Reviewerly/exHarmony huggingface dataset:
Two-Phase-Win Strategy for Improving the AlphaZero’s Strength

AlphaZero used a combination of Monte-Carlo Tree Search as well as deep neural networks that learned without human knowledge. It demonstrated that reinforcement learning by self-play could surpass the human champions. The great success of AlphaZero seems that every AI tasks can be trained and learned without any human knowledge as well as any human heuristics. But this paper presents another viewpoint: the AlphaZero approach is good at the perspective of overall situations, and miniMax search (with alpha-beta pruning) is adept in discovering partial solutions. Therefore, we introduce the Two-Phase-Win strategy to combine AlphaZero and miniMax search with alpha-beta pruning for improving AlphaZero’s strength. It has improved the strength of the AlphaZero approach applied to Connect4. The results of experiments show that the Two-Phase-Win strategy has 58% win rate against the AlphaZero approach and doesn’t lose any game in a 100-game match.
Motivation and It’s Connection with the Achievements in Math of University Students

Motivation is the main driving force in human behavior and activity, including the learning process. There are studies in which it is shown that the initial achievements of students directly depend on their intelligence, and further achievements in learning depend on their motivation. The aim of this study is to compare motivating factors: The research method is the analysis of the results of an anonymous online survey of students by the method of Rean A. A. & Yakunin V. A. with the addition of two possible motives that are directly related to mathematics. Statistics of responses to the questionnaire is studied using t-test and correlation. The study found that successful students have a significantly higher level of internal motivation than weaker students. According to our data, there is no significant difference in the levels of external and internal motivation between students of mathematical specialties and students studying mathematics as a subject necessary for further education. Students had a slight increase in internal motivation after a year of studying. Based on the research, we can confidently say that the teacher should stimulate the internal motivation of students.
New Research in the Field of Cognitive Virtual Chatbots

Enterprises working on chatbot systems have already started seeing benefits from their deployed cognitive virtual assistants. They are working to implement even more features to enrich conversation systems and improve the user experience.
Open Language Education

In this episode, we are discussing SMARTool (Strategic Mastery of Russian Tool), a free-to-use online resource for learners of the Russian language. Professor of Russian Laura Janda explains the pedagogical principles behind the tool and plans for future expansions, whereas IT engineer Radovan Bast shares his views on how the choice of sharing the code openly on GitHub serves the needs of the wider community of programmers as well as language learners.
Research on Vocabulary Identification and Misunderstanding in The Process of Translating Environmental Energy Science and Technology Documents into Chinese

Abstract English is an international language. English translation is one of the indispensable skills in international communication. The world has in-depth research and advanced technology on environmental energy. Therefore, how to translate advanced environmental energy articles can be effective. Learning the corresponding knowledge, and in the translation of professional energy science and technology vocabulary, meaning, we need to start from the scientific background of Chinese and English, analyze the importance of English translation into Chinese in the process of English translation in different contexts, through analysis of professional literature The key points that must be mastered in vocabulary and lexical translation in English translation, and then put forward the strategies to improve the vocabulary and lexical translation in English translation of energy science and technology literature. How to learn vocabulary, lexical translation vocabulary and meaning in English translation of energy-related manuscripts Lay the foundation and avoid misunderstandings.
International Cooperation for Cybercrime Prevention

网络通信技术的发展对于经济, 社会与国民带来巨大的影响力. 但在产生正向效益的同时，网络通信技术也逐渐成为犯罪人作案的主要工具. 当今, 跨国性质的网络电信诈骗犯罪在中国已成为主流的犯罪, 在犯罪特征上呈现跨国性, 隐蔽性，且被害人多，涉案金额也特别巨大，给社会, 国民以及国际关系带来严重的不利影响. 针对跨国性网络电信诈骗犯罪的日益增长的趋势，中国司法机关应当通过双边, 多边司法协助条约的签订, 提高引渡条约全球覆盖范围, 赃款追缴机制的完善, 大数据云平台的构建等着力于打造国际化, 区域化, 模式化的国际刑事司法协助互助平台，与其他各国通力合作，以能够有效的规制网络电信诈骗犯罪.
THE EFFECT OF KNOWLEDGE DEVELOPMENT AND KNOWLEDGE RETENTION ON PERFORMANCE OF SMALL AND MEDIUM-SCALED ENTERPRISES (SMES): A STUDY OF APPAREL SMES IN SRI LANKA

