A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one‐shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It (...) turns out that the success of the IA depends substantially on the ‘‘preference order’’ (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO. (shrink)

A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It (...) turns out that the success of the IA depends substantially on the ‘‘preference order’’ (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO. (shrink)

In a recent article published in this journal (van Deemter, Gatt, van der Sluis, & Power, 2012), the authors criticize the Incremental Algorithm (a well-known algorithm for the generation of referring expressions due to Dale & Reiter, 1995, also in this journal) because of its strong reliance on a pre-determined, domain-dependent Preference Order. The authors argue that there are potentially many different Preference Orders that could be considered, while often no evidence is available to determine which is a good (...) one. In this brief note, however, we suggest (based on a learning curve experiment) that finding a Preference Order for a new domain may not be so difficult after all, as long as one has access to a handful of human-produced descriptions collected in a semantically transparent way. We argue that this is due to the fact that it is both more important and less difficult to get a good ordering of the head than of the tail of a Preference Order. (shrink)

In this study a combination of computer tools for coupling and virtual screening is detailed, in 108 active molecules and 3620 decoys to find stabilizers for G quadruplex. To have more precise results, combinations of coupling programs with fifteen energy scoring functions were applied. The validation and evaluation of the metrics was done with the CompScore genetic algorithm. The results showed an increase in BEDROC and EF of 50% compared to other strategies, as well as reflecting early recognition (...) of active molecules. From these results it is possible to work with the molecules that showed a good early recognition and evaluate their effect as G4 stabilizers. This ensures more efficient and accurate results in the preclinical stage for the development of anticancer drugs. Keywords: Enrichment metrics; telomere; G quadruplex ; CompScore. References [1]M. Porru, P. Zizza, M. Franceschin, C. Leonetti and A. Biroccio. «EMICORON: A multi-targeting G4 ligand with a promising preclinical profile» 2017. Biochimica et Biophysica Acta - General Subjects, 1861, 1362–1370. [Online]. Available: https://doi.org/10.1007/s00294-018-0836-6. [2]K. Tomita. «How long does telomerase extend telomeres? Regulation of telomerase release and telomere length homeostasis». Current Genetics, 64, 1177–1181. 2018. [Online]. Available: https://doi.org/10.1016/j.bbagen. 2016.11.010. [3]M. Jafri, S. Ansari, M. Alqahtani and J. Shay. «Roles of telomeres and telomerase in cancer, and advances in telomerase-targeted therapies. Genome Medicine., 2016. [Online]. Available: https://doi.org/10.3390/ijms19020482. [4]J. Huppert and S. Balasubramanian. «G-quadruplexes in promoters throughout the human genome». 35, 406–413. 2007. [Online]. Available: https://doi.org/10.1016/j.bbapap.2017.06.012. [5]S. Joy, Vijayakumar, S. Choi and H. Sunhye. «Role of computer-aided drug design in modern drug discovery». Archives of Pharmacal Research. 2015. [Online]. Available: https://doi.org/10.1007/s12272-015-0640-5. [6]S. Asamitsu, S. Obata, Z. Yu, T. Bando and H. Sugiyama. «Recent Progress of Targeted G-Quadruplex-Preferred Ligands Toward Cancer Therapy». Molecules, 24, 429. 2019. [En línea]. Available: https://doi.org/10.3390/molecules24030429. [7]R. Monsen and J. Trent. «Biochimie G-quadruplex virtual drug screening : A review». Biochimie, 152, 134–148. 2018. [Online]. Available: https://doi.org/10.1039/c9cc06748e. [8]J. Beauvarlet, P. Bensadoun, E. Darbo, G. Labrunie, E. Richard, I. Draskovic and M. Djavaheri-mergny. «Modulation of the ATM / autophagy pathway by a G-quadruplex ligand tips the balance between senescence and apoptosis in cancer cells». 1–18. 2019. [Online]. Available: https://doi.org/10.1093/nar/gkz095. [9].Z. Crees, J. Girard, Z. Rios, G. Botting, K. Harrington and C. Shearrow. « Oligonucleotides and G-quadruplex stabilizers: targeting telomeres and telomerase in cancer therapy».2014. [Online]. Available: https://doi.org/10.2174/1381612820666140630100702. [10].M. Meier, A. Moya-torres, N. Krahn, M. Mcdougall, L. Orriss, E. Mcrae and T. Patel. «Structure and hydrodynamics of a DNA Gquadruplex with a cytosine bulge»., 1–13. 2018. [Online]. Available: https://doi.org/10.1093/nar/gky307. (shrink)

