Miracle-dz/UniFAR-FTUs huggingface dataset:
Selecting Answers to Questions from Web Documents by a Robust Validation Process

Question answering (QA) systems aim at finding answers to question posed in natural language using a collection of documents. When the collection is extracted from the Web, the structure and style of the texts are quite different from those of newspaper articles. We developed a QA system based on an answer validation process able to handle Web specificity. A large number of candidate answers are extracted from short passages in order to be validated according to question and passages characteristics. The validation module is based on a machine learning approach. It takes into account criteria characterizing both passage and answer relevance at surface, lexical, syntactic and semantic levels to deal with different types of texts. We present and compare results obtained for factual questions posed on a Web and on a newspaper collection. We show that our system outperforms a baseline by up to 48% in MRR.
Optimization of Trading Physics Models of Markets

We describe an end-to-end real-time S&P futures trading system. Inner-shell stochastic nonlinear dynamic models are developed, and canonical momenta indicators are derived from a fitted Lagrangian used by outer-shell trading models dependent on these indicators. Recursive and adaptive optimization using adaptive simulated annealing is used for fitting parameters shared across these shells of dynamics and trading models.
Joint channel allocation and power control for underlay D2D transmission

We study a joint channel allocation and power control problem for device-to-device (D2D) transmission underlaying a conventional single-cell cellular network. In such networks, direct transmissions are allowed among device pairs with local needs, provided that the adverse effects of D2D communications on cellular users is negligible and cellular users are given the priority in using limited wireless resources. Moreover, as D2D users are not in contact with the base station (BS), providing them with channel and/or network knowledge imposes excessive overhead. As a result, it becomes imperative to seek for new resource management mechanisms that fit the limitations of this concept. In this paper we consider a realistic model with respect to the information availability, and propose a joint channel allocation and power control scheme by using game- and graph theory. In particular, we first decompose the resource management problem into two cascaded channel allocation and power control problems, by proving a lower bound on the aggregate utility of cellular users. Afterwards we propose a centralized graph-theoretical channel allocation approach jointly for D2D and cellular users. Given the channel allocation, the subsequent power control problem is modeled as a game with incomplete information. We analyze the characteristics of this game and solve it in a distributed manner, by using a multi-agent Q-learning strategy. We evaluate the proposed resource allocation scheme both analytically and numerically.
Public relations and the new institutionalism: In search of a theoretical framework

The aim of this conceptual article is to study a possible dual relationship between public relations and the new institutionalism. How can neo-institutional organizational theory contribute to public relations research, including answering the question: How is public relations (corporate communication or strategic communication) institutionalized in private and public organizations? And how, vice versa, can public relations contribute to the neo-institutional study of organizations, including answering the question: How does public relations institutionalize? In the first part of the article, a short up-to-date précis of the new institutionalism is given demonstrating how this theoretical framework within organizational sociology has developed through various stages and in various directions. The focus is on the shift towards a more complex and interactive understanding of how and why organizations adopt institutionalized norms. In the second part of the article, the new interest in rhetorical, discursive and communicative aspects among neo-institutional scholars is discussed with special reference to Lammers’s theory of institutional messages. An outline of a new theory about how public relations institutionalizes is presented. Each of the two parts of the article ends with a set of research questions to be addressed by future cross-disciplinary research in public relations and neo-institutional organizational theory.
The Role of Intrinsically Unstructured Proteins in Neurodegenerative Diseases

The number and importance of intrinsically disordered proteins (IUP), known to be involved in various human disorders, are growing rapidly. To test for the generalized implications of intrinsic disorders in proteins involved in Neurodegenerative diseases, disorder prediction tools have been applied to three datasets comprising of proteins involved in Huntington Disease (HD), Parkinson's disease (PD), Alzheimer's disease (AD). Results show, in general, proteins in disease datasets possess significantly enhanced intrinsic unstructuredness. Most of these disordered proteins in the disease datasets are found to be involved in neuronal activities, signal transduction, apoptosis, intracellular traffic, cell differentiation etc. Also these proteins are found to have more number of interactors and hence as the proportion of disorderedness (i.e., the length of the unfolded stretch) increased, the size of the interaction network simultaneously increased. All these observations reflect that, “Moonlighting” i.e. the contextual acquisition of different structural conformations (transient), eventually may allow these disordered proteins to act as network “hubs” and thus they may have crucial influences in the pathogenecity of neurodegenerative diseases.
Explaining and Predicting Resistance to Computer Anxiety Reduction among Teacher Education Students

