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This article examines the phenomenon of fake news through a survey of university students in the United Kingdom. The survey, composed through a selection of factual and non-factual content/news and complemented through a validation tool, sought to assess the attitudes of these respondents to items of factual misinformation before and after these were verified with the tool. The findings from the survey present online misinformation as a very complex and unfolding phenomenon in terms of user behaviour, particularly when presented with an authentication tool. The majority of respondents failed in identifying factual from fake news posts. While respondents indicated mistrust in using third-party validation tools, the majority indicated a critical need for a verification tool that would support their quest and increase their trust in what they read online.

While we instantaneously recognize a face as attractive, it is much harder to explain what exactly defines personal attraction. This suggests that attraction depends on implicit processing of complex, culturally and individually defined features. Generative adversarial neural networks (GANs), which learn to mimic complex data distributions, can potentially model subjective preferences unconstrained by pre-defined model parameterization. Here, we present generative brain-computer interfaces (GBCI), coupling GANs with brain-computer interfaces. GBCI first presents a selection of images and captures personalized attractiveness reactions toward the images via electroencephalography. These reactions are then used to control a GAN model, finding a representation that matches the features constituting an attractive image for an individual. We conducted an experiment (N=30) to validate GBCI using a face-generating GAN and producing images that are hypothesized to be individually attractive. In double-blind evaluation of the GBCI-produced images against matched controls, we found GBCI yielded highly accurate results. Thus, the use of EEG responses to control a GAN presents a valid tool for interactive information-generation. Furthermore, the GBCI-derived images visually replicated known effects from social neuroscience, suggesting that the individually responsive, generative nature of GBCI provides a powerful, new tool in mapping individual differences and visualizing cognitive-affective processing.

Mobile Crowdsensing (MCS) has attained much attention for gathering distributed mobile users to complete large-scale sensing tasks. To ensure the task completion, enough users should be motivated to perform tasks. Thus, many existing works in MCS focus on proposing incentive mechanism in the offline scenario, where the information of all users is available to the platform. However, we argue that the actual MCS is usually an online scenario, where the platform does not know the user's information until they establish connections with the platform. Meanwhile, users connect to the platform randomly and will cut off the connection at any time. Hence, when accessed by a user, the platform needs to make an irrevocable decision instantly about whether to select the user or not, and decides a remuneration for the user without knowing future information. In this paper, we first propose a reverse-auction framework to model the interaction between the platform and users. Then, we present an online truthful incentive mechanism (OTIM) to motivate users, including online winner selection and remuneration determination strategies. Finally, massive simulations are conducted based on three real traces and the results illustrate that OTIM achieves truthfulness, individual rationality, computational efficiency and an approximately full budget utilization.

There is a growing interest in the neuroscience community on the advantages of multimodal neuroimaging modalities. Functional and structural interactions between brain areas can be represented as a network (graph) allowing us to employ graph-theoretic tools in multiple research directions. Researchers usually treated brain networks acquired from different modalities or different frequencies separately. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. We can incorporate this information from different modalities (multi-modal case), from different frequencies (multi-frequency case), or a single modality following a dynamic functional connectivity analysis (multi-layer, dynamic case). Researchers already used multi-layer networks to model brain disorders, to detect key hubs related to a specific function, to reveal structural-functional relationships, and to define more precise connectomic biomarkers related to brain disorders. However, the construction of a multilayer network depends on the selection of multiple preprocessing steps that can affect the final network topology. Here, we analyzed the fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total). We focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, we untangled specific combinations of researchers choices that yield repeatable topologies, giving us the chance to recommend best practices over consistent topologies.

This paper investigates how migration and location choice decisions depend on a large set of location characteristics, with particular focus on measuring the importance and nature of non-monetary costs of moving. We employ a stated-preference approach to elicit respondents’ choice probabilities for a set of hypothetical choice scenarios, using two waves from the NY Fed’s Survey of Consumer Expectations. Our stated probabilistic choice approach allows us to recover the distribution of individual-level preferences for location and mobility attributes without concerns about omitted variables and selection biases that hamper analyses based on observed mobility choices alone. We estimate substantial heterogeneity in the willingness-to-pay (WTP) for location characteristics and in moving costs, both across and within demographic groups. We find moving costs to be strongly associated with attachment to the current neighborhood and dwelling and to social networks. Our results indicate evidence of sorting into current locations based on preferences for location attributes as well as a strong negative association between respondents’ non-monetary moving costs and their moving expectations and actual mobility decisions.

Deep reinforcement learning (DRL)-based recommender systems have recently come into the limelight due to their ability to optimize long-term user engagement. A significant challenge in DRL-based recommender systems is the large action space required to represent a variety of items. The large action space weakens the sampling efficiency and thereby, affects the recommendation accuracy. In this article, we propose a DRL-based method called deep hierarchical category-based recommender system (DHCRS) to handle the large action space problem. In DHCRS, categories of items are used to reconstruct the original flat action space into a two-level category-item hierarchy. DHCRS uses two deep Q -networks (DQNs): 1) a high-level DQN for selecting a category and 2) a low-level DQN to choose an item in this category for the recommendation. Hence, the action space of each DQN is significantly reduced. Furthermore, the categorization of items helps capture the users' preferences more effectively. We also propose a bidirectional category selection (BCS) technique, which explicitly considers the category-item relationships. The experiments show that DHCRS can significantly outperform state-of-the-art methods in terms of hit rate and normalized discounted cumulative gain for long-term recommendations.

