Reviewerly/exHarmony huggingface dataset:
Improving robustness of deep neural networks via large-difference transformation

Recent research shows that previous model-agnostic methods that transform the input images before feeding them into the classifiers fail to defend against the adversarial examples. We assume that the small-difference transformations commonly used are the blame and therefore propose a new model-agnostic defense using a large-difference transformation. Specifically, we try to apply the novel primitive-based transformation that re-builds the input images by primitives of colorful triangles. In terms of the distortions required to completely break the defenses, our experiments on the ImageNet subset show that significantly large distortions (0.12) are needed to break the defense compared to other state-of-the-art model-agnostic defenses (0.05–0.06) under the strong attack method Backward Pass Differentiable Approximation (BPDA). This finding indicates that large difference transformation can improve the adversarial robustness, suggesting a promising new direction towards solving the challenge of adversarial robustness.
An Investigation of Multimedia Application in Spoken English Teaching at Vocational Schools

At present, multimedia has been increasingly applied in English teaching, which results in the possibilities of improving English teaching quality and promoting students' ability enhancement. This investigation is focused on multimedia application in spoken English at vocational schools, aimed to explore the functions and features of multimedia application and optimize the spoken English teaching. The research subjects are the fresh students in two classes of a vocational school. The research lasts five months. In the experimental group, multimedia is applied in spoken English teaching. In the control group, the traditional teaching method is adopted. The results indicate: First, students like multimedia assisted teaching method more than the traditional teaching method. Second, multimedia exerts great influences on students' spoken English learning including spoken English improvement, interest training, self-confidence enhancement, anxiety relief, initiative development, and the establishment of proper learning modes. Third, there are still some problems with multimedia application in spoken English teaching, such as much more information, quick courseware operation, and unclear key points.
Research on the effectiveness of experiential learning in immersive virtual reality

At present, the application of immersive virtual reality in teaching is relatively limited. This article studies a teaching example to explore the effectiveness and rationality of immesive virtual reality in education and learning. Through the comparative experiment of traditional teaching and immersive virtual reality teaching, the experimental results confirm that it is possible to use immesive virtual reality for experiential learning. Students' perception and participation are significantly enhanced, and it is easier to remember knowledge.
Applying Model-Driven Approach for Data Model Unification

A lot of scientific data sources currently are concentrated within data infrastructures supporting the whole cycle of data processing and management. Heterogeneous data are represented using various data models from the relational model to various NoSQL models. That is why data integration issues within data infrastructures become more and more important. This paper is devoted to the implementation of data model unification techniques considered as a formal basis for data interoperability, integration, and reuse within data infrastructures. The implementation applies a model-driven engineering approach, namely, formal methods allowing us to define data model syntax and semantic transformations of one data model to the other. To emphasize the advantages of the model-driven approach MDE-based implementation is compared with previous implementation utilizing metacompilation and term rewriting techniques.
Promoting health education through biology: The effectivity of a 5E-learning scenario on nutrition and digestive system topic towards high school students’ health literacy

Abstract As one of the sustainable development goals (SDGs), good health and wellbeing needs to be supported especially through science education program. Therefore, this research is aimed to examine the effect of a 5E learning scenario in biology instruction on nutrition and digestive system topic towards high school students’ health literacy. This was a quasi-experimental research that involved two groups of students from a public high school in Bantul region, Yogyakarta, Indonesia. One group played as a treatment group who learned the topic using 5E learning scenario, whilst another one as the control group who was taught by lecturing method. Pre-test and post-test instruments were employed to collect students’ health literacy data and analysed descriptively towards its indicators as well as statistically using Wilcoxon and Mann U Whitney. The findings show that the treatment group’s health literacy skill is significantly better than the control group (0.00; p:0.05). Therefore, it is concluded that the 5E-learning model implemented in this research evident more effective in fostering students’ health literacy.
A motion parameters estimating method based on deep learning for visual blurred object tracking