Abstract This study investigated how resistance to the reduction of computer anxiety among teacher education students can be explained and reliably predicted. Instrumentation for the study consisted of six self-report questionnaires: (1) the Computer Anxiety Scale, (2) the Computer Attitude Scale, (3) the Computer Experience Scale, (4) the Computer Knowledge Scale, (5) Rotter’s Internal-External Control Scale, and (6) the trait form of the State-Trait Anxiety Inventory. A stepwise multiple regression using backward deletion was used to find the dimensions along which computer anxiety can be explained and best predicted. The results indicated that the best predictors of retained computer anxiety were computer confidence, trait anxiety, computer knowledge, and computer liking, together accounting for 69% of the variance of computer anxiety following completion of a computer literacy course. The findings suggest that any efforts to treat retained computer anxiety in teacher education students should focus on building computer confidence and expanding students’ knowledge about computers.
Investigating neurophysiological correlates of metacontrast masking with magnetoencephalography

Early components of visual evoked potentials (VEP) in EEG seem to be unaffected by target visibility in visual masking studies. Bridgeman’s reanalysis of Jeffreys and Musselwhite’s (1986) data suggests that a later visual component in the VEP, around 250 ms reflects the perceptual effect of masking. We challenge this view on the ground that temporal interactions between targets and masks unrelated to stimulus visibility could account for Bridgeman’s observation of a U-shaped time course in VEP amplitudes for this later component. In an MEG experiment of metacontrast masking with variable stimulus onset asynchrony, we introduce a proper control, a pseudo mask. In contrast to an effective mask, the pseudomask should produce neither behavioral masking nor amplitude modulations of late VEPs. Our results show that effective masks produced a strong U-shaped perceptual effect of target visibility while performance remained virtually perfect when a pseudomask was used. The visual components around 250 ms after target onset did not show a distinction between mask and pseudomask conditions. The results indicate that these visual evoked potentials do not reveal neurophysiological correlates of stimulus visibility but rather reflect dynamic interactions between superimposed potentials elicited by stimuli in close temporal proximity. However, we observed a postperceptual component around 340 ms after target onset, located over temporal-parietal cortex, which shows a clear effect of visibility. Based on P300 ERP literature, this finding could indicate that working memory related processes contribute to metacontrast masking.
Adult CST‐null mice maintain an increased number of oligodendrocytes

The galactolipids galactocerebroside and sulfatide have been implicated in oligodendrocyte (OL) development and myelin formation. Much of the early evidence for myelin galactolipid function has been derived from antibody and chemical perturbation of OLs in vitro. To determine the role of these lipids in vivo, we previously characterized mice lacking galactocerebroside and sulfatide and observed abundant, unstable myelin and an increased number of OLs. We have also reported that mice incapable of synthesizing sulfatide (CST‐null) while maintaining normal levels of galactocerebroside generate relatively stable myelin with unstable paranodes. Additionally, Hirahara et al. ( 2004 ; Glia 45:269–277) reported that these CST‐null mice also contain an increased number of OLs in the forebrain, medulla, and cerebellum at 7 days of age. Here, we further the findings of Hirahara et al. by demonstrating that the number of OLs in the CST‐null mice is also increased in the spinal cord and that this elevated OL population is maintained through, at least, 7 months of age. Moreover, we show that the enhanced OL population is accompanied by increased proliferation and decreased apoptosis of oligodendrocytic‐lineage cells. Finally, through ultrastructural analysis, we show that the CST‐null OLs exhibit decreased morphological complexity, a feature that may result in decreased OL competition and increased OL survival. © 2009 Wiley‐Liss, Inc.
Experiencing special education: Former students of classes for the emotionally and behaviorally disordered talk about their schooling

In this study 23 former students of special classes for the emotionally and behaviorally disordered (EBD) located in mainstream school were interviewed about their school experiences. The qualitative data were gathered in two in-depth interviews in 1991 and 1995 in southern Finland. The pros and cons of special education are presented as positive and negative elements of special class treatment. The most positive elements were the special teacher and the small teaching group. The most common and significant negative element was the experience of being labeled. The placement of students with special needs in special classrooms has been criticized during recent years. The positive experiences of former EBD pupils shows that there is still need for this kind of intervention for some pupils and in certain phases of their school careers. However, the quality of education must be guaranteed with the help of individualized education plans (IEPs) in cooperation with the regular class teachers.
Soil Moisture Retrieval Using Neural Networks: Application to SMOS

