Reviewerly/exHarmony huggingface dataset:
Developing Foreign Language Motivation in the Context of Competency-based Approach

The subject of the research is language teaching process, theoretical and practical validity of the competency-based approach as the most effective approach in forming positive and sustainable motivation to foreign language learning. The purpose of the work is development of motivation to language learning, forming multilaterally developed linguistic individual through integration of competency-based approach in the teaching process. The research results obtained confirm the efficiency of competency-based approach in teaching and efficacy of using activities within this approach which enable to increase non-linguistic specialty students’ interest and sustainable motivation to language learning. Moreover, the research results indicate that competency-based approach, its result-orientation on necessary conditions facilitate long-term motivation and productivity of language proficiency.
Introspectiveness of Higher Education Students: Study and Development

The authors substantiated that the success of developing students’ introspectiveness is ensured by the created psychological and pedagogical conditions. A model of developing the introspectiveness as a personality quality of a future professional has been developed and tested. The results of this study create opportunities for further development of the problem in the context of the organization of lectures in the learning process for students of different courses and types of specialization. The article is intended for psychologists, tutors, education managers.
A Heuristic Prover for Elementary Analysis in Theorema

We present a plug-in to the Theorema system, which generates proofs similar to those produced by humans for theorems in elementary analysis and is based on heuristic techniques combining methods from automated reasoning and computer algebra. The prover is able to construct automatically natural-style proofs for various examples related to convergence of sequences as well as to limits, continuity, and uniform continuity of functions. Additionally to general inference rules for predicate logic, the techniques used are: the S-decomposition method for formulae with alternating quantifiers, use of Quantifier Elimination by Cylindrical Algebraic Decomposition, analysis of terms behavior in zero, bounding the $$\epsilon $$ -bounds, semantic simplification of expressions involving absolute value, polynomial arithmetic, usage of equal arguments to arbitrary functions, and automatic reordering of proof steps in order to check the admissibility of solutions to the metavariables. The problem of proving such theorems directly without using refutation and clausification is logically equivalent to the problem of satisfiability modulo the theory of real numbers, thus these techniques are relevant for SMT solving also.
Both Sides of the Coin: The Impact of Cyber Attacks on Business Value

Cyber incidents are hitting companies and making news headlines almost every day. It is critical to investigate the impacts of these incidents on the organizations’ business value. The stock price, a widely used measurement of business value, of every victimized company fluctuates differently after a data breach. This article summarizes the factors that drive the whirling of the coin and suggests more proactive cyber crisis management to turn incidents into opportunities to create positive long-term impacts and enhance cyber resilience.
Robust Liver Vessel Extraction Using DV-Net with D-BCE Loss Function

Recently, liver vessel segmentation has aroused widespread interests in medical image analysis. Accurately extracting blood vessels from the liver is a difficult task due to the complex vessel structures and image noise. To make the network better adapt to this complex feature, a deeper network is needed to fit this nonlinear transformation. In this work, we introduce the dense block structure into the V-net to construct a new Dense V-Net (DV-Net) and use data augmentation to segment the liver vessels from abdominal CT volumes with few training samples. Besides, we propose the D-BCE loss function to cope with the problem of dynamic changes in pixel ratio caused by 3D segmentation random patches, which can also control the trade-off between false positives and negatives. In this way, the proposed DV-Net structure can acquire a more powerful discrimination capability between vessel areas and non-vessel areas. Our method is tested on the public datasets from 3Dircadb. The average dice and sensitivity on the 3Dircadb dataset were 74.76% and 75.27% respectively. Experiments are also conducted in another public dataset from Medical Segmentation Decalthon and also obtain much higher accuracies. Moreover, our approach is automatic, accurate and robust for liver vessel extraction and can enjoy better convergence properties, making it more efficient and reliable in practice.
Facing the ball carrier in AI World Cup soccer

Robot soccer competitions have been around for a while and have been a great environment to develop AI algorithms in. One of these environments is the AI world cup. The AI world cup environment is a virtual environment where two teams with five robots each play a soccer match. This paper focuses on defending the attacker that is carrying the ball in the AI world cup environment. This is achieved by comparing two approaches, a rule-based algorithm, and a Deep Deterministic Policy Gradient rein- forcement learning algorithm. The two algorithms are based on a coaching framework build by the TU Delft. This framework implements the actions of the robots, such as moving, jumping, and kicking the ball, and the communication between the robots. The two approaches have been evaluated and the rule-based outperforms the reinforce- ment learning algorithm. Furthermore, a teamwork strategy between the goalkeeper and defender has been developed, where this teamwork is a significant improvement over the non-teamwork algorithm.
An In-depth Study on Internal Structure of Chinese Words