Tracking the specific object in the blurred scenes is one of the challenging problems in computer vision and image processing. The accuracy and performance of trackers within the blur frames usually demonstrate a severe decrease. Accordingly, this problem needs to be corrected for better tracking results. Thus, this study seeks to present the best solution. To this end, a novel deep learning approach is proposed for object tracking in the presence of motion blur and fast motion. The hidden information in the blurring kernel is useful for tracking a specific blurred object through a series of consecutive frames. In this study, this matter is evaluated from a new perspective to solve the problems of blurred object tracking and objects with highly fast motions using a convolutional neural network (CNN) and a particle filter. Therefore, the proposed framework has two phases. First, the kernel leading to blurring is estimated by CNN, and then by a particle filter and the probability distribution of motion information that has been achieved by the kernel estimation the object is tracked. The results demonstrate that the suggested method can enhance the accuracy of tracking compared with the state-of-the-art, especially when the amount of blur is high.
Role of Economic Freedom in Attracting Investments in the Context of Azerbaijan

Since foreign investments are crucial in developing economy and breaking poverty chain, the majority of developing countries, which suffer from the lack of investment savings in countries, enter the competition in attracting investments. In the competition, they realize that economic freedom has to be improved for investments to come to the economy as well as other freedom level indices. This paper analyzes the key factors needed to better off in order to attract investments, the linkage between foreign capital and the level of freedom of countries investigated in theory and practice, and Azerbaijanâ€™s current economic freedom level and improvements. Sharp decline in oil prices since 2015 that forced government to devaluate the currency twice lead to government reforms in non-oil sector in Azerbaijan. This study also investigates impact of Azerbaijan economic freedom level improved by economic and legislative reforms on economic freedom indices set by World Bank doing business and other organizations.
Comparative Analysis of Chatbots

A chatbot is computer software that employs Natural Language Processing and Pattern Recognition techniques to provide appropriate answers to questions posed by humans. In this paper, we are analyzing and comparing the total accuracy score of the following chatbots: Rose, Google Assistant, Siri, Machine Comprehension Chatbot, Mitsuku, Jabberwacky, ALICE and Eliza based on the answers provided by them to a set of predefined questions. The chatbots were broadly analyzed on three focal parameters: 1. Assessment of Factual Questions, 2. Assessment of Conversational Attributes, and 3. Evaluation of Exceptional queries. The paper produces conclusive comparisons and conclusions and then ranks these chatbots according to their performance in the above-mentioned focal points. These focal points help in assessing the chatbots according to their responses by assigning a rank to each chatbot concerning others. The final level is evaluated by averaging grades attained in the three parameters above.
Video Indexing Through Human Face

There are so many techniques for video indexing using face recognition, but all the methods are unable to reduce space and time complexity. This paper proposes a novel way to deal with separate data from video through the human face and ordering and recover it at whatever point vital. This work is centered around the accompanying advances: key frame extraction from the info video, face recognition from key frame, face distinguishing proof utilizing standardized identification, and video ordering through the barcode. First, we recognize a video as information in this work, and from it, each of the edges is obtained. Using the color histogram difference, the main frames are taken from all the frames present in the video. A color histogram is used to find the difference between two images and return the color histogram. Using the Viola–Jones algorithm, faces are identified from the extracted main frames. Using sliding window technique, the image gradient is created from face image and identifies the human face extracted from video using individual EAN-8 barcode for video indexing purposes. The main objective of this work is to identify human face from video and to represent them as EAN-8 linear video indexing barcode. This approach is useful for security purposes, video surveillance network, video description of communication channel, etc. It is useful for purposes of video indexing and retrieval. This reduces the bandwidth of the correspondence, storage space, and time complexity. The effect of this approach is to index the video as a linear EAN-8 barcode based on the human face present in the video after describing the individual human face of the video.
ISWC 2020

Presents information on the ISWC 2020 Conference.
Statistical Machine Translation for Myanmar Language Paraphrase Generation

In this paper, we applied a statistical machine translation (SMT) approach to generate Burmese paraphrases of input sentences and words in Burmese. The system trained 89K sentence pairs that are manually collected from Facebook Comments and daily conversation corpus and also 89K Burmese Paraphrase Words are collected from Burmese Wiktionary. We implemented three different statistical machine translation models; phrase-based, hierarchical phrase based, and the operation sequence model. Moreover, we used two segmentation units; character and syllable segmentation for comparing the machine translation performance. The performance of machine translation or paraphrase generation was measured in terms of BLEU, RIBES, chrF++, and WER scores for all experiments. However, automatic evaluation metrics are weak for judging whether the generated Burmese sentences and words “is a paraphrase” or “is not a paraphrase': And thus, we also conducted a human evaluation on both sentence-to-sentence and word-toword paraphrase generation results. We found that the results obtained using the BLEU and RIBES automatic evaluation metrics were misleading and as the human evaluation result the machine translation approach is suitable for Burmese paraphrase generation.
Graph Layout Based on Network Embedding and Improved Dimensionality Reduction

