This article aims to explore representation of the content knowledge of historical Malay manuscripts by extracting the event features using an event ontology framework. The manuscript used during the testing is Sulalatus Salatin (Sejarah Melayu ) by Abdul Ahmad Samad and it was published at University of Malaya Digital Library database. In aligning to a domain-specific ontology, the Simple Event Model (SEM) model is adopted and an event-based ontology for historical Malay manuscripts is designed. Information extraction approach is done manually (...) to extract events from the manuscript and mapped into Protégé editor. Competency questions were constructed and submitted to the Protégé editor using SPARQL to check the ontology capability of providing answers as well as to examine its correctness. Event-based ontology model assists in discovering and representing the content knowledge of historical Malay manuscripts and supports organisation of knowledge. All the main concepts are extracted from selected Malay manuscript and 17 concepts used to develop the event-based ontology model. The knowledge was verified by three domain experts in Malay manuscript. In the findings, the interrater reliability for Event and Actor instances is 84%, which means 16% of instances and its type are incorrect and need amendment. For Place, interrater reliability is 95% and 99% for Role. Meanwhile, the experts achieved 100% agreement for Time. In addition, the experts agreed that the concepts, properties and instances for Malay Manuscript Ontology and complied with the criteria of consistency, completeness, conciseness, expandability and ease of use. The development of the event-based model of an ontology-based system with a high level of semantic granularity reflects the various cultural riches and intellectual aspect stored in Malay manuscripts. This will enable systematic research of the knowledge embedded in the manuscripts and make it widely and easily accessible by everyone. (shrink)

Based on previous studies implicating increased functional connectivity within the self-referential brain network in major depressive disorder (MDD), and considering the functional roles of three distinct modules of such brain net (responsible for three-dimensional components of Selfhood) together with the documented abnormalities of self-related processing in MDD, we tested the hypothesis that patients with depression would exhibit increased connectivity within each module of the self-referential brain network and that the strength of these connections would correlate positively with depression severity. Applying (...) the electroencephalogram (EEG) operational synchrony analysis to extract three modules of the self-referential brain network in 12 medication-free depressive outpatients and 10 control subjects we have found an increase in the strength of EEG synchrony within all three modules in depressive patients (though non-significant for the right module). Furthermore, multiple regression analysis that used 3 factors (values of synchrony strength for all three modules) as input indicated that combined increase in the strength of synchrony in all three modules was positively associated with severity of depression. Taken together the findings of this study suggest that depression is primarily associated with hypersynchrony in all three modules of the brain self-referential network (the anterior module been responsible for “witnessing observation and first–person perspective”, the left posterior module been responsible for “reflective agency and narration” and the right posterior module been responsible for “bodily representational-emotional agency”), thus contributing to excessive self-focus, rumination, and body tension. (shrink)

How speech sounds are represented in the brain is not fully understood. The mismatch negativity (MMN) has proven to be a powerful tool in this regard. The MMN event-related potential is elicited by a deviant stimulus embedded within a series of repeating standard stimuli. Listeners construct auditory memory representations of these standards despite acoustic variability. In most designs that test speech sounds, however, this variation is typically intra-category: All standards belong to the same phonetic category. In the current paper, (...) inter-category variation is presented in the standards. These standards vary in manner of articulation but share a common phonetic feature. In the standard retroflex experimental block, Mandarin Chinese speaking participants are presented with a series of “standard” consonants that share the feature [retroflex], interrupted by infrequent non-retroflex deviants. In the non-retroflex standard experimental block, non-retroflex standards are interrupted by infrequent retroflex deviants. The within-block MMN was calculated, as was the identity MMN (iMMN) to account for intrinsic differences in responses to the stimuli. We only observed a within-block MMN to the non-retroflex deviant embedded in the standard retroflex block. This suggests that listeners extract [retroflex] despite significant inter-category variation. In the non-retroflex standard block, because there is little on which to base a coherent auditory memory representation, no within-block MMN was observed. The iMMN to the retroflex was observed in a late time-window at centro-parieto-occipital electrode sites instead of fronto-central electrodes, where the MMN is typically observed, potentially reflecting the increased difficulty posed by the added variation in the standards. In short, participants can construct auditory memory representations despite significant acoustic and inter-category phonological variation so long as a shared phonetic feature binds them together. (shrink)

Robert French has argued that a disembodied computer is incapable of passing a Turing Test that includes subcognitive questions. Subcognitive questions are designed to probe the network of cultural and perceptual associations that humans naturally develop as we live, embodied and embedded in the world. In this paper, I show how it is possible for a disembodied computer to answer subcognitive questions appropriately, contrary to French’s claim. My approach to answering subcognitive questions is to use statistical information extracted from (...) a very large collection of text. In particular, I show how it is possible to answer a sample of subcognitive questions taken from French, by issuing queries to a search engine that indexes about 350 million Web pages. This simple algorithm may shed light on the nature of human cognition, but the scope of this paper is limited to demonstrating that French is mistaken: a disembodied computer can answer subcognitive questions. (shrink)