Text is a new way to guide human image manipulation. Albeit natural and flexible, text usually suffers from inaccuracy in spatial description, ambiguity in the description of appearance, and incompleteness. We in this paper address these issues. To overcome inaccuracy, we use structured information (e.g., poses) to help identify correct location to manipulate, by disentangling the control of appearance and spatial structure. Moreover, we learn the image-text shared space with derived disentanglement to improve accuracy and quality of manipulation, by separating relevant and irrelevant editing directions for the textual instructions in this space. Our model generates a series of manipulation results by moving source images in this space with different degrees of editing strength. Thus, to reduce the ambiguity in text, our model generates sequential output for manual selection. In addition, we propose an efficient pseudo-label loss to enhance editing performance when the text is incomplete. We evaluate our method on various datasets and show its precision and interactiveness to manipulate human images.

Machine learning and its subfield deep learning techniques provide opportunities for the development of operator mental state monitoring, especially for cognitive workload recognition using electroencephalogram (EEG) signals. Although a variety of machine learning methods have been proposed for recognizing cognitive workload via EEG recently, there does not yet exist a review that covers in-depth the application of machine learning methods. To alleviate this gap, in this paper, we survey cognitive workload and machine learning literature to identify the approaches and highlight the primary advances. To be specific, we first introduce the concepts of cognitive workload and machine learning. Then, we discuss the steps of classical machine learning for cognitive workload recognition from the following aspects, i.e., EEG data preprocessing, feature extraction and selection, classification method, and evaluation methods. Further, we review the commonly used deep learning methods for this domain. Finally, we expound on the open problem and future outlooks.

The interest in the application of nonlinear methodologies for the analysis of EEG signals for automatic recognition of emotions has increased notably. A vast number of studies in the research field of emotions detection have focused on the identification of the four quadrants of the valence-arousal emotional model. In this work, the recently introduced dispersion entropy (DispEn) has been applied for the first time to discern between the four groups of emotions corresponding to the four quadrants. This entropy-based index has demonstrated a considerable performance when dealing with this problem. Concretely, frontal and parieto-occipital brain regions reported the most relevant results when identifying the four emotional groups. Furthermore, the implementation of a classification model based on a sequential forward selection (SFS) approach and a support vector machine (SVM) classifier provided an average accuracy of 89.54%. This result is comparable to similar works in the scientific literature, with the advantage that the controlled selection of a reduced number of input features by means of the SFS scheme allowed to give a clinical interpretation of the outcomes. Therefore, it has been possible to reveal new insights about the performance of the most relevant brain regions involved in the processing of emotional information.

Start-up accelerators have attracted increasing attention from start-ups in recent years. However, little is known about how they interact with start-ups and investors, how they prioritize a wide variety of their services, and how macro-level factors affect their performance. We use a game-theoretic approach to study mutual effects between an accelerator, a venture capitalist (VC), and start-ups. In the model, the accelerator selects start-ups and invests in them. During its program, it provides intensive mentorship for the selected start-ups and screens them to determine whether or not they are successful. Finally, it introduces successful start-ups to the VC to obtain financing for their ideas. Our results indicate that 1) the most vital role of an accelerator is its screening services. 2) Given limited resources of accelerators, prioritizing their services is the key to their efficacy. Screening, mentorship, and seed investment services possess the first, second, and third priorities, respectively. 3) Although accelerators are anywhere beneficial, macro-level heterogeneity of entrepreneurial ecosystems is associated to their efficacy. Their efficacy is higher (lower) in less (more) developed regions. 4) Institutional reforms targeting mentoring or entrepreneurial communities are unexpectedly detrimental to the efficacy of accelerators, while they may enhance start-ups’ equilibrium willingness to participate.

This study focuses on the iconographic channel of the graphic novel as a particular occurrence of silence. In Comics, images provide not only the data required for the development of narration; they also render available the concrete circumstances of the enunciation and often orient the reader towards the identification of language in action, or towards the selection of a particular communicative intention, a process which coincides with Saville-Troike’s silences carrying illocutionary force and perlocutionary effect (1985), or with Kurzon’s silences – intentional signifiers alternating with an utterable signified. Through the analysis of concrete scenes taken from three graphic novels dealing with sociopolitical contexts of conflict – Satrapi’s Persepolis (2000), Folman and Polonsky’s Waltz with Bashir (2009) and Sacco’s Palestine (2007) – we identify two different sets of arguments: (1) semi-silent arguments resulting from the interplay between verbal and visual language & (2) silent arguments emerging within an entirely visual, extra-linguistic scene, where images alone regulate the quantity or the quality of information given at a certain point of narration with the aim of leading the addressee to a certain tacit conclusion.

Exploring questions of representativeness, balance and comparability is essential to tailoring corpus design and compilation to research goals, and to ensuring the validity of research results. This is especially true when the target population of texts under examination is very large and transcends a restricted area of specialization and/or covers multiple genres, as in the case of texts translated in institutional settings. This paper describes the multilayered sequential approach to corpus building applied in a comparative study on legal translation in three of these settings. The approach is based on a full mapping and categorization of institutional texts from a legal perspective; it applies an innovative combination of stratified sampling techniques integrating quantitative and qualitative criteria adapted to the research aims. The resulting corpora, categorization matrix and selection records, together with the methodological detail provided, can be useful for building other multi-genre corpora in translation studies and further afield.

This article reports on an interactional practice found in one form of adversarial talk, arguments during protests, where participants work to ‘entice’ a particular answer from an opponent using an uncontroversial questions in order to challenge the opponent on the basis of their own answer. Based on a collection of arguments during protests posted to YouTube, this article uses conversation analysis (CA) in order to investigate the way in which participants employ these uncontroversial questions as ‘pre-challenges’, using speaker selection, recipient focused topics and a moral ordering of talk to work to obligate a particular answer from the recipient. The results of the analysis illustrate several ways in which participants manipulate epistemics, speaker selection, and recipient design as resources for enacting social conflict.

