Miracle-dz/UniFAR-FTUs huggingface dataset:
On levels of cognitive modeling

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a high level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes.
Notice of RetractionDeveloping an abstract model of data structures via model refinement

Data structure design typically involves creating an abstract model of data structures and defining an appropriate representation of the data structures on the basis of the abstract model. As standardized frameworks of data structures are widely used, the focus is increasingly placed on developing an abstract model that enables a seamless integration of framework classes into the intended application. In this paper, we describe an approach to developing an abstract model of data structures via model refinement. Our approach distinguishes design considerations that play different conceptual roles and use them incrementally to guide model development. We present a case study to demonstrate its effectiveness in data structure design.
Study Empowerment for inclusion

Il contributo ha come scopo principale quello di sottolineare come, anche in contesti universitari, un intervento pedagogico sul metodo di studio possa essere per tutti gli studenti, e in particolare per le persone con disabilita o con Disturbo Specifico di Apprendimento (DSA), una efficace via per l’autodeterminazione e per l’inclusione. A partire da una attenta analisi della situazione internazionale in termini di dispositivi offerti dai centri di eccellenza universitari internazionali che accolgono studenti con disabilita e DSA, presentiamo il Progetto Inclusione 3.0 nella sezione relativa alla costruzione di un percorso di Study Empowerment rivolto a studenti universitari con dislessia che, attraverso un sistema integrato di dispositivi tecnologici, permetta di creare luoghi e tempi di vita universitaria inclusiva.
Stability of distributed power and signature sequence control for CDMA Systems-a game-theoretic framework

The problem of power control and signature sequence adaptation in code-division multiple-access (CDMA) systems is studied under a game-theoretic framework. Each user tries to maximize his own utility function, which may be different from others. Sufficient conditions for the existence of an equilibrium point in this multi-objective optimization problem are identified. The methodology for analyzing this class of problems is illustrated by examples.
Paradigm Shift or Paradigm Paralysis? National Mental Health and Capacity Law and Implementing the CRPD in Scotland

The United Nations Convention on the Rights of Persons with Disabilities (CRPD) highlights the need to actively remove obstacles to, and promote, the full and equal enjoyment of human rights by persons with disabilities. This is challenging us to revisit existing conceptions about what is genuine equal and non-discriminatory enjoyment of human rights by persons with cognitive, intellectual and psychosocial disabilities and to accept that a real and fundamental culture change is required in order to achieve this. Whilst many states are seeking to address CRPD requirements in law and policy, including those identified in its Article 12, it is arguable that these do not go far enough in order to secure this culture change. This article considers three issues that need to be resolved as part of the process of achieving this paradigm shift, namely capacity assessments as thresholds for involuntary interventions, authorising involuntary interventions and support for the exercise of legal capacity, both generally and in the particular context of Scotland’s mental health and capacity laws. In doing so, it argues that it is debatable whether the CRPD paradigm shift can be realistically achieved by simply adapted or supplementing current legal and policy models.
Machine Vision System for Automatic Inspection of Bridges

In the bridge inspection, the information of cracks is important to maintain a bridge. Therefore, automatic crack detection is highly desirable for efficiency and objectivity of crack assessment. However, in general, it is very difficult to detect cracks automatically from noisy concrete surfaces. In this paper, we propose a machine vision system for automatic inspection of bridges. The proposed machine vision system can detect cracks in real time, and it has some utility functions for supervised manipulation. The performance of the proposed system has been evaluated with 100 noisy images. In terms of accuracy in detecting cracks, experimental results show that the proposed method is superior to the conventional methods for detecting cracks.
Cooperation in Science and Technology with Central and Eastern European Countries Copernicus Project Cp 93-7599 Pekads Integrated Knowledge Modelling Environment E X Claim: a Language for Operationalizing Commonkads Expertise Models Using Description Logics (report on Operationalization of Commonkad