Disillusionment is acknowledged to be a painful process with important personal and social consequences. However, scientific conceptualisations of the experience are inconsistent. Across four studies, we examined whether lay conceptions of disillusionment produce a consistent pattern of features. In Study 1 (N = 204), we extracted 19 features of disillusionment from open-ended participant definitions. In Study 2 (N = 131), participants rated the centrality of these features and indicated that features such as discovery, disappointment, and loss, were highly representative, while (...) features such as hopelessness, orientation, and truth, were more peripheral. In two further studies, we used experimental designs to test the diagnosticity of these features. In Study 3 (N = 155), participants rated vignettes descriptions as more disillusioning when they were based on more, rather than less, prototypical disillusionment features. Given that disappointment is a feature of disillusionment, we conducted Study 4 (N = 60) to test whether the extracted features effectively distinguish disillusionment from disappointment. Overall, we found evidence to suggest that disillusionment contains a consistent set of features, and represents a state of negative epistemic affect associated with the violation of core assumptions. These results create avenues for research on disillusionment, its antecedents and its consequences. (shrink)

People spontaneously discover new representations during problem solving. Discovery of a mathematical representation is of special interest, because it shows that the underlying structure of the problem has been extracted. In the current study, participants solved gear‐system problems as part of a game. Although none of the participants initially used a mathematical representation, many discovered a parity‐based, mathematical strategy during problem solving. Two accounts of the spontaneous discovery of mathematical strategies were tested. According to the automatic schema abstraction hypothesis, experience (...) with multiple, unique problem exemplars facilitates extraction of the parity relation. According to the comparison‐based abstraction hypothesis, explicitly comparing gear pathways that have different number, but the same parity, should result in extraction of parity. An event history analysis showed that accumulation of experiences with different‐number, same‐parity comparisons predicted discovery of parity; accumulation of unique exemplars did not. Results suggest that comparison‐based abstraction processes can lead to the discovery of a mathematical relation. (shrink)

. Night driving is one of the major factors which affects traffic safety. Although detecting oncoming vehicles at night time is a challenging task, it may improve traffic safety. If the oncoming vehicle is recognised in good time, this will motivate drivers to keep their eyes on the road. The purpose of this paper is to present an approach to detect vehicles at night based on the employment of a single onboard camera. This system is based on detecting vehicle headlights (...) by recognising their shapes via an SVM classifier which was trained for this purpose. A pairing algorithm was designed to pair vehicle headlights to ensure that the two headlights belong to the same vehicle. A multi-object tracking algorithm was invoked to track the vehicle throughout the time the vehicle is in the scene. The system was trained with 503 single objects and tested using 144 587 single objects which were extracted from 1410 frames collected from 15 videos and 27 moving vehicles. It was found that the accuracy of recognition was 97.9% and the vehicle recognition rate was 96.3% which indicates clearly the high robustness attained by this system. (shrink)

Background: Mental healthcare needs person-tailored interventions. Experience Sampling Method (ESM) can provide daily life monitoring of personal experiences. This study aims to operationalize and test a measure of momentary reward-related Quality of Life (rQoL). Intuitively, quality of life improves by spending more time on rewarding experiences. ESM clinical interventions can use this information to coach patients to find a realistic, optimal balance of positive experiences (maximize reward) in daily life. rQoL combines the frequency of engaging in a relevant context (...) (a ‘behavior setting’) with concurrent (positive) affect. High rQoL occurs when the most frequent behavior settings are combined with positive affect or infrequent behavior settings co-occur with low positive affect. Methods: Resampling procedures (Monte Carlo experiments) were applied to assess the reliability of rQoL using various behavior setting definitions under different sampling circumstances, for real or virtual subjects with low-, average- and high contextual variability. Furthermore, resampling was used to assess whether rQoL is a distinct concept from positive affect. Virtual ESM beep datasets were extracted from 1058 valid ESM observations for virtual and real subjects. Results: Behavior settings defined by Who-What contextual information were most informative. Simulations of at least 100 ESM observations are needed for reliable assessment. Virtual ESM beep datasets of a real subject can be defined by Who-What-Where behavior setting combinations. Large sample sizes are necessary for reliable rQoL assessments, except for subjects with low contextual variability. rQoL is distinct from positive affect. Conclusion: rQoL is a feasible concept. Monte Carlo experiments should be used to assess the reliable implementation of an ESM statistic. Future research in ESM should asses the behavior of summary statistics under different sampling situations. This exploration is especially relevant in clinical implementation, where often only small datasets are available. (shrink)

We bring together recent discussions on data capitalism and biocapitalization by studying value flows in consumer genomics firms—an industry at the intersection between health care and technology realms. Consumer genomics companies market genomic testing services to consumers as a source of fun, altruism, belonging and knowledge. But by maintaining a multisided or platform business model, these firms also engage in digital capitalism, creating financial profit from data brokerage. This is a precarious balance to strike: If these companies’ business models consist (...) of assetizing the pool of genomic data that they assemble, then part of their work has to revolve around obscuring to consumers any uncertainties that would potentially impinge on these processes of assemblage. We reflect on the nature of these practices and the market relationships that enable them, and we relate this reflection to debates around alternative market arrangements that would potentially mitigate the extractive tendencies of these and other digital health firms. (shrink)

As one of the important 21st-century skills, collaborative problem solving (CPS) has caught much attention in the assessment area. Two initiative approaches have been created: the human-to-human and human-to-agent modes. Between the two modes, the human-to-human interaction is much closer to the real-world situation and its process stream data can reveal more detailed information about the cognitive processes. In order to measure CPS ability effectively by this mode, how to extract indicators from the data and model it is crucial, while (...) the existing methods all have some disadvantages. In the present study, we proposed a new paradigm for extracting indicators and modeling the dyad data in the human-to-human mode. Specifically, both individual and group indicators were extracted from the data stream, which served as evidence for demonstrating CPS skills. In addition, a within-item multidimensional Rasch model was used to fit the dyad data. To test the validity of the paradigm, we developed five tasks following the asymmetric mechanism and integrated them into an online testing platform. Four hundred thirty-four students from China participated in the assessment and the online platform recorded their crucial actions with time stamps. The new paradigm was used to deal with the generated process stream data. Results showed that the model fitted well. The item parameter estimates and fitting indexes were acceptable, and students were well differentiated. In general, the new paradigm of extracting indicators and modeling the dyad data is feasible for improving the human-to-human assessment of CPS. Finally, the limitations of the current study and further research directions are discussed. (shrink)

