Reviewerly/exHarmony huggingface dataset:
Multi-loss-aware Channel Pruning of Deep Networks

Channel pruning, which seeks to reduce the model size by removing redundant channels, is a popular solution for deep networks compression. Existing channel pruning methods usually conduct layer-wise channel selection by directly minimizing the reconstruction error of feature maps between the baseline model and the pruned one. However, they ignore the feature and semantic distributions within feature maps and real contribution of channels to the overall performance. In this paper, we propose a new channel pruning method by explicitly using both intermediate outputs of the baseline model and the classification loss of the pruned model to supervise layer-wise channel selection. Particularly, we introduce an additional loss to encode the differences in the feature and semantic distributions within feature maps between the baseline model and the pruned one. By considering the reconstruction error, the additional loss and the classification loss at the same time, our approach can significantly improve the performance of the pruned model. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method.
Assessment for Effective Lecture on Knowledge Retention towards Student Learning with Web-based Educational Tools

This study assessed the effective lecture for knowledge retention towards student learning. The survey involved 42 university students (fourth year in Chemical Engineering and Sustainability Energy Department, UNIMAS) where all of them were tested before and after the class through a 10 questions quiz as assessment. From this quiz assessment, only 50% (21 students) of the students managed to answer at least 7 questions correctly before the lecture was delivered. After delivering lecture, 100% (42 students) of the students managed to answer 10 questions correctly. However, after four weeks, only 90% (38 students) of the students managed to score 10 questions correctly due to short knowledge retention.
Spatial entanglement patterns and Einstein-Podolsky-Rosen steering in a Bose-Einstein condensate

We demonstrate EPR steering in an atomic system. This is allowed by spin correlations between spatially separated regions, that we detect by performing high-resolution imaging of BECs in a spin-squeezed state.
STRUCTURAL-FUNCTIONAL MODEL OF PEDAGOGICAL SUPPORT FOR PROFESSIONAL TRAINING OF FUTURE TEACHERS

Problem and goal. In the process of professional training of the future teacher it is of particular importance to find effective ways to solve the problem of professional development of future specialists, one of which is scientifically based pedagogical support. The problem of studying the features of the content and organization of pedagogical support is considered in the studies of many domestic and foreign scientists. On the basis of previously conducted theoretical research, the author defines the concept of “pedagogical support of future teacher training”. The article presents a theoretically grounded structural and functional model of pedagogical support of professional training of future teachers. The methodology of the research consists in the analysis and generalization of the scientific research of the following Russian scientists: V.A. Adolph, E.A. Alexandrova, S.I. Arkhangelskiy, V.P. Bespalko, B.S. Gershunskiy, G.A. Ignatieva, V.V. Krayevskiy, N.N. Kuzmina and S.N. Chistyakova. Results. The author describes three stages in the development of the model –methodological, technological, and the analysis of the results and their approbation. The set of theoretical and methodological approaches for the development of the structural – functional model of pedagogical support of future teacher training (personal activity-based, system-based, axiological, reflexive) is substantiated. The characteristic features of the main structural components of the model (target, content, activity and effectiveness) are given. The target component includes the purpose, objectives and principles of support. The content component that determines the content of the directions of pedagogical support includes organizational and pedagogical conditions necessary for the effective training of the future teacher. The activity component is aimed at determining the subjects and forms of pedagogical support. The effectiveness component presupposes the development of the ability to assess the effectiveness of the process of pedagogical support. Conclusion. The developed structural- functional model of pedagogical support for professional training of future teachers reflects the essence and the main content of the process and is aimed at optimizing and improving the effectiveness of the educational process. The main aspects that are to be taken into account in the development of this model are as follows: the methodological approaches, the content of the structural components of the model, the conditions for the implementation of these components.
A Survey Study of Various Software Cost Effort Estimation in Perspective of India

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Sequence Analysis of Learning Behavior in Different Consecutive Activities

The purpose of this research is to study the possibility of identifying students, statistically, by analyzing their behavior in different consecutive activities. In this project, there are three different sorts of activities: animated example, basic example, and parameterized exercises. We extracted the behavior of each student from the log activities of the Mastery Grids platform. Additionally, we investigate by using unsupervised learning technique, whether there are common patterns, that students share or not while performing these activities. We conclude that we are able to identify students from their behavior, besides that there are some common patterns.
What Are Justification Logics?

