Reviewerly/exHarmony huggingface dataset:
Reducing Language Barriers to Domestic Violence

The instrumentalist approach refuses to illustrate or analyze in detail the complex relationship between aggression and language in subordinating abuse. Collective Aggression Politics, which makes collective violence a form of "controversial politics." Violence as "a kind of dialogue" containing systems where different groups make claims against each other and where the targets of those claims are likely to give rise to violent reactions. Debate centers on a shift from a peaceful to a violent discourse in which they live, throughout, under the heading of divisive politics.
Analysis of Y00 Protocol under Quantum Generalization of a Fast Correlation Attack: Toward Information-Theoretic Security

In our previous work, it was demonstrated that the attacker could not pin-down the correct keys to start the Y00 protocol with a probability of one under the assistance of unlimitedly long known-plaintext attacks and optimal quantum measurements on the attacker's quantum memory. However, there were several assumptions that the Y00 system utilized linear-feedback shift registers as pseudo-random-number generators, and a fast correlation attack was disabled by irregular mapping on the Y00 systems. This study generalizes such an attack to remove the assumptions of the previous work. The framework of the security analyses of this study reiterates two well-known results from the past: (1) Y00 systems would be cryptanalyzed when the system is not designed well; (2) the system is possibly information-theoretically secure when the system is designed well, although the attacker's confidence in the correct key increases over time but the success probability of key recovery does not reach unity in finite time; (3) the breach time of the shared keys is increasingly threatened with time. Hence, a key-refreshment procedure for the Y00 protocol is provided herein. Such security analyses are important not only in key refreshment but also in initial key agreement situations.
Librarianship at the Turn of the Century: Contribution of Y.A. Grikhanov

The article analyses the contribution of Y.A. Grikhanov to the development of librarianship and library science. The main activity trends of Y.A. Grikhanov were driven by the development tasks of the library sphere since the 1970s. The author highlights the key problems of library stocks knowledge and determines contribution of Y.A. Grikhanov to their resolution. He was one of the organizers of the system of Depository storage of library collections in the libraries of Russia; he worked in the Department of Libraries of the Ministry of Culture of the Russian Federation. The main priorities of the Department activities in 1991—1999 were the creation of the All-Russian information-library computer network, the formation of “National Program for the preservation of library stocks”, the determination of priorities of the state library policy. Y.A. Grikhanov is one of the founders of the Federal Law “On Librarianship” and “The Model Library Code for the CIS Member States”. “The Library Encyclopaedia”, prepared under the guidance of Y.A. Grikhanov, reflects the holistic period of development of library science in Russia. Contribution of Y.A. Grikhanov to library stocks knowledge, library law and philosophical basis of library theory is huge. He is the leading scientist and organizer who has influenced the development of librarianship in Russia and the CIS countries.
Implementasi Metode Term Frequency-Inverse Document FrequencyClass Frequency untuk Peringkasan Berita Online

This study aims to improve and implement the Term Frequency Inverse Document Frequency Method with the Class Frequency method in summarizing online news to save time for news readers in understanding news through news summaries. Data (text documents) used in this study amounted to 20 Indonesian language news documents obtained from the site http://www.kompas.com. The trial document is a collection of news from the economic sports and technology categories. Data were analyzed using the Term Frequency Inverse Document Frequency Class Frequency method. The results of this study, show that Class Frequency implementation can affect the accuracy of word weighting in the Term Frequency and Inverse Document Frequency methods where the test results obtain an average accuracy of up to 75% of the 20 documents tested by comparing system testing with manual testing.
Characterizing and Understanding GCNs on GPU

Graph convolutional neural networks (GCNs) have achieved state-of-the-art performance on graph-structured data analysis. Like traditional neural networks, training and inference of GCNs are accelerated with GPUs. Therefore, characterizing and understanding the execution pattern of GCNs on GPU is important for both software and hardware optimization. Unfortunately, to the best of our knowledge, there is no detailed characterization effort of GCN workloads on GPU. In this paper, we characterize GCN workloads at inference stage and explore GCN models on NVIDIA V100 GPU. Given the characterization and exploration, we propose several useful guidelines for both software optimization and hardware optimization for the efficient execution of GCNs on GPU.
Various dimensions and aspects of the legal problems of the blockchain technology

