Reviewerly/exHarmony huggingface dataset:
Improved neural network pruning algorithm based on contrast loss and simulated annealing

The neural network pruning algorithm reduces the computation and complexity of neural networks effectively. But the pruning scope is always difficult to define. The existing algorithms perform polarization operations on the scale factors, then the threshold is selected, and the pruning range is determined. However, there are still many candidate points in the selection of the threshold, and it is difficult to determine the optimal solution from them. Aiming at the above problems, this paper proposes an improved neural network pruning algorithm based on contrast loss and simulated annealing to determine the selection of threshold. Firstly, different training data from the same dataset are used to train multiple networks. The goal is to allow different data to improve the generalization ability of the network. Then, each network chooses its own pruning threshold at the same layer. When selecting the threshold, the stationary point close to zero among the candidate points is selected as the candidate set of pruning threshold. Different threshold values are used in this range to obtain the corresponding pruning structure. Next, these structures are compared and the pruned structure corresponding to the smallest difference in neural network structures is kept. Experiments on CIFAR-10 and MINIST datasets show that the proposed method outperforms the existing algorithms on accuracy, pruning rate, model storage and running time.
Some Difficulties In Writting Skills of English Majored Freshmen at Tay Do University, Vietnam

There is no denying that English is playing an important role in most regions of the world and is playing a leading role in the education and future careers of students. Moreover, writing skills are one of the indispensable skills in the developing society in general and in the integration process of Vietnam in particular. However, in the learning process, English learners, in general, the English major students of Tay Do University in particular face many obstacles in mastering writing skills. Therefore, the study “Some difficulties in writing skills of English majored freshmen at Tay Do University” is conducted to identify the challenges facing freshman students. The survey participants are 45 students randomly selected from class A, B, and C majoring in English Language Course 15. The survey tool used to collect the data is the questionnaire. The study will identify the challenges in writing faced by English majored freshmen at Tay Do University such as vocabulary, grammar, and background knowledge.
ON THE EFFECTIVENESS OF PARAGRAPH VECTOR MODELS IN DOCUMENT SIMILARITY ESTIMATION FOR TURKISH NEWS CATEGORIZATION

News categorization, which is a common application area of text classification, is the task of automatic annotation of news articles with predefined categories. In parallel with the rise of deep learning techniques in the field of machine learning, neural embedding models have been widely utilized to capture hidden relationships and similarities among textual representations of news articles. In this study, we approach the Turkish news categorization problem as an ad-hoc retrieval task and investigate the effectiveness of paragraph vector models to compute and utilize document-wise similarities of Turkish news articles. We propose an ensemble categorization approach that consists of three main stages, namely, document processing, paragraph vector learning, and document similarity estimation. Extensive experiments conducted on the TTC-3600 dataset reveal that the proposed system can reach up to 93.5% classification accuracy, which is a remarkable performance when compared to the baseline and state-of-the-art methods. Moreover, it is also shown that the Distributed Bag of Words version of Paragraph Vectors performs better than the Distributed Memory Model of Paragraph Vectors in terms of both accuracy and computational performance.
Adaptive gaze estimation based on channel attention mechanism

Attention mechanisms have been found to be effective for human gaze estimation. To address the problem that traditional attention has limited ability to extract higher-order contextual information in gaze estimation tasks, an ECA attention mechanism-based gaze estimation network is proposed, which aims to effectively exploit the channel relations of features through a global average pooling layer without dimensionality reduction, suppress some facial regions that do not contribute to gaze estimation, and activate subtle facial features that can improve gaze estimation. The model can take full advantage of the user's appearance, which helps to improve the accuracy of the gaze estimation model. In this paper, experiments are conducted on the MPIIGaze dataset, and the results show that the network based on the channel attention mechanism can reduce the estimation error, and the model proposed in this paper can achieve more accurate gaze estimation.
Transformer dual feature enhancement based pedestrian re-identification algorithm

Pedestrian re-identification is an integral part of surveillance video analysis, and the field of pedestrian re-identification has been growing rapidly in recent years with the development of deep learning. Most existing pedestrian re-identification methods are based on convolutional neural network deep learning models, and most studies consider learning representations from individual images, ignoring the potential interactions between them. In this paper, we propose a dual feature-enhanced pedestrian re-identification method based on Transformer architecture, which learns high discriminative features of pedestrians, suppresses the influence of irrelevant features on the network, and explicitly models the interactions between all input images to more fully learn the pedestrian feature representations from different perspectives, thus making the network more accurate and robust in overall recognition. Experiments with the model on four publicly available pedestrian re-identification datasets demonstrate that all evaluation metrics are higher than most of the current mainstream pedestrian re-identification models.
THYROID LESION DETECTION USING DEEP LEARNING TECHNIQUES

