography tests. The finest approach (...) for medical diagnosis and a wide range of uses is computed tomography. This kind of imaging offers elaborate cross-sectional pictures of skinny slices of the organic structure. However, the preprocessing and denoising methods of Lung CT scans may mask some important image features. To address this challenge, we propose a novel framework involving an optimization technique algorithm to solve a multilevel thresholding problem based on information theory to segment abnormal lung CT scans. The proposed framework will evaluate a sample of CT scan images taken from a well-known benchmark database. The evaluation results will assess subjectively and objectively to demonstrate the effectiveness of the proposed framework. (shrink)

Job seekers find themselves increasingly duped and misled by fraudulent job advertisements, posing a threat to their privacy, security and well-being. There is a clear need for solutions that can protect innocent job seekers. Existing approaches to detecting fraudulent jobs do not scale well, function like a black-box, and lack interpretability, which is essential to guide applicants’ decision-making. Moreover, commonly used lexical features may be insufficient as the representation does not capture contextual semantics of the underlying document. Hence, this paper (...) explores to what extent different categorizations of fraudulent jobs can be classified. In addition, this paper seeks to find what type of features are most relevant in classifying the type of fraudulent job. In this paper, we develop and validate a machine learning system for identifying identity theft, corporate identity theft and multi-level marketing amongst fraudulent job advertisements. We utilized four classes of features: empirical rule set-based features, bag-of-word models, most recent state-of-the-art word embeddings and transformer models for various machine learning classifiers. The machine learning models were validated by evaluating them on a publicly available job description dataset. Our results indicate that the word embeddings and transformer-based features consistently outperformed the handcrafted rule-set based features class. Ultimately, a Gradient Boosting classifier with a combination of empirical rule-set based features, parts-of-speech tags and bag-of-words vectors achieved the best performance with an F1-score of 0.88. (shrink)

The use of batteries became essential in our daily life in electronic devices, electric vehicles and energy storage systems in general terms. As they play a key role in many devices, their design and implementation must follow a thorough test process to check their features at different operating points. In this circumstance, the appearance of any kind of deviation from the expected operation must be detected. This research deals with real data registered during the testing phase of a lithium (...) iron phosphate—LiFePO4—battery. The process is divided into four different working points, alternating charging, discharging and resting periods. This work proposes a hybrid classifier, based on one-class techniques, whose aim is to detect anomalous situations during the battery test. The faults are created by modifying the measured cell temperature a slight ratio from their real value. A detailed analysis of each technique performance is presented. The average performance of the chosen classifier presents successful results. (shrink)

This study examined individual differences in sensitivity to human-like features of a robot’s behavior. The paradigm comprised a non-verbal Turing test with a humanoid robot. A “programmed” condition differed from a “human-controlled” condition by onset times of the robot’s eye movements, which were either fixed across trials or modeled after prerecorded human reaction times, respectively. Participants judged whether the robot behavior was programmed or human-controlled, with no information regarding the differences between respective conditions. Autistic traits were measured with the autism-spectrum (...) quotient questionnaire in healthy adults. We found that the fewer autistic traits participants had, the more sensitive they were to the difference between the conditions, without explicit awareness of the nature of the difference. We conclude that although sensitivity to fine behavioral characteristics of others varies with social aptitude, humans are in general capable of detecting human-like behavior based on very subtle cues. (shrink)

This study examined individual differences in sensitivity to human-like features of a robot’s behavior. The paradigm comprised a non-verbal Turing test with a humanoid robot. A “programmed” condition differed from a “human-controlled” condition by onset times of the robot’s eye movements, which were either fixed across trials or modeled after prerecorded human reaction times, respectively. Participants judged whether the robot behavior was programmed or human-controlled, with no information regarding the differences between respective conditions. Autistic traits were measured with the autism-spectrum (...) quotient questionnaire in healthy adults. We found that the fewer autistic traits participants had, the more sensitive they were to the difference between the conditions, without explicit awareness of the nature of the difference. We conclude that although sensitivity to fine behavioral characteristics of others varies with social aptitude, humans are in general capable of detecting human-like behavior based on very subtle cues. (shrink)

This paper aims to investigate the kinaesthetic experience of dance, and especially of pantomime dance in Lucian’s De Saltatione and in Libanius’ oration 64, A Reply To Aristides On Behalf Of The Dancers, from the perspective of the mechanical. Specifically, pantomime will be discussed in juxtaposition with the concept of mechanical automation. Until now, this aspect remains unexplored; however, this is of great importance, particularly if we take into consideration that from the Hellenistic period onwards theatrical automata and processions with (...) engineered artefacts were considered to be a popular entertainmentmechanism and, as such, they exerted great influence on the public’s aesthetic. In this respect, I intend first to survey the concept of pantomimic mimesis as a mechanical reproduction of motion, i.e. gestures and postures. Next, I shall detect the vocabulary of mechanisms/mechanisms’ function that is generally embedded in dance rhetoric by examining forced motion both in pantomime and ancient mechanics. (shrink)

