Reviewerly/exHarmony huggingface dataset:
Trunk-Branch Ensemble Convolutional Neural Networks for Large Scale, Few-Shot Video-to-Still Face Recognition

For the real-world face recognition, factors such as occlusion and pose-variant (cross face) would affect the identification/verification performance. In addition, large number of classes also increase the complexity, which makes verification/identification even harder. In order to deal with these issues, how to extract discriminative embeddings is a challenging task for the researchers. This research aims at video-to-still (V2S) face identification, which means given few images per person (p.p.) as our database (also called gallery), we tend to identify if a person in a video is someone in our database or not. We use end-to-end Trunk-Branch Ensemble Convolutional Neural Networks (TBE-CNN) combined with state-of-the-art InceptionNet to create informative image patches and boost the features for occlusion and pose-variant issues. The images for learning and identifying comprise occlusions, different face directions and also resolution issues. Moreover, we create a large scale, few shot video-to-still (still-to-still) face recognition dataset with different settings to evaluate the the models and find the preferred settings for real-world face recognition application. How to ensure the accuracy under these practical noises and settings is the goal of this research.
AN INVESTIGATION OF UNIVERSITY STUDENTS’ PERCEPTIONS AND ACCEPTANCE OF MOBILE TECHNOLOGY USE IN LEARNING ENGLISH AS A SECOND LANGUAGE IN CHINA

AN INVESTIGATION OF UNIVERSITY STUDENTS’ PERCEPTIONS AND ACCEPTANCE OF MOBILE TECHNOLOGY USE IN LEARNING ENGLISH AS A SECOND LANGUAGE IN CHINA
Partial Regularization of First-Order Resolution Proofs

Proofs are a key feature of modern propositional and first-order theorem provers. Proofs generated by such tools serve as explanations for unsatisfiability of statements. However, these explanations are complicated by proofs which are not necessarily as concise as possible. There are a wide variety of compression techniques for propositional resolution proofs, but fewer compression techniques for first-order resolution proofs generated by automated theorem provers. This paper describes an approach to compressing first-order logic proofs based on lifting proof compression ideas used in propositional logic to first-order logic. An empirical evaluation of the approach is included.
PEDAGOGICAL INTERACTION OF THE TEACHERS OF THE ARTS COLLEGES

The article reveals the significance of the concepts of "communication" and "pedagogical communication". The structure of pedagogical interaction, strategy and tactics is shown. The theoretical questions of relations of college teachers, which are carried out in the form of interaction and partnership, are considered. Indicates scientific approaches to the definition of the concept of "pedagogical communication". Communication is one of the main problems, through the prism, which studies questions of people's perception and understanding of each other. Communication helps to better understand the process of interpersonal interaction and interpersonal relationships. Interest in communication manifests representatives of different sciences, because it is a multidimensional and multilevel phenomenon. The existence of society can not be imagined outside the communication and interaction of people as its members with each other, so communication has always remained an object not only of everyday, but also of scientific attention. Communication is one of the universal realities of human existence, a specific kind and a necessary condition for its activity. The psychological climate in the pedagogical collective largely depends on the professional-pedagogical communication. Correctly organized professional and pedagogical communication not only provides an effective exchange of information, but also allows you to better know your colleague, to predict the features of further professional interaction. For successful communication, it is necessary to create conditions that would facilitate the self-actualization of the interlocutors and their productive cooperation. Further research, in our opinion, requires the issues of providing, organizing and regulating the relationship of professional and pedagogical communication of teachers of the college of artistic direction in the conditions of methodical work.
Virtualization Solutions Supporting Privacy and Data Protection in Online Activities

