Reviewerly/exHarmony huggingface dataset:
Smart Dress Trial

The Smart Dress Trial, a Deep Learning Project is an innovative and exciting development in the fashion industry, aimed at providing a personalized and engaging shopping experience for consumers. Due to the restricted size selection, lack of privacy, hygiene issues, erroneous sizing, time-consuming procedure, poor lighting, and lack of mirrors, trying items on in a store may be a stressful experience. The Adaptive Content Generating and Preserving Network (ACGPN) is a revolutionary visual try-on network that we suggest as a solution to this problem. In particular, ACGPN determines if the image's information has to be generated or maintained based on the reference image's anticipated semantic layout, which will change after the try-on, producing a photorealistic richly detailed garment try-on. ACGPN typically consists of three main modules: a module for semantic layout generation, second, clothes warping module, and third an inpainting module.
Questify: Your Intelligent Web Page Assistant for Quick Answers

Retaining and recalling information from web pages can often feel like a daunting task, especially when faced with the vast amount of information contained within an article. However, researchers have come up with an innovative solution to tackle this. In this study, we propose a hybrid question-answering system that uses Natural Language Processing (NLP) to logically describe the data on a web page and then react to user inquiries. The system consists of a web scraper, named entity recognition module, and classifiers to learn more about the information.The process begins with a web scraper, which gets the text from a certain online page and makes sure that the required data is gathered. The system learns more about the information by identifying and classifying items like persons, locations, organizations, or other pertinent phrases. The question-answering module uses a knowledge graph to provide accurate and comprehensive answers to user queries. Using benchmark datasets, we evaluated the system’s performance to confirm the efficacy of their strategy. Across several datasets, the F1 score, which is a metric for precision and recall, varied from 0.7 to 0.95. The system can serve as a useful resource for information retrieval from web sites, according to these results, which show that it is able to provide users trustworthy responses to their questions.
A fully automated framework for accelerating Deep Learning deployment for edge devices

Artificial Intelligence (AI) applications are increasingly shifting from cloud-centric approaches to edge-based ones across various fields, such as mobile phones, IoT, automotive, aerospace, and more. However, moving the AI computational effort from large and powerful servers to devices with limited size and power requires a thorough rethinking and optimization of algorithms to ensure optimal performance [2], especially in the space industry where the need for small, lightweight, power-efficient devices imposes even greater limits on computational power. Embedded accelerators often have limitations regarding the number of supported operators, or their performance may be suboptimal. Consequently, algorithms must be modified and trained multiple times using different configurations to achieve the expected results regarding numerical values and performance. Therefore, creating a common workflow that can accommodate the needs of Deep Learning Engineers during algorithm training and the requirements of many hardware platforms during inference can significantly accelerate the development of edge-tailored models. This paper presents tuning_TAYLOR, a framework for conversion, execution, and benchmarking of custom Deep Learning Neural Networks on various edge devices (including CPUs, GPUs, FPGAs, and ASICs) to reduce the effort and time needed to achieve desired results.
Perancangan Komputerisasi Analisis Kuantitatif Pendokumentasian Rekam Medis di UPTD Puskesmas Dompu Kota

Medical records can be used for various purposes with the condition that they must be of good quality. To maintain the quality of medical records, medical record analysis must be carried out periodically. To support medical record analysis activities, it is necessary to use technology and information. The purpose of this study was to design a computerized quantitative analysis of medical record documentation at the UPTD Puskesmas Dompu Kota.This type of research is research and development, the development method uses the waterfall method. Medical record analysis used quantitative analysis of medical records. How to collect data by observation and unstructured interviews. The programming language used is PHP while the database uses MySQL. Functional testing yields a value of 1 and efficiency testing using the GTmetrix tool with an Overal score of A, i.e. the higher the grade the better the performance, Performance 98% The higher the percentage the faster, Structure 95% The higher the percentage the better the design, lCP 0.81 seconds Maximum limit 1.2 seconds, TBT 0 seconds Maximum limit 0.15, CLS 0 seconds Maximum limit 0.1 seconds.Based on the functionality testing and GTmetrix testing, it can be concluded that the design of computerized analysis of medical record documentation at the UPTD Puskesmas Dompu Kota can be used to support medical record analysis activities
Integrating Cyber Physical System Security Concepts in Computer System Security Curriculum