A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures, $T_{b} $'s) complemented with active microwaves (C-band Advanced Scatterometer (ASCAT) backscattering coefficients), and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) . The reference SM data used to train the NN are the European Centre For Medium-Range Weather Forecasts model predictions. The best configuration of SMOS data to retrieve SM using an NN is using $T_{b} $'s measured with both H and V polarizations for incidence angles from 25° to 60°. The inversion of SM can be improved by ~10% by adding MODIS NDVI and ASCAT backscattering data and by an additional ~5% by using local information on the maximum and minimum records of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN-inverted SM is able to capture the temporal and spatial variability of the SM reference data set. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated against in situ measurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this paper allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near-real-time processor and data assimilation in weather prediction models.
Unsupervised word sense disambiguation in biomedical texts with co-occurrence network and graph kernel

This paper proposes an unsupervised word sense disambiguation method for the biomedical domain. In this paper, a network representation of co-occurrence data is first defined to represent both word senses and word contexts. The representation expresses textual context observed around a certain term as a network, where nodes are terms and edges are the number of co-occurrences between connected terms. A graph kernel is adopted as a similarity measure between terms and senses represented in networks. Candidate senses and ambiguous contexts are then compared directly in the representation space to resolve the word sense. It only needs the sense definitions and a large amount of unlabeled texts. The experiments in the biomedical domain show that the method outperforms a baseline method of vector representation. The performance of the proposed method is comparable to the state-of-the-art unsupervised word sense disambiguation methods.
A neural network classifier based on Dempster-Shafer theory

A new adaptive pattern classifier based on the Dempster-Shafer theory of evidence is presented. This method uses reference patterns as items of evidence regarding the class membership of each input pattern under consideration. This evidence is represented by basic belief assignments (BBA) and pooled using the Dempster's rule of combination. This procedure can be implemented in a multilayer neural network with specific architecture consisting of one input layer, two hidden layers and one output layer. The weight vector, the receptive field and the class membership of each prototype are determined by minimizing the mean squared differences between the classifier outputs and target values. After training, the classifier computes for each input vector a BBA that provides a description of the uncertainty pertaining to the class of the current pattern, given the available evidence. This information may be used to implement various decision rules allowing for ambiguous pattern rejection and novelty detection. The outputs of several classifiers may also be combined in a sensor fusion context, yielding decision procedures which are very robust to sensor failures or changes in the system environment. Experiments with simulated and real data demonstrate the excellent performance of this classification scheme as compared to existing statistical and neural network techniques.
Automatic Construction of Statechart-Based Anomaly Detection Models for Multi-Threaded Industrial Control Systems

Traffic of Industrial Control System (ICS) between the Human Machine Interface (HMI) and the Programmable Logic Controller (PLC) is known to be highly periodic. However, it is sometimes multiplexed, due to asynchronous scheduling. Modeling the network traffic patterns of multiplexed ICS streams using Deterministic Finite Automata (DFA) for anomaly detection typically produces a very large DFA and a high false-alarm rate. In this article, we introduce a new modeling approach that addresses this gap. Our Statechart DFA modeling includes multiple DFAs, one per cyclic pattern, together with a DFA-selector that de-multiplexes the incoming traffic into sub-channels and sends them to their respective DFAs. We demonstrate how to automatically construct the statechart from a captured traffic stream. Our unsupervised learning algorithms first build a Discrete-Time Markov Chain (DTMC) from the stream. Next, we split the symbols into sets, one per multiplexed cycle, based on symbol frequencies and node degrees in the DTMC graph. Then, we create a sub-graph for each cycle and extract Euler cycles for each sub-graph. The final statechart is comprised of one DFA per Euler cycle. The algorithms allow for non-unique symbols, which appear in more than one cycle, and also for symbols that appear more than once in a cycle. We evaluated our solution on traces from a production ICS using the Siemens S7-0x72 protocol. We also stress-tested our algorithms on a collection of synthetically-generated traces that simulated multiplexed ICS traces with varying levels of symbol uniqueness and time overlap. The algorithms were able to split the symbols into sets with 99.6% accuracy. The resulting statechart modeled the traces with a median false-alarm rate of as low as 0.483%. In all but the most extreme scenarios, the Statechart model drastically reduced both the false-alarm rate and the learned model size in comparison with the naive single-DFA model.
Resting-State fMRI Functional Connectivity Is Associated with Sleepiness, Imagery, and Discontinuity of Mind