The main purpose of the study is to see whether three is a relationship between knowledge development (KD) and knowledge retention (KR) on the performance of Small and Medium-scale Enterprises (SMEs) and also to understand which factor has a significant effect on the performance of the SMEs. The data was collected from the owners of export-oriented apparel SMEs through a self-administrated questionnaire and used SPSS 21 version to analyze the data statistically. The results suggested that KD and KR have a positive significant relationship with the performance of SMEs, jointly. In the individual effect, the KR is the most influential factor on the performance of SMEs. The findings further reveal that apparel SMEs are practicing KD and KR strategies at the work place at average level. Article visualizations:
Comprehension correlates of the occurrence and deletion of “de” in Mandarin “N1 (de) N2” structures

Based on corpus materials and on-line semantic judgment surveys, this paper investigates the comprehension differences related to the occurrence and deletion of “de” in the Mandarin “N1 (de) N2” structure. By applying PCA and LME modellings on a set of semantic survey data, this study provides a multi-level database of semantic measurements for a set of Chinese “N1 (de) N2” structures as well as a quantitative analysis regarding the correlation between structure-level and constituent-level semantic features. The research shows that:(1) The “de”-occurring structure is more likely to be interpreted as indefinite than the “de”-deletion structure. (2) Animacy of N1 is positively related to the grammaticality of the “de”-occurring structure, while animacy of N1 is negatively related to the grammaticality of the “de”-deletion structure. The research findings provide evidence for prototype effects in the process of language comprehension. We propose that in natural comprehension, there is a high-animacy bias for N1 regarding the “de”-occurring structure; while a low animacy interpretation for N1 is more prototypical for the “de”-deletion structure. Accordingly, the “de”-occurring structure tends to be interpreted as a possessive, while the “de”-deletion structure is more likely to be interpreted as a modifier-head structure.
On the observability of multiagent neural networks

We obtain a characterization of observability for a class of linear systems which appear in multiagentneural networks research. Due to the connection between mathematical concept of control dynamical systemsand cognitive control, there has been growing interest in the descriptive analysis of complex networks with lineardynamics obtaining considerable advances in the description of the properties both structural and dynamical aboutmany aspects from everyday life. Notwithstanding, much less effort has been devoted to studying the observabilityof the dynamics taking place on them. In this work, a review of observability concepts is presented and providesconditions for observability of the multiagent systems.
The Algorithm for Detecting Malfunctions by Means of the SURF Feature Points Based on Probability and Statistics

The malfunctions detection of the high voltage electric wire mainly obtains the detection target image periodically by the drone and sends it back to the ground to compare the images to determine whether there is a failure. However, since the drone acquires images at different times in the sky, each time the image obtained is tilted, there is a certain difference in contrast and exposure, which seriously interferes with the detection of the fault. At present, image-based malfunction detection methods mainly include: methods based on image grayscale, shape and texture features, wavelet transform and image segmentation. These methods are complicated to operate and have long running time, which is difficult to meet the experimental requirements. We can detect the malfunctions by the distribution of the matching points of the image. The algorithm based on probability and statistics can reduce the effects of rotation, translation and noise during image processing. Dividing the image into blocks and calculating the number of image feature points to determine if there is a fault is simple and effective. Experimental results show that the algorithm can effectively detect whether the specified area is faulty.
Quantum devices for memory reduction (Conference Presentation)

Memory is a precious commodity across many different areas. I will present two ways in which quantum devices can lower memory requirements. The first application is the simulation of stochastic processes, i.e. processes that exhibit some randomness. We have experimentally realized quantum simulations of classical stochastic processes. Our simulators have lower memory requirements than the optimal classical simulators. The second type of quantum device I will discuss is a quantum autoencoder, which autonomously learns how to compress quantum data. We have developed and experimentally realized a photonic quantum autoencoder that is trained based on sets of quantum states.
Supplementing training with data from a shifted distribution for machine learning classifiers: adding more cases may not always help

In this study, we show that when a training data set is supplemented by drawing samples from a distribution that is different from that of the target population, the differences in the distributions of the original and supplemental training populations should be considered to maximize the performance of the classifier in the target population. Depending on these distributions, drawing a large number of cases from the supplemental distribution may result in lower performance compared to limiting the number of added cases. This is relevant for medical images when synthetic data is used for training a machine learning algorithm, which may result in a mixed distribution for the training set. We simulated a twoclass classification problem and determined the performance of a linear classifier and a neural network classifier on test cases when trained with cases from only the target distribution, and when cases from a shifted, supplemental distribution are added to a limited number of cases from the target distribution. We show that adding data from a supplemental distribution for machine learning classifier training may improve the performance on the target test distribution. However, given the same number of training cases from a mixed distribution, the performance may not reach the performance of only training on data from the target distribution. In addition, the increase in performance will peak or plateau, depending on the shift in the distribution and the number of cases from the supplemental distribution.
Increasing the Detection Probability of Objects by Modified Hough Transform in Radioelectronic Surveillance Systems

