Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification Of Educational And Cognitive Tasks In The Military Higher Education Institution

Monitoring is one of the lecturer’s activity aspects which let to evaluate effectiveness of training and results of higher education in the form of competences. Various types of tasks to identify students’ level of training at the stage of thematic, on-going, midterm and formative assessment have been developed and presented in the Russian and foreign science. The requirements of the Federal State Educational Standards (FSES) to effective training in the system of Russian military education indicate competences, which can be regarded as the request for future military officers with comprehensive knowledge and analytical thinking. The authors propose the classification of educational and cognitive tasks (ECT), the parameters of which allow lecturers to compile them for developing and assessing military students skills and abilities, and of the generalised indicators of the methodological and didactic systems of educational and cognitive tasks. The typology of educational and cognitive tasks is built with regard to the methods of theoretical and methodological analysis, classification, and modelling. All types of educational and cognitive tasks used to monitor academic progress of military students can give a general impression of how students embrace and master the studied disciplines. The examples of educational and cognitive tasks used in the humanities cycle of educational disciplines are given.
Identification of opinion leaders in social network

Identification of the opinion leaders in the world's largest microblogging site, Twitter, is crucial problem of social network analysis. Twitter has quick information flow and has high impact on forming opinion on mass public. This paper presents the study based on various centrality mesaure approaches for finding key players for a specific political trend on Twitter. A novel weighted approach has been proposed for finding opinion leaders based on the centrality measures. Experiment has been conducted on the Twitter's data for the sit in procession of Pakistani politician Imran Khan in 2014. It has been found that Twitter opinion leadership makes a significant contribution to individuals' involvement in political reforms.
Categorical contrast and audience appeal: niche width and critical success in winemaking

Previous studies show that producers that span category boundaries exhibit lower fit to category schemas, accumulate less expertise, and elicit negative reactions from both critics and consumers. We propose that the negative reaction to category spanning also depends on another mechanism: widespread category spanning lowers categorical contrast--the sharpness of a category's boundaries. Lowered contrast blurs boundaries among categories due to the impairment of the comparison processes underlying evaluations and the growing disagreement about the meaning of the category. These processes lower the appeal of all products in a category and make it problematic for any offer to receive widespread acclaim. By making boundaries less salient, reduced contrast also lowers the advantages of category specialism. These propositions receive support in an analysis of style categories and ratings of Barolos and Barbarescos, elite Italian wines. Copyright 2010 The Author 2010. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved. , Oxford University Press.
A New Horizon Electronic Image Stabilization Algorithm Based on SVM

In this paper, SVM is used in an electronic image stablizing algorithm designed for the sea environment with the horizon feature. First, samples updated mechanism proposed makes SVM an online classifier to distinguish two regions. On this basis, through the binary image processing and the Hough transform, the horizon is detected and the motion compensation values are acquired. Finally, the Affine transform and Bilinear interpolation is adopted to rectify the image sequence. These strategies make the algorithm have advantages in both high accuracy and low computational complexity.
Trust based knowledge outsourcing for semantic Web agents

The semantic Web enables intelligent agents to "outsource" knowledge, extending and enhancing their limited knowledge bases. An open question is how agents can efficiently and effectively access the vast knowledge on the inherently open and dynamic semantic Web. The problem is not that of finding a source for desired information, but deciding which among many possibly inconsistent sources is most reliable. We propose an approach to agent knowledge outsourcing inspired by the use trust in human society. Trust is a type of social knowledge and encodes evaluations about which agents can be taken as reliable sources of information or services. We focus on two important practical issues: learning trust and justifying trust. An agent can learn trust relationships by reasoning about its direct interactions with other agents and about public or private reputation information, i.e., the aggregate trust evaluations of other agents. We use the term trust justification to describe the process in which an agent integrates the beliefs of other agents, trust information, and its own beliefs to update its trust model. We describe the results of simulation experiments of the use and evolution of trust in multiagent systems. Our experiments demonstrate that the use of explicit trust knowledge can significantly improve knowledge outsourcing performance. We also describe a collaborative trust justification technique that focuses on reducing search complexity, handling inconsistent knowledge, and avoiding error propagation.
Bagged Structure Learning of Bayesian Network

We present a novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data. Our approach relies on Efron’s bootstrap framework, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure. Unlike previous bootstrap or MCMC based approaches that are only aimed at recovering specific structural features, we learn a concrete density model that can be used for probabilistic generalization. To make use of our objective when some of the data is missing, we propose a bagged structural EM procedure that does not incur the heavy computational cost typically associated with a bootstrap-based approach. We compare our bagged objective to the Bayesian score and the Bayesian information criterion (BIC), as well as other bootstrap-based model selection objectives, and demonstrate its effectiveness in improving generalization performance for varied real-life datasets.
Mixing Time Power Laws at Criticality

