Reviewerly/exHarmony huggingface dataset:
Data Management System for Thesis Monitoring at STMIK IBBI Using B-Model

Actually thesis is the final task of a student, but a good supervisor must be able to adjust and spur the completion of the thesis in accordance with the characteristics and abilities of the students. For this reason, this Thesis Monitoring Information System is needed to assist in monitoring and enabling interaction between the supervisors and students if there are obstacles in the face-to-face guidance process. Every university has many students who have gotten jobs before completing their lectures needs to implement this thesis monitoring information system so as to optimize the thesis guidance process. This study used a B-Model which was an enhancement of the Waterfall method by emphasizing the maintenance phase. The results showed that the thesis monitoring information system can help lecturers and students in the guidance process so that it can be implemented well. System Effectiveness Testing is done using the Wilcoxon Signed-Rank Test.
RAL - Improving Stream-Based Active Learning by Reinforcement Learning

One of the main challenges associated with supervised learning under dynamic scenarios is that of periodically getting access to labels of fresh, previously unseen samples. Labeling new data is usually an expensive and cumbersome process, while not all data points are equally valuable. Active learning aims at labeling only the most informative samples to reduce cost. In this paradigm, a learner can choose from which new samples it wants to learn, and can obtain the ground truth by asking an oracle for the corresponding labels. We introduce RAL - Reinforced stream-based Active Learning -, a new active-learning approach, coupling stream-based active learning with reinforcement-learning concepts. In particular, we model active learning as a contextual-bandit problem, in which rewards are based on the usefulness of the system's querying behavior. Empirical evaluations on multiple datasets confirm that RAL outperforms the state of the art, both by improving learning accuracy and by reducing the number of requested labels. As an additional contribution, we release RAL as an open-source Python package to the machine-learning community.
Real-time Visual Tracking Based on Convolutional Neural Networks

Abstract Traditional target tracking is based on target detection. When the target changes significantly, such as occlusion, scale change, the update of the tracking model will waste a lot of space and time resources, resulting in a very slow tracking speed, which cannot meet the actual engineering needs. In view of the above situation, an end-to-end tracking strategy is proposed, which is simpler and faster than the existing technology. The proposed tracker only needs to detect the first frame image and use it as the input of the model, and set the multi-task loss function to predict the position of the next frame of the target and the size of the bounding box. This paper constructs a lightweight network architecture with an additional selection mechanism to avoid wasting resources for global search and matching. Through experiments, good results can be achieved on the standard data set, and tracking speeds close to one hundred frames per second are achieved, which is very competitive with existing advanced trackers.
Text Summarization using QA Corpus for User Interaction Model QA System

Document summarization is capable of generating user query relevant, precise summaries from the original document for user needs.To reduce the response time summary generation, QA corpus is built for similar questions and answer with help of learning model.It has been trained and tested by Quora duplicate and Yahoo!Answer datasets.The large QA corpus has been dynamically clustered with semantic features paves a way for efficient document's retrieval.Answers are produced from datasets or generate summaries for unanswerable from the available sources.Results obtained from statistical significance test with hypothesis testing and evaluation with standard metrics proves the significant improvement in generating text summarization using QA corpus.The outcome is better in the producing close proximity of answers for the given user query.
Smart contracts - the new form of the legal agreements

Abstract Today we are witnesses an explosion of online business, developed on the internet – a special environment that requires own resources and tools and it is governed by specific rules. In this context, a new type of technology has been developed – the distributed ledger system, which allowed the creation of a new form of the agreement - the smart contracts. Smart contract is the next step forward in the process of digitalized contracts, after using the PDF documents with electronic signatures, and it favors the businesses to be carried out completely automatically, without the need for human intervention, and to gain greater efficiency and reduction in costs. This paper will try to provide the answers to several questions, such as: what is a smart contract?; how smart contract will be used?; how smart contract will be enforced?; etc. Moreover, it will be emphasized the advantages of smart contract and the new developments such as “Ricardian” contracts representing more efficient and transparent agreements that can be drafted and enforced on platform. Most important issue of this paper consists in analysis of legal framework of smart contracts using the basic principles of contract law combined with blockchain regulations, taking into account changing the paradigm from “code is law” to “law is code”.
Recognition method of wide area measurement abnormal data based on GRA-IForest