A novel piecewise water flow technique for text line extraction from multi-skewed document images of handwritten text of different scripts is presented here. The basic water flow technique assumes that the hypothetical water flows from both left and right sides of the image frame. This flow of water fills up the gaps between consecutive objects but faces obstruction if any object lies in the path of the flow. All unwetted regions in the document image are then labeled distinctly to extract (...) the text lines. However, the technique fails when two neighboring text lines touch each other, as water gets obstructed by the touching segment. To get rid of this difficulty, we have modified the basic water flow technique by iteratively applying the same over the vertically segmented document images. The main purpose of this vertical segmentation is to localize the text line segment where two text lines get joined. These segments are then horizontally fragmented, and each fragment is placed suitably to the text line in which it actually belongs to. This way, the probable data loss during isolation of the touching text line segment is minimized. Both the techniques have been tested on three different databases, viz., CMATERdb 1.1.1, CMATERdb 1.1.2, and ICDAR2009 handwritten segmentation contest pages, respectively. The test results show that the present technique outperforms the basic one for all three databases. (shrink)

This paper studies the feature extraction and middle-level expression of Convolutional Neural Network convolutional layer glass broken and cracked at the scene of road traffic accident. The image pyramid is constructed and used as the input of the CNN model, and the convolutional layer road traffic accident scene glass breakage and crack characteristics at each scale in the pyramid are extracted separately, and then the depth descriptors at different image scales are extracted. In order to improve the discriminative power of (...) the depth descriptor, Hellinger kernel and Principal Component Analysis are used to perform nonlinear and linear transformations. Two aggregation strategies based on depth descriptors are proposed to form a global image representation. The classification experiment of the data set shows that Hellinger kernel, PCA transformation, and two aggregation strategies are all conducive to improving the classification accuracy. In addition, the convolutional layer road traffic accident scene glass breaking and crack feature coding can obtain better classification performance than the fully connected layer feature. We conducted dynamic impact tests on plate glass and Polyvinyl Butyral- laminated glass under different boundary conditions and studied the crack propagation and failure process of the glass under different impact speeds. The results show that there are radial cracks and circular cracks on the glass specimens under the impact load; the glass specimens show partial damage under high-speed impact and the characteristics of glass breaking and cracks at the scene of road traffic accidents; the four-frame plate glass is supported by sharp dagger-like fragments. This paper compares the energy absorption of glass specimens under different boundary conditions. The results show that the energy absorption effect of the four-point supporting glass specimen is generally stronger than that of the four-frame supporting glass. (shrink)

Half a million species of plants could be existing in the world. Classification of plants based on leaf features is a critical job as feature extraction from binary images of leaves may result in duplicate identification. However, leaves are an effective means of differentiating plant species because of their unique characteristics like area, diameter, perimeter, circularity, aspect ratio, solidity, eccentricity, and narrow factor. This paper presents the extraction of plant leaf gas alongside other features from the camera images or a (...) dataset of images by applying a convolutional neural network. The extraction of leaf gas enables identification of the actual level of chlorophyll and nitrogen which may help to interpret future predictions. Our contribution includes the study of texture and geometric features, analyzing ratio of Ch and N in both healthy and dead leaves, and the study of color-based methods via CNN. Several steps are included to obtain the results: image preprocessing, testing, training, enhancement, segmentation, feature extraction, and aggregation of results. A vital contrast of the results can be seen by considering the kind of image, whether a healthy or dead leaf. (shrink)

In extracting predictions from theories that describe a multiverse, we face the difficulty that we must assess probability distributions over possible observations, prescribed not just by an underlying theory, but by a theory together with a conditionalization scheme that allows for selection effects. This means we usually need to compare distributions that are consistent with a broad range of possible observations, with actual experimental data. One controversial means of making this comparison is by invoking the 'principle of mediocrity': that is, (...) the principle that we are typical of the reference class implicit in the conjunction of the theory and the conditionalization scheme. In this paper, I quantitatively assess the principle of mediocrity in a range of cosmological settings, employing 'xerographic distributions' to impose a variety of assumptions regarding typicality. I find that for a fixed theory, the assumption that we are typical gives rise to higher likelihoods for our observations. If, however, one allows both the underlying theory and the assumption of typicality to vary, then the assumption of typicality does not always provide the highest likelihoods. Interpreted from a Bayesian perspective, these results support the claim that when one has the freedom to consider different combinations of theories and xerographic distributions, one should favor the framework that has the highest posterior probability; and then from this framework one can infer, in particular, how typical we are. In this way, the invocation of the principle of mediocrity is more questionable than has been recently claimed. (shrink)