This article discusses the implementation of modified Hough transform for radioelectronic surveillance systems, such as radars, security systems, etc.
Recovery of a class of Binary Images from Fourier Samples

In this paper, we study the recovery of a certain type of shape images modeled as binary-valued images from few Fourier samples. In particular, the boundary of the shapes is assumed to be the zero level-set of a trigonometric curve. This problem was previously considered in [1], and it was shown that such images can be studied withing the framework of signals with finite rate of innovation. In particular, an annihilation filter is introduced to recover the trigonometric boundary curve. It is proved in [1] that 3Λ Fourier samples are sufficient to exactly recover the edges of a binary shape, where Λ is the bandwith of the trigonometric boundary curve. In this paper, we introduce a class of shapes (that include convex shapes as special cases) for which |2Λ + 1| Fourier samples are sufficient for exact recovery using the same annihilation filter. Simulation results support our theoretical results.
Investigating Alteration Zone Mapping Using EO-1 Hyperion Imagery and Airborne Geophysics Data

Hyperspectral remote sensing records reflectance or emittance data in a large sum of contiguous and narrow spectral bands, and thus has many information in detecting and mapping the mineral zones. On the other hand, the geological and geophysical data gives us some other fruitful information about the physical characteristics of soil and minerals that have been recorded from the surface. The Sarcheshmeh mining area located in the NW-trending Uromieh-Dokhtar magmatic belt within Central Iran is mainly of porphyry type, and is associated with extensive hydrothermal alterations. Due to the semi-arid type of climate with abundant rock exposure, this area is suitable for application of remote sensing techniques. In this work, we focus on generating the alteration maps around Cu porphyry copper deposits using the spectral angle mapper algorithm on Hyperion data by applying two filters named reduction to pole and analytical signal on a total magnetic intensity map and generating the Kd map from radiometry data. What is clear is the high importance of applying the adequate pre-processing on Hyperion data because of low signal-to-noise ratio. By comparing the known deposits in the region with the results obtained by applying the mentioned methods, it is revealed that not all the higher K radiometric values are entirely associated with the hydrothermal alteration zones, and in contrast, the potassic alteration map extracted from Hyperion imagery successfully corresponds to the alteration zones around the Sarcheshmeh mining area. Finally, the results particularly obtained from processing the Hyperion data are confirmed by indices of Cu porphyry deposits in the region.
Mediator between Computer Science and Classic Law Education: Russian Experience

The article discusses the need to introduce an interprofessional and interdisciplinary approach to solving legal problems in the field of law, information technology and information security and provides Russian experience in the transition to a digital economy and the training of competent personnel. The introduction of a mediator - a specialist as a link between the classical legal education and technical competencies and knowledge in the field of information technology and the information sphere as a whole is proposed. The article also provides non-trivial questions that are already facing lawyers and technical experts. This is the complex nature of the information sphere, which incorporates other branches of law and fields of activity, the cross-border nature of the Internet, and the development of artificial intelligence as a new subject of legal relations, which incorporates other entities (creators, developers, users, sellers, distributors), and etc. The article analyzes and proposes the possible introduction by universities of the relevant disciplines in the field of information technology, the study by specialists of the legal branch of the technical sciences and their use in professional practice for the best solution and counteraction of problems in the information sphere.
Exploring the Classification of Security Events using Sparse and Dense Representation of Text

With the availability of open source intelligence systems, such as the Integrated Crisis Early Warning System (ICEWS), news stories are constantly being monitored and analyzed. However, with the increasing amount of data available on the Internet, automated methods such as machine learning can be used to quantify and speedup analysis. Although projects such as ICEWS have a global focus, it is important that in South Africa, we focus on the local situation. Using WhatsApp as our new source, we evaluated a machine learning approach to automatically classify violent events, such as protests of mass violence, by using dense vector representations. Our experimental results indicated that dense vector representation did not necessarily improve the performance of the machine learning classifier.
Multiple-source Entity Linking with Incomplete Sources

This paper introduces a new entity linking task from a well-known online video application in industry, where both entities and mentions are represented by multiple sources but some of them may be missing. To address the issue of incomplete sources, it proposes a novel neural approach to model the linking relationship between a pair of an entity and a mention. To verify the proposed approach to this task, it further creates a large scale dataset including 70k examples. Experiments on this dataset empirically demonstrate that the proposed approach is effective over a baseline and particularly it is robust to the missing sources in some extent.
Miniaturization of the Hanbury Brown-Twiss configuration (Conference Presentation)