Abstract Aiming at the anomaly recognition method of large area measurement system (WAMS), a method based on Grey Mrrelatian Aru and Isolation Forest algorithm is proposed, considering the attributes of power data and the big data characteristics of large area measurement system. In this method, the relational matrix of power data attribute sequence is obtained through grey relational analysis, and the dependent attributes are selected according to the relational matrix for attribute sequence merging. Then, the data set is partitioned by cascaded isolated forest model and abnormal data are detected. Taking the data of a province’s wide-area measurement system as an example for experimental simulation, compared with LOF, Kmeans and IForest, under the same inspection time, the GRA-IForest model has achieved good inspection results and achieved a higher overall detection accuracy and lower standard deviation.
An Algebraic-Topological Approach to Processing Cross-Blockchain Transactions

The state-of-the-art techniques for processing cross-blockchain transactions take a simple centralized approach: when the assets on blockchain $X$, say $X$-coins, are exchanged with the assets on blockchain $Y$---the $Y$-coins, those $X$-coins need to be exchanged to a "middle" medium (such as Bitcoin) that is then exchanged to $Y$-coins. If there are more than two parties involved in a single global transaction, the global transaction is split into multiple local two-party transactions, each of which follows the above central-exchange protocol. Unfortunately, the atomicity of the global transaction is violated with the central-exchange approach: those local two-party transactions, once committed, cannot be rolled back if the global transaction decides to abort. In a more general sense, the graph-based model of (two-party) transactions can hardly be extended to an arbitrary number of parties in a cross-blockchain transaction. %from why to how In this paper, we introduce a higher-level abstraction of cross-blockchain transactions. We adopt the \textit{abstract simplicial complex}, an extensively-studied mathematical object in algebraic topology, to represent an arbitrary number of parties involved in the blockchain transactions. Essentially, each party in the global transaction is modeled as a vertex and the global transaction among $n+1$ ($n \in \mathbb{Z}$, $n &gt; 0$) parties compose a $n$-dimensional simplex. While this higher-level abstraction seems plausibly trivial, we will show how this simple extension leads to a new line of modeling methods and protocols for better processing cross-blockchain transactions.
Review of underwater polarization clear imaging methods

Underwater images often suffer from many typical problems: in a complex optical environment, the quality of underwater images drops sharply, and features such as color and brightness are often attenuated seriously, which makes it difficult to improve the quality of underwater images. Polarization imaging can effectively suppress underwater scattering. In the underwater imaging environment, according to the polarization characteristics of the signal, backscatter and forwardscatter light, the impact of different components on the image is solved. Based on the underwater physical imaging model and the principle of polarization imaging, the principle of underwater polarization imaging is described in detail, and several classic underwater polarization imaging methods are emphasized. The current underwater imaging technology based on polarization characteristics is summarized, and according to their actual effect, these methods are evaluated and analyzed. What's more, based on the advantages and disadvantages of the existing underwater polarization imaging technology and their actual results, the future development of the underwater polarization imaging technology is summarized.
Tackling Global Environmental Challenges Through ICT [Mobile Radio]

Sustainability through Information and Communication Technology (ICT)from governments and businesses to the millions of school children striking as part of Fridays for Future, the question of how to live a more sustainable life has become an increasing concern across society. Where do everyday consumers stand on this issue? Do their habits and actions reflect their perceptions? Do they consider information and communication technology (ICT) as a tool to help them address their potential impacts in such areas as the environment, sustainable development, and climate change.
Word2vec Skip-gram Dimensionality Selection via Sequential Normalized Maximum Likelihood

In this paper, we propose a novel information criteria-based approach to select the dimensionality of the word2vec Skip-gram (SG). From the perspective of the probability theory, SG is considered as an implicit probability distribution estimation under the assumption that there exists a true contextual distribution among words. Therefore, we apply information criteria with the aim of selecting the best dimensionality so that the corresponding model can be as close as possible to the true distribution. We examine the following information criteria for the dimensionality selection problem: the Akaike Information Criterion, Bayesian Information Criterion, and Sequential Normalized Maximum Likelihood (SNML) criterion. SNML is the total codelength required for the sequential encoding of a data sequence on the basis of the minimum description length. The proposed approach is applied to both the original SG model and the SG Negative Sampling model to clarify the idea of using information criteria. Additionally, as the original SNML suffers from computational disadvantages, we introduce novel heuristics for its efficient computation. Moreover, we empirically demonstrate that SNML outperforms both BIC and AIC. In comparison with other evaluation methods for word embedding, the dimensionality selected by SNML is significantly closer to the optimal dimensionality obtained by word analogy or word similarity tasks.
Improved Algorithm of the Video Moving Object Detection Based on ViBE