Abstract: Retama raetam (RR) is a traditional medicinal plant belongs to fabaceae family which grows in North Africa and East Mediterranean region. Locally, RR is used in several diseases including diabetes mellitus and hypertension. Thus, this study aims to investigate certain behavioral and central effects of methanolic extract of RR seeds in experimental animals (male Albino adult mice of 20 – 35 gm). Three exploratory behavioral models are used in this study, open field, elevated plus maze and light-dark box models, (...) in addition, picrotoxin induced seizure model in mice. In elevated plus-maze test of anxiety, a dose of 25 mg/kg of RR induced a significant increase in number of open arms entries and time spent on open arms of the maze compared to the control. In light-dark model, time spent on light area and number of light-dark transitions are significantly increased after treatment with 25 mg/kg RR extract of seeds. Open field model also revealed a significant increase in general motor activity of the mouse after 25 mg/kg of RR. On the other hand, a dose of 50 mg/kg of RR significantly induced a profound central and peripheral analgesic responses. In summary, this study concludes that RR seeds have a profound anxiolytic-like effect and analgesic response with delay in latency time of seizure induced by picrotoxin in mice. (shrink)

We present PhrasIS, a benchmark dataset composed of natural occurring Phrase pairs with Inference and Similarity annotations for the evaluation of semantic representations. The described dataset fills the gap between word and sentence-level datasets, allowing to evaluate compositional models at a finer granularity than sentences. Contrary to other datasets, the phrase pairs are extracted from naturally occurring text in image captions and news headlines. All the text fragments have been annotated by experts following a rigorous process also described in the (...) manuscript achieving high inter annotator agreement. In this work we analyse the dataset, showing the relation between inference labels and similarity scores. With 10K phrase pairs split in development and test, the dataset is an excellent benchmark for testing meaning representation systems. (shrink)

Learning in educational settings most often emphasizes declarative and procedural knowledge. Studies of expertise, however, point to other, equally important components of learning, especially improvements produced by experience in the extraction of information: Perceptual learning. Here we describe research that combines principles of perceptual learning with computer technology to address persistent difficulties in mathematics learning. We report three experiments in which we developed and tested perceptual learning modules to address issues of structure extraction and fluency in relation to algebra and (...) fractions. PLMs focus students’ learning on recognizing and discriminating, or mapping key structures across different representations or transformations. Results showed significant and persisting learning gains for students using PLMs. PLM technology offers promise for addressing neglected components of learning: Pattern recognition, structural intuition, and fluency. Using PLMs as a complement to other modes of instruction may allow students to overcome chronic problems in learning. (shrink)

Basu (2003): For observations to be of use in theory testing, they need to be transformed into evidence via a theoretical process. Evidence is theory-laden.

Considering the highly complex structure of quantum chaos and the nonstationary characteristics of speech signals, this paper proposes a quantum chaotic encryption and quantum particle swarm extraction method based on an underdetermined model. The proposed method first uses quantum chaos to encrypt the speech signal and then uses the local mean decomposition method to construct a virtual receiving array and convert the underdetermined model to a positive definite model. Finally, the signal is extracted using the Levi flight strategy based on (...) kurtosis and the quantum particle swarm optimization optimized by the greedy algorithm. The bit error rate and similarity coefficient of the voice signal are extracted by testing the source voice signal SA1, SA2, and SI943 under different SNR, and the similarity coefficient, uncertainty, and disorder of the observed signal and the source voice signal SA1, SA2, and SI943 verify the effectiveness of the proposed speech signal extraction method and the security of quantum chaos used in speech signal encryption. (shrink)

This paper describes formalizations of Tait's normalization proof for the simply typed λ-calculus in the proof assistants Minlog, Coq and Isabelle/HOL. From the formal proofs programs are machine-extracted that implement variants of the well-known normalization-by-evaluation algorithm. The case study is used to test and compare the program extraction machineries of the three proof assistants in a non-trivial setting.

We report three studies in which we applied a value dictionary to narratives. Our objective was to test a theory-driven value dictionary for extracting valuable information from autobiographical and narrative texts. In Studies 1 and 2, participants wrote short autobiographical narratives and in Study 3, participants wrote narratives based on ambiguous stimuli. Participants in all three studies also completed the Portrait Value Questionnaire as a self-report measure of values. Overall, our results demonstrate that it is possible to extract value-relevant (...) information from these narratives. Extracted values from autobiographical narratives showed average correlations of 0.07 and 0.12 with self-reports compared to an average correlation of 0.01 for the extracted values from implicit motive tasks. The correlations with self-reports were in line with previous validation studies. The most salient values in narratives diverged somewhat, with a stronger emphasis on achievement values compared to self-reports, probably due to the nature of salient episodes within one's life that require demonstrating success according to social standards. Benevolence values were consistently most important in both self-ratings and text-based scoring. The value structure emerging from narratives diverged from the theoretically predicted structure, yet broad personally vs. socially focused value dimensions were qualitatively discernible. We highlight opportunities and challenges for future value research using autobiographical stories. (shrink)

Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, Microsoft; (...) Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions. (shrink)

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research (...) exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. (shrink)

Cognitive test batteries are widely used in diverse research fields, such as cognitive training, cognitive disorder assessment, or brain mechanism understanding. Although they need flexibility according to their usage objectives, most test batteries are not available as open-source software and are not be tuned by researchers in detail. The present study introduces an open-source cognitive test battery to assess attention and memory, using a javascript library, p5.js. Because of the ubiquitous nature of dynamic attention in our daily (...) lives, it is crucial to have tools for its assessment or training. For that purpose, our test battery includes seven cognitive tasks, common in cognitive science literature. By using the test battery, we conducted an online experiment to collect the benchmark data. Results conducted on 2 separate days showed the high cross-day reliability. Specifically, the task performance did not largely change with the different days. Besides, our test battery captures diverse individual differences and can evaluate them based on the cognitive factors extracted from latent factor analysis. Since we share our source code as open-source software, users can expand and manipulate experimental conditions flexibly. Our test battery is also flexible in terms of the experimental environment, i.e., it is possible to experiment either online or in a laboratory environment. (shrink)