Abstract Nowadays, smart devices like computers, tablets, and smartphones allow transmitting the information everywhere, with high speed, over the World Wide Web. However, risks regarding data integrity, privacy and security when using the Internet, increased dramatically, as methods designed to exploit the system’s vulnerabilities are more and more sophisticated. Therefore the need for people working in professional environments to protect their private data when using unsecure connections, by employing advanced tools. There are multiple solutions, but we will focus on the use of virtualization software like VMware or Oracle Virtual Box, together with traditional privacy measures (use of proxies and VPN’s). Today’s smart devices store an important amount of data about their owners and, in most of the cases, people don’t even realize this. Installing and using protection means is often not enough. They have to be properly setup in order to ensure the desired level of security, or anonymity, when using the Internet, and require for the military personnel a good knowledge not only about cyber vulnerabilities and risks, but also technical capabilities and features of the employed security solutions. DISCLAIMER : This paper expresses the views, interpretations, and independent position of the authors. It should not be regarded as an official document, nor expressing formal opinions or policies, of NATO or the HUMINT Centre of Excellence (HCOE).
Lateral Dispersion Pattern of Main Indicators at the Glojeh Polymetallic Deposit, NW Iran

The criterion-base iterative stepwise Backward Elimination (BE) method was used to predict Au according to the main variables (Ag, Cu, Pb, and Zn). The optimization process of the quadratic polynomial model are carried out on different trenches. Whereas, Pb and Zn with Ag×Zn and Pb×Zn are significant to determine the lateral dispersion of Au. It means Zn is the predominant element in near surface zone. Therefore, it point out that the polymetallic (Au-Ag-Cu-Pb-Zn) high-sulfidation hydrothermal veins may be related to a porphyry deposit at depth. Laterally, 2D surface contour maps using kriging confirms all the results of the dispersion pattern of elements at Glojeh.
Polynomial-Time Probabilistic Reasoning with Partial Observations via Implicit Learning in Probability Logics

Standard approaches to probabilistic reasoning require that one possesses an explicit model of the distribution in question. But, the empirical learning of models of probability distributions from partial observations is a problem for which efficient algorithms are generally not known. In this work we consider the use of bounded-degree fragments of the “sum-of-squares” logic as a probability logic. Prior work has shown that we can decide refutability for such fragments in polynomial-time. We propose to use such fragments to decide queries about whether a given probability distribution satisfies a given system of constraints and bounds on expected values. We show that in answering such queries, such constraints and bounds can be implicitly learned from partial observations in polynomial-time as well. It is known that this logic is capable of deriving many bounds that are useful in probabilistic analysis. We show here that it furthermore captures key polynomial-time fragments of resolution. Thus, these fragments are also quite expressive.
Galois extensions, positive involutions and an application to unitary space-time coding

We show that under certain conditions every maximal symmetric subfield of a central division algebra with positive unitary involution $ (B, \tau) $ will be a Galois extension of the fixed field of $ \tau $ and will 'real split' $ (B, \tau) $. As an application we show that a sufficient condition for the existence of positive involutions on certain crossed product division algebras over number fields, considered by Berhuy in the context of unitary space-time coding, is also necessary, proving that Berhuy's construction is optimal.
Run length coding and efficient compression of hexagonal raster data based on Gosper curve

Abstract. Compared with regular quadrilateral grid, regular hexagonal grid is isotropy and has higher cell compactness and sampling density. This gives regular hexagonal grid advantages in visual display, spatial analysis, and many other aspects. However, the studies of raster data mainly focus on regular quadrilateral grid, and various encoding methods are also focused on it. The researches on hexagonal raster data are relatively insufficient.In this paper, encoding and compression for regular hexagonal grid are studied. By introducing Gosper curve which has good spatial aggregation and takes into account the morphological structure of regular hexagonal grid, the bidirectional correlation between Gosper curve and regular hexagonal grid is established. Then, a new encoding framework is built to determine the Gosper coding of each grid unit. The lossless compression is completed by performing run-length coding on adjacent coding sets in the target region.
Text Assisted Insight Ranking Using Context-Aware Memory Network

Extracting valuable facts or informative summaries from multi-dimensional tables, i.e. insight mining, is an important task in data analysis and business intelligence. However, ranking the importance of insights remains a challenging and unexplored task. The main challenge is that explicitly scoring an insight or giving it a rank requires a thorough understanding of the tables and costs a lot of manual efforts, which leads to the lack of available training data for the insight ranking problem. In this paper, we propose an insight ranking model that consists of two parts: A neural ranking model explores the data characteristics, such as the header semantics and the data statistical features, and a memory network model introduces table structure and context information into the ranking process. We also build a dataset with text assistance. Experimental results show that our approach largely improves the ranking precision as reported in multi evaluation metrics.
Preparation of Presentations to Demonstrate the Results of Student Work on the Information Systems Development (Recommended Practices)