This article focuses on how register considerations informed and guided the design of the spoken component of the British National Corpus 2014 (Spoken BNC2014). It discusses why the compilers of the corpus sought to gather recordings from just one broad spoken register – ‘informal conversation’ – and how this and other design decisions afforded contributors to the corpus much freedom with regards to the selection of situational contexts for the recordings. This freedom resulted in a high level of diversity in the corpus for situational parameters such as recording location and activity type, each of which was captured in the corpus metadata. Focussing on these parameters, this article provides evidence for functional variation among the texts in the corpus and suggests that differences such as those observed presently could be analysable within the existing frameworks for analysis of register variation in spoken and written language, such as multidimensional analysis.

ON ASSIGNING PRAGMATIC FUNCTIONS IN ENGLISH J. Lachlan Mackenzie and M. Evelien Keizer Abstract This paper presents a discussion of the treatment of the pragmatic functions Topic and Focus in Functional Grammar (Dik: 1989, ch. 1 3 ) . Two questions will be addressed: (a) the theoretical question of how the interface between the static grammar (dealing with a discourse as the product of text-creating activity; and the dynamic theory of verbal interaction (dealing with discourse as the ongoing text-creating process itself) is handled with regard to pragmatic functions; and (b) the practical question whether the reader of Dik (1989) finds a set of proposals that can be operationalized in the analysis of linguistic material. With regard to the former question we conclude that in the present FG treatment of Topic and Focus, the static and the dynamic approaches do not connect and that as a result, the speaker's selection of constituents for Topic or Focus function is left unaccounted for. As for the second question, we show that Dik's proposal can be put into practice, but that the resultant analysis suffers from a number of inconsistencies and unclarities. Finally, we argue that most of these inconsistencies and unclarities can be solved if (1) we accept a different classification of Focus, and (2) assume that Topic assignment is irrelevant in English, as there is no consistent way in which Topic constituents are given special treatment, and P1-placement can be accounted for without having to resort to Topic function.

This paper is a study of how deaf children in Hong Kong acquire Chinese relative clauses. The relative clause is reported to be a difficult structure for deaf children (Friedmann & Szterman 2006). While it may be true for postnominal relative clauses, it is unclear whether prenominal relative clauses are equally difficult for deaf children. This paper explores this question by examining deaf children’s comprehension and production of Chinese relative clauses via an elicited production task, a picture selection task and a dots-connecting task, which are all presented in written format. In addition to deaf children, typically developing Cantonese children and Cantonese adults with high Chinese proficiency are also recruited for comparison. The results show that deaf children fall behind typically developing Cantonese children in production. But deaf children with higher Chinese proficiency can perform similarly with typically developing Cantonese children. The error types and the types of non-RC responses produced by deaf children are also present in the data of typically developing Cantonese children, suggesting that deaf children do not undergo a different pathway in the development of relative clauses. While typically developing Cantonese children demonstrate non-significant subject advantage in production but object advantage in comprehension, deaf children do not demonstrate subject-object asymmetry in production and object advantage in comprehension.

Delusions are rigid beliefs held with high certainty despite contradictory evidence. Notwithstanding decades of research, we still have a limited understanding of the computational and neurobiological alterations giving rise to delusions. In this review, we highlight a selection of recent work in computational psychiatry aimed at developing quantitative models of inference and its alterations, with the goal of providing an explanatory account for the form of delusional beliefs in psychosis. First, we assess and evaluate the experimental paradigms most often used to study inferential alterations in delusions. Based on our review of the literature and theoretical considerations, we contend that classic draws-to-decision paradigms are not well-suited to isolate inferential processes, further arguing that the commonly cited ‘jumping-to-conclusion’ bias may reflect neither delusion-specific nor inferential alterations. Second, we discuss several enhancements to standard paradigms that show promise in more effectively isolating inferential processes and delusion-related alterations therein. We further draw on our recent work to build an argument for a specific failure mode for delusions consisting of prior overweighting in high-level causal inferences about partially observable hidden states. Finally, we assess plausible neurobiological implementations for this candidate failure mode of delusional beliefs and outline promising future directions in this area.

Federated machine learning which enables resource-constrained node devices (e.g., Internet of Things (IoT) devices, smartphones) to establish a knowledge-shared model while keeping the raw data local, could provide privacy preservation and economic benefit by designing an effective communication protocol. However, this communication protocol can be adopted by attackers to launch data poisoning attacks for different nodes, which has been shown as a big threat to most machine learning models. Therefore, we in this paper intend to study the model vulnerability of federated machine learning, and even on IoT systems. To be specific, we here attempt to attacking a popular federated multi-task learning framework, which uses a general multi-task learning framework to handle statistical challenges in federated learning setting. The problem of calculating optimal poisoning attacks on federated multi-task learning is formulated as a bilevel program, which is adaptive to arbitrary selection of target nodes and source attacking nodes. We then propose a novel systems-aware optimization method, called as ATTack on Federated Learning (ATFL), to efficiently derive the implicit gradients for poisoned data, and further attain optimal attack strategies in the federated machine learning. This is an earlier work, to our knowledge, that explores attacking federated machine learning via data poisoning. Finally, experiments on several real-world datasets demonstrate that when the attackers directly poison the target nodes or indirectly poison the related nodes via using the communication protocol, federated multi-task learning model is sensitive to both poisoning attacks.