Unlike English letters, Chinese characters have rich and specific meanings.Usually, the meaning of a word can be derived from its constituent characters in some way.Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information.This work proposes to model the deep internal structures of Chinese words as dependency trees with 11 labels for distinguishing syntactic relationships.First, based on newly compiled annotation guidelines, we manually annotate a word-internal structure treebank (WIST) consisting of over 30K multi-char words from Chinese Penn Treebank.To guarantee quality, each word is independently annotated by two annotators and inconsistencies are handled by a third senior annotator.Second, we present detailed and interesting analysis on WIST to reveal insights on Chinese word formation.Third, we propose word-internal structure parsing as a new task, and conduct benchmark experiments using a competitive dependency parser.Finally, we present two simple ways to encode word-internal structures, leading to promising gains on the sentence-level syntactic parsing task.
Improved Parallel RSA Cryptosystem with GPU for Network Information Security

Asymmetric cryptography is commonly used to safeguard computer network security. Particularly, RSA algorithm is one of the most predominantly deployed crypto-algorithms. However, the standard RSA algorithm relies on multiple big-number modular exponentiation operations, which increase the execution time for encryption and decryption. In this research, we focus on the parallelization of several accelerated methods of RSA in a GPU environment. We have analyzed and developed improved parallel algorithms based on two recently improved variants of the RSA algorithm, implementing and optimizing them with NVIDIA-P100 Graphic Card. The accelerated parallel RSA algorithms we proposed and demonstrated under the specific hardware configuration achieved parallel speed-up factors up to 57 and 42 in encrypting and decrypting messages of lengths up to 8192-bytes, respectively.
MBS-NET: A MOVING-CAMERA BACKGROUND SUBTRACTION NETWORK FOR AUTONOMOUS DRIVING

Abstract. Background subtraction aims at detecting salient background which in return provides regions of moving objects referred to as the foreground. Background subtraction inherently uses the temporal relations by including time dimension in its formulation. Alternative techniques to background subtraction require stationary cameras for learning the background. Stationary cameras provide semi-constant background images that make learning salient background easier. Still cameras, however, are not applicable to moving camera scenarios, such as vehicle embedded camera for autonomous driving. For moving cameras, due to the complexity of modelling changing background, recent approaches focus on directly detecting the foreground objects in each frame independently. This treatment, however, requires learning all possible objects that can appear in the field of view. In this paper, we achieve background subtraction for moving cameras using specialized deep learning approach, the Moving-camera Background Subtraction Network (MBS-Net). Our approach is robust to detect changing background in various scenarios and does not require training on foreground objects. The developed approach uses temporal cues from past frames by applying Conditional Random Fields as a part of the developed neural network. Our proposed method have a good performance on ApolloScape dataset (Huang et al., 2018) with resolution 3384 × 2710 videos. To the best of our acknowledge, this paper is the first to propose background subtraction for moving cameras using deep learning.
Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS)

In a visual-based real detection system using computer vision, the most important thing that must be considered is the computation time. In general, a detection system has a heavy algorithm that puts a strain on the performance of a computer system, especially if the computer has to handle two or more different detection processes. This paper presents an effort to improve the performance of the trash detection system and the target partner detection system of a trash bin robot with social interaction capabilities. The trash detection system uses a combination of the Haar Cascade algorithm, Histogram of Oriented Gradient (HOG) and Gray-Level Coocurrence Matrix (GLCM). Meanwhile, the target partner detection system uses a combination of Depth and Histogram of Oriented Gradient (HOG) algorithms. Robotic Operating System (ROS) is used to make each system in separate modules which aim to utilize all available computer system resources while reducing computation time. As a result, the performance obtained by using the ROS platform is a trash detection system capable of running at a speed of 7.003 fps. Meanwhile, the human target detection system is capable of running at a speed of 8,515 fps. In line with the increase in fps, the accuracy also increases to 77%, precision increases to 87,80%, recall increases to 82,75%, and F1-score increases to 85,20% in trash detection, and the human target detection system has also improved accuracy to 81%, %, precision increases to 91,46%, recall increases to 86,20%, and F1-score increases to 88,42%.
Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.
Tingkat Literasi Informasi Siswa MAN 1 Jembrana Menggunakan BIG 6 Model