‘À quoi sert la littérature?’, Deleuze opens his book on Leopold von Sacher-Masoch with a question that this paper will pose again, trying to propose an answer to it, and to put to test the methodological approach that can be obtained from that answer: what are the uses of literature and, more specifically, how could the philosopher use literature, what could be done with it? To this end, this paper will focus on Deleuze’s symptomatological conception of literature. The second (...) part of the paper will be dedicated to the reading of two novels, 1984 by George Orwell and The Circle by Dave Eggers. The novels will be put in in a relation of resonation with two concepts sketched in Deleuze’s Post-scriptum sur les sociétés de contrôle: Foucauldian disciplinary societies and control societies. These novels will be considered as signaletic material, symptomatologies of two conditions of society, from which this paper proposes to extract two types of signs: Signs of Order and Signs of Suggestion. These signs will be made react with the concepts of Foucault and Deleuze, in order to see if they can produce the ground for a therapeutic. The paper will therefore combine literary symptomatologies and philosophical aetiology, in the attempt to make them react together, Foucault and Orwell, Eggers and Deleuze, to extract and describe signs from the literary material considered that may be brought to the fore only after the consideration of the causes of the most important symptoms of discipline and control. (shrink)

Cet article présente une étude sur corpus de patrons morpho-syntaxiques associés à des hypothèses interprétatives sur les noms déverbaux événementiels. Il confronte notamment certaines propositions de Grimshaw (1990) avec des usages attestés repérés en corpus et suggère de nouvelles hypothèses descriptives. L'extraction des usages attestés est réalisée à l'aide du concordancier ' Corpus Workbench ' sur 3 années du quotidien régional ' L'Est Républicain ' telles que mises.

This paper explores multiple experimental interventions in molecular biology. By “multiple,” we mean that molecular biologists often use different modes of experimental interventions in a series of experiments for one and the same subject. In performing such a series of experiment, scientists may use different modes of interventions to realize plural goals such as testing given hypotheses and exploring novel phenomena. In order to illustrate this claim, we develop a framework of multiple modes of experimental interventions to analyze a series (...) of experiments for a single subject. Our argument begins with a brief characterization of Craver and Darden’s taxonomy of experiments, because the taxonomy they have made implies various modes of interventions. We propose to extract two interventional directions and two interventional effects from their taxonomy as the basis of classification. The vertical or inter-level direction means that an intervention is performed between different levels of organization and the horizontal or inter-stage direction means that an intervention is performed between different stages of a mechanism. Interventions may produce an excitatory or an inhibitory effect. As a consequence, we can classify modes of interventions according to different directions and effects. We illustrate our claims by doing a case study of the PaJaMo experiment, which is a series of experiments for a single subject. The final goal in this paper is to provide a taxonomy of characteristics of experimentation in which the PaJaMo experiment is adequately located. (shrink)

Positioning technologies, such as GPS are widespread in society but are used only sparingly in behavioural science research, e.g., because processing positioning technology data can be cumbersome. The current work attempts to unlock positioning technology potential for behavioural science studies by developing and testing a research tool to analyse GPS tracks. This tool—psyosphere—is published as open-source software, and aims to extract behaviours from GPSs data that are more germane to behavioural research. Two field experiments were conducted to test application (...) of the research tool. During these experiments, participants played a smuggling game, thereby either smuggling tokens representing illicit material past border guards or not. Results suggested that participants varied widely in variables, such as course and speed variability and distance from team members in response to the presence of border guards. Subsequent analyses showed that some of these GPS-derived behavioural variables could be linked to self-reported mental states, such as fear. Although more work needs to be done, the current study demonstrates that psyosphere may enable researchers to conduct behavioural experiments with positioning technology, outside of a laboratory setting. (shrink)

Metacognition, the ability to monitor and evaluate our own cognitive processes, confers advantages to individuals and their own judgment. A more recent hypothesis, however, states that explicit metacognition may also enhance the collective judgment of groups, and may enhance human collaboration and coordination. Here, we investigate this social function hypothesis of metacognition with arguably one of the oldest collaboration problems humans face, common-pool resource use. Using an agent-based model that simulates repeated group interactions and the forming of collective judgments about (...) resource extraction, we show that (1) in “kind” environments (where confidence and judgment accuracy correlate positively), explicit metacognition may allow groups to reach more accurate judgments compared to groups exchanging object-level information only; while (2) in “wicked” environments (where confidence and judgment accuracy correlate negatively), explicit metacognition may protect groups from the large judgment errors yielded by groups using metacognitive information for individual-level learning only (implicit metacognition). With explicit metacognition, this research highlights a novel mechanism which, among others, provides a testable explanation of the often-observed finding that groups all over the world communicate to enhance common property use. (shrink)