Research is often guided by maps of elementary dimensions, such as core traits, foundations of morality, and principal stereotype dimensions. Yet, there is no comprehensive map of prejudice dimensions. A major limiter of developing a prejudice map is the ad hoc sampling of target groups. We used a broad and largely theory-agnostic selection of groups to derive a map of principal dimensions of expressed prejudice in contemporary American society. Across a series of exploratory and confirmatory studies, we found three principal factors: Prejudice against marginalized groups, prejudice against privileged/conservative groups, and prejudice against unconventional groups (with some inverse loadings for conservative groups). We documented distinct correlates for each factor, in terms of social identifications, perceived threats, personality, and behavioral manifestations. We discuss how the current map integrates several lines of research, and point to novel and underexplored insights about prejudice. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

It is not a secret that many leadership development programs are not producing their intended results, and that the majority of first-time managers are simply not effective. Even though leadership development is a high priority for organizations, most companies recognize that their programs have much to improve in selecting and developing first time managers. Perhaps it is time to reconsider the assumptions that these programs are based upon. First, while organizations tend to promote high performing individual contributors, the mindsets of an effective individual contributor do no translate to effective leadership mindsets. Second, leadership development programs generally assume that new managers can change behavior by focusing on skill development. These programs fail to identify, name, and address the underlying mindsets that influence individual action. In this article, I highlight research on leadership and leadership mindsets that can replace the assumptions of underperforming leadership selection and training programs. Building on leadership mindset research, I make recommendations to help organizations better select and train new managers.

Describing systems in terms of choices and their resulting costs and rewards offers the promise of freeing algorithm designers and programmers from specifying how those choices should be made; in implementations, the choices can be realized by optimization techniques and, increasingly, by machine-learning methods. We study this approach from a programming-language perspective. We define two small languages that support decision-making abstractions: one with choices and rewards, and the other additionally with probabilities. We give both operational and denotational semantics. In the case of the second language we consider three denotational semantics, with varying degrees of correlation between possible program values and expected rewards. The operational semantics combine the usual semantics of standard constructs with optimization over spaces of possible execution strategies. The denotational semantics, which are compositional, rely on the selection monad, to handle choice, augmented with an auxiliary monad to handle other effects, such as rewards or probability. We establish adequacy theorems that the two semantics coincide in all cases. We also prove full abstraction at base types, with varying notions of observation in the probabilistic case corresponding to the various degrees of correlation. We present axioms for choice combined with rewards and probability, establishing completeness at base types for the case of rewards without probability.

Interest in unintended discrimination that can result from implicit attitudes and stereotypes (implicit biases) has stimulated many research investigations. Much of this research has used the Implicit Association Test (IAT) to measure association strengths that are presumed to underlie implicit biases. It had been more than a decade since the last published treatment of recommended best practices for research using IAT measures. After an initial draft by the first author, and continuing through three subsequent drafts, the 22 authors and 14 commenters contributed extensively to refining the selection and description of recommendation-worthy research practices. Individual judgments of agreement or disagreement were provided by 29 of the 36 authors and commenters. Of the 21 recommended practices for conducting research with IAT measures presented in this article, all but two were endorsed by 90% or more of those who felt knowledgeable enough to express agreement or disagreement; only 4% of the totality of judgments expressed disagreement. For two practices that were retained despite more than two judgments of disagreement (four for one, five for the other), the bases for those disagreements are described in presenting the recommendations. The article additionally provides recommendations for how to report procedures of IAT measures in empirical articles.

Niche construction theory concerns how organisms can change selection pressures by altering the feature–factor relationship between themselves and their environment. These alterations are standardly understood to be brought about through two kinds of organism–environment interaction: perturbative and relocational niche construction. We argue that a reconceptualization is needed on the grounds that if a niche is understood as the feature–factor relationship, then there are three fundamental ways in which organisms can engage in niche construction: constitutive, relational, and external niche construction. We further motivate our reconceptualization by showing some examples of organismic activities that fall outside of the current categorization of niche construction, but nonetheless should be included. We end by discussing two objections to niche construction and show how our reconceptualization helps to undercut these objections. ispartof: British Journal for the Philosophy of Science vol:73 issue:2 pages:351-372 status: published

Poetry has a prominent history in Arabic literature. The classical Arabic poetry has 16 m that vary in rhythm and target purpose. Chanting a poem eloquently requires knowing the poem’s meter and obtaining a diacritized version of its verses (letters inscribed with their short vowels); diacritics are often not inscribed in Arabic texts. This work proposes solutions to classify input Arabic text into the 16 poetry meters and prose. It also investigates the automatic diacritization of Arabic poetry. We adopt machine learning approach using a large dataset of 1657 k verses of poems and prose to develop neural networks to classify and diacritize Arabic poetry. We propose deep and narrow recurrent neural networks with bidirectional long short-term memory cells for solving these problems. The proposed model classifies the input text with an average accuracy of 97.27%, which is significantly higher than previous work. We also propose a solution that achieves an accuracy that approaches 100% when multiple verses of the same poem are available through predicting the class from the aggregate probabilities of the multiple verses. Diacritizing poetry is much harder than diacritizing prose due to the poet’s meticulous selection of phrases and relaxation of some diacritization rules.

Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.

Objectives : While COVID-19 had a profound impact on healthcare, its effects on medical students are less clear. This study explored the effects of COVID-19 safety measures on medical students’ specialty selection and career choices. It further considers the potential differential effects of COVID by gender. Design Between June and November 2020 at a Midwestern medical university, medical students with an anticipated graduation in 2021 through 2023 participated in virtual focus group sessions, which explored students’ transition to remote learning during COVID-19, perception of gender bias within medical education, and personal and professional goals. Nine focus groups were held, with two to six students per session (N = 22). Focus groups were video recorded, transcribed verbatim, and data were deidentified. Transcripts were coded and analyzed using consensual qualitative analysis to identify themes. Results : Our analysis captured three themes: (1) Impact of Institutional Decision-Making due to COVID-19, (2) Impact of Unstructured Time on Professional and Personal Decision Making, and (3) Impact of Societal Pressures, Gender Bias, and Mentorship on Career Planning. Conclusions : COVID-19 disrupted important learning opportunities for medical students. Mentorship and shadowing are critical in helping students make career and specialty decisions, particularly for women. The loss of these opportunities may have lasting career impacts for all students.