Miniaturized versions of nowadays tabletop setups will be necessary for a successful commercialization of quantum cryptography and computing. Within this contribution, we present a concept for downsizing the Hanbury Brown-Twiss configuration. The design is based on optical simulations, with the aim of finding the best compromise of detection efficiency and level of miniaturization. Since scattering effects are important for evaluating the system’s performance, a complete scattering analysis got performed.
APPLYING ASSOCIATION RULE MINING FOR INDIRECT DEPENDENCIES ELIMINATION

Data Mining is defined as a progress used to extract usable data from a larger set of any raw data. It examine a data patterns in large batches of data using two or more software. It is an application of data mining, businesses can learn about their customers and develop more effective strategies related to various business functions. This paper defines the association rules to describe the indirect dependences. First, an algorithm is proposed to identify a product, sets of items, and characteristics that are given to the transactional database. There some indirect dependences, which refer to the relationship between discontinuous activities in business activities. To recognize anomaly detection for business process is the hindmost goal.
An Aural Skills Introduction to Twelve-Tone Music

While the number of theoretical topics for the post-tonal classroom now expands beyond set theory and serialism, these techniques remain pillars of twentieth-century study. As a result, it is worth refining our pedagogical approach for these post-tonal fundamentals, striving to make them both more intellectually accessible and musically relevant. This lesson plan is an aural skills introduction to twelve-tone music that can be implemented in both the written and skills settings. Through a three-step process that can be replicated with other dodecaphonic works, the end goal is a detailed pitch and narrative analysis of Luigi Dallapiccola’s “Vespro, tutto riporti” from his Cinque Frammenti di Saffo.
LibGuides: The Library is Open (Online): Instructional Resources Online

This guide provides updates to library services during the Covid-19 Pandemic. Resources and best-practices for continuity of instruction via online platforms.
Reflections on: Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization

We present a family of four novel methods for embedding knowledge graphs into real-valued tensors that capture the ordered relations found in RDF. Unlike many previous models, these can easily use prior background knowledge from users or existing knowledge graphs.We demonstrate our models on the task of predicting new facts on eight different knowledge graphs, achieving a 5% to 50% improvement over existing systems. Through experiments, we derived recommendations for selecting the best model based on knowledge graph characteristics. We also give a provably-convergent, linear tensor factorization algorithm.
Analysis of Facebook contents of Police Department of Pakistan in the context of Good Governance

The Sindh University Research Journal (Science Series) is an international, peer reviewed, open access multidisciplinary science journal, regularly published by faculty of Natural Sciences, University of Sindh, Jamshoro, Sindh, Pakistan since 1964. The journal is accredited and financially supported by Higher Education Commission Pakistan and included in “Y” category of Higher Education’s Journal recognition system (HJRS). The journal is indexed in major online indexing services including Google Scholar, CrossRef, etc. The journal publishes original scientific research in the form of research articles, review papers, short communications, mini reviews, case studies, data sources, and case reports pertaining to all fields of Natural Science. The journal is a quarterly publication i.e., publishes four issues in a calendar year during March, June, September, and December.
Study on Establishing Early Warning Model of Soil Salinization Disaster

Abstract This paper introduced the selection of evaluation factors of soil salinization hazard warning model and the calculation of weight of various factors. We used the fuzzy optimal BP neural model to predict the development degree of future soil salinization, which can effectively reduce the impact on soil salinization disasters.
Complementary Discriminative Correlation FiltersBased on Collaborative Representation for VisualObject Tracking

In recent years, discriminative correlation filter(DCF) based algorithms have significantly advanced the state of the art in visual object tracking. The key to the success of DCF is an efficient discriminative regression model trainedwith powerful multi-cue features, including both hand-crafted and deep neural network features. However, the tracking performance is hindered by their inability to respond adequately to abrupt target appearance variations. This issue is posed by the limited representation capability of fixed image features. In this work, we set out to rectify this shortcoming by proposing a complementary representation of a visual content. Specifically, we propose the use of a collaborative representation betweensuccessive frames to extract the dynamic appearance information from a target with rapid appearance changes, which results in suppressing the undesirable impact of the background. The resulting collaborative representation coefficients are combinedwith the original feature maps using a spatially regularised DCF framework for performance boosting. The experimental results on several benchmarking datasets demonstrate the effectiveness and robustness of the proposed method, as compared with anumber of state-of-the-art tracking algorithms.
THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES AS AN OPPORTUNITY IN EDUCATION: A CASE STUDY ON THE CHALLENGE OF STRENGTHENING DIGITAL SKILLS IN TEACHERS

Information about the paper titled "THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES AS AN OPPORTUNITY IN EDUCATION: A CASE STUDY ON THE CHALLENGE OF STRENGTHENING DIGITAL SKILLS IN TEACHERS" at IATED Digital Library