The authors discuss typical mistakes that students pursuing a degree in "Applied Information Sciences" make while defending the results of traineeship, course projects and graduate qualification works.The
Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-Convex Optimization

We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical algorithms, for optimizing univariate sub-problems with nice properties. From the other side, it also augments probabilities to receive desired results, by embedding advanced techniques in optimizations of realistic tasks. Integrating both numerical algorithms and advanced techniques together, TECU is proposed in a unified framework for solving a class of non-convex problems. Although the task embedded strategies bring inaccuracies in sub-problem optimizations, we provide a realizable criterion to control the errors, meanwhile, to ensure robust performances with rigid theoretical analyses. By respectively embedding ADMM and a residual-type CNN in our algorithm framework, the experimental results verify both efficiency and effectiveness of embedding task-oriented strategies in coordinate descent for solving practical problems.
Composite Binary Decomposition Networks

Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the fullprecision counterparts. In this paper, we propose the composite binary decomposition networks (CBDNet), which first compose real-valued tensor of each layer with a limited number of binary tensors, and then decompose some conditioned binary tensors into two low-rank binary tensors, so that the number of parameters and operations are greatly reduced comparing to the original ones. Experiments demonstrate the effectiveness of the proposed method, as CBDNet can approximate image classification network ResNet-18 using 5.25 bits, VGG-16 using 5.47 bits, DenseNet-121 using 5.72 bits, object detection networks SSD300 using 4.38 bits, and semantic segmentation networks SegNet using 5.18 bits, all with minor accuracy drops.1
Introduction to big data recommender systems—volume 2

The rapid development of e-commerce websites and social networking applications has drastically increased the volumes of online generated data, leading to the term big data. With the rise in Internet population to 3.2 billion worldwide, on the average, 2.5 quintillion bytes of data is generated on daily basis [1]. Such greater volumes of data introduced information overload problem, when it is difficult to find the most relevant information from numerous diverse sources, e.g., websites, blogs, e-commerce, and social networking applications. The growing size of data has forced the research community to think beyond the simple search problem to the next level of filtering of pertinent information [2]. Past few years have seen significant progress in the development of powerful and intelligent tools to process and analyze the complex patterns in big data to extract the knowledge that is more meaningful for users. The potential ability to create intelligence from the analysis of raw data has been successfully applied to diverse areas, such as business, industry, sciences, social media, and e-commerce, to name a few. The ever-growing volume, complexity, and dynamicity of online information have necessitated the use of recommender systems as an appropriate tool for facilitating and accelerating the process of information engineering. The recommender systems apply numerous knowledge discovery techniques on users&apos; historical and contextual data (e.g., location, time, preference, weather, device, and mood) to suggest information, products, and services that best match the user&apos;s preferences [3].
Coherent manipulation of a three-dimensional maximally entangled state

A scheme to generate three dimensional OAM-maximally-entangled photon-pairs via spontaneous-parametric-down-conversion is demonstrated, which can perform both amplitude and phase modulations of the generated state and shows great advantages over traditional post-selection method.
Fostering Lecturers’ Teaching Creativity through Games

The purpose of this study is to see and analyze the use of games in fostering lecturers’ creativity at Language Development Center of IAIN Padangsidimpuan. The investigation in this study covers several points i.e. what games do the lecturers apply in teaching, how do they create the games, and what are the advantages of using games in teaching? This is a type of qualitative descriptive study in which it focuses on analyzing the data in depth in a form of data elaboration and generalization. This study was done at Language Development Center of IAIN Padangsidimpuan. Ten lecturers were chosen as participants of this study. Then, in collecting the data, interview and observation were used. After that, the data were analyzed descriptively in form on data elaboration and narration. Moreover, it was found that games could foster lecturers’ creativity in teaching, and there were many kinds of games that were applied by lecturers such as True/False, Cards, Pictures and Drawing, Guessing, and Miming. Finally, it was also found that there were many advantages derived from using games i.e. (1) It improves lecturers’ confidence, (2) It also can create communicative teaching and learning, and (3) It can increase the use of media and make the media more interesting.
Komparasi Algoritma Naive Bayes Dengan Algoritma Genetika Pada Analisis Sentimen Pengguna Busway