Precise prediction of risk for dyslexia among children’s in earlier stages is a significant long-term aim in the field of cognitive computing. Producing such accurate results for detection of dyslexia from a dataset which consist of low-quality dataset and the presence of vague information is the toughest challenge among researchers. This paper aims at developing an evolving model to handle the impreciseness in the detection of dyslexia more intelligently. In this work, each instance is described in a neutrosophic domain by defining a membership degree of truthiness, indeterminacy, and falsity. These instances are neutrosophically clustered by applying Neutrosophic C-Means clustering (NCM) which forms four different clusters namely dyslexia, no dyslexia, control/revision and hyperactivity or other issues. The outlier and noise are the special categories of indeterminacy which often occurs in real datasets are promptly discovered and clustered. NCM is optimized by introducing Artificial Bacterial Foraging (ABF), especially when there is vagueness or imprecision in the selection of cluster centroids. With the merits of global searching, ABF selects more promising clustering during cluster re-computation. The interpreted results confirm that the role played by proposed ONCMC-ABF algorithm produces better results in the prediction of dyslexia with the low-quality dataset.

Sleep requires that we disconnect from the environment, losing the ability to promptly respond to stimuli. There must be at least one essential function that justifies why we take this risk every day, and that function must depend on the brain being offline. We have proposed that this function is to renormalize synaptic weights after learning has led to a net increase in synaptic strength in many brain circuits. Without this renormalization, synaptic activity would become energetically too expensive and saturation would prevent new learning. There is converging evidence from molecular, electrophysiological, and ultrastructural experiments showing a net increase in synaptic strength after the major wake phase, and a net decline after sleep. The evidence also suggests that sleep-dependent renormalization is a smart process of synaptic down-selection, comprehensive and yet specific, which could explain the many beneficial effects of sleep on cognition. Recently, a key molecular mechanism that allows broad synaptic weakening during sleep was identified. Other mechanisms still being investigated should eventually explain how sleep can weaken most synapses but afford protection to some, including those directly activated by learning. That synaptic down-selection takes place during sleep is by now established; why it should take place during sleep has a plausible explanation; how it happens is still work in progress.

For a rapid dissemination of information during crisis events, official agencies and disaster relief organizations have been utilizing social media platforms, which are susceptible to rumor propagation. To minimize the impact of rumors with limited time and resources, the agencies and social media companies not only need to wisely choose the cases to clarify amongst the numerous cases, but they should also make an informed decision on the timing of clarification. Reacting fast can be misjudged as an obvious best policy as partial/imprecise information may fail to contain the impact of the rumors. On the other hand, investment in terms of time, effort, and money to clarify with more complete information also allows the rumors to spread with their full force during the learning phase, thereby making the process of decision-making very challenging. The objective of this paper is to determine the optimal strategies for the official agencies and social media companies by developing two novel sequential game-theoretic models, namely “Rumor Selection for Clarification” and “Learning for Rumor Clarification”, that can help decide which rumor to clarify and when to clarify, respectively. Results from this study indicate that posting verified information on social media reduces the uncertainties involved in rumor transmission, thereby enabling social media users to make informed decisions on whether to support or oppose the rumor being circulated. This verification needs to be obtained within reasonable limits of time and cost to keep the learning process worthwhile.

This exploratory investigation aims to develop a managerial decision model that assist international firms and investors in selecting the right valuable foreign country for their international operations. Two comparative and rich-information case studies were purposefully selected from among Arabian large firms. Additionally, six international investment decisions were investigated within these two cases and data were collected by in-depth interviews, follow-up interviews and questionnaire instrument. Within and cross-case analyses were undertaken in the current investigation. The findings demonstrated that a methodical and international experience-based strategic decision-making process was adopted to attain a profitable foreign country choice. The decision process comprised five critical managerial phases; that is, (1) analysis of internationalization drivers, (2) searching and development of location determinants and country alternatives, (3) obtaining adequate and related-information about potential countries, and, ultimately, assessment for these alternatives, (4) selection of a final beneficial foreign country choice, and, finally, (5) effective implementation of the selected country choice. The research findings provide theoretical and practical implications to the internationalization of firms as well as international investment. Further, it provides significant methodological contributions to international business research in relation to an effective multiple case study approach to capture elements of the international market selection process.

This paper offers a rationale for the typefaces chosen for the publication Technologies of Romance: Part II written by Paul O’Kane and celebrated at a symposium held at the Science Museum in November 2018. In setting out this rationale the paper broadens its considerations to reflect on the extent to which typefaces might in themselves usefully be considered as technologies of Romance. It locates the practice of noting in print the typeface used in a given publication both historically and within contemporary practice. The recording of the decisions informing the making of a book starts to hint at the nature of the book itself as a technology, and allows for some discussion of the values of ‘making’ books within both past and present contexts of production, including the challenge of digital readers. Elaboration on the decisions concerning the use of a given typeface for a publication then opens out a discussion of the intention behind selection, and both the functional and emotional ambitions of the typeface choice. The associative aims informing selection of a typeface are explored against an understanding of the associative embedding of meaning within aspects of the terminology of typefaces, as well as the scripts they represent and their designed forms. The paper explores for example the imperial associations embedded in the character shapes of the basic letterforms, as well as the technological implications of the terms such as uppercase and lowercase and the problematic colonial implications of the term ‘Latin’ to describe the script in question. In considering the archival possibilities of typefaces as technologies the paper concludes by reflecting on examples of digital typeface design practice, which have actively sought to re-imagine a previous era of letterpress typesetting technology and the possibilities for investigating ongoing Romantic ideals within this very particular and highly specialised design discipline.