Justification logic began with Sergei Artemov’s work providing an arithmetic semantics for intuitionistic logic. As part of that work, a small number of explicit modal logics were introduced—logics in which there was a structure of terms that kept track of not just what was a necessary truth, but w hy it was necessary. These explicit modal logics were connected with standard modal logics such as S4, T, K, and others using Realization Theorems, essentially saying that modal operators concealed an underlying informational structure. Since Artemov’s work, the phenomenon of justification logic has turned out to be very broad. For instance, I have shown that infinitely many modal logics have justification counterparts. In this paper I will sketch the basics and try to give some of the ideas behind formal justification proofs, justification semantics, and realization theorems.
Constructivism in epistemology and in human sciences

The projective constructivism as a cultural attitude and the constructivism in epistemology are discussed. The radical constructivism as a project of substituting natural processes with artificial ones is analyzed. The authors study the constructivist conception of knowledge in general, the constructivist philosophy of scientific knowledge in particular. The following thesis is substantiated: a complete control of the human being over the world is impossible, and realizing of the radical constructivism as life and cultural attitude is not a triumph of human freedom, but rather its loss. Epistemological constructivism is not a true interpretation of cognition and cannot be a fruitful strategy in science. The authors defend the position of constructive realism as a life, cultural and cognitive attitude.
An Effective Recommendation Algorithm Based on Multi-Source Information

This paper proposes an effective recommendation algorithm based on multi-source information, which employs the user feature information and image feature information to handle the problems in recommender system, such as data sparsity, cold start user problem and cold start item problem.The proposed algorithm is as follow.Firstly, this paper presents a denoising auto-encoder to handle the problem of data sparsity and cold start user problem.It can learn the hidden features with nonlinearity of user and item.In addition, the paper proposes collaborative filtering algorithm based on multi-features of items.This approach employs the convolutional neural network to extract features of the image.Then combine the features of the image and the activities of the users to solve the problems of data sparsity and cold start item problem.The proposed method mentioned above is tested with dataset called MovieLens.The results of the experiment show that the proposed method has competitive performance.
Rearing of Local Earthworms in The Mixed Media of Bagasse and Fish Waste

This study aims to determine the preference of earthworms to a media, their population growth in the media, and their ability to produce vermicompost. This study was designed with a completely randomized design consisted of three (3) treatments and each treatment consisted of 7 replications. The treatments in this study were 3 species of local earthworms (Pheretima javanica, Pheretima asiatica, and Pontoscolex corethrurus). The rearing media used was the mixture of bagasse and fish waste. The parameters studied were the time needed by the earthworms to enter the media, the earthworms population growth rate, and the time needed by the earthworms in producing vermicompost. The results showed the three species tested could be lived on mixed media of bagasse and fish waste. The worm's preference to the media was determined based on the time of earthworms entering media. The fastest was Pheretima javanica (0.23 minutes), followed by Pheretima asiatica (3.09 minutes) and Pontoscolex corethrurus (6.94 minutes) (p&lt;0.05). The population growth rate between Pheretima javanica (5.85) and Pheretima asiatica (5.71) was not significantly different, but both significantly higher than Pontoscolex corethrurus (0.29 individual/month). The time for vermicompost formation in Pheretima javanica (6 weeks) and Pheretima asiatica (6 weeks) was not significantly different, but they were significantly lower from the time for vermicompost formation in Pontoscolex corethrurus (8 weeks).
Towards Encoding of the Transition Relation in Dialogue Games Model Checking

We can understand a protocol as a set of rules used by the communicating entities i.e. people or computers. These rules specify allowed interactions between them. Every day people use protocols unconsciously during their conversations since they help to achieve the goal of the conversation (e.g. a compromise, a persuasion). In the paper, we focus on argumentative dialogues in which players can perform actions representing speech acts like claim, question, scold etc. Since we consider dialogues which have an emotional undertow we want to design a system for semantic verification of properties of dialogue games with emotional reasoning. This framework is based on interpreted systems designed for a dialogue protocol in which participants have emotional skills. The GERDL language is used for a dialogue game specification. We present the idea of encoding rules describing the dialogue game given in this language. We want to verify some properties of dialogue games and we focus on the reachability property that can take into account emotions and commitments of players.
Dynamic improvements of static surrogates in direct search optimization