Cyber physical systems (CPS) are becoming increasingly prevalent in our daily lives, from connected vehicles and medical devices to critical infrastructure systems. As there is growing emphasis both in industry and academia on the security aspects of CPS, students should have sufficient foundational knowledge about these topics as they transition into their professional careers. This manuscript describes the integration of modern CPS security concepts into the traditional required Computer System Security undergraduate course at Texas State University. Content is introduced through a group course project, where students reviewed and replicated results from a recent relevant CPS security paper. Papers were self-selected by groups based upon ten recommendations covering content from emerging CPS security topics, such as connected vehicle security and industrial IoT. Based upon the success of this initial implementation, future efforts to expand the scope of CPS coverage in future course iterations are proposed.
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation

Artificial neural networks (ANNs) have been broadly utilized to analyze various data and solve different domain problems. However, neural networks (NNs) have been considered a black box operation for years because their underlying computation and meaning are hidden. Due to this nature, users often face difficulties in interpreting the underlying mechanism of the NNs and the benefits of using them. In this paper, to improve users’ karning and understanding of NNs, an interactive learning system is designed to create digit patterns and recognize the m in real time. To help users clearly understand the visual differences of digit patterns (i.e., $0\sim 9$) and their results with an NN, integrating visualization is considered to present all digit patterns in a two-dimensional display space with supporting multiple user interactions. An evaluation with multiple datasets is conducted to determine its usability for active learning In addition, informal user testing is managed during a summer workshop by asking the workshop participants to use the system.
Integrating Scrum Project Management in Information Technology Capstone Course

Agile project management has replaced the traditional Waterfall as the main project management method in IT industry. Scrum is the most popular Agile framework, which may greatly improve productivity. In this paper, we introduce the pedagogical methods applied in teaching Scrum project management in an IT capstone course. We integrate the Waterfall and the Scrum methods in the capstone project procedure, to combine the planning of Waterfall and the agility of Scrum. The hybrid method enables students to experience the benefits and differences of these methods while working on the project. We conducted a pre-survey and a post-survey to assess the outcome of the applied pedagogical methods. Survey results show that using Scrum in students’ capstone projects is an efficient way for them to learn Scrum project management, and, in addition, using Scrum is helpful in the implementation of students’ projects. The surveys indicate that most students recommend using Scrum project management in this and later IT Project courses.
Рsychological determinants of development of social competences in the structure of social maturity of future law officers

The article reveals the psychological determinants of the development of social competences in the structure of social maturity of future law enforcement officers. Special attention is paid to social competences, which are necessary for the development of social maturity of future law enforcement officers, which is one of the fundamental challenges of modern times. The models of social competences in the structure of social maturity of future law enforcement officers were analyzed, which include: skills necessary for recruitment and preservation of satisfactory and supportive relationships, and orientation regulating the use of these skills. Competencies of emotional and social intelligence in the structure of social maturity of future law enforcement officers are a practically and theoretically consistent, reliable and justified approach to the assessment and personality development of future law enforcement officers. It is emphasized that social cross-context competences, which can take place in various spheres of interaction and adaptation, are currently gaining more relevance. Existential skills are the basis - the ability to manage oneself, life guidelines that allow future law enforcement officers to navigate in life conditions, in search of themselves, in organizing their activities, realizing themselves (Life skills approach in Europe). Social competencies in the structure of social maturity of future law enforcement officers form complex skills that allow them to function successfully in various social and professional situations. One of the conditions for the effective social functioning of future law enforcement officers is the possession of emotional and social competencies that ensure the formation of social maturity of future law enforcement officers. Key words: social maturity, social competences, future law enforcement officers, emotional intelligence, social cross-contextual competences.
Event-Driven FaaS Workflows for Enabling IoT Data Processing at the Cloud Edge Continuum