Lesions are damaged regions in organs or tissues caused by diseases such as tumors.As tumors grow, they can spread to surrounding tissues, leading to the development of thyroid cancer, a type of highly treatable endocrine cancer.Some people have no symptoms.Others may notice a lump in the neck.Although more than 90 percent of identified thyroid nodules are clinically insignificant benign lesions, in 4 to 6.5 percent of cases, these nodules are due to thyroid cancer.Thyroid cancer often does not show any symptoms in its early stages, and its cause is not well understood.Symptoms may include a lump in the neck, but often go unnoticed.Ultrasonic images can be used to determine if a swelling is a harmless benign lesion or cancer.Our proposed project aims to develop a deep-learning system that can accurately classify ultrasonic images of the thyroid into benign or cancerous in a short time, allowing doctors to provide prompt treatment if necessary.
Internet piracy

The rapid development of new communication technologies has led to the widespread use of the Internet and digital media. While fast and easy access to information has benefited society in many ways, it has also led to the development and proliferation of cybercrimes. One such type of cybercrime is Internet piracy, which involves using the Internet to steal copyrighted intellectual property, such as music, movies, video games, and software. Internet continues to increase as more intellectual property is created and can be easily shared and distributed digitally over shared networks. This theft of intellectual property negatively impacts the world economy, job opportunities, employee earnings, and tax revenues for many nations. With such widespread use and great global impact, it is important to understand and examine Internet piracy. This chapter presents a brief review of Internet piracy including key definitions, history, and factors associated with internet piracy offending. Finally, this chapter reviews various initiatives that have emerged to combat Internet piracy ranging from anti-piracy education campaigns to legal actions made by both industries and law enforcement agencies.
Photovoltaic Power Prediction Method Based on Combined Similar Days and BiLSTM

Abstract The PV power output between similar days has great similarity. It can improve the accuracy of the prediction results based on valid similar days. In this paper, we proposed a novel combined similar day selection method based on multiple periods. Firstly, the meteorological characteristic quantity is established, and then the historical similar period is selected by using the combination index of grey correlation degree and cosine similarity in different periods, and multiple combined similar days are constructed. Then, using the historical power value and meteorological data of the corresponding period, the photovoltaic power of the prediction day is predicted by bi-long short-term memory (BiLSTM) neural network in different periods. The photovoltaic power prediction results of the day to be predicted are corrected according to the power errors of multiple combined similar days. The results show that this method has better prediction accuracy.
LEARNING TO WRITE EXPLANATORY TEXT USING THE PICTURE-TO-PICTURE METHOD FOR STUDENTS OF MTs NURUL HIKMAH BEKASI

Learning to write for some students is still difficult. Writing requires practice and skill acquisition like vocabulary. Appropriate and effective methods also determine the success of learning to write. One method that emphasizes collaborative learning is the picture-to-picture cooperative method. The research in this paper used the Kemmis and McTaggart classroom action methods through action, observation, and reflection. The students who participated were 37 students in class 9 of Mts Nurul Hikmah Bekasi. The research used 2 cycles. In the first and second cycles, the researcher provided explanatory writing material through the picture-to-picture method. The theme of the picture is the picture of natural phenomena. There is an increase of 3 points between cycles 1 and 2. Learning to write through the picture-to-picture method can improve the results of students' ability to write explanatory texts. Teachers are expected to be able to complete this method with several other media, such as augmented reality applications, power points, and several other media. Hence, there are variations in teaching in the classroom.
An Improved Slow Coherent Groups Recognition Method for Dynamic Equivalence of Large Power System

Abstract The slow coherent groups recognition method is one of the dynamic equivalence techniques, which is based on the singular perturbation principle. Its algorithm performance is widely recognized. In order to reduce the solving difficulty and time consuming, this method ignores the generator stator damping effect. But during a fault, the generator stator damping effect directly affects the oscillation amplitude and frequency of the rotor angular curve. If the damping characteristics of the original system are ignored, when the damping torque coefficients of each generator vary greatly, the coherence between generators will be affected, and the accuracy of the simplified equivalent system will inevitably decrease. This paper proposes a new method based on the traditional slow coherent groups’ recognition method. The new method, which is based on the second-order model of the generator, can consider the generator stator damping effect without increasing the algorithm complexity. The active and reactive power of the system are decoupled. The eigenvalues of the system model and the general solution of the second-order differential equations are obtained through rigorous derivation. It also proposes a two-scale coherent groups recognition criterion. Finally, the effectiveness of the new method is verified based on the IEEE-39-Bus system.
Security research of power system based on Blockchain and 5G Technology