This study aims to determine the level of information literacy of students in MAN 1 Jembrana. This research uses descriptive quantitative method. The population in this study were students of class XI MAN 1 Jembrana with a total of 200 people. Sampling in this study was 10% using the random sampling method using the Slovin approach. The data collection technique used in this study is a closed questionnaire. The results show that the level of information literacy of students in MAN 1 Jembrana is included in the good category, by obtaining an overall average score of 3.15..
EDGE: An Enticing Deceptive-content GEnerator as Defensive Deception

Cyber deception defense mitigates Advanced Persistent Threats (APTs) with deploying deceptive entities, such as the Honeyfile.The Honeyfile distracts attackers from valuable digital documents and attracts unauthorized access by deliberately exposing fake content.The effectiveness of distraction and trap lies in the enticement of fake content.However, existing studies on the Honeyfile focus less on this perspective.In this work, we seek to improve the enticement of fake text content through enhancing its readability, indistinguishability, and believability.Hence, an enticing deceptive-content generator, EDGE, is presented.The EDGE is constructed with three steps: extracting key concepts with a semantics-aware K-means clustering algorithm, searching for candidate deceptive concepts within the Word2Vec model, and generating deceptive text content under the Integrated Readability Index (IR).Furthermore, the readability and believability performance analyses are undertaken.The experimental results show that EDGE generates indistinguishable deceptive text content without decreasing readability.In all, EDGE proves effective to generate enticing deceptive text content as deception defense against APTs.
Sentiment Analysis of Film Reviews Based on BI-GRU+Attention+Capsule Fusion

In this paper, we introduce a novel model called BGAC for text semantic analysis task.The proposed network is a new integration of two Bi-GRU, self-Attention mechanism and Capsule network architecture.In our experiment on the task of sentiment analysis in dataset IMDB (a public film review dataset), our model achieve the state-of-the-art results.We compare it with six other deep learning models, such as LSTM, CNN, GRU, BI-GRU, CNN+GRU and GRU+CNN.The results of the experiments show that the experimental effect of the bidirectional GRU fusion self-attention mechanism and the capsule network outperforms than the other six neural network models.In addition, the experiments show that combination of GRU with CNN is better than that combination of CNN and GRU, and the combination of CNN with GRU performs better than employ CNN model alone.The accuracy of using single CNN is successively higher than that of LSTM, BI-GRU and GRU model.Our model which the combination of the BI-GRU, Attention and Capsule network introduced in this paper achieves the highest accuracy, precision and F1 score.In conclusion, the bidirectional GRU with self-attention mechanism and capsule network model significantly improves the accuracy of text sentiment classification task.
Investigation of Security Challenges from the Perspective of Stakeholders in IoT

Internet of Things (IoT) has become one of the major study concerns and prospects in recent times. The ecosystem that makes the interconnection between person, objects, device in a secured acceptable and useful manner is in the boundary of Internet of Things. One of the major concerns in IoT is its security and the technologies which are working behind. The security measures are taken under a vast amount of studies and applications. The concept of this research is to consider the existing technologies that are working for security assurance and the challenges which are faced by different angles of participants and manufacturers due to make IoT a secure electronic ecosystem. Basically, the focus over the security challenges are on the stakeholders and they are user, manufacturer and service provider.
Quality control of digital representations of manuscript texts: proposal of a standards-based method

Abstract This study aims to propose a quality control method for digitized versions of manuscript documents that will be relevant for paleographical and codicological analysis. The methodology applied consisted of a systematic review of papers related to automated analysis of the physical characteristics of handwritings and document supports in the field of digital paleography, as well as of the numerous standards that have been emerging in the field of image engineering for quality assessment in digital image recordings. We also worked with a sample of 275 digital representations of pages or double pages of manuscript documentation dating to between the 12th and 17th centuries. As a result of this study, we propose a taxonomy of physical attributes of the handwritings and of their documentary supports that must be represented in the digital image with a high level of fidelity and without any distortions that could lead scholars to erroneous interpretations of the physical and formal characteristics of the original documents. On the basis of this taxonomy, we identified a set of typical distortions caused by digitization processes that can affect the recording quality of the physical attributes previously proposed, as well as a set of parameters and metrics for measuring quality that can be used to create a sufficiently exhaustive quality model. We also detected a series of limitations which, if not properly managed, can compromise the effectiveness of these types of controls.
Research on Impact of COVID-19 on the Student Management of Vocational and Technical Colleges in China Based on Emerging Network Technologies