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to investigate the functional architecture of the healthy human brain and how it is affected by learning, lifelong development, brain disorders or pharmacological intervention. Non-sensory experiences are prevalent during rest and must arise from ongoing brain activity, yet little is known about this relationship. Here, we used two runs of rs-fMRI both immediately followed by the Amsterdam Resting-State Questionnaire (ARSQ) to investigate the relationship between functional connectivity within ten large-scale functional brain networks and ten dimensions of thoughts and feelings experienced during the scan in 106 healthy participants. We identified 11 positive associations between brain-network functional connectivity and ARSQ dimensions. ‘Sleepiness’ exhibited significant associations with functional connectivity within Visual, Sensorimotor and Default Mode networks. Similar associations were observed for ‘Visual Thought’ and ‘Discontinuity of Mind’, which may relate to variation in imagery and thought control mediated by arousal fluctuations. Our findings show that self-reports of thoughts and feelings experienced during a rs-fMRI scan help understand the functional significance of variations in functional connectivity, which should be of special relevance to clinical studies.
How perceived threat increases synchronization in collectively moving animal groups

Nature is rich with many different examples of the cohesive motion of animals. Previous attempts to model collective motion have primarily focused on group behaviours of identical individuals. In contrast, we put our emphasis on modelling the contributions of different individual-level characteristics within such groups by using stochastic asynchronous updating of individual positions and orientations. Our model predicts that higher updating frequency, which we relate to perceived threat, leads to more synchronized group movement, with speed and nearest-neighbour distributions becoming more uniform. Experiments with three-spined sticklebacks (Gasterosteus aculeatus) that were exposed to different threat levels provide strong empirical support for our predictions. Our results suggest that the behaviour of fish (at different states of agitation) can be explained by a single parameter in our model: the updating frequency. We postulate a mechanism for collective behavioural changes in different environment-induced contexts, and explain our findings with reference to confusion and oddity effects.
Information Integration for Heterogeneous Data Sources

Information Retrieval from heterogeneous information systems is required but challenging at the same as data is stored and represented in different data models in different information systems.Information integrated from heterogeneous data sources into single data source are faced upon by major challenge of information transformation- were in different formats and constraints in data transformation are used in data integration for the purpose of integrating information systems, at the same is not cost effective. This paper introduces ideaof Information integration based on search criteria from heterogeneous data sources into single data source. Every element of information source such as entity, field, and relation is mapped to component of new single text source-created every time heterogeneous information systems are searched and result is saved into new text file. This approach allows us to create new text file and delete existing file, modifiying wrapper, making modifications later and managing data retrieval in a simple unified style. This architecture is flexible enough to incorporate variety of data models and query capabilities by various protocols. It is possible to select logically tied information from all available legacy data sources.
A proposed High-Resolution Time-Frequency Distribution for the Analysis of Multicomponent and Speech Signals

Abstract— In this paper, we propose a novel time-frequencydistribution (TFD) for the analysis of multi-component signals. In particular, we use synthetic as well as real-life speech signals toprove the superiority of the proposed TFD in comparison to someexisting ones. In the comparison, we consider the cross-termssuppression and the high energy concentration of the signal around its instantaneous frequency (IF). Keywords — Cohen’s Class, Multicomponent signal, Separable Kernel, Speech signal, Time- frequency resolution. I. I NTRODUCTION HE spectrogram, a smoothed version of the well-knownWigner-Ville distribution (WVD), has been widely usedin speech applications [1], [2], [3], [4]. The spectrogram,which is in general a cross-terms free time-frequencydistribution (TFD), suffers from the undesirable trade-offbetween the time concentration and the frequencyconcentration. To address the problem of cross-termssuppression, while keeping a high time-frequency resolution,other TFDs have been proposed. Among these, one can citethe smoothed pseudo WVD (SPWVD) [9], the Zhao-Atlas-Marks distribution (ZAMD) [5] and the B-distribution (BD)[6], just to name a few. In this paper, we present a new distribution for the analysis of multicomponent signals. This distribution, inspired from the Butterworth kernel quadraticTFD [8], has the ability of suppressing the cross-terms while keeping a high-resolution in the time-frequency plane. Toassess the performance of this proposed distribution, we alsopropose to compare it to some existing ones known for theircross-terms suppression property. In the comparisonexamples, we use synthetic as well as real-life data from aspeech application. The comparison results show the highperformance of the proposed TFD in dealing with non-stationary multicomponent signals.The paper is organized as follows: In Section 2, a theoretical aspect of some TFR interest is presented. A proposed hightime-frequency resolution quadratic TFD is introduced inSection 3. In Section 4, simulations and comparison examplesas well as a discussion are presented. Section 5 concludes thepaper.II. THEORETICAL BACKGROUND FOR QUADRATICTFD