This report presents Ex Claim, a hybrid language for knowledge representation and reasoning. Developed as an operationalization language for the KADS knowledge based systems development methodology, Ex Claim has a meta-level architecture: it structures the knowledge on three levels, namely the domain, inference and task level. An extension of a description logic is used for implementing the domain level. The inference and task levels are procedural and support non-determinism (inferences and tasks being backtrackable). This in turn requires a non-monotonic domain level. Description logics (term subsumption languages) o er a set of inference services (not available in other KR languages) which are extremely useful in knowledge modelling. Such inference services include domain-level deduction, semantic consistency veri cation and automatic classi cation of concepts. Note that most of the existing KBS development tools and environments do not provide any facilities for model consistency or completeness checking. We argue that such validation and veri cation facilities are extremely important in assisting a knowledge engineer in developing models. In fact, these might be the main facilities a user would expect from a computer-aided KBS development tool.
SEED-GROWING HEART SEGMENTATION IN HUMAN ANGIOGRAMS

Segmentation, Unsupervised clustering, Mean shift, Cardiac images, Human heart, Left ventricle. In this paper an image segmentation scheme that is based on combinations of a nonparametric technique and a seed based clustering algorithm is reported. The method has been applied to clinical unsubtracted angiograms of the human heart. The first step of the method consists in applying a mean shiftbased filter in order to improve the left ventricle cavity information in angiographic images. Second, the initial seed is semi automatically generated from the aortic valve manual localization by a specialist. Third, each angiographic image is segmented using a clustering algorithm that begins with the seed which is grown until image pixels associated to the left ventricle cavity are clustered. A validation is performed by comparing the estimated contours with respect to contours manually traced by a cardiologists. From this validation stage the maximum of the average contour error considering six angiographic sequences (a total of 178 images) is 7.30 %.
Adaptive Development Data Selection for Log-linear Model in Statistical Machine Translation

This paper addresses the problem of dynamic model parameter selection for log-linear model based statistical machine translation (SMT) systems. In this work, we propose a principled method for this task by transforming it to a test data dependent development set selection problem. We present two algorithms for automatic development set construction, and evaluated our method on several NIST data sets for the Chinese-English translation task. Experimental results show that our method can effectively adapt log-linear model parameters to different test data, and consistently achieves good translation performance compared with conventional methods that use a fixed model parameter setting across different data sets.
A joint exploration of executive subcomponents in binge drinking

Abstract Background: Executive deficits have widely been reported in young binge drinkers over the last decade, but uncertainty remains regarding the specificity of these deficits and their variation across executive subcomponents. The current study aimed to offer a theoretically grounded and specific exploration of the differential deficits observed across executive functions in binge drinkers. Method: A total of 40 university students (20 binge drinkers: 10 women; 20 matched controls: 12 women) performed three validated neuropsychological tasks, each exploring a specific executive function, namely, shifting, updating, and inhibition (specifically resistance to distractor interference). Tasks were presented to participants in pseudo-randomized order. Repeated measure analyses of variance were performed to compare group performance. Results: A dissociation was observed across executive tasks regarding group differences: compared with controls, binge drinkers demonstrated preserved performance for shifting and updating abilities, but impaired inhibition. These results support the central role of inhibitory control in excessive alcohol consumption. In contrast to severe alcohol use disorders, binge drinking does not appear to be related to a general executive deficit. Conclusions: In view of the pivotal role played by inhibition impairments in the emergence of severe alcohol use disorders, these findings suggest that individualized evaluation and rehabilitation programs focusing on this inhibitory control subcomponent may improve control abilities in early stages of alcohol-related disorders.
A novel background subtraction for intelligent surveillance in wireless network

In wireless network, the resource limitations and process speed are the main constraints. They make many traditional background subtraction methods unable to be applied in wireless network. Vibe algorithm is a newly proposed background subtraction technique and its computational load is very low. That makes it available in the wireless network. However, Vibe is a pixel-level algorithm and it does not take the information of region into consideration. So it can be easily influenced by the background disturbance, noise and illumination change. In this paper a novel background subtraction algorithm which can be used in wireless network is proposed. To deal with these problems, the proposed algorithm employs graph-based image segmentation to segment every frame and construct the region-level information. With the region-level information, the proposed algorithm provides several strategies such as classification of regions, modification in neighboring regions and fast elimination of ghost region to improve the result of Vibe algorithm. Experiment results demonstrate that the proposed method can effectively extract the moving objects in various categories of scenes.
DotKnot: pseudoknot prediction using the probability dot plot under a refined energy model

