Reviewerly/exHarmony huggingface dataset:
APPLICATION OF COMPUTER EDUCATIONAL GAMES IN HIGHER LEGAL EDUCATION (AN EXPERIMENT EXPERIENCE)

The article analyzes the cognitive effect of the use of computer educational games in the future lawyers training at university. The relevance of studying this issue is due to the search for modern higher education of optimal educational practices that allow one to achieve the goals it faces. The authors of the article put forward a hypothesis that computer educational games have a significant cognitive effect. The article describes an experiment aimed at establishing the cognitive result of using a game in the educational process when conducting classes with students of the Faculty of Law from the subjective (students’ opinion) and objective (teachers’ opinion) points of view. For this purpose, in their own pedagogical practice, the authors use the computer educational game “Criminal Case” developed by them, designed to train lawyers’ professional competencies of an investigator. The authors conduct a study in which, using the methods of pedagogical observation, interviewing, questioning, as well as the method of expert assessments, they evaluate the ability of a computer educational game, using the example of the game “Criminal Case”, to train a student the professional competencies of an investigator. The results obtained during the experiment confirmed a high cognitive effect of the use of computer educational games in teaching lawyers, both in the opinion of students, who indicated that they acquired new knowledge, systematization of existing knowledge after training through the game, and in the opinion of teachers, based on objective data from the results of testing students to determine their level of knowledge. As a result, the authors formulate the conclusion that computer educational games have many positive qualities that affect both the acquisition of theoretical knowledge and practical skills, and therefore they can successfully supplement traditional forms of education. The results of the study can be used to study this issue further.
REGULARITIES AND PRINCIPLES OF FORMING THE LIFE STYLE OF STUDENTS OF HIGHER EDUCATION INSTITUTIONS OF CULTURE

The article contains a detailed description of the patterns and principles of the formation of students’ lifestyle of universities of culture of the Russian Federation. The educational and cultural environment of the higher educational institution of the Russian Federation, according to the author, is a complex, multicomponent, constantly changing education depending on the cultural context. This pedagogical phenomenon is a unique form of the existence of students of higher education institutions of culture in the Russian socio-cultural space, where conditions and opportunities for full self-expression of their individuality are created. The author focuses on the educational environment as an important factor in the organization of the socio-cultural space of students of higher education institutions of culture, which is defined for these students as educational and cultural and aimed at creating optimal conditions for the professional formation and social development of students’ personality of higher education institutions of culture in the context of socio-cultural communication. The educational environment of cultural universities contains cross-cultural, organizational-technological, educational-professional and socio-educational components in which students’ lifestyle, life activity and life creation are taught. At the same time, the study of socio-pedagogical support for the formation of students’ lifestyle of higher education institutions of culture by its role in increasing the effectiveness of this process in the educational and cultural environment of higher education institutions of the Russian Federation allowed the author to identify general social and pedagogical, special and specific principles of the formation of the lifestyle of students of higher education institutions of culture.
Feature parameters extraction and affective computing of voice message for social media environment

Voice message in social media environment includes a large number of conversation natural languages, which increases the difficulty of emotion tagging and affective computing. In order to solve the above difficulties, this paper analyzes the cognitive differences between the semantic and acoustic features of voice message from the perspective of cognitive neuroscience, and presents a voice feature extraction method based on EEG (Electroencephalogram) experiments, and gets the representation of 25 acoustic feature parameter vectors. Meanwhile, we proposed an affective computing method based on PAD (Pleasure-Arousal-Dominance) dimension emotional space according to the above parameters. Experiments show that the method can effectively solve the affective computing problem of voice message. Overall, there are two main contributions of this paper. Firstly, it comprehensively analyzes the emotional cognitive feature of voice message in social media environment from the perspectives of cognitive neural mechanism, voice acoustic feature and text semantics. Secondly, the segmented affective computing method for voice message based on acoustic feature parameters and PAD emotional state model is proposed.
Smart contract vulnerability detection based on variant LSTM

The smart contracts of Ethereum have brought an essential contribution to the development of blockchain. Nowadays, an increasing number of smart contracts are being deployed on Ethereum, which brings prosperity to Ethereum while also bringing many security risks. According to reports, several attacks due to the smart contract vulnerability have caused huge losses to Ethereum. Therefore, detecting smart contract security vulnerabilities is of great importance. However, the existing work is not sufficient to fully perform this task. For this reason, we propose a variant of the LSTM model to detect smart contract vulnerabilities at the bytecode level. In our variant LSTM model, we interact hidden state of the model with the input sequence multiple times. In this way, the variant model is able to capture more potential features in the bytecode for learning. We used a dataset of 34822 unique smart contracts for training and testing. The results show that the variant LSTM model outperforms the general LSTM model and improves in most metrics.
Survey on Different Micro Services, Recommendation Model and AI based Chatbot used for Recruitment System