Background: ViBE (Visual Background Extractor) is an algorithm with a variety of advantages in video moving object detection which utilizes a pixel-level background modeling. However, it is not suitable for distinguishing the scene of drastic change, adapts poorly to the sudden change of the illumination and may lost the object easily, because this algorithm uses a fixed threshold to distinguish the background from the foreground. Methods: In this paper, an improved ViBE algorithm is proposed, which an adaptive dynamic threshold method is introduced for classification of the foreground and the background in the changing scenes. When reconstructing the model it required for drastic change of illumination, Otsu algorithm is used to judge the threshold and select the appropriate frame to complete the reconstruction to achieve quick adapt to the light. Results: Experimental results show that the proposed algorithm has higher recall value, better precision and F value comparing to the original algorithm. The improved algorithm has the highest classification accuracy among other similar algorithms and therefore the improved algorithm significantly improves the detection results. Conclusion: After analyzing the principle of ViBE algorithm, this paper proposed improvements to it from two aspects to aim at its deficiency. Taking into account of the dynamic changes of different environments, the change factor was proposed to measure the dynamic degree of background. According to the value of the factor, adaptive clustering was obtained and clustering threshold was updated to improve the adaptability of the algorithm to the dynamic environments. The improved ViBE algorithm can find the appropriate frame to reconstruct model structure in the case of abrupt light change, which can quickly adapt itself to the light change and be more accurate in the object detection.
Discovering Useful Sentence Representations from Large Pretrained Language Models

Despite the extensive success of pretrained language models as encoders for building NLP systems, they haven't seen prominence as decoders for sequence generation tasks. We explore the question of whether these models can be adapted to be used as universal decoders. To be considered "universal," a decoder must have an implicit representation for any target sentence $s$, such that it can recover that sentence exactly when conditioned on its representation. For large transformer-based language models trained on vast amounts of English text, we investigate whether such representations can be easily discovered using standard optimization methods. We present and compare three representation injection techniques for transformer-based models and three accompanying methods which map sentences to and from this representation space. Experiments show that not only do representations exist for sentences from a variety of genres. More importantly, without needing complex optimization algorithms, our methods recover these sentences almost perfectly without fine-tuning the underlying language model at all.
Didactic engineering as a methodology of organizing automated educational activities

The study shows that from a historical point of view, effective educational systems are one of the main elements of the «pushing forward» development of society. A distinctive feature of our era is that in it, another degree of freedom is added to the cognitively active space with two degrees of freedom, and the space of activity is transformed into a space with three degrees of freedom: cognitive, virtual, and real. Because of this, automated systems with elements of «artificial intelligence» penetrated into all spheres of life, filling the space with three degrees of freedom with new objects of life. Overall, this led to the emergence of a «digital economy» with enormous potential for the growth of labor productivity. Didactic engineering – a methodology for organizing automated educational activities in space with three degrees of freedom that arose due to historical necessity and committed to solve the problem of providing the digital economy with personnel. Currently, in the framework of didactics and psychology, many pedagogical problems are solved at the stages of verbal, graphic, tabular modeling. The paper shows that solutions for the design of automated educational systems are insufficient. It is also pointed out that didactic engineering is a continuation of classical didactics, supplementing it with the stages of mathematical and simulation modeling. An example of solving a pedagogical problem based on an engineering approach is given.
Based on Normal Cloud Model and D-S Evidence Theory Comprehensive Evaluation of Operation State of Fire Control System

The characteristic information of fire control system is miscellaneous, which reflects the operation status of fire control system from different levels. However, due to the inaccuracy of measurement process, the evaluation criteria are not uniform.This will cause great ambiguity and uncertainty for real-time control of the health state of the fire control system.The traditional fuzzy comprehensive evaluation model only considers the fuzziness of the index but ignores the randomness. The cloud model theory considers both the fuzziness and the randomness of things. The model is applied to the fire control system. The analytic hierarchy process (AHP) is applied to obtain the correlation degree between the index and the operation level.In the overall evaluation of fire control system, the weight of feature state information is obtained by the method based on evidence trust degree, which avoids the influence of subjective weight on the overall evaluation results.D-S evidence theory is used to evaluate the overall operation status of fire control system.The validity of this method for real-time health control of fire control system is verified by an example.
The digital watermarking algorithm using discrete chaotic maps