The deepening application of 5G network in the power system has greatly improved the operation efficiency of the power dispatching system and the transmission speed of the power network, but at the same time, it also brings serious hidden dangers to the security of the power system. This paper provides a new solution for 5G networks and services based on blockchain technology to improve data security, privacy and decentralization. The paper deeply studies the integration of 5G and blockchain technology, strengthens the storage and management of 5G data through blockchain technology, and optimizes 5G network from the architecture to enhance the security of 5G ecosystem. Finally this paper explores the application of the combination of 5G and blockchain in the power industry. Through the secondary reconstruction of 5G and blockchain technology, a deep integration architecture with the power system is formed to improve the security of 5G network carrying power business and ultimately ensure the safe operation of the energy Internet.
Economic Benefit Post-evaluation for Technical Revamping of Power Grid Projects

On the basis of the variations of the technical revamping of the power grid projects with cross purposes ,and diverse contents is hard to put into practice the economic and efficiency analysis of the power grid projects. So a new project economic benefit post evaluation model of the technical revamping of the power grid project is presented based on the method of before and after comparison. A proper project economic benefit post evaluation criterion is introduced along with a theory of life cycle costs and a evaluation index system of the technical revamping of the power network projects is constructed. On the foundation of the theory of life cycle costs, the innovative economic benefit post evaluation model can provide reliable methods and criterion for the rationality analysis and the decision risk control and the economic benefit assessment of the technical revamping of the power grid projects.
Human Motion Capture Based on MEMS Sensor

Abstract In order to realize the monitoring of human joint rehabilitation, a human motion capture and recognition system is constructed by using micro electro mechanical system (MEMS) sensor nodes. A two-stage extended Kalman filter algorithm is proposed for multi-sensor data fusion. The error matrix between the coordinate system of sensor node and the coordinate system of body was calculated by using the stationary posture calibration. The root mean squared error (RMSE) of the computed joint angle time series is less than 0.5°. The feature of joint angle time series was extracted and the support vector machine (SVM) classification model based on particle swarm optimization (PSO) was established. The experimental results show that the SVM algorithm optimized by PSO has better recognition effect than BP neural network. The average recognition rate can reach more than 97%. The human motion capture system designed in this paper can effectively realize human motion capture, recognition and joint rehabilitation monitoring.
Speaker Recognition through Deep Learning Techniques

Deep learning has now become an integral part of today's world and advancement in the field of deep learning has gained a huge development. Due to the extensive use and fast growth of deep learning, it has captured the attention of researchers in the field of speaker recognition. A detailed investigation regarding the process becomes essential and helpful to the researchers for designing robust applications in the field of speaker recognition, both in speaker verification and identification. This paper reviews the field of speaker recognition taking into consideration of deep learning advancement in the present era that boosts up this technology. The paper continues with a systematic review by firstly giving a basic idea of deep learning and its architecture with its field of application, then entering into the high-lighted portion of our paper i.e., speaker recognition which is one of the important applications of deep learning. Here we have mentioned its types, different processing techniques, challenges that come across in this technology, performance evaluation criteria, deep learning implementation frameworks, and lastly various databases used in the field of speaker identification (SI) and Speaker Verification (SV).
DEMIX: Domain-Enforced Memory Isolation for Embedded System

Memory isolation is an essential technology for safeguarding the resources of lightweight embedded systems. This technique isolates system resources by constraining the scope of the processor's accessible memory into distinct units known as domains. Despite the security offered by this approach, the Memory Protection Unit (MPU), the most common memory isolation method provided in most lightweight systems, incurs overheads during domain switching due to the privilege level intervention. However, as IoT environments become increasingly interconnected and more resources become required for protection, the significant overhead associated with domain switching under this constraint is expected to be crucial, making it harder to operate with more granular domains. To mitigate these issues, we propose DEMIX, which supports efficient memory isolation for multiple domains. DEMIX comprises two mainelements-Domain-Enforced Memory Isolation and instruction-level domain isolation-with the primary idea of enabling granular access control for memory by validating the domain state of the processor and the executed instructions. By achieving fine-grained validation of memory regions, our technique safely extends the supported domain capabilities of existing technologies while eliminating the overhead associated with switching between domains. Our implementation of eight user domains shows that our approach yields a hardware overhead of a slight 8% in Ibex Core, a very lightweight RISC-V processor.
Obstacles Scientific Research in the Departments of Curriculum and Instructional Methods in Jordanian Universities from the of Perspectives Graduate Students