We study the mixing time of some Markov chains converging to critical physical models. These models are indexed by a parameter beta and there exists some critical value betac where the model undergoes a phase transition. According to physics lore, the mixing time of such Markov chains is often of logarithmic order outside the critical regime, when beta ne betac, and satisfies-some power law at criticality, when beta = betac. We prove this in the two following settings: 1. Lazy random walk on the critical percolation cluster of "mean-field" graphs, which include the complete graph and random d-regular graphs. The critical mixing time here is of order Theta(n). This answers a question of Benjamini, Kozma and Wormald. 2. Swendsen-Wang dynamics on the complete, graph. The critical mixing time, here is of order Theta(n1/4). This improves results of Cooper, Dyer, Frieze and Rue. In both settings, the main tool is understanding the Markov chain dynamics via properties of critical percolation on the underlying graph.
The unfolding and resurfacing of information systems (IS) knowledge over the last 25 years: A systemic perspective

Currently there are many perspectives aiming to account for what the field of information systems (IS) is or should be. For some, IS is conceived as a reference and stable discipline with dominant or core knowledge to export to other disciplines. To others, IS still requires a more eclectic/fluid perspective to enable IS be adaptable to emerging challenges and possibilities. In both types of perspectives, there is a need to appreciate fully how IS-knowledge has effectively unfolded through time in broader contexts of social sciences. In line with an emerging research orientation to conceive the IS field as both dominant and eclectic, we propose a systemic framework to account for these stages. We identify key IS knowledge concepts/themes emerging from analysing 4,100 abstracts from articles in eight IS journals covering EU/US regions over the last 25 years. Our findings suggest that such knowledge does not accumulate but rather, some IS key concepts like systems, information and use resurface over time, leading to draw important implications for IS education, research and practice.
Corporate Job Ladders in Europe: Wage Premia for University vs. High School Level Positions

Investment in human capital is a central issue in the literature on economic growth. The purpose of this study is to shed light on the economic incentives for investment in university education across countries. An empirical investigation of earnings for private-sector engineers and business administrators in seven European countries - Belgium, Denmark, France, Germany, Italy, Sweden and the United Kingdom - is presented. The analysis is based on a large micro-data set that is ideally suited for international comparisons. It contains information on earnings, age, occupation, responsibility level, industry and firm size. Standardized wage premia for university vs. high school level positions are computed for each country and field of work. The results indicate that the wage premia are higher for business administrators than for engineers in all the countries considered and that the premia for engineers are remarkably similar across countries. Aggregation over fields of work, which is not uncommon in studies on the returns to education, therefore seems to be questionable practice when comparing the returns in different countries.
Correspondences From Epipolar Plane Images , Experimental Evaluation

The method seeking correspondences in Epipolar Plane Images (EPI) is presented. The principal idea is to employ dense sequence to get more information which could guide the correspondence algorithm. Theoretically a simple technique finding homogeneous straight lines in EPI suffices to establish correspondences. This paper verifies the method in real experiments. The paper builds on results of [ 6]. Real data uncovered several optical and mechanical phenomena which introduce additional problems. They are revealed in the paper one by one and undone.
Forecasting Portfolio Risk in Normal and Stressed Markets

The instability of historical risk factor correlations renders their use in estimating portfolio risk extremely questionable. In periods of market stress correlations of risk factors have a tendency to quickly go well beyond estimated values. For instance, in times of severe market stress, one would expect with certainty to see the correlation of yield levels and credit spreads to go to -1, even though historical estimates will miss this region of correlation. This event might lead to realized portfolio risk profile substantially different than what was initially estimated. The purpose of this paper is to explore the effects of correlations on fixed income portfolio risks. To achieve this, we propose a methodology to estimate portfolio risks in both normal and stressed times using confidence weighted forecast correlations.
JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method

Different types of J-proteins perform distinct functions in chaperone processes and diseases development. Accurate identification of types of J-proteins will provide significant clues to reveal the mechanism of J-proteins and contribute to developing drugs for diseases. In this study, an ensemble predictor called JPPRED for J-protein prediction is proposed with hybrid features, including split amino acid composition (SAAC), pseudo amino acid composition (PseAAC), and position specific scoring matrix (PSSM). To deal with the imbalanced benchmark dataset, the synthetic minority oversampling technique (SMOTE) and undersampling technique are applied. The average sensitivity of JPPRED based on above-mentioned individual feature spaces lies in the range of 0.744–0.851, indicating the discriminative power of these features. In addition, JPPRED yields the highest average sensitivity of 0.875 using the hybrid feature spaces of SAAC, PseAAC, and PSSM. Compared to individual base classifiers, JPPRED obtains more balanced and better performance for each type of J-proteins. To evaluate the prediction performance objectively, JPPRED is compared with previous study. Encouragingly, JPPRED obtains balanced performance for each type of J-proteins, which is significantly superior to that of the existing method. It is anticipated that JPPRED can be a potential candidate for J-protein prediction.
Wireless communication glove apparatus for motion tracking, gesture recognition, data transmission, and reception in extreme environments