Abstract: Nowadays, it's all about AI-powered chatbots and recommender systems to make user experiences better. The recommender system uses machine learning to give you personalized content suggestions, and the chatbot gives you real-time support. This article looks at a new way to combine these technologies to make user interactions more smooth and engaging. The recommender systems analyze your data and use advanced algorithmsto give you recommendations that match your preferences. This helps you get more engagement and conversions. But what happens when you need help or have questions? That's where the recommendations system and microservices come in.
PENERAPAN DIGITAL BOOK SEBAGAI MODUL PEMBELAJARAN PADA MATA KULIAH KONSTRUKSI BANGUNAN I PENDIDIKAN TEKNIK BANGUNAN FAKULTAS TEKNIK UNIVERSITAS NEGERI JAKARTA

This research departs from the problems that occur in the field, currently there are still many lecturers who have not used a variety of learning media, and according to the characteristics and needs of students in line with technological and IT developments. This study aims to find out how the application of the e-Learning Module learning media using E-Books as an interactive learning medium in the Building Construction 1 Course in the Building Engineering Education Study Program, UNJ, and its feasibility as an interactive learning medium in the Building Construction 1 Course in the Education Study Program. Building Engineering, UNJ. The research is using qualitative methods with the model of definition, design, and implementation. The research subjects were 2 lecturers and 63 students from the 2018-2019 class. The analysis used is descriptive analysis. The results of the study concluded that: (1) the application of the e-Learning Module learning media using E-Books as interactive learning media in the Building Construction I course at the Building Engineering Education Study Program, UNJ Jakarta was considered effective with a practical student percentage of 81.11% including the category very effective, especially used as online learning, and (2) the e-Learning module uses E-Books as interactive learning media in the Building Construction Course I at the Building Engineering Education Study Program, UNJ Jakarta which is very practical and feasible as an interactive learning medium in Building Construction I course at the Building Engineering Education Study Program, UNJ Jakarta, both based on the content of the material (88.50%), or based on the media aspect (83.33%), especially in online learning.
Adaptive Scalable Video Streaming (ASViS): An Advanced ABR Transmission Protocol for Optimal Video Quality

Multimedia video streaming, identified as the dominant internet data consumption service, brings forth challenges in consistently delivering optimal video quality. Dynamic Adaptive Streaming over HTTP (DASH), while prevalent, often encounters buffering problems, causing video pauses due to empty video buffers. This study introduces the Adaptive Scalable Video Streaming (ASViS) protocol as a solution. ASViS incorporates scalable video coding, a flow-controlled User Datagram Protocol (UDP), and deadline-based criteria. A model is developed to predict the behavior of ASViS across varying network conditions. Additionally, the effects of diverse parameters on ASViS performance are evaluated. ASViS adjusts data flow similarly to the Transmission Control Protocol (TCP), based on bandwidth availability. Data are designed to be discarded by ASViS according to video frame deadlines, preventing outdated information transmission. Compliance with RFC 8085 ensures the internet is not overwhelmed. With its scalability feature, ASViS achieves the highest possible image quality per frame, aligning with Scalable Video Coding (SVC) and the available data layers. The introduction of ASViS offers a promising approach to address the challenges faced by DASH, potentially providing more consistent and higher-quality video streaming.
Accounting for Transfer of Learning Using Human Behavior Models

An important characteristic of human learning and decision-making is the flexibility with which we rapidly adapt to novel tasks. To this day, models of human behavior have been unable to emulate the ease and success with which humans transfer knowledge in one context to another. Humans rely on a lifetime of experience and a variety of cognitive mechanisms that are difficult to represent computationally. To address this problem, we propose a novel human behavior model that accounts for human transfer of learning using three mechanisms: compositional reasoning, causal inference, and optimal forgetting. To evaluate this proposed model, we introduce an experiment task designed to elicit human transfer of learning under different conditions. Our proposed model demonstrates a more human-like transfer of learning compared to models that optimize transfer or human behavior models that do not directly account for transfer of learning. The results of the ablation testing of the proposed model and a systematic comparison to human data demonstrate the importance of each component of the cognitive model underlying the transfer of learning.
FIP Brisbane 2023: Personalised and Precision Medicine

Accepted abstracts under the theme: Personalised and Precision Medicine (n=36)
A Likelihood Probability-Based Online Summarization Ranking Model