The present work is in a context of derivative-free optimization involving direct search algorithms guided by surrogate models of the original problem. These models are classified into two categories: static surrogates and dynamic models. This work introduces the quadratic hybrid model (HQM), that dynamically corrects information from a static surrogate. Instead of bringing an additive or multiplicative correction, the HQM generalizes these two types of corrections by considering the static model as an input variable of the quadratic model. Numerical tests are performed with the Mads algorithm on three multidisciplinary and one simulation-based engineering problems. The results show that the contribution of the HQM to the Mads algorithm is to solve problems at greater precision for the same computational budget.
User-based collaborative filtering approach for content recommendation in OpenCourseWare platforms

A content recommender system or a recommendation system represents a subclass of information filtering systems which seeks to predict the user preferences, i.e. the content that would be most likely positively "rated" by the user. Nowadays, the recommender systems of OpenCourseWare (OCW) platforms typically generate a list of recommendations in one of two ways, i.e. through the content-based filtering, or user-based collaborative filtering (CF). In this paper, the conceptual idea of the content recommendation module was provided, which is capable of proposing the related decks (presentations, educational material, etc.) to the user having in mind past user activities, preferences, type and content similarity, etc. It particularly analyses suitable techniques for implementation of the user-based CF approach and user-related features that are relevant for the content evaluation. The proposed approach also envisages a hybrid recommendation system as a combination of user-based and content-based approaches in order to provide a holistic and efficient solution for content recommendation. Finally, for evaluation and testing purposes, a designated content recommendation module was implemented as part of the SlideWiki authoring OCW platform.
Harvesting Visual Objects from Internet Images via Deep Learning Based Objectness Assessment

The collection of internet images has been growing in an astonishing speed. It is undoubted that these images contain rich visual information that can be useful in many applications, such as visual media creation and data-driven image synthesis. In this paper, we focus on the methodologies for building a visual object database from a collection of internet images. Such database is built to contain a large number of high-quality visual objects that can help with various data-driven image applications. Our method is based on dense proposal generation and objectness-based re-ranking. A novel deep convolutional neural network is designed for the inference of proposal objectness, the probability of a proposal containing optimally-located foreground object. In our work, the objectness is quantitatively measured in regard of completeness and fullness, reflecting two complementary features of an optimal proposal: a complete foreground and relatively small background. Our experiments indicate that object proposals re-ranked according to the output of our network generally achieve higher performance than those produced by other state-of-the-art methods. As a concrete example, a database of over 1.2 million visual objects has been built using the proposed method, and has been successfully used in various data-driven image applications.
Value And Semantic Psychometric Creativity And Creativity Self-Assessment Correlates

This article is devoted to the examination of the linkage between different types of creativity and value personal orientation structure. The issue arises from, on the one hand, many researches belief that creativity could be assumed as value, which gives the meaning of life, while creative self-realization is the leading life purpose and crucial point, on the other hand, the level of this issue knowledge is quite low (especially considering the link between creativity and parameters of value and semantic sphere). The application of the psychological testing and correlates analysis method reveals the connection between value orientation and psychometric creativity, namely, verbal creativity and divergent mindset, and also the rate of personal creativity characteristics self-evaluation. The sample consists of 135 people aged from 18 to 30, Novosibirsk Universities’ students of different training fields and working specialists. The data analysis let to depict the type of value, connected with both characteristics of verbal and non-verbal creativity, divergent mindset and with personal creativity characteristics. This is the type of value “Self-reliance”, which core feature is the independence of mindset and behavioral choices. Revealed connections give an opportunity to examine the creative peculiarities as the source for self-reliance value expression and, vice versa, independence of mindset and behavior could be the factor, stimulating creative process and creative activity.
A use case centric survey of Blockchain: status quo and future directions