Military personnel need better ways to communicate in hostile, noisy, silence-mandated, and/or extreme environments. Typing on a keyboard is difficult and impractical while wearing comprehensive protective clothing. Wireless data gloves were researched and developed to transmit and receive ASCII code and other signals as hand gestures. Two categories of glove prototypes were constructed: gloves with and without a haptic-IO capability. All data gloves detect motion, such as gestures, using magnetic sensors. Non-haptic gloves only transmit static and dynamic gestures. Haptic gloves have vibro-mechanical devices on the fingertips for feedback about transmitted signals and for covert-signal reception. Many potential communications applications include hazardous and covert military operations, space operations, fire fighting, mining, training, underwater use, and aids for the visually and hearing impaired.
Neurophysiology of intra- and inter-species emotional interactions. Personality trait effect, P300 and N300 ERPs measures

Emotional empathy plays a crucial role in social intra-species and inter-species interactions. However the role of interspecies interactions and of some personality components was underestimated. The present research explored electrophysiological correlates of affective processing in relation to emotionally valenced human-human (HH) and human-animal (HA) interactions. Further, we explored the link between such cortical responses and personality empathic profile as measured by the Balanced Emotional Empathy Scale (BEES) and the Interpersonal Reactivity Index (IRI). Both HH and HA interactions was associated to a significant increase of N300 and P300 deflections in response to positive and negative compared with neutral interactions. However, whereas N300 was mainly influenced by stimuli valence and was frontally distributed, P300 seemed to be mainly modulated by the stimuli attentional relevance and showed even a posterior distribution. Finally, a significant association was found between emotional empathy trait (BEES) and N300 amplitude. Results are discussed in light of the significance of empathic traits in mediating species-specific and species-aspecific relationships.
Learning Deep Intrinsic Video Representation by Exploring Temporal Coherence and Graph Structure

Learning video representation is not a trivial task, as video is an information-intensive media where each frame does not exist independently. Locally, a video frame is visually and semantically similar with its adjacent frames. Holistically, a video has its inherent structure--the correlations among video frames. For example, even the frames far from each other may also hold similar semantics. Such context information is therefore important to characterize the intrinsic representation of a video frame. In this paper, we present a novel approach to learn the deep video representation by exploring both local and holistic contexts. Specifically, we propose a triplet sampling mechanism to encode the local temporal relationship of adjacent frames based on their deep representations. In addition, we incorporate the graph structure of the video, as a priori, to holistically preserve the inherent correlations among video frames. Our approach is fully unsupervised and trained in an end-to-end deep convolutional neural network architecture. By extensive experiments, we show that our learned representation can significantly boost several video recognition tasks (retrieval, classification, and highlight detection) over traditional video representations.
A novel RFID-based shipping containers location and identification solution in multimodal transport

In this paper, a new integrated solution is proposed through moving passive RFID and GPS equipment into shipping container to make the cargos and container monitored and located even if the container is loaded, stored, hoisted and transported. New solution can complete the function of not only passive RFID products identification but also active RFID container tracking of long distance. All kinds of customhouses, checkpoints and container dock themselves even donpsilat need to build RFID network to manage cargos and container, as all real-time information can be inquired through EPC network by authorized users. In addition, this architecture can completely solve collision problem and enhance the information security, automatic manifest, adaptability of geographic area and anti-theft.
Hormonal regulation of microsomal cytochrome P450 2C11 in rat liver and kidney.

