Reviewerly/exHarmony huggingface dataset:
Neural network-based motion vector estimation algorithm for dynamic image sequences

With the rapid development of deep learning, convolutional neural networks have gradually become the main means to extract features of dynamic image sequences. The motion vector estimation algorithm, as the key to the stability of image sequences, directly affects the performance of image stabilization systems, so the motion estimation algorithm for convolutional neural networks is necessary. The study proposes an improved convolutional neural network based on loss-free function, and applies it to the extraction of dynamic image features. On this basis, the motion estimation algorithm is then optimised by combining grey-scale projection and block matching methods. The experimental results show that the new loss-free function-based convolutional neural network has better recognition capability with an error rate of only 15% in dynamic image recognition. The accuracy of the optimised motion estimation algorithm is as high as 95.1% with a PSNR value of 16.636, which is higher than that of the traditional grey-scale projection algorithm. In terms of video processing, the improved algorithm has a higher PSNR value than the search block matching method, the bit-plane matching method and the full search block matching method, with a higher steady image accuracy and high operational efficiency, providing a new research idea for the improvement of motion estimation algorithms. In general, the proposed algorithm is a significant improvement over the current mainstream algorithms in terms of image accuracy, processing performance and number of operations, and it provides a new research idea for the improvement of motion estimation algorithms.
CEFI: Command Execution Flow Integrity for Embedded Devices

As embedded devices are widely used in increasingly complex settings (e.g., smart homes and industrial control systems), one device is usually connected with multiple entities, such as mobile apps and the cloud. Recent research has shown that privilege separation vulnerabilities, which allow violations of authority between different entities, are occuring in IoT systems. Because such vulnerabilities can be exploited without violating static control flow and data flow, existing CFI and DFI solutions cannot prevent them. We present CEFI, the first method to enforce integrity of command execution on embedded devices after deployment. CEFI provides fine-grained Command Execution Flow Integrity by preventing external commands from being executed on control flow paths belonging to interaction channels that are not authorized to perform them. Using minimal manual annotations as a starting point, CEFI statically determined the legal path set (from the start to the end point) and instruments the program to verify the legitimacy of the command execution at runtime by checking whether the calling context is consistent between the runtime executed path and statically obtained legal path set. We evaluate our prototype with five real-world firmware samples, and show that CEFI has an average performance overhead of just 0.18%, an average memory overhead of 0.19%, and that CEFI can effectively protect embedded devices against attacks on privilege separation vulnerabilities even if they do not violate control flow.
INNOVATIVE TECHNOLOGIES AND METHODS OF TEACHING IN NIVERSITIES OF UKRAINE

The article considers innovative technologies and methods of training, as used in the system of modern educational process in the IHE of Ukraine. The demand for the transition of innovative education, in particular in higher school, is caused by the challenges of the present day, and lies in priority directions of the state policy of Ukraine in the context of integration of the domestic educational sector into the European and world space. As a result of the research, it was determined that for higher education it is necessary to form a systematic approach to the analysis of professional tasks, strategic, ability to social mobility, aspiration to self-identification, self-expression and self-improvement during life. The authors of the article analyzed the establishment and development of innovative schools, whose activity is aimed at the formation of new, alternative concepts and pedagogical system, analyzed the use of the rating system of control of educational process, organization and evaluation of the quality of knowledge of the applicants. According to the results of the study it is determined that innovative educational activity is a complex process, which requires constructive management and significantly changes the educational process, allowing to solve problems of formation of individual educational prospects. The authors note that at introduction of innovative technologies in the pedagogical process of the educational system of the IHE there is an increase of professional competence of future specialists – participants of innovation processes, improvement of qualitative indicators of educational achievements of the competitors. At the same time, the regional system of education in general is being modernized, the development of the IHE on the basis of search, development and implementation of innovative pedagogical technologies is being monitored, and the scientific and methodological support of the educational institution development is being provided.
The essence of readiness for creative professional activities of future primary teachers