Objectives: This study aimed at exploring the obstacles scientific research in the departments of curriculum and instructional methods in Jordanian universities.&#x0D; Methods: A questionnaire of 59 paragraphs was developed by the researcher to figure out the perspectives of graduate students. The questionnaire was distributed to five domains: working conditions, publishing procedures, research teams, research reviewers, and writing research. It was distributed on (71) of Graduate Students in five universities.&#x0D; Results: The study showed that the five areas confirmed the existence of obstacles facing graduate students in conducting scientific research to varying degrees, ranging from very high levels to low levels, and were arranged in descending order as follows: researchers, working conditions, publication procedures, research arbitration, writing research, and that the areas of scientific research obstacles vary according to the level of study (Master, Ph.D.), the number of and to publish it, more often than graduate students of the doctoral level face.&#x0D; Conclusion: The study recommended the importance of training graduate students (Masters and Ph. D.) in the Departments of curricula and teaching methods, on how to design scientific research, identify its problem and stages of implementation, statistical treatments, increase the issues of university educational journals, select arbitrators who adhere to the deadlines set in the arbitration of research, urge civil society institutions to provide financial and moral support for outstanding scientific research and work to employ the results of scientific research in economic development programs.
Improved Lower Bounds on the On-line Chain Partitioning of Posets of Bounded Dimension

An on-line chain partitioning algorithm receives a poset, one element at a time, and irrevocably assigns the element to one of the chains. Over 30 years ago, Szemerédi proved that any on-line algorithm could be forced to use $$\left( {\begin{array}{c}w+1\\ 2\end{array}}\right)$$ chains to partition a poset of width w. The maximum number of chains that can be forced on any on-line algorithm remains unknown. In a survey paper by Bosek et al., it is shown that Szemerédi’s argument could be improved to obtain a lower bound almost twice as good. Variants of the problem were considered where the class is restricted to posets of bounded dimension or where the poset is presented via a realizer of size d. In this paper, we prove two results. First, we prove that any on-line algorithm can be forced to use $$(2-o(1))\left( {\begin{array}{c}w+1\\ 2\end{array}}\right)$$ chains to partition a 2-dimensional poset of width w. Second, we prove that any on-line algorithm can be forced to use $$(2-\frac{1}{d-1}-o(1))\left( {\begin{array}{c}w+1\\ 2\end{array}}\right)$$ chains to partition a poset of width w presented via a realizer of size d.
The category of understanding in the process of forming postgraduate students' research competencies

Introduction . The capacity to solve research problems is one of the key universal competencies of future highly qualified specialists. Meanwhile, the process of understanding occupies the central position in research activity. However, the problem is that, unlike most epistemological procedures, the experience of understanding, deeply personal and often unique, is difficult to explore and transmit. Purpose setting . In this connection, the purpose of the research carried out at the Department of Philosophy, Sociology and Religious Studies of the KSPU named after V. P. Astafiev during 2017–2021 was to trace the dynamics in postgraduate students» ideas about the phenomenon of understanding. More precisely, to identify their reflected experience of understanding and, accordingly, the features of formation of research competencies. Methodology and methods of the study . The target group included about 50 respondents annually. A typical and non-standardized interview, a survey, a participant observation, elements of the comparative method and longitudinal study, content analysis, logical analysis, and the phenomenological method were used. Surveys, conversations, unstructured interviews conducted among students not only allowed to reveal their ideas about the phenomenon of understanding, but also become a tool for forming these ideas, a means of learning and development. At the same time, the educational process itself acquires the features of a scientific search. Results . The answers showed that students clearly distinguish epistemological concepts, choose the right definitions, evaluate statements. 75 % of respondents did not make any obvious mistakes. Data were obtained concerning the perception of the difference between the natural sciences and the humanities, between explanation and understanding, as well as data concerning attitudes to a number of other problems. Conclusion . It has been established that by the end of the first year of study, postgraduate students demonstrated a number of stable achievements: they were aware of the key position of understanding in the system of epistemological procedures and categories; they were able to see the multidimensionality of understanding, its non-reducibility to other epistemological procedures and categories; they master post-nonclassical cognitive strategies and are open to new paradigms in science, education, and social practice. As the comparative analysis shows, the dynamics of students' epistemological preferences is reflecting the trends observed in science and education (a turn from a normative approach to an interpretative-humanistic one, and some other trends).