Efficient layout of complex network remains a challenging problem. Based on network embedding and an improved dimensionality reduction technique, we propose a new graph layout method in this work. First, the network embedding method is used to generate the low dimensional dense vector representation of nodes, called embedding, which captures a given node's one-order and high-order neighbourhood node information. And then an improved tsNET approach is proposed to project this embedding to 2D space resulting in a good graph layout. Finally, we evaluate our method comparing with state-of-the-art methods by measuring the layout quality, visual inspection and analyzing the computational complexity. The experiment demonstrates the superiority of our method, and it can produce excellent layouts.
Just Shake Them Together: Imitation-Resistant Secure Pairing of Smart Devices via Shaking

In traditional device-to-device (D2D) communication based on wireless channel, identity authentication and spontaneous secure connections between smart devices are essential requirements. In this paper, we propose an imitation-resistant secure pairing framework including authentication and key generation for smart devices, by shaking these devices together. Based on the data collected by multiple sensors of smart devices, these devices can authenticate each other and generate a unique and consistent symmetric key only when they are shaken together. We have conducted comprehensive experimental study on shaking various devices. Based on this study, we have listed several novel observations and extracted important clues for key generation. We propose a series of innovative technologies to generate highly unique and completely randomized symmetric keys among these devices, and the generation process is robust to noise and protects privacy. Our experimental results show that our system can accurately and efficiently generate keys and authenticate each other.
TO THE QUESTION OF THE IMPACT OF ARTIFICIAL INTELLIGENCE ON MODERN EDUCATION

The article is devoted to the study of the influence of artificial intelligence on modern education, analysis of the prospects of artificial intelligence application in higher education institutions and problems arising as a result. The authors note that the future of higher education is inextricably linked to the development of information technologies and intellectual machines. The prospects of artificial intelligence open up new opportunities in teaching and learning in higher education institutions with powerful potential to change even the management system of educational institutions itself. The authors study the history of artificial intelligence since the 14th century, when Raimund Lullius proposed the idea of implementing reasoning and thought processes in an intellectual machine. The article uses methods of complex theoretical and descriptive analysis. The scientific novelty of the work is that the authors of the article have found that the effectiveness of use of artificial intelligence in education can be represented by the following functions: automation, integration, acclimation, distinction, identification. The authors argue that the increasing use of artificial intelligence in universities and schools also puts ethical questions at the forefront. Now organisations must consider what type of data is collected, how that information is used and what controls are in place to protect the privacy of students and schoolchildren. Practical significance of the work: in addition to functions reflecting the effectiveness of the use of artificial intelligence in the educational process, the authors have determined the positive aspects of the introduction of artificial intelligence in education. The results of the study: the authors of the article conclude that at present universities need to rethink their function and pedagogical models of education in relation to artificial intelligence, as higher education institutions open up extensive opportunities due to the application of artificial intelligence in the educational process.
THE ROLE OF GAME TEACHING TECHNOLOGIES IN THE PROFESSIONAL FORMATION OF STUDENTS - FUTURE LAWYERS

The article is devoted to identifying the role of gaming teaching technologies in the professional development of students - future lawyers. In the article, the author proves the thesis that gaming teaching technologies contribute to the implementation of such functions as: an activating function aimed at stimulating the cognitive interest of students both in theoretical knowledge and practical skills for the implementation of successful professional activities; developing, aimed at the formation of sustainable professional mobility on the basis of mastered professional and general professional competencies; psychological, which consists in the formation of the main mental cognitive processes, including: attention, imagination, thinking, speech, memory; communicative, aimed at developing the ability to business communication; educational, contributing to the regulation of interpersonal relations on the basis of social-perceptual interaction. Analysis and interpretation of the results of numerous studies made it possible to conclude in the article that in the course of the educational process, an integrated solution of the tasks of teaching, upbringing and the formation of the student's personality is carried out in the framework of training a highly qualified lawyer with the ability to fiercely compete in the modern labor market, expressing the most important guarantees of ensuring successful employment. The game technology of teaching in the article is presented as a reproduction of a genuine professional situation in which all students participate and have the opportunity to realize their individual status roles, in which simplified models of future professional activities are expressed. The main purpose of the article is to establish the role and place of gaming teaching technologies in the formation and development of a student's personality as a future qualified lawyer, ready for successful creative professional activity.
Machine Translation Customization via Automatic Training Data Selection from the Web