Recognition of sarcastic statements has been a challenge in the process of sentiment analysis. A sarcastic sentence contains only positive words conveying a negative sentiment. Therefore, it is tough for any automated machine to identify the exact sentiment of the text in the presence of sarcasm. The existing systems for sarcastic sentiment detection are limited to the text scripted in English. Nowadays, researchers have shown greater interest in low resourced languages such as Hindi, Telugu, Tamil, Arabic, Chinese, Dutch, Indonesian, (...) etc. To analyse these low resource languages, the biggest challenge is the lack of available resources, especially in the context of Indian languages. Indian languages are very rich in morphology which pose a greater challenge for the automated machines. Telugu is one of the most popular languages after Hindi among Indian languages. In this article, we have collected and annotated a corpus of Telugu conversation sentences in the form of a question followed by a reply for sarcasm detection. Further, a set of algorithms are proposed for the analysis of sarcasm in the corpus of Telugu conversation sentences. The proposed algorithms are based on hyperbolic features namely, Interjection, Intensifier, Question mark and Exclamation symbol. The achieved accuracy is 94%. (shrink)

Le Tractatus particulares est un ouvrage en quatre parties attribué à Abraham Ibn Ezra (ca. 1089-ca. 1161), qui nous est parvenu en deux traductions latines. Le présent article montre que, malgré les attestations dans les incipits et les explicits des deux traductions latines, le Tractatus particulares ne peut pas être un ouvrage authentique d’Ibn Ezra, ni un recueil d’écrits de sa plume. L’essentiel du Tractatus particulares est constitué de traductions de l’arabe en hébreu, réalisées bien après la mort d’Ibn Ezra (...) et associées à son nom par la suite. L’article entend aussi montrer que la source hébraïque du Tractatus particulares était sans doute conçue comme un ouvrage astrologique complet, une sorte d’encyclopédie astrologique médiévale. (shrink)

To investigate whether implicit detection occurs uniformly during change blindness with single or combination feature stimuli, and whether implicit detection is affected by exposure duration and delay, two one-shot change detection experiments are designed. The implicit detection effect is measured by comparing the reaction times of baseline trials, in which stimulus exhibits no change and participants report “same,” and change blindness trials, in which the stimulus exhibits a change but participants report “same.” If the RTs (...) of blindness trials are longer than those of baseline trials, implicit detection has occurred. The strength of the implicit detection effect was measured by the difference in RTs between the baseline and change blindness trials, where the larger the difference, the stronger the implicit detection effect. In both Experiments 1 and 2, the results showed that the RTs of change blindness trials were significantly longer than those of baseline trials. Whether under set size 4, 6, or 8, the RTs of the change blindness trials were significantly longer than those in the baseline trials. In Experiment 1, the difference between the baseline trials’ RTs and change blindness trials’ RTs of the single features was significantly larger than that of the combination features. However, in Experiment 2, the difference between the baseline trials’ RTs and the change blindness trials’ RTs of single features was significantly smaller than that of the combination features. In Experiment 1a, when the exposure duration was shorter, the difference between the baseline and change blindness trials’ RTs was smaller. In Experiment 2, when the delay was longer, the difference between the two trials’ RTs was larger. These results suggest that regardless of whether the change occurs in a single or a combination of features and whether there is a long exposure duration or delay, implicit detection occurs uniformly during the change blindness period. Moreover, longer exposure durations and delays strengthen the implicit detection effect. Set sizes had no significant impact on implicit detection. (shrink)

Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. Firstly, multiple frames of images are read in time sequence and converted into grayscale images. The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture. The correlation of the dynamic texture of adjacent frames and the matching degree of SIFT (...) features of two frames were compared, and the predetection results were obtained according to the matching results. Next, compared with the next frame of the dynamic texture and SIFT feature whose step size is lower than the human eye refresh frequency, the final result is obtained. Through experiments on multiple groups of different types of film and television data, high recall rate and accuracy rate can be obtained. The algorithm in this paper can detect the gradual change lens with the complex structure and obtain high detection accuracy and recall rate. A lens boundary detection algorithm based on fuzzy clustering is realized. The algorithm can detect sudden changes/gradual changes of the lens at the same time without setting a threshold. It can effectively reduce the factors that affect lens detection, such as flash, movies, TV, and advertisements, and can reduce the influence of camera movement on the boundaries of movies and TVs. However, due to the complexity of film and television, there are still some missing and false detections in this algorithm, which need further study. (shrink)