Much business ethics and corporate social responsibility literature suggests, implicitly or explicitly, that firms ought to engage in activities that can be characterized as philanthropy, namely, expending resources beyond what is required by law and market norms to promote others’ welfare at the expense of firm profits. However, this literature has struggled to provide a normative framework for evaluating corporate philanthropy, although scholars have noted that such expenditures can potentially remedy market failures and provide public goods more efficiently. I articulate (...) two specific rationales that can justify corporate philanthropy based on considerations of welfare economics: 1) firms making strategic but high-risk investments in activities that are likely to generate positive externalities even if they prove unprofitable and 2) firms possessing a strong comparative advantage in their ability to address a social problem at lower social cost. Moreover, these rationales can be evaluated by a concept I develop called the philanthropy multiplier, indicating the ratio of net positive externalities to net costs. I suggest that firms consider publicizing their philanthropy multipliers, and I discuss theoretical and practical implications. (shrink)

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing (...) objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals. (shrink)

Role-based database management systems are most widely used for information storage and analysis but are known as vulnerable to insider attacks. The core of intrusion detection lies in an adaptive system, where an insider attack can be judged if it is different from the predicted role by performing classification on the user’s queries accessing the database and comparing it with the authorized role. In order to handle the high similarity of user queries for misclassified roles, this paper proposes a deep (...) metric neural network with strategic sampling algorithm that properly extracts salient features and directly learns a quantitative measure of similarity. A strategic sampling method of heuristically generating and learning training pairs through Monte Carlo search is proposed to select a training pair that can represent the entire dataset. With the TPC-E–based benchmark data trained with 11,000 queries for 11 roles, the proposed model produces the classification accuracy of 95.41%, which is the highest compared with the previous models. The results are verified through comparison of quantitative and qualitative evaluations, and the feature space modelled in the neural network is analysed by t-SNE algorithm. (shrink)

Drawing on a qualitative, multi-case study of three kinds of geographically tethered gig work—ride-hailing, delivery, and domestic services platforms—in the United States, I examine how workers anticipate the influences of metrics, live with metrics, and cope with algorithmic precarity. Data for this project include in-depth interviews with 50 gig workers about their efforts to interpret and manage metrics as part of their everyday work practices. The analysis reveals that participants were anxious about metrics primarily because of (...) the disciplinary outcomes, that are, the threat of job loss and the valued job features. It also directs attention to how workers felt and experienced customer-sourced ratings and system-generated behavioral metrics variously across platforms. Information asymmetries and the perceived lack of control also intensified a sense of powerlessness among participants. While participants articulated strategies that aimed at managing the uncertainty of customer-sourced ratings—and more precisely, the work-related uncertainty created by “difficult customers”—throughout service interactions, their feelings of anxiety could not be resolved. Furthermore, the (in)visibility of metrics, the settings of platform-mediated worker–customer interactions, and workers’ platform dependence contributed to the varying disciplinary power of metrics. The study contributes to understanding how metrics as affective measures mediate the trilateral relationship between platforms, workers, and customers in the gig economy. (shrink)

This paper researches and analyzes the evaluation of the competitiveness of ice and snow tourism, uses the improved fuzzy neural network algorithm to process the system flow diagram of ice and snow tourism development through the function and characteristics of the power system of ice and snow tourism, and finally selects more than 40 indicators of the three subsystems of resources, economy, and culture. Based on the construction of cloud fuzzy neural network model, the above method is used for (...) experimental comparison analysis, and experiments are conducted through University of California Irvine dataset and engineering examples to compare with the traditional cloud model, fuzzy neural network, and BP neural network to analyze the operation efficiency, accuracy rate, and several rules of the algorithm. Through the experimental comparative analysis, the cloud fuzzy neural network can fully take into account the randomness and fuzziness of the data, optimize the generation of cloud rules, avoid multidimensional rule disasters, and ensure the operational efficiency of the algorithm; the accuracy rate of the algorithm is improved relative to that of the traditional technology, and it applies to a variety of datasets. And the software is used to test the ice and snow tourism industry system dynamics model to realize the correctness and robustness testing of the model. After the constructed model can reflect the real situation within the error range, the final policy simulation of the model is carried out. (shrink)