Non-stigmatizing depictions of plus-size lovers of fashion are not easily found in traditional mass media, but in social media, fat fashion enthusiasts can readily be found. The purpose of this study was to investigate the little-studied phenomenon of plus-size YouTube content creators who make videos about fat fashion. To that end, thirteen individuals who wear plus-size women’s apparel took part in semi-structured interviews. A major theme found in the data analysis was transgression. Interview participants were staunch advocates of breaking discriminatory, unspoken societal rules that constrained them, especially in relation to dress. Three subthemes were found in relation to transgression: visibility, representation and agency. Embracing visibility, as seen in interviewees’ performance of fatness in public and in social media, may help to portray fatness as a human characteristic that is just as ‘normal’ as thinness. Serving as a positive role model to others was also embraced by participants, who hoped to assist their viewers in dealing with the consequences of living in the fatphobic US culture. The interviewees demonstrated and promoted agency through fashion, in marked contrast to their past experiences of being powerless and disparaged because of their size. Through celebration of mainstream, conforming fashion, the fat fashion vloggers are transgressive by joyfully wearing styles which previously were discouraged or often unavailable for plus-size consumers.

Visualizing data in sports videos is gaining traction in sports analytics, given its ability to communicate insights and explicate player strategies engagingly. However, augmenting sports videos with such data visualizations is challenging, especially for sports analysts, as it requires considerable expertise in video editing. To ease the creation process, we present a design space that characterizes augmented sports videos at an element-level (what the constituents are) and clip-level (how those constituents are organized). We do so by systematically reviewing 233 examples of augmented sports videos collected from TV channels, teams, and leagues. The design space guides selection of data insights and visualizations for various purposes. Informed by the design space and close collaboration with domain experts, we design VisCommentator, a fast prototyping tool, to eases the creation of augmented table tennis videos by leveraging machine learning-based data extractors and design space-based visualization recommendations. With VisCommentator, sports analysts can create an augmented video by selecting the data to visualize instead of manually drawing the graphical marks. Our system can be generalized to other racket sports (e.g., tennis, badminton) once the underlying datasets and models are available. A user study with seven domain experts shows high satisfaction with our system, confirms that the participants can reproduce augmented sports videos in a short period, and provides insightful implications into future improvements and opportunities.

How evaluative are legal texts? Do legal scholars and jurists speak a more descriptive or perhaps a more evaluative language? In this paper, we present the results of a corpus study in which we examined the use of evaluative language in both the legal domain as well as public discourse. For this purpose, we created two corpora. Our legal professional corpus is based on court opinions from the U.S. Courts of Appeals. We compared this professional corpus to a public corpus, which is based on blog discussions on the internet forum Reddit. While many linguistic phenomena can give insights into evaluativity, we investigated the use of a wide selection of evaluative adjectives (more specifically, thick adjectives) to gain a more comprehensive picture of the degree of evaluativity in the legal domain. Our analysis shows that legal professionals use thick terms less evaluatively than laypeople, which suggests that legal texts are less evaluative than ordinary discussions. This result, more generally, supports the philosophical idea that thick concepts may vary in their evaluative intensity.

Knowledge of transitive relationships between items can contribute to learning the order of a set of stimuli from pairwise comparisons. However, cognitive mechanisms of transitive inferences based on rank order remain unclear, as are relative contributions of reward associations and rule-based inference. To explore these issues, we created a conflict between rule- and reward-based learning during a serial ordering task. Rhesus macaques learned two lists, each containing five stimuli that were trained exclusively with adjacent pairs. Selection of the higher-ranked item resulted in rewards. “Small reward” lists yielded two drops of fluid reward, whereas “large reward” lists yielded five drops. Following training of adjacent pairs, monkeys were tested on novels pairs. One item was selected from each list, such that a ranking rule could conflict with preferences for large rewards. Differences between the corresponding reward magnitudes had a strong influence on accuracy, but we also observed a symbolic distance effect. That provided evidence of a rule-based influence on decisions. RT comparisons suggested a conflict between rule- and reward-based processes. We conclude that performance reflects the contributions of two strategies and that a model-based strategy is employed in the face of a strong countervailing reward incentive.

Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can make database more intelligent (AI4DB). For example, traditional empirical database optimization techniques (e.g., cost estimation, join order selection, knob tuning, index and view selection) cannot meet the high-performance requirement for large-scale database instances, various applications and diversified users, especially on the cloud. Fortunately, learning-based techniques can alleviate this problem. On the other hand, database techniques can optimize AI models (DB4AI). For example, AI is hard to deploy in real applications, because it requires developers to write complex codes and train complicated models. Database techniques can be used to reduce the complexity of using AI models, accelerate AI algorithms and provide AI capability inside databases. Thus both DB4AI and AI4DB have been extensively studied recently. In this paper, we review existing studies on AI4DB and DB4AI. For AI4DB, we review the techniques on learning-based configuration tuning, optimizer, index/view advisor, and security. For DB4AI, we review AI-oriented declarative language, AI-oriented data governance, training acceleration, and inference acceleration. Finally, we provide research challenges and future directions.

Car ferry services constitute an important part of the transportation network in several parts of the world, especially in areas with limited alternative modes of transport. A central problem facing decision makers is the socially optimal capacity of ferry services. However, the literature has not examined all the decision variables the are relevant to decision makers in a simultaneous framework, only partially. We add to the literature by treating all the relevant decisions variables in a simultaneous framework, which enables a more complete representation of optimal capacity, than partial frameworks. Our proposed methodology also includes the cost of not being able to board the first arriving departure, which is an essential cost in the case of car ferries with too low capacity. We apply the methodology to a case study of three major ferry crossings in Norway. Results indicate that a too large capacity is provided. Thus, local policy makers should consider revising the current service levels. Other policy makers may enact better decisions based on the findings we provide. Sensitivity test suggests that the method used to estimate the number of users not being able to board due to capacity concerns may be improved. This, however, does not alter our main conclusion.