There are three main problems in track fastener defect detection based on image: The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. The potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. The detection of the track fastener (...) clip will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network is proposed. Firstly, the track fastener image segmentation method is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder and Restricted Boltzmann Machine, and the features are fused. Finally, the similarity measurement method Mahalanobis Distance is used to detect defects in track fasteners. The effectiveness of the proposed method is verified by real Pandrol track fastener images. (shrink)

Higher animals need to identify and track material objects because they depend on interactions with them for nutrition, reproduction, and social interaction. This paper investigates the perception of material objects. It argues, first, that material objects are tagged, in all five external senses, as bearers of the features detected by them. This happens through a perceptual process, here entitled Generalized Completion, which creates the appearance of objects that have properties that transcend the activation of sensory receptors. The paper shows, secondly, (...) that material objects are privileged subjects for perceived motion and interaction. That is, they are perceived as subjects for these properties while their parts seem to be subjects only derivatively. Material objects are the only perceptual subjects that are both multisensory and privileged. (shrink)

Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our (...) procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data. (shrink)

Detecting Intrusions and anomalies is becoming much more challenging with new attacks popping out over a period of time. Achieving better accuracies by applying benchmark classifier algorithms used for identifying intrusions and anomalies have several hidden data mining challenges. Although neglected by many research findings, one of the most important and biggest challenges is the similarity or membership computation. Another challenge that cannot be simply neglected is the number of features that attributes to dimensionality. This research aims to come up (...) with a new membership function to carry similarity computation that can be helpful for addressing feature dimensionality issues. In principle, this work is aimed at introducing a novel membership function that can help to achieve better classification accuracies and eventually lead to better intrusion and anomaly detection. Experiments are performed on KDD dataset with 41 attributes and also KDD dataset with 19 attributes. Recent approaches CANN and CLAPP have showed new approaches for intrusion detection. The proposed classifier is named as UTTAMA. UTTAMA performed better to both CANN and CLAPP approaches w.r.t overall classifier accuracy. Another promising outcome achieved using UTTAMA is the U2R and R2L attack accuracies. The importance of proposed approach is that the accuracy achieved using proposed approach outperforms CLAPP, CANN, SVM, KNN and other existing classifiers. (shrink)

The pitfalls of the Wadding edition of John Duns Scotus illustrate a general feature of the study of medieval philosophy: the gap that separates the authentic words of the medieval thinker one wishes to study from the Latin words one sees on the pages of a printed edition — and further still from the English words one sees in a translation. The aim of this essay is to make clear both the nature and the size of that gap, (...) not in order to dismay prospective students of medieval philosophy, but in order to explain the hazards in such a way that students can equip themselves properly to meet them. I will begin by discussing in a general way the channels of transmission by which medieval philosophy has made its way down to us. I then turn to three specific cases by which I illustrate some of those general points as they apply to texts of different sorts and from different periods. Along the way I draw attention to the kinds of errors that are liable to be introduced at the various stages of transmission between a medieval lecturer’s spoken words and the text of a modern critical edition, and I outline the tools and techniques that the careful historian of medieval philosophy will use in order to minimize such errors, especially where no critical edition is available. In the second half of the essay I turn to problems of translation. I provide an example that shows how a reader can sometimes detect errors in a translation even without checking the Latin text, and another to illustrate how translations sometimes reflect controversial views about how a text is to be interpreted. I then conclude with a look at the translation of particular terms, discussing a number of standard translations that are apt to be misleading, and giving some idea of the range of translation of certain key terms. (shrink)

A brain tumor is an abnormal mass or growth of a cell that leads to certain death, and this is still a challenging task in clinical practice. Early and correct diagnosis of this type of cancer is very important for the treatment process. For this reason, this study aimed to develop computer-aided systems for the diagnosis of brain tumors. In this research, we proposed three different end-to-end deep learning approaches for analyzing effects of local and deep features for brain MRI (...) images anomaly detection. The first proposed system is Directional Bit-Planes Deep Autoencoder which extracts and learns local and direction features. The DBP-DAE by decomposition of a local binary pattern into eight bit-planes extracts are directional and inherent local-structure features from the input image and learns robust feature for classification purposes. The second one is a Dilated Separable Residual Convolutional Network which extracts high and low-level features. The main advantage of this approach is that it is robust and shows stable results regardless to size of image database and to solve overfitting problems. To explore the effects of mixture of local and deep extracted feature on accuracy of classification of brain anomaly, a multibranch convolutional neural network approach is proposed. This approach is designed according to combination of DBP-DAE and DSRCN in an end-to-end manner. Extensive experiments conducted based on brain tumor in MRI image public access databases and achieves significant results compared to state-of-the-art algorithms. In addition, we discussed the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s. (shrink)