The pandemic spread of the COVID-19 epidemic disrupted the normal teaching order of China's vocational and technical colleges. A series of measures to respond to the epidemic, such as postponing the start of school and online education based on emerging network technologies, have caused the students management in vocational and technical colleges to face multiple challenges. In the post-epidemic era, how to promote the validity and efficiency of the student management of vocational and technical colleges based on emerging network technologies becomes an important research question. After sufficient data mining and analysis, this research puts forward some advices to optimize the student management and education system of vocational and technical colleges. The vocational and technical colleges can adopt a unified college student management information system to break through information barriers and realize resource sharing; make full use of emerging network technologies to target the interests of college students and guide their value orientation and public opinion orientation; create multiple organizations Alliance-style management, in-depth exploration of students' learning and living conditions, realizing precise assistance and other coping strategies for student problems, and further improving the efficiency and quality of student management.
Social Partnership As A Mechanism For Developing Students Flexible Skills

This article is devoted to the problem of social partnership as a leading mechanism for educating students' flexible skills.Various forms of social partnership are the leading tool for educating the flexible skills of young people in the region, which are so in demand in modern conditions on the labor market.The study of the phenomenon of "social partnership" and the mechanisms of education of flexible skills of students can enrich both of these phenomena.A high level of formation of various aspects of empathy was revealed in an average of a quarter of students, they are emotionally responsive, they are characterized mainly by a positive attitude when communicating, they tend to create an atmosphere of openness and trust.High results in the course of studying emotional intelligence were revealed in terms of "Awareness of their feelings and emotions", i.e. most students accept their emotions and are aware of them.At the same time, the remaining indicators mainly revealed medium and low levels, i.e. students are not clearly aware of their own feelings, do not accept them to a greater extent, and have difficulties in managing them.Students with low and medium levels of emotional intelligence experience difficulties with managing other people's emotions and relieving emotional stress in stressful situations.Thus, it can be concluded that the level of development of flexible skills of students is at a low level, while social partnership can become a key tool for their education.
LightSeg: a light-weight network for real-time semantic segmentation

Semantic segmentation is an intensive prediction task with a wide range of applications, such as mobile phones, robots, autonomous driving and other mobile devices. The computing power of these mobile devices is often very limited, which limits the application of semantic segmentation. This paper designs a simple and efficient end-to-end real-time segmentation algorithm. Our algorithm model is an asymmetric encoder-decoder structure, which is composed of two parts: the first part is a lightweight encoder, which consists of a dual-path fast downsampling module and a lightweight three-stage feature extractor; the second part is an asymmetric decoding classifier, which consists of a mutual attention feature fusion module, a lightweight atrous spatial pyramid pooling module, and a classifier. Our method has only 0.64M parameters, and achieves 68.4% mIoU, 67.5 FPS with an image of 10242048 resolutions, also reachs 203.8 FPS, 61.9% mIoU with 5121024 resolutions, on the Cityscapes test set on GTX1080Ti. Comprehensive experiments show that our method can reach the state-of-the-art level on the Cityscapes dataset in terms of accuracy and speed.
Calliar: an online handwritten dataset for Arabic calligraphy

Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy is designed manually by experts with aesthetic insights. In the past few years, there has been a considerable effort to digitize such type of art by either taking a photograph of decorated buildings or drawing them using digital devices. The latter is considered an online form where the drawing is tracked by recording the apparatus movement, an electronic pen, for instance, on a screen. In the literature, there are many offline datasets with diverse Arabic styles for calligraphy. However, there is no available online dataset for Arabic calligraphy. In this paper, we illustrate our approach for collecting and annotating an online dataset for Arabic calligraphy called Calliar, which consists of 2,500 sentences. Calliar is annotated for stroke, character, word, and sentence-level prediction. We also propose various baseline models for the character classification task. The results we achieved highlight that it is still an open problem.