With technological developments in IT, many of the effective regulations start to have problems to answer the new technologic features and possibilities. These developments take advantage of the legal loophole and uncontrollably grow in worldwide. One another aspect of the developments on the IT is we start to talk about not only national legal gaps, but also international legal gaps due to the nature of the IT technology worldwide network. One of the most promising technology since 2010 is blockchain technology, which is the technology that is open source, software-based, peer to peer technology. It uses a distributed ledger, to store users' transactions. Due to the emerging technology of blockchain and cryptocurrencies together, differences of the blockchain technology and cryptocurrencies could not be understood well. The blockchain is a technology, behind of the cryptocurrencies. Cryptocurrency is just a small part of the main driven technology of the blockchain.There have been several legal concerns of the services that stored or provided a base on the blockchain technology. There is some research to try to determine which kind of legal gaps may be emerged due to blockchain technology. In addition, the big part of the research on the topic examines current legal problems of the blockchain services. However, it has not been studied is the wide scope of the impact of the blockchain technology in the different legal fields of law.This study is going to include extensive research into the motivations to blockchain legal frame on the several fields of the law. We will also examine the different solutions for the problem or legal gaps of the blockchain services in order to shape the legal framework of the blockchain technology.
Structural-Aware Sentence Similarity with Recursive Optimal Transport

Measuring sentence similarity is a classic topic in natural language processing. Light-weighted similarities are still of particular practical significance even when deep learning models have succeeded in many other tasks. Some light-weighted similarities with more theoretical insights have been demonstrated to be even stronger than supervised deep learning approaches. However, the successful light-weighted models such as Word Mover's Distance [Kusner et al., 2015] or Smooth Inverse Frequency [Arora et al., 2017] failed to detect the difference from the structure of sentences, i.e. order of words. To address this issue, we present Recursive Optimal Transport (ROT) framework to incorporate the structural information with the classic OT. Moreover, we further develop Recursive Optimal Similarity (ROTS) for sentences with the valuable semantic insights from the connections between cosine similarity of weighted average of word vectors and optimal transport. ROTS is structural-aware and with low time complexity compared to optimal transport. Our experiments over 20 sentence textural similarity (STS) datasets show the clear advantage of ROTS over all weakly supervised approaches. Detailed ablation study demonstrate the effectiveness of ROT and the semantic insights.
PENINGKATAN HASIL BELAJAR MELALAUI MEDIA BLENDED LEARNING BERBASIS EDMODO

Education as a face to fice learning system becomes a modern learning system, innovating in the delivery of learning. The purpose of this study, 1) developing edmodo-based blended learing learning media on Arabic text Membeca material, 2) knowing the feasibility of edmodo-based blended learning media, and 3) knowing the attractiveness of students and instructors in using edmodo-based blended learning learning media. While this type of research uses development research (R&D) with qualitative and quantitative approaches with ADDIE design models. The results of this study indicate Edmodo-based Media Blended Learning according to Media Experts and the material is declared feasible and gets an average percentage of 85%. while the examination by 2 expert teachers on average score of 82% and the results of field tests conducted on students get a score of 84%, see the results of the scores above that edmodo-based blended learing learning media on Membeca material Arabic text can be used in convey learning..
Identification of Relevant Input Variables for Prediction of Output PV Power Using Artificial Neural Network Models

At present, generating energy from renewable sources is an important topic and is attracting significant attention because of its many benefits. Recent technological developments have made generating renewable energy from various sources such as the solar, sun, wind, geothermal energy, With the continuous increase of grid-connected photovoltaic (PV), high-precision PV power prediction is increasingly important. Extant deterministic forecasting methods do not facilitate fully effective dispatching decisions or power grid risk analysis. To find the most influencing variables for PV power prediction, this paper proposes a model for predicting the output PV power, different combinations of weather variables were used to develop this model. The determination coefficient of the proposed model is 0.98501 with an RMSE value of 30.663. The proposed model was tested on new data, the results showed that the model works with a good preferment and that the prediction quality depends on the time of year with a determination coefficient of 0.9972, 0.9856, 0.9487 and 0.9942 for summer, autumn, winter and spring respectively.
Understanding Mechanical Effects on the Dynamics of Nascent Adhesions