In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features (...) from the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value. (shrink)

It is widespread that the consumers browse relevant reviews for reference before purchasing the products when online shopping. Some stores or users may write deceptive reviews to mislead consumers into making risky purchase decisions. Existing methods of deceptive review detection did not consider the valid product review sets and classification probability of feature weights. In this research, we propose a deceptive review detection algorithm based on the target product identification and the calculation of the Metapath feature (...) weight, noted as TM-DRD. The review dataset of target product is modeled as a heterogeneous review information network with the feature nodes. The classification method of graph is used to detect the deceptive reviews, which can improve the efficiency and accuracy of deceptive review detection due to the sparsity, imbalance of deceptive reviews, and the absence of category probability of feature weight calculation. The TM-DRD algorithm we proposed is validated on the real review dataset Yelp and compared with the SpEagle, NFC, and NetSpam algorithm. The experiment results demonstrate that the TM-DRD algorithm performs better than the other method with regard to the accuracy and efficiency. (shrink)

The main objective of this book is to present the theory of general relativity in a direction that will be intelligible, informative, and interesting to the individual reader. Many of the texts about general relativity are either too thin on detail or too narrow in scope; this book was written with the aim of rectifying these shortcomings.

In all systems of health care there are certain essential levels of care and service. These take the form of self-care within the family unit; primary professional care by general medical nursing or social practitioners within a local neighbourhood; general specialist care in a district and super-specialist care in a region. Each of these has its own special roles and responsibilities and each is considered in this paper.

The social network has become the primary medium of rumor propagation. Moreover, manual identification of rumors is extremely time-consuming and laborious. It is crucial to identify rumors automatically. Machine learning technology is widely implemented in the identification and detection of misinformation on social networks. However, the traditional machine learning methods profoundly rely on feature engineering and domain knowledge, and the learning ability of temporal features is insufficient. Furthermore, the features used by the deep learning method based on natural (...) language processing are heavily limited. Therefore, it is of great significance and practical value to study the rumor detection method independent of feature engineering and effectively aggregate heterogeneous features to adapt to the complex and variable social network. In this paper, a deep neural network- based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of social network event without feature engineering and domain knowledge. The experimental results show that the feature aggregation model has achieved 94.4% of accuracy as the best performance in recent works. (shrink)

There is controversy as to whether children with developmental language disorder (DLD) and those with high-functioning autism plus language impairment (HFA-LI) share similar language profiles. This study investigated the similarities and differences in the production of Chinese negative sentences by children with DLD and children with HFA-LI to provide evidence relevant to this controversy. The results reflect a general resemblance between the two groups in their lower-than-TDA (typically developing age-matched) performance. Both groups encountered difficulties in using negative markers, which (...) suggests that they might be impaired in feature agreement. Slight differences were detected between the two groups. Specifically, children with DLD experienced difficulties with the agreement on the feature [+telic] and that on the feature [+dynamic], while children with HFA-LI had difficulties with the agreement on the feature [+dynamic] and that on the feature [−dynamic]. This study supports the idea of a common symptomatology for the two disorders. More importantly, it suggests that these two disorders, DLD and HFA-LI, are not altogether the same in terms of language impairment. This paper concludes that general labels should not be simply attached to any children with language disorders. Instead, atypical language is very worthy of further analysis in the categorization of language disorders. (shrink)

The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show (...) that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%. (shrink)

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s (...) prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance. -/- . (shrink)

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we (...) propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload. (shrink)

Astrobiology aims to determine the distribution and diversity of life in the universe. But as the word “biosignature” suggests, what will be detected is not life itself, but an observation implicating living systems. Our limited access to other worlds suggests this observation is more likely to reflect out‐of‐equilibrium gasses than a writhing octopus. Yet, anything short of a writhing octopus will raise skepticism about what has been detected. Resolving that skepticism requires a theory to delineate processes due to life and (...) those due to abiotic mechanisms. This poses an existential question for life detection: How do astrobiologists plan to detect life on exoplanets via features shared between non‐living and living systems? We argue that you cannot without an underlying theory of life. We illustrate this by analyzing the hypothetical detection of an “Earth 2.0” exoplanet. Without a theory of life, we argue the community should focus on identifying unambiguous features of life via four areas: examining life on Earth, building life in the lab, probing the solar system, and searching for technosignatures. Ultimately, we ask, what exactly do astrobiologists hope to learn by searching for life? (shrink)