The amount of digital text-based consumer review data has increased dramatically and there exist many machine learning approaches for automated text-based sentiment analysis. Marketing researchers have employed various methods for analyzing text reviews but lack a comprehensive comparison of their performance to guide method selection in future applications. We focus on the fundamental relationship between a consumer’s overall empirical evaluation, and the text-based explanation of their evaluation. We study the empirical tradeoff between predictive and diagnostic abilities, in applying various methods to estimate this fundamental relationship. We incorporate methods previously employed in the marketing literature, and methods that are so far less common in the marketing literature. For generalizability, we analyze 25,119 products in nine product categories, and 260,489 reviews across five review platforms. We find that neural network-based machine learning methods, in particular pre-trained versions, offer the most accurate predictions, while topic models such as Latent Dirichlet Allocation offer deeper diagnostics. However, neural network models are not suited for diagnostic purposes and topic models are ill equipped for making predictions. Consequently, future selection of methods to process text reviews is likely to be based on analysts’ goals of prediction versus diagnostics.

This paper gives recommendations for selecting, implementing and sustainably operating ERP systems. We indicate special aspects which are important from our point of view. The paper addresses practitioners who are responsible for ERP systems, especially IT and project managers. The structure of the paper matches the three main phases of an ERP system’s lifecycle within an enterprise: selection, implementation and operations. General process models are given for selection and implementation of ERP systems. Our suggestions stretch from project management, business process reengineering, system selection criteria, reporting and customizing to choosing key users, data migration, and user training. Operations of ERP systems are commented according to the views defined by the ARIS concept. We are focusing on organizational issues, but give also remarks on business process maintenance, exploitation of ERP functions, and data management. While other publications give rather general advice, recommendations in this paper are selected to be use-oriented and easy to apply. The recommendations do not depend on any particular ERP system.

The paradigm of Internet of Things (IoT) has attracted much attention in both academia and industry areas in the past decades. Recently, the integration of IoT and social networks is advocated to promote further development. This article focuses on the applications, solutions, and challenges of Social Internet of Things (SIoT) over collaborative edge computing, which exploits the advantages of both mobile edge computing and social relationships among SIoT users. First, applications of collaborative offloading, caching, and streaming data processing are illustrated, followed by representative social-aware solutions, including auction, coalition game, and federated learning. Finally, several research challenges are described. The main contributions of this article are as follows: 1) we specify the role of social ties in traditional applications of IoT and its impacts on individual's selection; 2) we elaborate the reasons why the presented three approaches can be applied in SIoT; and 3) the discussed challenges can contribute to the future development of secure, robust, and intelligent SIoT frameworks.

Visual information often quickly dominates people’s judgments of others’ competence and performance, including in the selection of leaders and decision makers. Reviewing recent research on static and dynamic visual cues, we discuss how people extrapolate judgments of competence and performance from visual information. We highlight how these two streams of research contribute to understanding performance perceptions and offer future avenues for research integrating the consideration of both static and dynamic visual cues.

In list proportional representation (PR) systems, parties shape political selection. We propose a theory of party list choice and elections in list PR systems. Our results describe how a party allocates candidates of heterogenoeus quality across list ranks depending on (1) its electoral goals and (2) its competitive environment. We test our predictions on the universe of Swedish local politicians from 1991 to 2014. While parties assign better candidates to higher ranks at all ballot levels, the pattern is most pronounced among electorally advantaged parties, i.e., those with the strongest prospect of controlling the executive. These results contrast with existing accounts of candidate selection, which emphasize that parties prioritize candidate quality in their nomination strategies only when constrained by electoral incentives. Our results, instead, suggest that the principal demand for high-quality politicians derives from parties, rather than voters.

Thesis is a scientific work written by undergraduate students that discusses a particular topic or field based on the results of a literature review written by experts, the results of field research, or the results of development (experiments). The problem in this research is that the process of selecting the best thesis is still subjective without regard to other criteria and is also done manually which makes the selection process less efficient. The purpose of this research is to create a system for selecting the best student thesis. The method used in this research is the Composite Performance Index (CPI). Methods of data collection techniques by means of observation, interviews, documentation. The results of this study are the Composite Performance Index (CPI) method can be used for selecting the best student thesis and the results obtained for the first rank are A1 with a value of 218.75, the second rank is A2 with a value of 198.75 and the third rank is A4 with a value of 178.75.

Scientific communities confer many forms of credit --- both implicit and explicit --- on their successful members, and it has long been argued that the motivation provided by these forms of credit helps to shape a community's collective attention toward different lines of research. The allocation of scientific credit, however, has also been the focus of long-documented pathologies: certain research questions are said to command too much credit, at the expense of other equally important questions; and certain researchers (in a version of Robert Merton's Matthew Effect) seem to receive a disproportionate share of the credit, even when the contributions of others are similar.Here we show that the presence of each of these pathologies can in fact increase the collective productivity of a community. We consider a model for the allocation of credit, in which individuals can choose among projects of varying levels of importance and difficulty, and they compete to receive credit with others who choose the same project. Under the most natural mechanism for allocating credit, in which it is divided among those who succeed at a project in proportion to the project's importance, the resulting selection of projects by self-interested, credit-maximizing individuals will in general be socially sub-optimal. However, we show that there exist ways of allocating credit out of proportion to the true importance of the projects, as well as mechanisms that assign credit out of proportion to the relative contributions of the individuals, that lead credit-maximizing individuals to collectively achieve social optimality. These results therefore suggest how well-known forms of misallocation of scientific credit can in fact serve to channel self-interested behavior into socially optimal outcomes.

In general, family planning can be defined as an effort to regulate the number of pregnancies in such a way that it will have a positive impact on the mother, baby, father and family concerned and will not cause harm as a direct result of the pregnancy. Family planning is an attempt to regulate the population or the use of methods to regulate fertilization to help a person or family achieve certain goals. The purpose referred to here is an intentional arrangement of pregnancy by the family, which is not against the applicable laws or regulations and also the morals of Pancasila and for the welfare of the family. Giving understanding to couples of childbearing age that there are various kinds of contraceptives and their benefits, so that people can choose any contraceptive that suits the individual. With more benefits than side effects, especially to keep the pregnancy distance, and a prosperous family can be achieved. It is hoped that with careful family planning, pregnancy is something that is really expected so that it will avoid the act of terminating a pregnancy with abortion. Ciamis Regency is in the province of West Java, where the area is directly adjacent to two regencies, namely Banjar and Tasikmalaya. Its location in the center of the city makes the Benteng village not like a village in general.