RNA pseudoknots are functional structure elements with key roles in viral and cellular processes. Prediction of a pseudoknotted minimum free energy structure is an NP-complete problem. Practical algorithms for RNA structure prediction including restricted classes of pseudoknots suffer from high runtime and poor accuracy for longer sequences. A heuristic approach is to search for promising pseudoknot candidates in a sequence and verify those. Afterwards, the detected pseudoknots can be further analysed using bioinformatics or laboratory techniques. We present a novel pseudoknot detection method called DotKnot that extracts stem regions from the secondary structure probability dot plot and assembles pseudoknot candidates in a constructive fashion. We evaluate pseudoknot free energies using novel parameters, which have recently become available. We show that the conventional probability dot plot makes a wide class of pseudoknots including those with bulged stems manageable in an explicit fashion. The energy parameters now become the limiting factor in pseudoknot prediction. DotKnot is an efficient method for long sequences, which finds pseudoknots with higher accuracy compared to other known prediction algorithms. DotKnot is accessible as a web server at http://dotknot.csse. uwa.edu. au.
Food Constituent Estimation for Lifestyle Disease Prevention by Multi-Task CNN

ABSTRACT Unbalanced nutrition due to an unhealthy diet may increase the risk of developing lifestyle diseases. Many mobile applications have been released to record everyday meals for the health-conscious to enable them to improve their dietary habits. Most of these applications only base their food classification on an image of the food, requiring the user to manually input information about the ingredients such as the calories and salinity. To address this problem, food ingredient estimation from food images has been attracting increasing attention. Automatic ingredient estimation could possibly strongly alleviate the process of food-intake estimation and dietary assessment. In this paper, we propose an automatic food ingredient estimation method from food images by using multi-task CNN. We focus on classification of the food category and estimation of the calorie content and salinity for lifestyle disease prevention. Two-stage transfer learning using a large number of food category recognition image databases is applied to train our multi-task CNN for improved ingredient estimation. We experimentally analyze the relationship between the food category and salinity by using multi-task CNN.
Accuracy of Breath-hold CT in Treatment Planning for Lung Stereotactic Ablative Radiotherapy

PURPOSE The objectives of this study are (1) to measure concordance of tumor position on breath-hold (BH) computed tomography (CT) scans relative to the natural tumor path during free breathing (FB) and (2) to evaluate the benefits of the breathing monitoring device Abches (Apex Medical, Tokyo) for stereotactic ablative radiotherapy (SABR) treatment planning. METHODS In 53 lung cancer patients treated with CyberKnife™ robotic radiosurgery system, FB four-dimensional computerized tomography (4DCT) and end-expiration (EE) BH CT images were obtained. Extent of natural tumor motion was assessed with rigid registration derived from end-inspiration (EI) and EE phases of the 4DCT. Tumor displacement in BH scans relative to the natural tumor path was measured relative to the EE 4DCT phase. RESULTS Mean tumor motion (+/- 1 SD) during natural FB was 1 ± 1 mm, 2 ± 2 mm, and 6 ± 6 mm in medio-lateral, anterior-posterior, and cranio-caudal directions, respectively. Tumor position on BH CT scan was closer to EE than EI 4DCT phase for 35/53 patients (66%). Difference of BH tumor position vs. EE state was 4 ± 3 mm. Gross tumor displacements perpendicular to natural tumor path were as great as 11 mm (anterior-posterior) and were seen with or without the breathing monitoring device. CONCLUSION Tumor position during BH CT may not accurately correspond to positions observed on FB 4DCT. Hence, accurate and custom 4D analysis for each individual patient is recommended for treatment planning, especially those involving BH acquisitions.
Co-simulating power systems and communication network for accurate modeling and simulation of PMU based wide area measurement systems using a global event scheduling technique

In this paper, we describe our global event-driven co-simulation framework GECO that we developed to co-simulate power systems dynamics with data network activities. The GECO framework utilizes global event scheduling across two different simulators with distinct simulation disciplines - to eliminate common synchronization errors often found in federated simulation platforms. We also illustrate the use of the GECO framework on two PMU-based WAMS applications: cyber-attacks impacts analysis on all-PMU state estimation, and PMU-based out-of-step protection. The experimental results described in this paper not only show the efficacy of the GECO framework but also illustrate the utility of GECO in WAMS modeling and simulation.
Cerebrospinal Fluid Based Diagnosis in Alzheimer’s Disease