With the continuous development of “Internet + Education”, online learning has become a hot topic of concern. Decision tree is an important technique for solving classification problems from a set of random and unordered data sets. Decision tree is not only an effective method to generate classifier from data set, but also an active research field in data mining technology. The decision tree mining algorithm can classify the data, grasp the teaching process of the teacher, and analyze the overall performance (...) of the students, so as to realize the dynamic management of the educational administration and help the educational administration personnel to make the right decision, with more reasonable allocation of resources. This paper evaluates students’ academic performance based on the learning behavior data of online learning, so as to intervene in students’ learning in advance, which is the key problem that needs to be solved at present. Taking students’ learning attitude, completion of homework, and attendance as factors, the paper uses decision tree technology to analyze the factors affecting students’ performance, and evaluates students’ performance. Firstly, this paper collects the high-dimensional behavioral characteristic data of students’ online learning and conducts correlation analysis after preprocessing the behavioral characteristic data. Then, the decision tree C4.5 algorithm is used to construct a performance evaluation model. Students’ performance is evaluated by the model, and the evaluation accuracy is about 88% compared with actual performance. Finally, through the model analysis, it is concluded that the video task point completion is the most influential in students’ achievement, followed by chapter test completion and chapter test average score, and the course interaction amount and homework average score are the least influential in students’ achievement, which has a practical reference value for effectively serving online learning and teachers’ teaching. (shrink)

Novelty is a key concept to understand creativity. Evaluating a piece of artwork or other creation in terms of novelty requires comparisons to other works and considerations about the elements that have been reused in the creative process. Human beings perform this analysis intuitively, but in order to simulate it using computers, the objects to be compared and the similarity metrics to be used should be formalized and explicitly implemented. In this paper we present a study on relevant elements (...) for the assessment of novelty in computer-generated narratives. We focus on the domain of folk-tales, working with simple plots and basic narrative elements: events, characters, props and scenarios. Based on the empirical results of this study we propose a set of computational metrics for the automatic assessment of novelty. Although oriented to the implementation of our own story generation system, the measurement methodology we propose can be easily generalized to other creative systems. (shrink)

When people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners' comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving (...) higher level goals such as changing the listener's beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice-giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi-attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness. (shrink)

When people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners' comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving (...) higher level goals such as changing the listener's beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice‐giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi‐attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness. (shrink)

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of (...) 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine, adaptive boosting, random forest, and K-nearest neighbor models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination, Nash–Sutcliffe coefficient, and error in the root mean square ratio to the standard deviation of the measured data. The results demonstrated that the XGBoost model has the highest prediction performance with, followed by the SVM model with, RF, the AdaBoost model with and the KNN model with. A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable. (shrink)

In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that (...) do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

Open Shop Scheduling Problem is one of the most important scheduling problems in the field of engineering and industry. This kind of problem includes m machines and n jobs, each job contains a certain number of operations, and each operation has a predetermined processing time on its corresponding machine. The order of processing of these operations affects the completion times of all jobs. Therefore, the purpose of OSSP is to achieve a proper order of processing of jobs using specified machines, (...) so that the completion time of all jobs is minimized. In this paper, the strengths and limitations of three methods are evaluated by comparing the results of solving the OSSP in large-scale and small-scale benchmarks. In this case, the minimized completion time and total tardiness are considered the objective functions of the adapted methods. To solve small-scale problems, we adapt a mathematical model called Multiobjective Mixed Linear Programming. To solve large-scale problems, two metaheuristic algorithms including Multiobjective Parallel Genetic Algorithm and Multiobjective Parallel Simulated Annealing are adapted. In experimental results, we randomly generated small-scale problems to compare MOMILP with the Genetic Algorithm and Simulate Annealing. To compare MOPSA and MOPGA with the state of the art and show their strengths and limitations, we use a standard large-scale benchmark. The simulation results of the proposed algorithms show that although the MOPSA algorithm is faster, the MOPGA algorithm is more efficient in achieving optimal solutions for large-scale problems compared with other methods. (shrink)

The probability ranking conclusion is an extension of the absolute form evaluation conclusion. Firstly, the random simulation evaluation model is introduced; then, the general idea of converting the traditional evaluation method to the random simulation evaluation model is analyzed; on this basis, based on the rule of “further ensuring the stability of the ranking chain on the basis of increasing the possibility of the ranking chain,” two methods of solving the probability ranking conclusion are given. Based (...) on the rule of “further guaranteeing the stability of the ranking chain on the basis of improving the likelihood of the ranking chain,” two methods are given to solve the likelihood conclusion. This paper argues that this absolute form of conclusion hinders the approximation of the theory to the essence of the actual problem and is an important reason for the problem of “non-consistency of multi-evaluation conclusions.” To address this problem, a stochastic simulation-based comprehensive evaluation solution algorithm based on the idea of “Monte Carlo simulation” is proposed, and the corresponding ranking method is investigated, which is characterized by generating evaluation conclusions with probability information, and thus has more advantages than the absolute conclusion form in terms of problem interpretability. The method is characterized by the generation of evaluation conclusions with probabilistic information and thus has more advantages than the absolute conclusion form in terms of problem interpretation. Because of the independence of the stochastic simulation solution method, it is applied to the “bottom-up” evaluation model as an example, and a novel autonomous evaluation method is constructed. Finally, the application of the stochastic simulation evaluation model is illustrated by an example and compared with the absolute form evaluation. The evaluation model is an extension of the traditional evaluation model, which can further broaden the practical application of comprehensive evaluation theory. (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)