Millions of memes are created and shared every day on social media platforms. Memes are a great tool to spread humour. However, some people use it to target an individual or a group generating offensive content in a polite and sarcastic way. Lack of moderation of such memes spreads hatred and can lead to depression like psychological conditions. Many successful studies related to analysis of language such as sentiment analysis and analysis of images such as image classification have been performed. (...) However, most of these studies rely only upon either one of these components. As classifying meme is one problem which cannot be solved by relying upon only any one of these aspects, the present work identifies, addresses, and ensembles both the aspects for analyzing such data. In this research, we propose a solution to the problems in which the classification depends on more than one model. This paper proposes two different approaches to solve the problem of identifying hate memes. The first approach uses sentiment analysis based on image captioning and text written on the meme. The second approach is to combine features from different modalities. These approaches utilize a combination of glove, encoder-decoder, and OCR with Adamax optimizer deep learning algorithms. Facebook Challenge Hateful Meme Dataset is utilized which contains approximately 8500 meme images. Both the approaches are implemented on the live challenge competition by Facebook and predicted quite acceptable results. Both approaches are tested on the validation dataset, and results are found to be promising for both models. (shrink)

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...) We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines. (shrink)

The Test of Basic Auditory Capabilities is a battery of auditory-discrimination tasks and speech-identification tasks that has been normed on several hundred young normal-hearing adults. Previous research with the TBAC suggested that cognitive function may impact the performance of older adults. Here, we examined differences in performance on several TBAC tasks between a group of 34 young adults with a mean age of 22.5 years and a group of 115 older adults with a mean age of 69.2 years recruited (...) from the local community. Performance of the young adults was consistent with prior norms for this age group. Not surprisingly, the two groups differed significantly in hearing loss and working memory with the older adults having more hearing loss and poorer working memory than the young adults. The two age groups also differed significantly in performance on six of the nine measures extracted from the TBAC with the older adults consistently performing worse than the young adults. However, when these age-group comparisons were repeated with working memory and hearing loss as covariates, the groups differed in performance on only one of the nine auditory measures from the TBAC. For eight of the nine TBAC measures, working memory was a significant covariate and hearing loss never emerged as a significant factor. Thus, the age-group deficits observed initially on the TBAC most often appeared to be mediated by age-related differences in working memory rather than deficits in auditory processing. The results of these analyses of age-group differences were supported further by linear-regression analyses with each of the 9 TBAC scores serving as the dependent measure and age, hearing loss, and working memory as the predictors. Regression analyses were conducted for the full set of 149 adults and for just the 115 older adults. Working memory again emerged as the predominant factor impacting TBAC performance. It is concluded that working memory should be considered when comparing the performance of young and older adults on auditory tasks, including the TBAC. (shrink)

Cet article présente une étude sur corpus de patrons morpho-syntaxiques associés à des hypothèses interprétatives sur les noms déverbaux événementiels. Il confronte notamment certaines propositions de Grimshaw (1990) avec des usages attestés repérés en corpus et suggère de nouvelles hypothèses descriptives. L’extraction des usages attestés est réalisée à l’aide du concordancier « Corpus Workbench » sur 3 années du quotidien régional L’Est Républicain telles que mises à disposition sur le site du centre de ressources du CNRTL.

Information is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from such information and knowledge. Visualization is a key tool and has become one of the most significant platforms for interpreting, extracting, and communicating information. The current study is an endeavour toward data modelling and (...) user knowledge by using a rough set approach for extracting meaningful insights. The technique has used different rough set algorithms such as K-nearest neighbours, decision rules, decomposition tree, and local transfer function classifier for an experimental setup. The approach has found its accuracy for the optimal use of data modelling and user knowledge. The experimental setup of the proposed method is validated by using the dataset available in the UCI web repository. Results of the proposed study show that the model is effective and efficient with an accuracy of 96% for KNN, 87% for decision rules, 91% for decision trees, 85.04% for cross validation architecture, and 94.3% for local transfer function classifier. The validity of the proposed classification algorithms is tested using different performance metrics such as F-score, precision, accuracy, recall, specificity, and misclassification rates. For all these performance metrics, the KNN classifier outperformed, and this high performance shows the applicability of the KNN classifier in the proposed problem. (shrink)

The dynamics brought about by the adoption of the International Financial Reporting Standards have a direct impact on the various Colombian economic sectors; highlighting the Oil and Natural Gas Extraction sector as one of the most important in terms of wealth generation and exports of the nation. For this reason, the present study was directed in order to analyze the indicators of Liquidity and Profitability in the Colombian oil sector between 2011 and 2021. For the development of the study, a (...) quantitative investigation is proposed with a documentary base where it is carried out an analysis of secondary sources from the Superintendence of Companies from which financial data of the Oil and Natural Gas Extraction sector in Colombia is taken, on which a descriptive processing is carried out and subsequently a correlational analysis between the liquidity and profitability indicators. The results show a level of correlation between the liquidity indicators, referring to the Current Ratio and Acid Test, and those of Profitability, Return on Total Assets, and Return on Equity. It is concluded that the correlation between these variables would be explained by the rates and sales volumes of said sector; which constantly carries out transactions of high sums of money accompanied by multiple policies and methods to ensure the transaction and thus not unbalance the receipt of income by the industry. (shrink)

The scarcity of abnormal data leads to imbalanced data in the field of monitoring tool wear conditions. In this paper, a novel multi-index generative adversarial network is proposed to detect the tool wear conditions subject to imbalanced signal data. First, the generator in the MI-GAN is trained to produce fake normal signals, and the discriminator computes scores of testing signals and generated signals. Next, the generator detects abnormal signals based on the performance of imitating testing signals, and the discriminator will (...) compute the scores of testing signals and generated signals. Subsequently, two indexes, i.e., L 2 -norm and temporal correlation coefficient, are put forward to measure the similarity between generated signals and testing signals. Finally, our decision-making function further combines L 2 -norm and CORT with two discriminator scores to determine the tool conditions. Experimental results show that our method obtains 97% accuracy in tool wear detection based on imbalanced data without manual feature extraction, which outperforms traditional machine learning methods. (shrink)