The algorithm of formation, embedding and extracting of digital watermarks using discrete chaotic maps is considered. The key feature of the developed algorithm is the formation of the watermark using the discrete chaotic map and the restoration of this watermark after the image transmission via the communication channel. This is possible through the use of the developed method that searches for initial values of discrete chaotic map variables used in the formation of the watermark. The algorithm testing re sults are given. It is established that the proposed algorithm is able to identify the modified image areas. The algorithm is resistant to image conversion in JPEG format and to distortion up to 14 % of the image pixel values due to the presence of noise. Thus, the proposed algorithm can be used to solve real problems of the defense of digital images transmitted by communication channels.
Evolution of the Specialty «Advertising and Public Relations»: history, current state and the trends for development

The paper describes the history of the formation of the specialty “Advertising and PR” and the author’s vision of hidden conflict between modern requirements to the set of professional knowledge of young professionals in the field of advertising and public relations, and the content of the main learning outcomes in the form of a set of professional competences, which will have bachelors enrolled in 2019 entering the labor market in foreseeable future The logic of the article: the first part examines the history of the formation of the specialty and describes the transformation of the main requirements for the education of the graduate, separately describes the specifics of the current level of development of the bachelor’s degree (specialty); the second part provides an analytical overview of the needs of the modern professional sphere of advertising and public relations; the last part of the article represents the analysis of the competence models of bachelor’s training offered by Russian federal universities in the field of educational services, from the point of view of meeting the expectations of the professional environment.
Pengaruh Minat Baca terhadap Keterampilan Menulis Teks Eksplanasi Siswa SMA Negeri 1 Lembah Melintang

&lt;p align="center"&gt;&lt;span lang="EN-US"&gt;This study aims to study whether there was interest interested in learning explanatory text writing students. This type of research is quantitative with a 2x2 &lt;em&gt;factorial design&lt;/em&gt;. Students are grouped into two classes, namely the experimental class and the control class. The experimental class in this study was in class XI IPA 1, while the control class was in XI IPA 3. The instruments used to collect data were reading interest questionnaires and performance tests for explanatory text writing skills. The results of this study are the following two things. First, explanatory text writing skills of students who have high reading interest in the experimental class are better than students who have high reading interest in the control class. Second, explanatory text writing skills of students who have low reading interest in the experimental class are better than students who have low reading interest in the control class.&lt;/span&gt;&lt;/p&gt;
Spectral analysis for a multi-dimensional split-step quantum walk with a defect

This paper studies the spectrum of a multi-dimensional split-step quantum walk with a defect that cannot be analysed in the previous papers. To this end, we have developed a new technique which allow us to use a spectral mapping theorem for the one-defect model. We also derive the time-averaged limit measure for one-dimensional case as an application of the spectral analysis.
Deep Learning for Social Media Sentiment Analysis

Social media is used as a tool by many people to express their opinions. Sentiment analysis for social media is very important, as it allows information to be obtained about public opinion on government performance. The goal of this research is to learn about the opinions of Surabaya citizens, using deep learning methods. The data are extracted from the official Twitter accounts of the Surabaya government and a private radio station in Surabaya. The data are grouped into two categories: positive and negative sentiments. This research is conducted in three steps: data pre-processing, sentiment classification, and visualization. Data pre-processing is required before modelling approaches are applied. It is used to transform the unstructured text data into structured data. The data pre-processing consists of case folding, tokenizing, and the removal of stop words. Deep learning methods are then applied to the data. A Backpropagation Neural Network (BNN) and a Convolutional Neural Network (CNN) are used to perform the sentiment classification. The BNN and CNN are compared using various metrics, such as precision, sensitivity, and area under the receiver operating characteristic curve (AUC). A word cloud is then used to visualize the data and find the most frequent words in each class. The results show that the sentiment classification with CNN is better than that with the BNN because the values for the precision, sensitivity and AUC are higher.
Modern approaches to professional development of teachers of higher educational institutions

Professional development is one of the types of professional training for University teachers to develop their level of theoretical knowledge and competencies, improve practical skills and abilities that increase in accordance with the requirements of state educational standards for the quality of education in higher education institutions. This article attempts to review and analyze the current system of professional development in Russia, various approaches to its organization, technologies and forms. The combination of professional knowledge and information and communication technical skills is extremely important today for the professional and personal development of staff of higher educational institutions and improving the quality of training of students. Despite serious theoretical justification and diverse experience of professional development of teaching staff in universities, the current system, as practice has shown, is far behind the requirements of the time and needs urgent improvement, both in the content of courses of disciplines and in their practical implementation.