This article presents the design and development of a genetic algorithm to generate long-range transmit codes with low autocorrelation side lobes for radar to minimize target profile estimation error. The GA described in this work has a parallel processing design and has been used to generate codes with multiple constellations for various code lengths with low estimated error of a radar target profile.

Integrating large language models (LLMs) like GPT-4 into medical ethics is a novel concept, and understanding the effectiveness of these models in aiding ethicists with decision-making can have significant implications for the healthcare sector. Thus, the objective of this study was to evaluate the performance of GPT-4 in responding to complex medical ethical vignettes and to gauge its utility and limitations for aiding medical ethicists. Using a mixed-methods, cross-sectional survey approach, a panel of six ethicists assessed LLM-generated responses to eight (...) ethical vignettes. The main outcomes measured were relevance, reasoning, depth, technical and non-technical clarity, as well as acceptability of GPT-4’s responses. The readability of the responses was also assessed. Of the six metrics evaluating the effectiveness of GPT-4’s responses, the overall mean score was 4.1/5. GPT-4 was rated highest in providing technical (4.7/5) and non-technical clarity (4.4/5), whereas the lowest rated metrics were depth (3.8/5) and acceptability (3.8/5). There was poor-to-moderate inter-rater reliability characterised by an intraclass coefficient of 0.54 (95% CI: 0.30 to 0.71). Based on panellist feedback, GPT-4 was able to identify and articulate key ethical issues but struggled to appreciate the nuanced aspects of ethical dilemmas and misapplied certain moral principles. This study reveals limitations in the ability of GPT-4 to appreciate the depth and nuanced acceptability of real-world ethical dilemmas, particularly those that require a thorough understanding of relational complexities and context-specific values. Ongoing evaluation of LLM capabilities within medical ethics remains paramount, and further refinement is needed before it can be used effectively in clinical settings. (shrink)

As a way to address both ominous and ordinary threats of artificial intelligence, researchers have started proposing ways to stop an AI system before it has a chance to escape outside control and cause harm. A so-called “big red button” would enable human operators to interrupt or divert a system while preventing the system from learning that such an intervention is a threat. Though an emergency button for AI seems to make intuitive sense, that approach ultimately concentrates on the point (...) when a system has already “gone rogue” and seeks to obstruct interference. A better approach would be to make ongoing self-evaluation and testing an integral part of a system’s operation, diagnose how the system is in error and to prevent chaos and risk before they start. In this paper, we describe the demands that recent big red button proposals have not addressed, and we offer a preliminary model of an approach that could better meet them. We argue for an ethical core that consists of a scenario-generation mechanism and a simulation environment that are used to test a system’s decisions in simulated worlds, rather than the real world. This EC would be kept opaque to the system itself: through careful design of memory and the character of the scenario, the system’s algorithms would be prevented from learning about its operation and its function, and ultimately its presence. By monitoring and checking for deviant behavior, we conclude, a continual testing approach will be far more effective, responsive, and vigilant toward a system’s learning and action in the world than an emergency button which one might not get to push in time. (shrink)

In this paper, we propose an adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis and prediction through the study and analysis of sports videos. The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. The sparse generative model is constructed by combining L1 regularization and subspace representation, which retains sufficient representational power (...) while dealing with outliers. To overcome the shortcomings of the traditional multiplicative fusion mechanism, this paper proposes an adaptive selection mechanism based on Euclidean distance, which aims to detect deteriorating models in time during the dynamic tracking process and adopt corresponding strategies to construct more reasonable likelihood functions. Based on the Bayesian citation framework, the adaptive selection mechanism is used to combine the sparse discriminative model and the sparse generative model. Also, different online updating strategies are adopted for the template set and Principal Component Analysis subspace to alleviate the drift problem while ensuring that the algorithm can adapt to the changes of target appearance in the dynamic tracking environment. Through quantitative and qualitative evaluation of the experimental results, it is verified that the algorithm proposed in this paper has stronger robustness compared with other classical algorithms. Our proposed visual object tracking algorithm not only outperforms existing visual object tracking algorithms in terms of accuracy, success rate, accuracy, and robustness but also achieve the performance required for real-time tracking in terms of execution speed on the central processing unit. This paper provides an in-depth analysis and discussion of the adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis. Using a variety of county-level algorithms for analysis and multiple solutions to improve the accuracy of the results, we obtain a more efficient and accurate algorithm. (shrink)