The article discusses the features of the professional training of future primary school teachers under conditions specially created for it. Modern approaches to determining the readiness for creative activity of future primary school teachers are analyzed. It is proved that the readiness of applicants for higher education for professional activities is a complex psychological formation and the result of special professional education, upbringing and self-education and is characterized by a professional orientation of the individual to obtain professional knowledge and skills and a creative educational and educational activity. The problem of readiness for creative professional activity of future primary school teachers has been studied through the disclosure of its content and the definition of structural components, with the help of which it is possible to most clearly explore the process itself and the result of pedagogical influence. It has been determined that the essence of readiness for creative professional activity of future primary school teachers implies the gradual mastering of one’s own inner world by the personality, which becomes a means of self-knowledge and application of the acquired knowledge and skills in further professional activities; provides an increase in the creative potential of the individual, which allows you to form the skills of professional activity; forms «special» creativity as the ability to create, which is associated with the professional field of activity. It has been determined that the readiness for creative professional activity of future primary school teachers as an integrative personal education lies in the unity of positive motivation for the creative mastery of the teacher’s profession (motivational component), an integrated system of knowledge about the essence of technology of creative professional activity of a teacher (cognitive component) and teaching and educational activities (activity component).
The Basic Problems of Educational Principles: Teaching

Teaching is the most purposeful, planned and organized educational activities in school education activities, and it is the basic way for schools to achieve the goal of education and promote the development of students. Because of this, teaching has become one of the basic problems of educational research. On the basis of explaining the relationship between education and teaching, this paper focuses on the classification of teaching objectives, the organization of teaching content, the selection of teaching methods and media, so as to provide theoretical basis and method guidance for effective teaching.
A Study on the Corresponding Expressions of ‘令人’ Causative Using Parallel Corpus - Focused on the Teaching of A→B Translation between Chinese and Korean

최근 한국어교육의 다변화 경향에 따라 몇몇 대학에서 ‘외국어로서의 한국어 번역’, 즉 KFLT(Korean as a Foreign Language for Translation) 전공이 개설되고 있지만 아직까지 특화된 교육과정 및 교육 방안이 부족한 실정이다. 따라서 KFLT 특성에 맞춘 차별화된 교육내용과 교수방안을 축적하고 이를 구체화, 정교화해 나가는 연구가 필요하다. 본고는 중한 A→B 방향 번역시 난점으로 지적되는 사동문 번역 관련 교수학습의 내용과 방안을 다루었다. 특히 감정을 표시하는 기능으로 자주 쓰이는 중국어 令人 사동 구문에 주목하여 병렬말뭉치를 통해 그 특징과 한국어 대응 양상을 파악하였으며, 나아가 이를 중국인 KFLT 학습자 대상 A→ B 번역 수업의 내용으로 구성하였다. 본 연구의 결과는 중국인 KFLT 전공 학습자를 위한 각종 수업에 즉시 적용할 수 있으며 향후 KFLT교육 컨텐츠 구축의 자료로도 활용할 수 있을 것으로 기대된다. 또한 직관에 의존할 수 없는 A→ B 번역에 병렬말뭉치를 도입하여 실질적 대안을 제시하였다는 의의가 있다. Recently, KFLT(Korean as a Foreign Language for Translation) major has been being opened in some universities due to the trend of diversification of Korean language education, but specialized curriculum and methods are still insufficient. For this reason, more researches are needed to accumulate differentiated educational contents and teaching plans tailored to the characteristics of KFLT, and to give shape and refine them. As a part of that, this paper dealt with teaching and learning contents and methods related to the translation of causative construction, which is pointed out as a difficulty in the A→B translation between Chinese and Koran. In particular, focusing on "令人" causative construction which is often used to express emotions, we identified the characteristics of it and Korean corresponding expressions through parallel corpus. And furthermore, using the results, we composed the contents for Chinese KFLT learners in A→B translation class. The results of this study can be immediately applied to various classes for Chinese KFLT major learners and are expected to be used as data for the construction of KFLT education contents in the future. It is also meaningful that a practical alternative was proposed by introducing a parallel corpus to A→B translation, which cannot be relied on intuition.
Application of virtual reality as an alternative training media as complement of ship bridge simulator (case study: port of tanjung emas semarang)

Abstract The application of computer technology such as 3D modeling, simulators, virtual reality, and augmented reality, will always provide benefits to the maritime sector. The object of study used in this research is the physical bridge simulator at Poltekpel Surabaya which is used as a virtual simulator using virtual reality. Through the application of virtual reality technology, it can be developed into an educational facility for the cadets there which does not only rely on 2D or 3D forms but can be used as a simulator where users can interact directly in the application. With this simulator, it can save in terms of manufacturing costs and in terms of usage it becomes more effective and practical. In this simulator application the user can participate in a virtual 3D environment that is simulated like a real environment. With a simulator using virtual reality, users can learn how to operate a ship like a captain without having to visit the ship’s bridge directly. Simulators with virtual reality will provide a virtual learning environment that can be accessed easily anywhere and anytime. Based on the results of the cadet assessment test questionnaire consisting of 7 questions with 30 respondents, it can be concluded that the VR application for the Bridge Simulator is in the very good category because the application is easy to understand by the respondents and the 3D results from the ship’s bridge at Poltekpel are the same with those in the simulator application and for the results of the questionnaire calculations have a total value of 908 which is between 840 to 1050, or 87% in the percentage calculation.
Swarm Robots Search for Multiple Targets Based on Historical Optimal Weighting Grey Wolf Optimization