Congestion major cities in Indonesi caused by the proliferation of the use of private vehicles. Some expressing he thinks about busway user through the social media and other web site, This opinion can be used as a sentiment analysis to see if the user busway proposes a review of positive or negative. The results of the analysis sentiment can help in the sight of and evaluate the use of busway, also expected to improve and transjakarta facility from so they tend to have an opinion positive. Based on the results of the analysis, sentiment it is hoped people will switch to using the will of course will reduce congestion. In the study also added the stages preprocesing by using the framework gataframework to complete the process that cannot be done on tools rapidminer. The methodology that was used in this research was it is anticipated that analysis the sentiment of the by the application of an genetic algorithm for an election features with an algorithm naive bayes. From the results of the testing to the case in research it is found that classification algorithm naive bayes based genetic algorithm having the kind of accuracy that good enough 88,55 % and value of auc reached 0,813 % with the level of the diagnosis classifications good. So that in this research classification algorithm naive bayes based genetic algorithm can be recommended as algorithms classifications good enough to analyze the busway user sentimen. Based on analysis is expected to private transport users will switch to using the busway will reduce congestion
Correction to: Language, Script, and Font Recognition

Owing to an unfortunate oversight the second author Niladri Sekhar Dash was missing in the initially published html version of this chapter. He has now been added.
Knowledge Resource Quality, Knowledge Management ProcessCapability, and Effectiveness – A Simulation Approach

This paper analyses the importance of knowledge resource quality in the context of a knowledge managementenvironment in Information Technology sector. The exogenous variable of this study is knowledge resource quality,and the endogenous variables are knowledge management process capability and knowledge managementeffectiveness. This research is conducted in the context of information technology sector in India. System Dynamicsapproach is used for this research which consists of five different steps of problem identification, systemconceptualization, model formulation, simulation & validation, and policy analysis & improvement. Data for thisresearch was collected through a survey questionnaire. The survey was conducted in large Indian IT companies, andthe data was collected from 423 knowledge workers of 63 IT companies in India. Multiple linear regression equationswere developed between the independent and dependent variables of the study. These equations were used todevelop the stock and flow diagram, and the model was simulated under four different scenarios. The results highlight the importance and leverage provided by knowledge resource quality for improving KM process capability andeffectiveness. Managerial implications are provided to help IT managers to take strategic and operational decisionsfor improving KM performance
Monitoring Kamera CCTV Untuk Perangkat Mobile Dengan Menggunakan Smartphone

With the progress of the times, technology has also developed, especially application technology that shows very rapid progress, infrastructure in information technology that can support the creation of a reliable system or application has also experienced rapid development as well. The technology is used so that we can monitor or monitor the situation and situation of different places or regions by using a CCTV monitoring application, by using this application we are as if we will be in one place in front of us, the coverage that we can monitor is not limited territory, different places, islands, countries and even different continents can monitor each other. The development of computer network technology that is different in area every day is also growing and the scope is wider, not only in one area coverage, it can even become a global (world) coverage, namely using the internet, with this internet facility, then to use devices that have connected to computer networks from different areas of the place can be affordable with CCTV monitoring applications can even reach a wide area.
Evaluasi Penerapan Sistem Informasi Smart Prodi dengan Pendekatan Delone Mclean dan Framework Cobit 5

Implementation of Information Systems used to improve the quality of service in the field of education at STMIK Amikom Purwokerto, the increasing problems in STMIK Amikom Purwokerto have not yet had a difference in terms of information systems to increase the system's prosperity. The method used is descriptive method using quantitative. The concept of this study uses the theory of DeLone and McLean combined with the COBIT 5 Framework which consists of 6 indicators including system quality, information, services, users, user satisfaction and net benefits. The results of this study are information systems that have been quite good and successful in the range of 0.80 the highest value of system quality, this explains about system users who have been carried out as daily activities to help use the operational process. The lowest value is service quality (0.50). This explains the quality of service received well needs improvement. And found some services that are less good and between good variables. Benefits of users, found to be related between information system development and information system maturity level (H0 rejected), but opposed not too strong or significant (0.38).