Presently, the State Grid Corporation of China has accumulated a large amount of maintenance records for power primary equipment. Unfortunately, most of these records are unstructured data which lead to difficultly analyze and utilize them. The emergence of natural language processing technology and deep learning methods provide a solution for unstructured text data. This paper proposes a progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment. Firstly, the textual characteristics and word (...) separation difficulties of maintenance records are analyzed, then 7 main entity categories of power technical terms from unstructured maintenance records are chosen, and 3452 maintenance records are labeled by these categories, which is so called EPE-MR training dataset. Secondly, the standard test reports, standard maintenance, and fault analysis reports for three types of power primary equipment are employed as corpus to train character embedding in order to obtain certain words representation ability of maintenance records. After that, progressive multilevel radicals feature extraction module is designed to get detailed and fine semantic information in a hierarchical manner. Further, radicals feature representation and character embedding are concatenated and sent to BiLSTM module to extract contextual information in order to improve Chinese entity recognition ability. Moreover, CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records. Finally, comparative experiments on public MSRA dataset, China People’s Daily corpus, and EPE-MR dataset are implemented, respectively, which show the effectiveness of the proposed method. (shrink)

The aim of the present study was to investigate the effect of observing other’s movements on subsequent performance in bottlenose dolphins. The imitative ability of non-human animals has intrigued a number of researchers. So far, however, studies in dolphins have been confined to intentional imitation concerned with the explicit request to imitate other agents. In the absence of instruction to imitate, do dolphins (un)intentionally replicate other’s movement features? To test this, dolphins were filmed while reaching and touching a stimulus (...) before and after observing another dolphin (i.e., model) performing the same action. All videos were reviewed and segmented in order to extract the relevant movements. A marker was inserted post hoc via software on the videos upon the anatomical landmark of interest (i.e., rostrum) and was tracked throughout the time course of the movement sequence. The movement was analyzed using an in-house software developed to perform two-dimensional (2D) post hoc kinematic analysis. The results indicate that dolphins’ kinematics is sensitive to other’s movement features. Movements performed for the “visuomotor priming” condition were characterized by a kinematic pattern similar to that performed by the observed dolphin (i.e., model). Addressing the issue of spontaneous imitation in bottlenose dolphins might allow ascertaining whether the potential or impulse to produce an imitative action is generated, not just when they intend to imitate, but whenever they watch another conspecific’s behavior. In closing, this will clarify whether motor representational capacity is a by-product of factors specific to humans or whether more general characteristics such as processes of associative learning prompted by high level of encephalization could help to explain the evolution of this ability. (shrink)

We used several cue–target SOAs and three different degrees of cue predictability , to investigate the role of awareness of cue–target predictability on cueing effects. A group of participants received instructions about the informative value of the cue, while another group did not receive such instructions. Participants were able to extract the predictive value of a spatially peripheral cue and use it to orient attention, whether or not specific instructions about the predictive value of the cue were given, and no (...) matter their ability to correctly report it in a post-test questionnaire. In the non-predictive block, bad estimators who received no instructions showed regular cueing effects, while good estimators exhibited smaller and non-significant facilitatory effects at the short SOA and an absence of significant IOR at longer SOAs. However, for the instructions group, the pattern of results reversed. (shrink)

High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy is (...) tested using the decision tree, support vector machine, and K-nearest neighbor classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy. (shrink)

In the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene data is of great importance in cancer diagnosis and treatment. In this paper, we propose a novel method for identifying differentially expressed genes based on tensor robust principal component analysis, which extends the matrix method to the processing of multiway (...) data. To identify differentially expressed genes, the plan is carried out as follows. First, multiview data containing cancer gene expression data from different sources are prepared. Second, the original tensor is decomposed into a sum of a low-rank tensor and a sparse tensor using TRPCA. Third, the differentially expressed genes are considered to be sparse perturbed signals and then identified based on the sparse tensor. Fourth, the differentially expressed genes are evaluated using Gene Ontology and Gene Cards tools. The validity of the TRPCA method was tested using two sets of multiview data. The experimental results showed that our method is superior to the representative methods in efficiency and accuracy aspects. (shrink)

IntroductionBisphenol A is highly debated and studied in relation to a variety of health outcomes. This large variation in the literature makes BPA a topic that is prone to selective use of literature, in order to underpin one’s own findings and opinion. Over time, selective use of literature, by means of citations, can lead to a skewed knowledge development and a biased scientific consensus. In this study, we assess which factors drive citation and whether this results in the overrepresentation of (...) harmful health effects of BPA.MethodsA citation network analysis was performed to test various determinants of citation. A systematic search identified all relevant publications on the human health effect of BPA. Data were extracted on potential determinants of selective citation, such as study outcome, study design, sample size, journal impact factor, authority of the author, self-citation, and funding source. We applied random effect logistic regression to assess whether these determinants influence the likelihood of citation.ResultsOne hundred sixty-nine publications on BPA were identified, with 12,432 potential citation pathways of which 808 citations occurred. The network consisted of 63 cross-sectional studies, 34 cohort studies, 29 case-control studies, 35 narrative reviews, and 8 systematic reviews. Positive studies have a 1.5 times greater chance of being cited compared to negative studies. Additionally, the authority of the author and self-citation are consistently found to be positively associated with the likelihood of being cited. Overall, the network seems to be highly influenced by two highly cited publications, whereas 60 out of 169 publications received no citations.ConclusionIn the literature on BPA, citation is mostly driven by positive study outcome and author-related factors, such as high authority within the network. Interpreting the impact of these factors and the big influence of a few highly cited publications, it can be questioned to which extent the knowledge development in human literature on BPA is actually evidence-based. (shrink)