This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are (...) proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis. (shrink)

In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the (...) generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles — parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets. (shrink)

Governments and citizens of nearly every nation have been compelled to respond to COVID-19. Many measures have been adopted, including contact tracing and risk assessment algorithms, whereby citizen whereabouts are monitored to trace contact with other infectious individuals in order to generate a risk status via algorithmic evaluation. Based on 38 in-depth interviews, we investigate how people make sense of Health Code, the Chinese contact tracing and risk assessment algorithmic sociotechnical assemblage. We probe how people accept or resist (...) Health Code by examining their ongoing, dynamic, and relational interactions with it. Participants display a rich variety of attitudes toward privacy and surveillance, ranging from fatalism to the possibility of privacy to trade-offs for surveillance in exchange for public health, which is mediated by the perceived effectiveness of Health Code and changing views on the intentions of institutions who deploy it. We show how perceived competency varies not just on how well the technology works, but on the social and cultural enforcement of various non-technical aspects like quarantine, citizen data inputs, and cell reception. Furthermore, we illustrate how perceptions of Health Code are nested in people’s broader interpretations of disease control at the national and global level, and unexpectedly strengthen the Chinese authority’s legitimacy. None of the Chinese public, Health Code, or people’s perceptions toward Health Code are predetermined, fixed, or categorically consistent, but are co-constitutive and dynamic over time. We conclude with a theorization of a relational perception and methodological reflections to study algorithmic sociotechnical assemblages beyond COVID-19. (shrink)

This response discusses the experiment reported in Krahmer et al.’s Letter to the Editor of Cognitive Science. We observe that their results do not tell us whether the Incremental Algorithm is better or worse than its competitors, and we speculate about implications for reference in complex domains, and for learning from ‘‘normal” (i.e., non-semantically-balanced) corpora.

Extended redundancy analysis is a statistical method that relates multiple sets of predictors to response variables. In ERA, the conventional approach of model evaluation tends to overestimate the performance of a model since the performance is assessed using the same sample used for model development. To avoid the overly optimistic assessment, we introduce a new model evaluation approach for ERA, which utilizes computer-intensive resampling methods to assess how well a model performs on unseen data. Specifically, we suggest several (...) new model evaluation metrics for ERA that compute a model’s performance on out-of-sample data, i.e., data not used for model development. Although considerable work has been done in machine learning and statistics to examine the utility of cross-validation and bootstrap variants for assessing such out-of-sample predictive performance, to date, no research has been carried out in the context of ERA. We use simulated and real data examples to compare the proposed model evaluation approach with the conventional one. Results show the conventional approach always favor more complex ERA models, thereby failing to prevent the problem of overfitting in model selection. Conversely, the proposed approach can select the true ERA model among many mis-specified models. (shrink)

Intelligent communication processing in English aims to obtain effective information from unstructured text data using various text processing techniques. Text vector representation and text similarity calculation are important fundamental tasks in the whole field of natural language processing. In response to the shortcomings of existing sentence vector representation models and the singularity of text similarity algorithms, improved models and algorithms are proposed based on a thorough study of related domain technologies. This paper presents an in-depth and comprehensive study (...) of text vectorization representation and text similarity calculation algorithms in the field of natural language processing. The existing text vectorized representation models and text similarity computation algorithms are described, and their shortcomings are summarized to provide a basis for the background and significance of this paper, as well as to provide ideas for improvement directions. It is experimentally verified that the sentence vector model proposed in this paper achieves higher accuracy than the SIF sentence vector model for text classification tasks. In the task of text similarity computation, it achieves better results in three evaluation metrics: accuracy, recall, and F1 value. The algorithm also improves the computational efficiency of the model to a certain extent by removing feature words with low feature contribution. The algorithm first improves the deficiencies of the traditional word-shift distance algorithm by defining multifeature fusion weights and realizes a text similarity calculation algorithm based on multifeature weighted fusion with better similarity calculation results. Then, a linear weighting model is constructed to further combine the similarity calculation results of the hierarchical pooled IIG-SIF sentence vectors to realize the multimodel fusion text similarity calculation algorithm. (shrink)