Continuum Computing encompasses the integration of diverse infrastructures, including cloud, edge, and fog, to facilitate seamless migration of applications based on their specific needs, ensuring optimal satisfaction of their requirements. The primary obstacles in this particular context mostly pertain to the incapacity to promptly respond to changes in the environment or the quality of service (QoS) constraints of the application, as well as the incapability to maintain an application in a stateless manner, hence impeding its relocation without the risk of data loss. The objective of this research is to tackle the aforementioned issues through the introduction of a framework based on Function-as-a-Service (FaaS) and event-driven architecture. This framework enables the decomposition, localization, and relocation of applications inside a Continuum infrastructure, facilitated by a rule engine that is both system and data-aware.
Phylogeny-Inspired Soft Prompts For Data-to-Text Generation in Low-Resource Languages

William Soto Martinez, Yannick Parmentier, Claire Gardent. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
Phase Unwrapping Method Using Adaptive AI Model for the Application of Industrialization and Precision Metrology Field

Phase unwrapping method based on Residual Auto Encoder Network is proposed in this chapter. Phase unwrapping is regarded as a multiple classification problem, and it will be solved by the trained network model. Through training and validation stages, optimal network models can be served as predictors of wrap count distribution map of wrapped phase. Then merge the wrapped phase and count together to complete unwrapping. Software simulation and hardware acquisition are the sources of training dataset. To further improve accuracy of unwrapping, image analysis-based optimization method is designed that can remove misclassification and noise points in initial result. In addition, phase data stitching by Iterative Closest Point is adopted to realize dynamic resolution and enhance the flexibility of method. Point diffraction interferometer and multi-step phase extraction technique is the foundation of proposed method. It can be concluded from experiments that the proposed method is superior to state-of-art ones in accuracy, time efficiency, anti-noise ability, and flexibility.
Beyond Surface Linguistics

Contemporary artificial intelligence has advanced markedly toward mimicking human intelligence. Despite linguistic proficiency, machines remain bereft of genuine text comprehension, leading to perceived, albeit superficial, intelligence. This chapter introduces a straightforward test that highlights the non-human-like cognition of machines such as ChatGPT. Eschewing the prevalent approach of progressively complex testing, the long memory test highlights ChatGPT's inability to function at a human level. Central to this assessment, the test mandates reliable information retention, a feat the transformer architecture of GPT fails to achieve.
Adaptive Learning in IoT-Based Smart City Applications

Internet of things (IoT) based smart city applications rely on constant data collection and accurate data analytics, yet the fast-changing nature of such data often causes the performance of machine learning models to deteriorate over time. Adaptive learning has been increasingly utilized in these applications in recent years as a viable solution to this problem. Moreover, IoT applications are vulnerable to various security threats due to their large-scale deployment, resource-constrained devices, and diverse protocols. This has led to an increased interest in efficient security and intrusion detection mechanisms tailored for IoT environments. In this chapter, the authors first focus on methods to address the issue of concept drift in time series streaming data for IoT-based smart city applications, such as weather, flood, and energy consumption forecasting, through adaptive learning. Furthermore, the authors examine adaptive learning-based security solutions to various attacks in different domains of the dynamic smart city landscape.
MOTIVATION AS A CONFLICT MANAGEMENT TOOL FOR TEACHING STAFF

Staff motivation is a crucial aspect of a successful organization, which is determined by the extent to which the existing incentive system takes into account the employee motivation. Influence on working behavior through motivation, with which the value is determined by the fact that, relying on it, the manager gets the opportunity from subordinates to the goals of the 155 organization to get the appropriate behavior. This allows the manager to meet the needs of his employees to increase the level of satisfaction with their work. The motivation for each employee is individual. Employees have different requirements, different personal characteristics, skills, abilities, interests and inclinations. They have different expectations from their employers. However, with the dynamic work of the organization, it is not always possible to avoid a conflict situation that negatively affects the staff. The important components of the conflict situation are the desire of the parties, the strategy and tactics of their behavior, as well as the perception of the conflict situation. The organization can influence all these components with the help of various tools, including the motivation system. Improving the motivation system is one of the tools for increasing the effectiveness of conflict management in the organization. The article considers the effectiveness and features of motivation as a conflict management tool in the organization.
Interactive Causality-Enabled Adaptive Machine Learning in Cyber-Physical Systems