This study investigates the problem of swarm robots searching for multiple targets in an unknown environment. We propose the Historical Optimal Weighting Grey Wolf Optimization (HOWGWO) algorithm based on an improved grouping strategy. In the HOWGWO algorithm, we gather and update every individual grey wolf’s historical optimal position and rank grey wolves based on the merit of their historical optimal position. The position of the prey is dynamically estimated by the leader wolf, and all grey wolves move towards the prey’s estimated position. To solve the multi-target problem of swarm robots search, we integrate the HOWGWO algorithm with an improved grouping strategy and divide the algorithm into two stages: the random walk stage and the dynamic grouping stage. During the random walk stage, grey wolves move randomly and update their historical optimal positions. During the dynamic grouping stage, the HOWGWO algorithm generates search auxiliary points (SAPs) by adopting an improved grouping strategy based on individual grey wolves’ historical optimal positions. These SAPs are then utilized for grouping grey wolves to search for different prey. The SAPs are re-generated using the optimum historical positions of every single grey wolf after positions have been updated, rather than just those belonging to a specific group. The effectiveness of the proposed HOWGWO algorithm is extensively assessed in 30 dimensions using the CEC 2017 test suite, which simulates unimodal, multimodal, hybrid, and composition problems. Then, the obtained results are compared with competitors, including GWO, PSO and EGWO, and the results are statistically analyzed through Friedman’s test. Ultimately, simulations are performed to simulate the problem of searching multiple targets by swarm robots in a real environment. The experimental results and statistical analysis confirm that the proposed HOWGWO algorithm has a fast convergence speed and solution quality for solving global optimization problems and swarm robots searching multiple targets problems.
Stealth Address Schemes With Fast Retrievability Based On Subgroup Membership Assumptions Related To Factoring

Abstract Stealth address is a known technique to ensure the privacy (anonymity) of a recipient participating in a certain transaction in a distributed blockchain scenario. However, most existing stealth address schemes require linear judge time and search time $\mathcal{O}(n)$, where $n$ is the number of transactions of a certain block, so the only way to claim transactions for a recipient is to traverse the transaction list to find out whether an ever-arrived transaction belongs to him. To overcome this drawback, we proposed the notion of Fast Stealth Address (FSA), a novel approach that simultaneously preserves privacy and improves search efficiency of recipients. We give a generic construction of FSA scheme under subgroup membership assumption related to factoring and instantiate concrete schemes based on specific number-theoretic assumptions. Our framework mainly improves on two aspects: (i) allowing constant recognize time $\mathcal{O}(1)$ to judge whether a certain block contains recipient’s transactions and (ii) allowing logarithmic search time $\mathcal{O}(\log{n})$ to find out the precise transactions intended for a recipient. We formalize the security model of an FSA scheme and provide provable security analysis to ensure the security of our constructions. Besides, we implement our schemes to measure their real-world performance on several metrics and give comparison results to stealth address scheme utilized by Monero.
Arduino Based Restaurant Menu Ordering System

Abstract The role of restaurants, canteens, other forms of eatery and food service outlets are becoming very big part of most economies at different levels. However improvement in customer experience while making orders plays a very critical role in having a smooth and efficient menu ordering process. This article presents a novel hybrid approach that tackles the challenges associated with menu ordering especially in the post-covid era; both from software and hardware perspectives. First, a web-application - powered by ReactJS and GraphQL, which enables order request anywhere at any time was developed. In addition, is the implementation of hardware with mobility feature on trips to the restaurant using two Arduino microcontrollers (transmitter and receiver). The result allows the user to browse through a catalogue, check-in and out on a Thin-Film-Transistor (TFT) liquid-crystal display (LCD) and deliver a seamless experience to the customer.
Madvex: Instrumentation-Based Adversarial Attacks on Machine Learning Malware Detection