In the last 20 years the Turing test has been left further behind by new developments in artificial intelligence. At the same time, however, these developments have revived some key elements of the Turing test: imitation and adversarialness. On the one hand, many generative models, such as generative adversarial networks, build imitators under an adversarial setting that strongly resembles the Turing test. The term “Turing learning” has been used for this kind of setting. On the other hand, (...) AI benchmarks are suffering an adversarial situation too, with a ‘challenge-solve-and-replace’ evaluation dynamics whenever human performance is ‘imitated’. The particular AI community rushes to replace the old benchmark by a more challenging benchmark, one for which human performance would still be beyond AI. These two phenomena related to the Turing test are sufficiently distinctive, important and general for a detailed analysis. This is the main goal of this paper. After recognising the abyss that appears beyond superhuman performance, we build on Turing learning to identify two different evaluation schemas: Turing testing and adversarial testing. We revisit some of the key questions surrounding the Turing test, such as ‘understanding’, commonsense reasoning and extracting meaning from the world, and explore how the new testing paradigms should work to unmask the limitations of current and future AI. Finally, we discuss how behavioural similarity metrics could be used to create taxonomies for artificial and natural intelligence. Both testing schemas should complete a transition in which humans should give way to machines—not only as references to be imitated but also as judges—when pursuing and measuring machine intelligence. (shrink)

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

“D-linked” wh-phrases such as 'which car' are known to increase the acceptability of sentences with island violations. One influential account of this attributes the effect to working memory: the D-linked filler is easier to retrieve at the site of the gap and this leads to the amelioration in acceptability. Such an account predicts that this effect should occur in general with non-trivial wh-dependencies, not just in island environments. An experiment is presented here to test this prediction. Wh-questions with both (...) D-linked and bare wh-phrases and with both island and non-island embedded clauses are presented to participants, who rate their acceptability on a 7-point scale. Results show that D-linking significantly increases acceptability in both island and non-island environments, in accord with analyses that attribute the effect to working memory. In addition, the increase in acceptability is uniform in both types of environments, suggesting that the island effect itself may not be attributable to working memory. (shrink)

So far, the conceptualization and measurement of parental burnout have been deduced from those of job burnout. As a result, it is unclear whether current measures of parental burnout constitute the best representation of the parental burnout construct/syndrome: the possibility cannot be excluded that some dimensions ought to be added, which would change the structure and definition of parental burnout. In this study, the conceptualization and measurement of parental burnout were approached using an inductive method, in which the parental burnout (...) phenomenon was (re)constructed based solely on the testimonies of burned-out parents. Items extracted from their testimonies were presented to a sample of French-speaking and English-speaking parents (N = 901) and submitted to factor analyses. An identifiable parental burnout syndrome including four dimensions was found (exhaustion in one’s parental role, contrast with previous parental self, feelings of being fed up with one’s parental role and emotional distancing from one’s children). The resulting instrument, the Parental Burnout Assessment (PBA) presents good validity. Factorial invariance across gender and languages was also found. Finally, the results of this study replicate previous findings that psychological traits of the parents, parenting factors and family functioning account for more variance in parental burnout than sociodemographic factors. (shrink)

The appropriate understanding and fast processing of lengthy legal documents are computationally challenging problems. Designing efficient automatic summarization techniques can potentially be the key to deal with such issues. Extractive summarization is one of the most popular approaches for forming summaries out of such lengthy documents, via the process of summary-relevant sentence selection. An efficient application of this approach involves appropriate scoring of sentences, which helps in the identification of more informative and essential sentences from the document. In this work, (...) a novel sentence scoring approach DCESumm is proposed which consists of supervised sentence-level summary relevance prediction, as well as unsupervised clustering-based document-level score enhancement. Experimental results on two legal document summarization datasets, BillSum and Forum of Information Retrieval Evaluation (FIRE), reveal that the proposed approach can achieve significant improvements over the current state-of-the-art approaches. More specifically it achieves ROUGE metric F1-score improvements of (1−6)% and (6−12)% for the BillSum and FIRE test sets respectively. Such impressive summarization results suggest the usefulness of the proposed approach in finding the gist of a lengthy legal document, thereby providing crucial assistance to legal practitioners. (shrink)

There is a need for an extensive study on undergraduate students' attitudes towards the kalām course and its teaching process in the faculties of theology and Islamic studies, which are higher religious education institutions. In order to meet this need, a 30-item survey was prepared in a 5-point Likert structure that measures the attitude towards the kalām course. This questionnaire was applied to 1007 randomly selected undergraduate students studying in faculties of theology and Islamic studies in Turkey in the 2021-2022 (...) academic year. In this sample, 69.9% (N=704) of the participants were women, and 30.1% (N=303) were men. In the analyses made with the data obtained, it was seen that the questionnaire had a high incidence of internal consistency; and its suitability for factor extraction was calculated with the Bartlett Sphericity Test and the Kaiser-Meyer-Olkin (KMO) sample adequacy test. The KMO value was 0.934, and the Chi-square value [X2= 8083,719 p. (shrink)