Integrin-based adhesions play a critical role as force-transmission sites which mediate motile behaviour in numerous eukaryotic cell types. Theoretical models of these adhesions have mainly focused on how mechanochemical perturbations of stable adhesions lead to different modes of growth or shrinkage; however, the dynamics of adhesion assembly from a single molecular complex to its macromolecular steady state remains poorly understood, particularly when the chemical bonds involved are under tension. We developed a biophysical model of adhesion to give insight into the dynamics of adhesion assembly and, using stochastic simulations of the model, investigate the mechanical conditions which allow for the assembly process described above. We employed a novel machine-learning technique (called Conditional Expectation Analysis) on previously published super-resolution imaging data to quantify the relationship between adhesion size and the number of molecules it contains, revealing a non-linear trend that is uniquely captured by our biophysical model. In order to link adhesion dynamics to cell motility, we coupled an existing partial differential equation (PDE) model of a self-organizing lamellipodium in crawling cells to our biophysical model. Firstly, we showed that the complex spatio-temporal patterns of activity produced by the PDE model can be deciphered using a simplified ordinary differential equation (ODE) model. Applying slow-fast analysis on the ODE model, we showed that it exhibits a canard explosion and possesses five regimes of behavior, including stalling, waving, bistability stochastic waving and smooth motile. Secondly, we incorporated mechanical effects seen in the biophysical model (e.g., adhesion reinforcement, disassembly in response to force and growth beyond a threshold in force) ot the ODE model. Our results provided important insight into how microscopic mechanical effects are integrated at the cellular level to produce different modes of motility.
KLDivNet: An unsupervised neural network for multi-modality image registration

Multi-modality image registration is one of the most underlined processes in medical image analysis. Recently, convolutional neural networks (CNNs) have shown significant potential in deformable registration. However, the lack of voxel-wise ground truth challenges the training of CNNs for an accurate registration. In this work, we propose a cross-modality similarity metric, based on the KL-divergence of image variables, and implement an efficient estimation method using a CNN. This estimation network, referred to as KLDivNet, can be trained unsupervisedly. We then embed the KLDivNet into a registration network to achieve the unsupervised deformable registration for multi-modality images. We employed three datasets, i.e., AAL Brain, LiTS Liver and Hospital Liver, with both the intra- and inter-modality image registration tasks for validation. Results showed that our similarity metric was effective, and the proposed registration network delivered superior performance compared to the state-of-the-art methods.
Multi-Fusion Chinese WordNet (MCW) : Compound of Machine Learning and Manual Correction

Princeton WordNet (PWN) is a lexicon-semantic network based on cognitive linguistics, which promotes the development of natural language processing. Based on PWN, five Chinese wordnets have been developed to solve the problems of syntax and semantics. They include: Northeastern University Chinese WordNet (NEW), Sinica Bilingual Ontological WordNet (BOW), Southeast University Chinese WordNet (SEW), Taiwan University Chinese WordNet (CWN), Chinese Open WordNet (COW). By using them, we found that these word networks have low accuracy and coverage, and cannot completely portray the semantic network of PWN. So we decided to make a new Chinese wordnet called Multi-Fusion Chinese Wordnet (MCW) to make up those shortcomings. The key idea is to extend the SEW with the help of Oxford bilingual dictionary and Xinhua bilingual dictionary, and then correct it. More specifically, we used machine learning and manual adjustment in our corrections. Two standards were formulated to help our work. We conducted experiments on three tasks including relatedness calculation, word similarity and word sense disambiguation for the comparison of lemma's accuracy, at the same time, coverage also was compared. The results indicate that MCW can benefit from coverage and accuracy via our method. However, it still has room for improvement, especially with lemmas. In the future, we will continue to enhance the accuracy of MCW and expand the concepts in it.
Proposal of a Skill Training Method for UP Problem Suppression Based on Analysis of Ecological Cognitive Load and Multiple-dimensional Conducting Skills

In this study, we examined multiple-dimensional conducting skills (MCSs) and the ecological cognitive loads (ECLs) imposed on the competitors of the 44th WorldSkills Competition (WSC) “Information network cabling” competition, and then conducted an analysis of the causes of underperformance (UP) problems. We also collected knowledge about developmental structures that lead to structural differences in the cognitive loads that result from proficiency level differences and the suppression of cognitive loads. Herein, We clarified the conducting skills to be acquired at each developmental stage in the unlocking training loads method to suppress and avoid the UP problems in High pressure situations.
Improving Recommendations Using Traceability and Machine Learning

The intelligent filtering systems known as Recommender systems, are important tools meant to assist users in their choices and solve the issue of information overload, as they can predict and provide suggestions, intended to meet users’ interests and expectations. For this purpose, the recommendation process uses different data which is extracted from the users’ preferences and interests (User Profile) and the items’ characteristics. These systems employ the same techniques but different approaches that exploit textual and history data from users’ activities, taken either from their profiles or from other valuable resources and are often combined to provide the most suitable results. However, due to the increasing volume of data, the recommendation becomes more redundant and the same elements get proposed repeatedly. In this paper, we intend to discuss the benefits of using traceability and machine learning and how they can improve the recommendation and make it more efficient and reliable.
A Tale of Old Walhalla - Subject - Index to the AMA archive