Automatic typography is important because it helps designers avoid highly repetitive tasks and amateur users achieve high-quality textual layout designs. However, there are often many parameters and complicated aesthetic rules that need to be adjusted in automatic typography work. In this paper, we propose an efficient deep aesthetics learning approach to generate harmonious textual layout over natural images, which can be decomposed into two stages, saliency-aware text region proposal and aesthetics-based textual layout selection. Our method incorporates both semantic features and visual perception principles. First, we propose a semantic visual saliency detection network combined with a text region proposal algorithm to generate candidate text anchors with various positions and sizes. Second, a discriminative deep aesthetics scoring model is developed to assess the aesthetic quality of the candidate textual layouts. We build a new Textual Layout Aesthetics dataset with dense annotations of each image and design a reasonable evaluation metric to compare our method with richer baselines. The results demonstrate that our method can generate harmonious textual layouts in various actual scenarios with better performance.

Unsupervised domain adaptation (UDA) for the semantic segmentation of remote sensing images is challenging since the same class of objects may have different spectra while the different class of objects may have the same spectrum. To address this issue, we propose a class-aware generative adversarial network (CaGAN) for UDA semantic segmentation of multisource remote sensing images, which explicitly models the discrepancies of intraclass and the interclass between the source domain images with labels and the target domain images without labels. Specifically, first, to enhance the global domain alignment (GDA), we propose a transferable attention alignment (TAA) procedure to add more fine-grained features into the adversarial learning framework. Then, we propose a novel class-aware domain alignment (CDA) approach in semantic segmentation. CDA mainly includes two parts: the first one is adaptive category selection, which is to alleviate the class imbalance and select the reliable per-category centers in the source and target domains; the second one is adaptive category alignment, which is to model the intraclass compactness and interclass separability from source-only, target-only, and joint source and target images. Finally, the CDA plays as a penalty of GDA to train GaGAN in an alternating and iterative manner. Experiments on domain adaptation of space to space, spectrum to spectrum, both space-to-space and spectrum-to-spectrum data sets demonstrate that CaGAN outperforms the current state-of-the-art methods, which may serve as a starting point and baseline for the comprehensive applications of semantic segmentation in cross-space and cross-spectrum remote sensing images.

Few studies of conflict analysis have analyzed the conflict situations from the perspective of the consistency degree of cliques. Quantifying the conflict degree is instructive for weakening or resolving conflicts. In this paper, we propose the improved Pawlak model of conflict analysis, a common consistency measurement is introduced to quantify the consistency degree of cliques, which explains the internal causes of conflict situation from a new perspective. In addition, the proposed consistency measurement reflects the fit between feasible strategy and clique, which inspires us to formulate the criteria for selecting feasible strategies in a three-valued situation table. Moreover, considering that many-valued ratings are more powerful and effective to describe vague information, a novel model of fuzzy conflict analysis is established with a fuzzy conflict function and a fuzzy consistency measurement in a fuzzy situation table, which could degenerate into the improved Pawlak model of conflict analysis. The criteria of selecting feasible strategies is illustrated by an NBA labor negotiation. Finally, the performance of proposed models is compared with other advanced models.

This research investigated how word production is influenced by six feature-based semantic variables (number of semantic features, intercorrelational density, number of near semantic neighbors, semantic similarity, typicality, and distinctiveness). We simultaneously investigated effects of the six semantic variables on spoken picture naming in a large group of participants (n = 87), while controlling for other psycholinguistic variables. Across analyses, number of semantic features was the most consistent predictor with a facilitatory effect on naming latency and accuracy. In addition, inhibitory effects were found on naming accuracy for intercorrelational density and on naming latency for distinctiveness. The facilitatory effect of number of semantic features is suggested to stem from stronger semantic activation with an increasing number of semantic features, which results in facilitated selection of the word's lexical representation. In contrast, the inhibitory effect of intercorrelational density is most easily accounted for by increased competition at the lexical level. The mechanism underlying the inhibitory effect of distinctiveness is unclear. These findings indicate that future research on factors affecting word retrieval should also control for effects of number of semantic features, intercorrelational density, and distinctiveness. They also suggest that effects of the other semantic variables (e.g., semantic neighbors) reported in the literature were potentially overestimated due to insufficient control of other semantic and/or psycholinguistic variables. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Threat superiority effects describe the reaction time advantage for locating threatening objects in a visual search paradigm, compared to locating visually similar non-threatening objects. They are widely reported for threats of both natural (snakes and spiders) and man-made (guns and knives) origins. Across two experiments, the current study contrasts threat superiority effects for natural and man-made targets. When targets are not depicted held, snakes and spiders tended to exhibit larger threat superiority effects, and were searched for with additional caution, than were guns and knives. When snakes and spiders were depicted held and weapons wielded, systematic differences between the natural and man-made threats disappeared. This means the advantage for threats of natural origin observed when all targets were depicted not held may be attributable to differences in animation – snakes and spiders are alive and may strike at any time if in your vicinity, whereas a weapon can only inflict harm if wielded. From these data there is no evidence that evolved visual sensitivities to the basic shapes of venomous animals support faster detection and response times to these animals than can occur to targets such as guns and knives, whose shapes must be learned. The selection pressures that led to the evolution of such sensitivities (observable even in infancy) may therefore lie in protecting young children and babies from envenomation, before they even have the cognitive capacity to understand the dangers that snakes and spiders pose.