Multiobjective evolutionary algorithms have witnessed prosperity in solving many-objective optimization problems over the past three decades. Unfortunately, no one single MOEA equipped with given parameter settings, mating-variation operator, and environmental selection mechanism is suitable for obtaining a set of solutions with excellent convergence and diversity for various types of MaOPs. The reality is that different MOEAs show great differences in handling certain types of MaOPs. Aiming at these characteristics, this paper proposes a flexible ensemble framework, namely, ASES, which is (...) highly scalable for embedding any number of MOEAs to promote their advantages. To alleviate the undesirable phenomenon that some promising solutions are discarded during the evolution process, a big archive that number of contained solutions be far larger than population size is integrated into this ensemble framework to record large-scale nondominated solutions, and also an efficient maintenance strategy is developed to update the archive. Furthermore, the knowledge coming from updating archive is exploited to guide the evolutionary process for different MOEAs, allocating limited computational resources for efficient algorithms. A large number of numerical experimental studies demonstrated superior performance of the proposed ASES. Among 52 test instances, the ASES performs better than all the six baseline algorithms on at least half of the test instances with respect to both metrics hypervolume and inverted generational distance. (shrink)

The no-wait flow-shop scheduling problem with sequence-dependent setup times and release times is a typical NP-hard problem. This paper proposes an enhanced differential evolution algorithm with several fast evaluating strategies, namely, DE_FES, to minimize the total weighted tardiness objective for the NFSP with SSTs and RTs. In the proposed DE_FES, the DE-based search is adopted to perform global search for obtaining the promising regions or solutions in solution space, and a fast local search combined with three presented strategies is designed (...) to execute exploitation from these obtained regions. Test results and comparisons with two effective meta-heuristics show the effectiveness and robustness of DE_FES. (shrink)

BackgroundCommunity engagement in research has gained momentum as an approach to improving research, to helping ensure that community concerns are taken into account, and to informing ethical decision-making when research is conducted in contexts of vulnerability. However, guidelines and scholarship regarding community engagement are arguably unsettled, making it difficult to implement and evaluate.DiscussionWe describe normative guidelines on community engagement that have been offered by national and international bodies in the context of HIV-related research, which set the stage for similar work (...) in other health related research. Next, we review the scholarly literature regarding community engagement, outlining the diverse ethical goals ascribed to it. We then discuss practical guidelines that have been issued regarding community engagement. There is a lack of consensus regarding the ethical goals and approaches for community engagement, and an associated lack of indicators and metrics for evaluating success in achieving stated goals. To address these gaps we outline a framework for developing indicators for evaluating the contribution of community engagement to ethical goals in health research.SummaryThere is a critical need to enhance efforts in evaluating community engagement to ensure that the work on the ground reflects the intentions expressed in the guidelines, and to investigate the contribution of specific community engagement practices for making research responsive to community needs and concerns. Evaluation mechanisms should be built into community engagement practices to guide best practices in community engagement and their replication across diverse health research settings. (shrink)

In this paper, we use a pseudo-algorithmic procedure for assessing an AI-generated text. We apply the Comprehensive Assessment Procedure for Natural Argumentation (CAPNA) in evaluating the arguments produced by an Artificial Intelligence text generator, GPT-3, in an opinion piece written for the Guardian newspaper. The CAPNA examines instances of argumentation in three aspects: their Process, Reasoning and Expression. Initial Analysis is conducted using the Argument Type Identification Procedure (ATIP) to establish, firstly, that an argument is present and, secondly, its specific (...) type in terms of the argument classification framework of the Periodic Table of Arguments (PTA). Procedural Questions are then used to test the acceptability of the argument in each of the three aspects. The analysis shows that while the arguments put forward by the AI text generator are varied in terms of their type and follow familiar patterns of human reasoning, they contain obvious weaknesses. From this we can conclude that the automated generation of persuasive, well-reasoned argumentation is a far more difficult task than the generation of meaningful language, and that if AI systems producing arguments are to be persuasive, they require a method of checking the plausibility of their own output. (shrink)