A Tale of Old Walhalla - Subject - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Kompozitsiia: immersivnyi diskurs professii i bazovaia paradigma khudozhestvenno-proektnoi podgotovki

The article is devoted to the consideration of composition role in architecture and design, which seems to be a kind of propaedeutic block of the profession, immersive discourse, as well as the basic paradigm of art-project training. The author of the article studies the actual compositional techniques and concepts of modern project practice, interpreted as internal mechanisms and patterns of life in the professional space, a special system of «entry» into the specifics of art and project activities. The author outlines that formed on the basis of such an ideology, the author’s course on the basics of composition reflects key aspects of a compositional language formation taking into account immersive perception and serves as a way of introducing into the profession, mastering compositional thinking. The author comes to the conclusion that proposed approach allows students to integrate into the profession organically, sets the forms of immersing in art and project realities.
IbM Teacher of Early Childhood Education in Mempawah Hilir Sub District for Learning Tools Designing Based on 2013 Curriculum

The main goal of this IbM was to improve (1) knowledge and skills of the teacher from Malikian to design APE (2) knowledge and skills of the teacher from Tanjung and MAlikian to design K-13 based RKH, (3) knowledge and skills of the teacher from MAlikian and Tanjung on designing K-13 based evaluation instrument. The output of this IbM was (1) 8 APE, (2) 16 RKH, (3) 16 evaluation instrument. Methods of this activity were (1) giving a course to teachers how to design APE, RKH, and K-13 based evaluation instrument, (2) training teachers how to design APE, RKH, and instrument evaluation. (3) giving guidance of implementation of APE, RKH and K-13 based instrument evaluation. Evaluation of this activity was done two times which was done after the course and training activities. As a result, this IbM was successfully done without mistakes and obstacles.
Multi-task Reinforcement Learning with a Planning Quasi-Metric

We introduce a new reinforcement learning approach combining a planning quasi-metric (PQM) that estimates the number of steps required to go from any state to another, with task-specific "aimers" that compute a target state to reach a given goal. This decomposition allows the sharing across tasks of a task-agnostic model of the quasi-metric that captures the environment's dynamics and can be learned in a dense and unsupervised manner. We achieve multiple-fold training speed-up compared to recently published methods on the standard bit-flip problem and in the MuJoCo robotic arm simulator.
On the Data Blasé, Statistical Imaginaries, and the Cashier-less Bodega

The banality of everyday data is a hallmark of the 21st century. At a moment when computational techniques involving big data and machine learning are in the process of parsing all walks of life, we might revisit notions of the gigantic, its transformation of the quantitative into the qualitative, and the corresponding subjectivities produced through […]
Web Vulnerability Measures for SMEs

An investigation was conducted into web vulnerabilities in commonly used web application templates and frameworks (WAFs) systems such as Joomla, WordPress, Moodle and C #.Net framework. A web vulnerability scoring scheme was developed and used to record metrics of the vulnerabilities associated with the web application templates and frameworks. A custom web application was also developed purported to demonstrate how the vulnerabilities could be shielded in web application frameworks (WAFs). The investigations and implementations were guided by Open Web Application Security Project. The study found some of the most common vulnerabilities in the frameworks and templates at different levels. The choice of Content Management Systems (CMS) templates and WAFs for web application systems development can then be guided by this study.
An empirical analysis of information encoded in disentangled neural speaker representations

The primary characteristic of robust speaker representations is that they are invariant to factors of variability not related to speaker identity. Disentanglement of speaker representations is one of the techniques used to improve robustness of speaker representations to both intrinsic factors that are acquired during speech production (e.g., emotion, lexical content) and extrinsic factors that are acquired during signal capture (e.g., channel, noise). Disentanglement in neural speaker representations can be achieved either in a supervised fashion with annotations of the nuisance factors (factors not related to speaker identity) or in an unsupervised fashion without labels of the factors to be removed. In either case it is important to understand the extent to which the various factors of variability are entangled in the representations. In this work, we examine speaker representations with and without unsupervised disentanglement for the amount of information they capture related to a suite of factors. Using classification experiments we provide empirical evidence that disentanglement reduces the information with respect to nuisance factors from speaker representations, while retaining speaker information. This is further validated by speaker verification experiments on the VOiCES corpus in several challenging acoustic conditions. We also show improved robustness in speaker verification tasks using data augmentation during training of disentangled speaker embeddings. Finally, based on our findings, we provide insights into the factors that can be effectively separated using the unsupervised disentanglement technique and discuss potential future directions.