This paper presents an assessment of blockchain technology based on the Emerging Technology Analysis Canvas (ETAC) to evaluate the drivers and potential outcomes. The ETAC is a framework to critically analyze emerging technologies. The assessment finds that blockchain can fundamentally transform the world. It is ready for specific applications in use cases such as digital currency, lightweight financial systems, ledgers, provenance, and disintermediation. However, Blockchain faces significant technical gaps in other use cases and needs at least 5-10 years to come to full fruition in those spaces. Sustaining the current level of effort (e.g. startups, research) for this period of time may be challenging. We also find that the need and merits of decentralized infrastructures compared to centralized and semi-centralized alternatives is not always clear. Given the risk involved and significant potential returns, we recommend a cautiously optimistic approach to blockchain with the focus on concrete use cases. The primary contributions of this paper are a use case centric categorization of the blockchain, a detailed discussion on challenges faced by those categories, and an assessment of their future.
All optical actively tunable quantum signal de-multiplexer based on sum frequency generation

an actively tunable quantum signal de-multiplexer for multi-channel energy-time entangled state based on sum frequency generation is reported. Such device can de-multiplex any signal frequency and preserves entanglement after up-conversion.
BGP Signaled IPsec Tunnel Configuration

This document defines a method of using BGP to signal IPsec tunnelconfiguration along with NLRI, it uses and extends tunnelencapsulation attribute as specified in [I-D.ietf-idr-tunnel-encaps]for IPsec tunnel.
Local Bandwidth Estimation via Mixture of Gaussian Processes.

Real world data often exhibit inhomogeneity - complexity of the target function, noise level, etc. are not uniform over the input space. We address the issue of estimating locally optimal kernel bandwidth as a way to describe inhomogeneity. Estimated kernel bandwidths can be used not only for improving the regression/classification performance, but also for Bayesian optimization and active learning, i.e., we need more samples in the region where the function complexity and the noise level are higher. Our method, called kernel mixture of kernel experts regression (KMKER) follows the concept of mixture of experts, which is constituted of several complementary inference models, the so called experts, where in advance a latent classifier, called the gate, predicts the best fitting expert for each test input to infer. For the experts we implement Gaussian process regression models at different (global) bandwidths and a multinomial kernel logistic regression model as the gate. The basic idea behind mixture of experts is, that several distinct ground truth functions over a joint input space drive the observations, which one may want to disentangle. Each expert is meant to model one of the incompatible functions such that each expert needs its individual set of hyperparameters. We differ from that idea in the sense that we assume only one ground truth function which however exhibits spacially inhomogeneous behavior. Under these assumptions we share the hyperparameters among the experts keeping their number constant. We compare KMKER to previous methods (which cope with inhomogeneity but do not provide the optimal bandwidth estimator) on artificial and benchmark data and analyze its performance and capability for interpretation on datasets from quantum chemistry. We also demonstrate how KMKER can be applied for automatic adaptive grid selection in fluid dynamics simulations.
Experiments on Open-Set Speaker Identification with Discriminatively Trained Neural Networks

This paper presents a study on discriminative artificial neural network classifiers in the context of open-set speaker identification. Both 2-class and multi-class architectures are tested against the conventional Gaussian mixture model based classifier on enrolled speaker sets of different sizes. The performance evaluation shows that the multi-class neural network system has superior performance for large population sizes.
Detecting quantum features in the real world

Quantum resources embedded in physical systems can be destroyed by noisy transmission channels and/or by nonidealities of detectors. Here, we face such a problem by considering some nonclassicality criteria in the mesoscopic intensity domain.
Methodological Bases Of Network Analysis Covering Problems Of Individual Social Adaptation

Current trends in modernization of present-day higher education determine the demand for the development of network processes and practices in ensuring the key tasks of training initiative and competent specialists. Herewith, it is necessary to develop scientifically based ways and means of social adaptation of an individual under the conditions of evolving network reality of education. The present paper covers social, theoretical and practical relevance of the problem. The authors describe the existing approaches to its solution in an interdisciplinary context. Special attention is paid to the peculiarities of the network approach and aspects of its development in line with creating conditions for the individual’s social adaptation in education. The purpose of the article is to substantiate the theoretical and methodological foundations of the heuristic potential of the network approach to the problems of social adaptation and individual’s viability in higher education. Current approaches to the analysis and improvement of individuals’ social adaptation strategies under current conditions of network practices demonstrate an insufficient level of methodological problem understanding. The study of the methodological foundations of the network problems of individual social adaptation in education is based on the general methodological principle of the normative and interpretive analysis, which allows integrating the data of modern social and humanitarian knowledge in the construction of theoretical images of social phenomena. The article describes the novelty and the theoretical significance of the problem.