The Alzheimer’s disease (AD) is the most frequent form of dementia worldwide. The major neuropathological hallmarks of the disease are loss of neurons and synapse, senile plaques (extracellular aggregates primarily composed of s-amyloid; As) and neurofibrillary tangles (aggregates of hyperphosphorylated forms of the microtubule-associated tau protein) throughout cortical and limbic regions of the brain. The definite diagnosis still requires histopathological conformation according to the criteria, however, in recent years substantial progress has been made in the area of early biomarker development. The use of cerebrospinal fluid as a testing platform is very promising because the CSF protein composition can reflect the pathological processes of the brain and because it is easily accessible by a lumbar puncture. Some proteins and peptides such as s-amyloid 1-42 (A┚142), s-amyloid 1-40 (A┚1-40), total tau (tau) and hyperphosphorylated tau (p-tau) have been reported to meet the criteria for a biomarker. Another series of publications reported transthyretin, isoprostane, BACE1 activity and other proteins and enzymes as a potential biomarkers in AD. Whereas the biomarkers mentioned first have been studied extensively and were suggested to be included into clinical AD criteria, less information is available on the others. This review will focus on the importance of CSF based biomarkers in AD, covers the data available from the literature and highlights their role in the differential diagnosis of dementia.
Measuring Network Vulnerability Based on Pathology

This paper compares disease with network vulnerability by their definitions and characteristics. A mapping between disease and vulnerability is built based on their similarities. We put forward a novel model of vulnerabilities in computer networks by simulating the reverse of cause-result of disease. Based on the model, a quantitative metric for vulnerabilities of computer networks is presented. The complexity of the algorithm for computing the metric is O(|V|2X|S|), where V and S stand for set of vulnerabilities and set of network states. By analyzing different structures of the vulnerability model, we found that the value reflecting vulnerability decreases when the model is more linear.
GUI ripping: reverse engineering of graphical user interfaces for testing

Graphical user interfaces (GUIs) are important parts oftoday's software and their correct execution is required toensure the correctness of the overall software. A populartechnique to detect defects in GUIs is to test them by executingtest cases and checking the execution results. Testcases may either be created manually or generated automaticallyfrom a model of the GUI. While manual testingis unacceptably slow for many applications, our experiencewith GUI testing has shown that creating a model that canbe used for automated test case generation is difficult.We describe a new approach to reverse engineer a modelrepresented as structures called a GUI forest, event-flowgraphs and an integration tree directly from the executableGUI. We describe "GUI Ripping", a dynamic process inwhich the software's GUI is automatically "traversed" byopening all its windows and extracting all their widgets(GUI objects), properties, and values. The extracted informationis then verified by the test designer and used to automaticallygenerate test cases. We present algorithms for theripping process and describe their implementation in a toolsuite that operates on Java and Microsoft Windows' GUIs.We present results of case studies which show that ourapproach requires very little human intervention and is especiallyuseful for regression testing of software that is modifiedfrequently. We have successfully used the "GUI Ripper"in several large experiments and have made it availableas a downloadable tool.
Sexual Fluidity: An Integrative Review

Historically, sexuality had been considered a fundamental, biologically determined characteristic of humans. Lately, better protection of human rights and recognition of non-traditional relationships have been leading to acceptance towards gay, transgendered and bisexual people. Nonetheless, little advancement has been made into fully understanding the intricacies of human sexuality and recent research has found that sexuality may not be fixed after all; instead, it appears to be more variable and fluid. This integrative review on sexual fluidity has drawn four discussed themes: Sexuality as a Continuum, Sexual Fluidity of Women, Sexual Agency and Hetero/Homosexuality Binary of Men. Several questions call for more research into understanding sexual fluidity across the lifespan and the development of initiatives to help individuals to both understand and accept this trait. Furthermore, advocacy is needed to ensure equal rights and freedoms without discrimination, both socially and economically.