Machine translation (MT) systems, especially when designed for an industrial setting, are trained with general parallel data derived from the Web. Thus, their style is typically driven by word/structure distribution coming from the average of many domains. In contrast, MT customers want translations to be specialized to their domain, for which they are typically able to provide text samples. We describe an approach for customizing MT systems on specific domains by selecting data similar to the target customer data to train neural translation models. We build document classifiers using monolingual target data, e.g., provided by the customers to select parallel training data from Web crawled data. Finally, we train MT models on our automatically selected data, obtaining a system specialized to the target domain. We tested our approach on the benchmark from WMT-18 Translation Task for News domains enabling comparisons with state-of-the-art MT systems. The results show that our models outperform the top systems while using less data and smaller models.
A Spatial Domain Technique for Digital Image Authentication and Tamper Recovery

Nowadays, digital watermarking techniques are used to protect the integrity and authenticity of digital image and to provide capabilities for self-recovery of tampered locations. In this paper, a blind watermarking technique for image authentication and recovery of tampered area is proposed. Here the watermarking is done in block-wise manner by dividing the original image into non-overlapping blocks of 4*4 sizes. Here watermark data is consisting of authentication data and recovery data. Authentication data for each block is embedded in same block and recovery data is embedded in mapped block. Watermark data is generated using self-embedding techniques. This proposed scheme is checked against different types of attacks and different percentages of content modification of original image. The experimental results represent accurate detection and localization of tamper and high-quality recovery.
Generic quantum hardware accelerators for conventional systems

Abstract Quantum mechanics proposes, universe is a sum of a generic building block. Different orientation (i.e., angle, phase, amplitude, etc.) and summation of blocks forms entities. When differentiated, building blocks used for formation of entity are termed as basis. Following computational theory, these basis are termed as computational basis. Classical computers possess binary basis. Quantum system possess exponential computational power because of infinite computational basis. When computing solution to a problem, it's found in state space. Deterministic model (Conventional) requires both correct and incorrect solution set. For problems of probabilistic nature with plenty of variables (NP and P problems), computing solution requires exponential time, as entire state space is scanned. Furthermore, if solution is incomputable, the computation will never complete as solution is missing from both sets. Probabilistic model (Quantum) conducts a guided state space search and possess greater information carrying capacity per bit. Therefore, Quantum Accelerators (QA) are ideal for solving such problems. Resulting implementation of a Generic Quantum Hardware Accelerator (GQHA) is described via algorithms, mathematical models and microarchitecture. Next, a competitive industrial analysis and virtual implementation in a cloud environment is defined. Finally, it's proven that GQHA can replace conventional accelerators to produce faster and reliable results.
COVID-19’s Impact on the Telecommunications Companies

Now the world is witnessing most significant challenges due the Covid-19 crisis. Beyond health effects, it has social and economic effects. With the enormous amount of data available and the widespread use of social web globally, research can and should use it to provide solutions. Customer satisfaction is known to affect customer churn (customers leaving companies), which is a problem affecting many companies in competitive and volatile markets – like the current one. One easily available open source of customer opinions are tweets – more relevant now in the online world. Whilst Natural Language Processing (NLP) on tweets is not new, few studies target customer satisfaction, and NLP body of research on Arabic tweets is modest; we are not aware of any other study on this during a global pandemic. Our research thus aims to propose a new model based on Twitter mining to measure customer satisfaction during Covid-19, as well as compare customer satisfaction before and during the crisis. This is a use case for the largest Telecom companies in Saudi Arabia, and we involve the popular method of Sentiment Analysis (SA) for the task. We additionally propose a new Saudi lexicon and apply it to monitor real-time customer satisfaction on Twitter using three different transfer network models on Arabic sentiment analysis. Also, this research evaluates using these models on Arabic Sentiment Analysis as the first study comparing between three different transfer network models on Arabic text.