WebAssembly (Wasm) is a low-level binary format for web applications, which has found widespread adoption due to its improved performance and compatibility with existing software. However, the popularity of Wasm has also led to its exploitation for malicious purposes, such as cryptojacking, where malicious actors use a victim's computing resources to mine cryptocurrencies without their consent. To counteract this threat, machine learning-based detection methods aiming to identify cryptojacking activities within Wasm code have emerged. It is well-known that neural networks are susceptible to adversarial attacks, where inputs to a classifier are perturbed with minimal changes that result in a crass misclassification. While applying changes in image classification is easy, manipulating binaries in an automated fashion to evade malware classification without changing functionality is non-trivial. In this work, we propose a new approach to include adversarial examples in the code section of binaries via instrumentation. The introduced gadgets allow for the inclusion of arbitrary bytes, enabling efficient adversarial attacks that reliably bypass state-of-the-art machine learning classifiers such as the CNN-based Minos recently proposed at NDSS 2021. We analyze the cost and reliability of instrumentation-based adversarial example generation and show that the approach works reliably at minimal size and performance overheads.
Super-resolution reconstruction of sea surface pollutant diffusion images based on deep learning models: a case study of thermal discharge from a coastal power plant

While remote sensing images could convey essential information of surface water environment, the low spatial resolution limits their application. This study carried out a series of experiment tests of thermal discharge from a coastal power plant and constructed an image dataset HY_IRS, representing the transport and diffusion of discharged heated water in tidal waters. Two image super-resolution (SR) reconstruction models based on deep learning (DL), ESPCN and ESRGAN, were trained based on this dataset and then used to reconstruct high-resolution remote sensing images. It shows that the two DL models can markedly improve the spatial resolution of the surface diffusion image of thermal discharging, with the PSNR improved by 8.3% on average. The trained two models were successfully used to improve the spatial resolution of thermal infrared remote sensing SST images from Landsat8 TIRS, indicating that the SR model based on DL has a good effect and a crucial application prospect in the field of improving the resolution of pollutant diffusion remote sensing images.
Semi-supervised learning models for document classification: A systematic review and meta-analysis

The continuous increase of digital documents on the web creates the need to search for information patterns that allow the categorization of organizational documents to generate knowledge in an institution. An Artificial Intelligence technique for this purpose is text classification, it for its application uses labels (previously categorized documents) with supervised (with labels) or unsupervised (without labels) training models. Both traditional models with their advantages and disadvantages have been joined into semi-supervised models that extract the best qualities of each one, however, the labeling process involves resources and time that try to be optimized to improve classification accuracy.&#x0D; An analysis of the different semi-supervised models would show us the advantages of their training and the way how the structure of each of them affects the accuracy of their classification. In the present study, a classification structure of the semi-supervised models in the classification of documents is proposed to analyze their qualities and categorization process, through an SLR (Revision of systematic literature) that extracts performance metrics from the identified studies to perform a meta-analysis through forest plots.&#x0D; To define the search strategy for studies, the PICOC (Population, Intervention, Comparison, Outputs, Context) method has been used, it is supported by the research question defines a search string, which has allowed the collection of 228 research, these are filtered with the PRISMA declaration method and the determination of exclusion criteria, in this way 35 researches are selected for the present study.&#x0D; The analysis of the selected studies identifies a structure for the different semi-supervised learning models, and a scheme of their work process is obtained, it has been used to extract advantages, disadvantages, and performance metrics. Through a meta-analysis with forest diagrams, the classification accuracy performance of the researches in each learning model is evaluated, determining as results that regardless of the characteristics of its process, active learning (0.89) and assembled learning (0.83) present the best performance levels.
Complex Knowledge Graph Embeddings Based on Convolution and Translation

Link prediction involves the use of entities and relations that already exist in a knowledge graph to reason about missing entities or relations. Different approaches have been proposed to date for performing this task. This paper proposes a combined use of the translation-based approach with the Convolutional Neural Network (CNN)-based approach, resulting in a novel model, called ConCMH. In the proposed model, first, entities and relations are embedded into the complex space, followed by a vector multiplication of entity embeddings and relational embeddings and taking the real part of the results to generate a feature matrix of their interaction. Next, a 2D convolution is used to extract features from this matrix and generate feature maps. Finally, the feature vectors are transformed into predicted entity embeddings by obtaining the inner product of the feature mapping and the entity embedding matrix. The proposed ConCMH model is compared against state-of-the-art models on the four most commonly used benchmark datasets and the obtained experimental results confirm its superiority in the majority of cases.