Abstractive summarization models frequently utilize decoding strategies, like beam search, which cause issues, such as a vast search space and exposure bias during decoding. To address this problem, we propose an online ranking model for summarization based on the likelihood probability. Our approach aims to establish a correlation between the output probability of a candidate summary and its quality, assigning higher output probabilities to summaries of better quality. Consequently, the ranking model can assess and select the best summary from several candidate summaries by contrasting their output probabilities during the ranking stage, thereby enhancing the performance of the summary model across various metrics. Simultaneously, our model adopts online sampling at each training step and incorporates information from the inference stage into the training process, which effectively mitigates the exposure bias that arises from the inconsistency between the model training and inference processes. Empirical results show that the proposed model performs impressively on the CNNDM and LCSTS public datasets. Compared with the baseline model, our online summarization ranking model yielded a 3.97, 2.55, and 4.12 increase in ROUGE-1, ROUGE-2, and ROUGE-L, respectively. Overall, experiments reaffirm the relevance of the ranking stage and the impact of the model in optimizing other metrics.
Pengembangan Aplikasi Penjualan Sepatu Berbasis Web Menggunakan Metode Rapid Application Development

In today's modern era, where advances in technology and information are developing very rapidly coupled with current advances in technology and information, it has made it easier for customers to carry out transactions and carry out their daily activities. Several shops selling goods, one of which is a shoe store in the Gowa area, are mostly still having trouble getting customers around the store because the location is less crowded by the public. This study aims to create a web-based shoe sales application to try to present shoe sales information to customers quickly and accurately and make it easier for customers to make purchases. The floating method used in developing this web-based shoe sales application system uses the RAD (Rapid Application Development) method. From the results of discussing this web-based shoe sales application, of course, it can be used as a solution to existing problems. With this application it will be very useful in promoting products to a wider range.
Global economic transformations and their impact on economic growth in Iraq from 2003 to 2020

The volume of global economic transfers could lead to increased investments by Iraqi and foreign institutions in Iraqi infrastructure and various economic projects. Increased investment may, in turn, increase GDP, create jobs, and improve the lives of citizens. During the period in question, Iraq was affected by unstable security conditions, including war, internal conflict, and terrorist attacks. These security challenges may affect Iraq's ability to attract economic remittances and implement the investments necessary for economic growth. The oil sector is one of Iraq's most important economic sectors and is greatly affected by global economic transformations. Historically, after the collapse of the former Iraqi regime in 2003, oil production increased, contributing to economic growth. Economic growth in Iraq may slow during specific periods due to global economic shifts, such as falling oil prices and the impact of the 2008 global financial crisis. Security challenges, corruption, and lack of investment in non-oil sectors may harm economic growth in Iraq. Therefore, this study aimed to conduct a standard analytical study of the role of remittances in supporting economic growth in Iraq from 2003 to 2020. The study concluded that global economic remittances positively impact economic growth at a significance level of 5%.
Effect of Using the U-Dictionary Application for Students’ Pronunciation on Grade Eight in Teaching Speaking at SMP Swasta Taman Asuhan Pematang Siantar

The objective of this research was to find out the effect of the U-Dictionary Application for the students’ pronunciation on grade eight in teaching speaking at SMP Swasta Taman Asuhan Pematang Siantar. The method used quantitative with quasi-experimental research method with pre-test and post-test design. Two classes provided the data: the experimental class and the control class. In this research, VIII-A was the experimental class, while VIII-B was the control class. There were 28 students in each class, for a total sample size of 56. The pre-test mean score for the experimental class was 64.75, whereas the mean score for the control class was 64.50. Conversely, the experimental class’s post-test mean score was 78.92, whereas the control class’s post-test mean score was 69.57. The data was then examined by the researchers using a t-test. The T-test showed a greater degree of significance (2,7 &gt; 1.674) compared to the T-table. Results showed that using the U-Dictionary Application for students’ pronunciation on grade eight teaching speaking at SMP Swasta Taman Asuhan Pematang Siantar had a substantial impact. Ha was accepted and H0 was rejected.
Developing English Textbook for Pre-school Children