The current use of citation-based metrics to evaluate the research output of individual researchers is highly discriminatory because they are uniformly applied to authors of single-author articles as well as contributors of multi-author papers. In the latter case, these quantitative measures are counted, as if each contributor were the single author of the full article. In this way, each and every contributor is assigned the full impact-factor score and all the citations that the article has received. This has a (...) multiplication effect on each contributor's citation-based evaluative metrics of multi-author articles, because the more contributors an article has, the more undeserved credit is assigned to each of them. In this paper, I argue that this unfair system could be made fairer by requesting the contributors of multi-author articles to describe the nature of their contribution, and to assign a numerical value to their degree of relative contribution. In this way, we could create a contribution-specific index of each contributor for each citation metric. This would be a strong disincentive against honorary authorship and publication cartels, because it would transform the current win-win strategy of accepting honorary authors in the byline into a zero-sum game for each contributor. (shrink)

In a rigorous systematic review, Dukhanin and colleagues categorize metrics and evaluative tools of the engagement of patient, public, consumer, and community in decision-making in healthcare institutions and systems. The review itself is ably done and the categorizations lead to a useful understanding of the necessary elements of engagement, and a suite of measures relevant to implementing engagement in systems. Nevertheless, the question remains whether the engagement of patient representatives in institutional or systemic deliberations will lead to improved clinical (...) outcomes or increased engagement of individual patients themselves in care. Attention to the conceptual foundations of patient engagement would help make this systematic review relevant to the clinical care of patients. (shrink)

Harmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algorithm relies on three operators for performing improvisation: memory consideration, pitch adjustment, and random consideration. In order to improve algorithm efficiency, we use roulette wheel and tournament selection in memory consideration, replace (...) the pitch adjustment and random consideration with a modified polynomial mutation, and enhance the obtained new harmony with a modified β-hill climbing algorithm. Such modification can help to maintain the diversity and enhance the convergence speed of the modified HS algorithm. β-hill climbing is a recently introduced local search algorithm that is able to effectively solve different optimization problems. β-hill climbing is utilized in the modified HS algorithm as a local search technique to improve the generated solution by HS. Two algorithms are proposed: the first one is called PHSβ–HC and the second one is called Imp. PHSβ–HC. The two algorithms are evaluated using 13 global optimization classical benchmark function with various ranges and complexities. The proposed algorithms are compared against five other HSA using the same test functions. Using Friedman test, the two proposed algorithms ranked 2nd (Imp. PHSβ–HC) and 3rd (PHSβ–HC). Furthermore, the two proposed algorithms are compared against four versions of particle swarm optimization (PSO). The results show that the proposed PHSβ–HC algorithm generates the best results for three test functions. In addition, the proposed Imp. PHSβ–HC algorithm is able to overcome the other algorithms for two test functions. Finally, the two proposed algorithms are compared with four variations of differential evolution (DE). The proposed PHSβ–HC algorithm produces the best results for three test functions, and the proposed Imp. PHSβ–HC algorithm outperforms the other algorithms for two test functions. In a nutshell, the two modified HSA are considered as an efficient extension to HSA which can be used to solve several optimization applications in the future. (shrink)

Third generation prospect theory is a theory of choices and of judgments of highest buying and lowest selling prices of risky prospects, i.e., of willingness to pay and willingness to accept. The gap between WTP and WTA is sometimes called the “endowment effect” and was previously called the “point of view” effect. Third generation prospect theory combines cumulative prospect theory for risky prospects with the theory that judged values are based on the integration of price paid or price (...) received with the consequences of gambles. In TGPT, the discrepancy between WTP and WTA is due to loss aversion-losses have greater absolute utility than gains of the same value. TGPT was developed independently of similar developments by Birnbaum and Zimmermann :145–187, 1998) and Luce. This paper reviews theoretical and empirical findings, to show that TGPT fails as a descriptive model of both choices and judgments. Evidence refutes three implications of TGPT, but they are consistent with configural weight models in which loss aversion is not needed to describe the results. In the configural weight models, buyers place greater weight on lower consequences, attributes or estimates of value compared to sellers, who place greater configural weight on higher aspects of an object or prospect. (shrink)

This paper proposes a two-level metaheuristic consisting of lower- and upper-level algorithms for the job-shop scheduling problem with multipurpose machines. The lower-level algorithm is a local search algorithm used for finding an optimal solution. The upper-level algorithm is a population-based metaheuristic used to control the lower-level algorithm’s input parameters. With the upper-level algorithm, the lower-level algorithm can reach its best performance on every problem instance. Most changes of the proposed two-level metaheuristic from its original variants are in the lower-level (...) algorithm. A main purpose of these changes is to increase diversity into solution neighborhood structures. One of the changes is that the neighbor operators of the proposed lower-level algorithm are developed to be more adjustable. Another change is that the roulette-wheel technique is applied for selecting a neighbor operator and for generating a perturbation operator. In addition, the proposed lower-level algorithm uses an adjustable delay-time limit to select an optional machine for each operation. The performance of the proposed two-level metaheuristic was evaluated on well-known benchmark instances. The evaluation’s results indicated that the proposed two-level metaheuristic performs well on most benchmark instances. (shrink)

Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. It selects information and similarity relationships based on the user’s history and collects others that are the same as the user’s hobbies. User’s evaluation information is to generate recommendations. The main research is the inadequate combination of context information and the mining of new points of interest in the context-aware recommendation process. On the (...) basis of traditional recommendation technology, in view of the characteristics of the context information in music recommendation, a personalized and personalized music based on popularity prediction is proposed. Recommended algorithm is MRAPP. The algorithm first analyzes the user’s contextual information under music recommendation and classifies and models the contextual information. The traditional content-based recommendation technology CB calculates the recommendation results and then, for the problem that content-based recommendation technology cannot recommend new points of interest for users, introduces the concept of popularity. First, we use the memory and forget function to reduce the score and then consider user attributes and product attributes to calculate similarity; secondly, we use logistic regression to train feature weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results. Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy. Through theoretical proof and simulation experiments, the effectiveness of the MRAPP algorithm is demonstrated. (shrink)

This paper examines flexibility in ad hoc networks and suggests that, even with cross-layer design as a mechanism to improve adaptation, a fundamental limitation exists in the ability of a single optimization function, defined a priori, to adapt the network to meet all quality-of-service requirements. Thus, code implementing multiple algorithms will have to be positioned within the network. Active networking and programmable networking enable unprecedented autonomy and flexibility for ad hoc communication networks. However, in order to best leverage the (...) results of active and programmable networking, metrics that indicate the nature and location of required flexibility need to be developed. The primary contribution of this paper is to propose a metric that couples network topological rate of change with the ability of a generic service to move itself to an optimal location in concert with the changing network. This metric points to a fundamental tradeoff among adaptation (changing service location), performance (sophistication or estimated minimum code size of the service), and the network’s ability to tune itself to a changing ad hoc network topology. (shrink)

This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing (...) new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints. (shrink)

A growing body of empirical evidence suggests the existence of a Paradox of Choice, whereby a larger choice set leads to a lower expected payoff to the decisionmaker. These empirical findings contradict traditional choice-theoretic results in microeconomics and even social psychology, suggesting profound yet unexplained aspects of choice settings/behavior. The Paradox remains largely as a theoretically-rootless empirical phenomenon. We neither understand the mechanisms and conditions that generate it, nor whether the Paradox stems from choice behavior, choice setting, or both. We (...) present a general search model of consumer choice under incomplete information to examine the theoretical existence and conditions for the Paradox. The model departs from traditional microeconomic choice theory in assuming no “cognitive free lunches.” Search and evaluative efforts require time, focus, and opportunity cost in the model, as in real life. The model shows that conditions for Paradox readily exist, diminishing returns to potential value of the choice in choice set size is a necessary condition for a Paradox outcome, that a Paradox outcome can occur with or without a “choice overload” response by the decisionmaker, and that avoidance of a Paradox outcome is an impossibility in some choice settings (primitive to setting regardless of choice behavior). Lastly, we develop a pareto-improving choice set reduction algorithm that eliminates weakly dominated choice set elements and presents the restricted choice set to the decisionmaker. As such, it is shown that the algorithm acts to treat or mitigate the Paradox of Choice, as well as to pareto-improve choice-supporting evaluation more generally. Like established AI recommender systems, the novel algorithm filters choices. In the present case, the filtering is based upon identification of weakly dominated choice set elements. (shrink)

Polarization is a topic of intense interest among social scientists, but there is significant disagreement regarding the character of the phenomenon and little understanding of underlying mechanics. A first problem, we argue, is that polarization appears in the literature as not one concept but many. In the first part of the article, we distinguish nine phenomena that may be considered polarization, with suggestions of appropriate measures for each. In the second part of the article, we apply this analysis to evaluate (...) the types of polarization generated by the three major families of computational models proposing specific mechanisms of opinion polarization. (shrink)

We have developed a formal model of certain types of riddles, and implemented it in a computer program, JAPE, which generates simple punning riddles. In order to test the model, we evaluated the behaviour of the program, by having 120 children aged eight to eleven years old rate JAPE-generated texts, human-generated texts, and non-joke texts for "jokiness" and funniness. This confirmed that JAPE's output texts are indeed jokes, and that there is no significant difference in funniness or jokiness between JAPE"s (...) most comprehensible texts and published human-generated jokes. (shrink)