The current study was conducted to investigate hormonal regulation of cytochrome P450 2C11 (CYP2C11) in rat liver and kidney of adult male rats. In two experiments, hypophysectomy (Hx) resulted in decreased (P < .05) hepatic CYP2C11 apoprotein and mRNA levels. Growth hormone (GH) replacement of Hx rats prevented the decline in hepatic CYP2C11 apoprotein and mRNA levels, whereas, subcutaneous injection of testosterone had no effect. Rat pituitary extract is equally effective in intact or castrated Hx rats in preventing the decline in hepatic CYP2C11 apoprotein and mRNA levels. Specific neutralization of rat GH by sheep anti-rat GH serum reduced (P < . 05) serum IGF-I concentrations, hepatic CYP2C11 apoprotein and mRNA levels. Hx of male rat resulted in decreased (P < .05) renal CYP2C11 apoprotein and mRNA levels, and treatment with GH failed to prevent these effects; however, supplementation of Hx rats with testosterone or rat pituitary extract prevented the Hx-induced decrease of renal CYP2C11 apoprotein and mRNA levels, and the effects of rat pituitary extract occurred only in intact rats. Neutralization of rat GH by anti-rGH significantly reduced (P < .05) CYP2C11 mRNA levels and serum T concentrations but not serum LH concentrations. These results indicate that although hepatic CYP2C11 is regulated by GH, rat renal CYP2C11 is regulated primarily by gonadal steroids.
Properties of Active Galaxies Deduced from H I Observations

We have completed a new survey for H I emission for a large, well-defined sample of 154 nearby (z≲ 0.1) galaxies with type 1 (broad-line) active galactic nuclei (AGNs). We make use of the extensive database of H I and optical parameters, presented in a companion paper, to perform a comprehensive appraisal of the cold gas content in active galaxies and to seek new strategies to investigate the global properties of the host galaxies and their relationship to their central black holes. After excluding objects with kinematically anomalous line profiles, which occur with high frequency in the sample, we show that the black hole mass obeys a strong, roughly linear relation with the host galaxy’s dynamical mass, calculated by combining the H I line width and the optical size of the galaxy. Black hole mass follows a looser, though still highly significant, correlation with the maximum rotation velocity of the galaxy, as expected from the known scaling between rotation velocity and central velocity dispersion. Neither of these H I-based correlations is as tight as the more familiar relations between black hole mass and bulge luminosity or velocity dispersion, but they offer the advantage of being insensitive to the glare of the nucleus and therefore are promising new tools for probing the host galaxies of both nearby and distant AGNs. We present evidence for substantial ongoing black hole growth in the most actively accreting AGNs. In these nearby systems, black hole growth appears to be delayed with respect to the assembly of the host galaxy but otherwise has left no detectable perturbation to its mass-to-light ratio, as judged from the Tully-Fisher relation, or its global gas content. The host galaxies of type 1 AGNs, including those luminous enough to qualify as quasars, are generally gas-rich systems, possessing a cold interstellar medium reservoir at least as abundant as that in inactive galaxies of the same morphological type. This calls into question current implementations of AGN feedback in models of galaxy formation that predict strong cold gas depletion in unobscured AGNs.
Models of Architecture: Reproducible Efficiency Evaluation for Signal Processing Systems

The current trend in high performance and embedded signal processing consists of designing increasingly complex heterogeneous hardware architectures with non-uniform communication resources. In order to take hardware and software design decisions, early evaluations of the system non-functional properties are needed. These evaluations of system efficiency require high-level information on both the algorithms and the architecture. In this paper, we define the notion of Model of Architecture (MoA) and study the combination of a Model of Computation (MoC) and an MoA to provide a design space exploration environment for the study of the algorithmic and architectural choices. A cost is computed from the mapping of an application, represented by a model conforming a MoC onto an architecture represented by a model conforming an MoA. The cost is composed of a processing-related part and a communication-related part. It is an abstract scalar value to be minimized and can represent any non-functional requirement of a system such as memory, energy, throughput or latency.
Dynamic turning control of a quadruped robot using nonlinear oscillators

The authors have proposed a dynamic turning control system of a quadruped robot by using nonlinear oscillators. It is composed of a spontaneous locomotion controller and voluntary motion controller. In this article, capability of dynamic turning motion of the proposed control system is verified through numerical simulations and hardware experiments: Various turning speed and orientation make the motion of the robot asymmetry in terms of duty ratio, stride and center of pressure. The proposed controller actively and adaptively controls redundant DOF to cancel the dynamic asymmetry and established stable turning motion at various locomotion speed and turning orientation.
Influence of audio coding on stereophonic acoustic echo cancellation

Stereophonic acoustic echo cancellation has been found more difficult than echo cancellation in mono due to a high correlation between the two audio channels. Different methods to decorrelate the channels have been proposed so that the stereophonic echo canceller identifies the true echo paths and its convergence rate increases. It is shown that the use of a perceptual audio coder effectively reduces the correlation between the channels and thus convergence to the true echo paths is insured. Furthermore, in those frequency regions where the encoder introduced quantization noise which is below the global perceptual masking threshold, an extra amount of inaudible noise can be added to the channels. Thereby the channel correlation is further decreased and the solution is stabilized. In subband audio coders with high frequency resolution only minor modifications are needed in the decoder.