This present research is motivated by lack of English teaching materials for pre-school children and the need for an appropriate English textbook. The aim of this research is to develop an English textbook for English for pre-school children. This research adopted Research and Development which employed three stages, namely exploration stage, design stage, and validation stage. Data were collected through document analysis and interviews. The result of need analysis pointed out that pre-school teachers need an English textbook for teaching. In the design phase, the textbook is carefully designed to meet the teachers and children’s need. The textbook includes topics related to children’s real life such as greeting, alphabet, numbers, colors, fruits, animals, family, day, and month. Whereas the activities are designed joyfully. For example, matching the words, drawing pictures, finding pictures that are different, cutting, and so on. The textbook also used colorful images to attract students’ attention to read and to stimulate their knowledge. The textbook was deemed valid. Considering these findings, the English textbook could be used in teaching English for pre-school children.Keyword: English textbook, teaching English, pre-school children&#x0D;
Language Crime by Students in the Communication Process on Campus

This study aims to identify and analyze language crimes that occur in the communication process among students on campus using a forensic linguistic approach. The research method used is a qualitative descriptive method with note-taking techniques. The respondents in this study are students of the Indonesian Christian University of Toraja. The results of the study indicate that language crimes in communication among students on campus occur in several forms of verbal harassment using animal names such as bai or pig, asu or dog, buaya for crocodile, and balao for rat. In addition, there is also language crime using the bombo or devil. This can affect the mental health and well-being of the victimized students. Lecturers and student organizations have an important role as supervisors and enforcers of regulations, as well as providing education and guidance on language ethics within the campus environment.
Designing and implementing an information steganography system for transmitting encrypted text messages in digital images

This danger can undoubtedly be eliminated using cryptography and steganography. And Combining encryption with steganography can assist in achieving high levels of data security, making this approach superior to using either of these techniques alone to send data across an unreliable route that is open to hacking. Visible steganography is one of the most secure steganography techniques. When the data text is inserted, the image's color frequency may vary slightly, which would be highly evident to anyone. We suggest levels of data security to get around the image's apparent behavior, where the language of the data is first transformed into an unintelligible cipher. An additional degree of protection is added by incorporating the cipher into an encrypted picture file before dividing it into shares. As a result, the ideas of steganography and cryptography are both applied to offer two levels of security and then a visual cryptography system is used to partition the picture into shares so that it may be transferred over a network channel.
MDCN: Multi-scale Dilated Convolutional Enhanced Residual Network for Traffic Sign Detection

Detecting small, multi-scale, and easily obscured traffic signs in real-world scenarios presents a persistent challenge. This paper proposes an approach that utilizes a multi-scale feature pyramid module to capture hierarchical features, facilitating robust detection of traffic signs across varying viewing angles and scales. To aggregate features at different scales and eliminate background interference, we employ a superposition of null convolution kernels with varying dilation rates, expanding the perceptual field from small to large. This effectively covers the object distribution across multiple scales while enhancing the resolution of the final output feature map for improved small target localization. Our method has demonstrated its effectiveness and superiority over several state-of-the-art approaches through extensive experiments conducted on two public traffic sign detection datasets.
Quantum-aided secure deep neural network inference on real quantum computers

Abstract Deep neural networks (DNNs) are phenomenally successful machine learning methods broadly applied to many different disciplines. However, as complex two-party computations, DNN inference using classical cryptographic methods cannot achieve unconditional security, raising concern on security risks of DNNs’ application to sensitive data in many domains. We overcome such a weakness by introducing a quantum-aided security approach. We build a quantum scheme for unconditionally secure DNN inference based on quantum oblivious transfer with an untrusted third party. Leveraging DNN’s noise tolerance, our approach enables complex DNN inference on comparatively low-fidelity quantum systems with limited quantum capacity. We validated our method using various applications with a five-bit real quantum computer and a quantum simulator. Both theoretical analyses and experimental results demonstrate that our approach manages to operate on existing quantum computers and achieve unconditional security with a negligible accuracy loss. This may open up new possibilities of quantum security methods for deep learning.
A Heterogeneous Sensor Deployment Method Based on Whale Optimization Algorithm

In order to better cooperate between sensors in the complex and changeable battlefield environment, it is necessary to rationally deploy multi-source heterogeneous sensors. Various sensors serving weapon systems have different functions, powers, and precisions. Therefore, a sensor evaluation model is proposed to calculate the evaluation indicators of several commonly used sensors, aiming at the requirements of real-time perception and acquisition of information in the complex battlefield situation and the sensor’s own parameter performance. Subsequently, the sensor evaluation index combined with the coverage index, interference index, and other indicators is used as the optimization objective function to study the heterogeneous sensor deployment method based on the whale optimization algorithm. The objective is to achieve the sensor location deployment that meets the optimal index. The simulation results demonstrate that the whale optimization algorithm effectively avoids falling into local optima and enhances the utilization rate of sensor resources in the research of heterogeneous sensor deployment.