This chapter describes an adaptive machine learning (ML) method for the utilization of unlabeled data for continual model adaptation after deployment. Current methods for the usage of unlabeled data, such as unsupervised and semi-supervised methods, rely on being both smooth and static in their distributions. In this chapter, a generic method for leveraging causal relationships to automatically associate labels with unlabeled data using state transitions of asynchronous interacting cause and effect events is discussed. This self-labeling method is predicated on a defined causal relationship and associated temporal spacing. The theoretical foundation of the self-supervised method is discussed and compared with its contemporary semi-supervised counterparts using dynamical systems theory. Implementations of this method to adapt action recognition ML models in semiconductor manufacturing and human assembly tasks as manufacturing cyber-physical systems (CPS) are provided to demonstrate the effectiveness of the proposed methodology.
PEDAGOGICAL TECHNOLOGIES FOR THE DEVELOPMENT OF STUDENTS’ CRITICAL THINKING COMPONENTS

The article presents the observation of private experience in the process of university education in the context of educational reforms and specifically shows the methods of stimulating and developing students’ critical thinking.Accordingly, we consider debates, group discussions and case studies used during seminars and practical exercises. The article reveals students independent thinking and the ability to transcendental reflection.Taking into account the fact that the main goal of continuous reforms of pedagogical education is to make radical changes in the process of formation of professional and personal qualities of the future teacher in accordance with modern requirements, we analyze the implementation of that process during university studies. The study was carried out on the basis of the analysis of the results of the seminar and practical exercises. The findings of the research, shows together with the formation of critical thinking, students develop group cooperation and interpersonal communication skills. The need for the mentioned qualities is explained by the fact that various difficulties arise during the professional activity of a pedagogue under modern conditions. Overcoming these difficulties is possible only through cooperative forms of work, group thinking, constructive communication, and identifying original solutions to problems.
Uluslararası Ticareti Geliştiren Bir Araç Olarak Blockchain

The foundation of the world economy is the international commerce system, but it faces difficulties like fraud, inefficiency, and a lack of confidence. The transformational potential of blockchain technology to improve the international trade landscape includes paperwork, delays, and hazards that complex relationships between importers, exporters, financial institutions, and regulatory authorities. Blockchain presents itself as a revolutionary force that provides efficiency, security, and transparency. Blockchain's smart contracts speed up verification of letters of credit (LCs) in trade finance. The precise blockchain security contributes to safety and fraud prevention by thwarting unauthorized access and tampering. This study explores the case studies to determine how blockchain technology might resolve issues, speed up transactions, and restore confidence in the global commerce ecosystem. Blockchain’s use ultimately is able to reshape trade procedures, promoting a climate of dependability, transparency, and cooperative development.
Impact of mass media on Rural tribal youth (With special Reference to Electronic media)

Today, the scope and expansion of mass media is increasing all over the world. Its use is increasing rapidly, especially among the youth in rural areas. In today's time, mass media has become such a part of the daily life of the young generation that it is impossible to imagine the present and future without it. Today the young generation is going through electronic media. Today, every person sitting in any corner of the world is connected to electronic media, but electronic media has two aspects, one positive and the other negative. In today's time, electronic media has become the main medium of mass communication. Smart phone has been the biggest medium for this. Everything can be accessed with the help of smart phone. In the presented paper, an attempt is being made to understand how electronic media is influencing the young generation.
A Comparative Analysis between Karatsuba, Toom-Cook and NTT Multiplier for Polynomial Multiplication in NTRU on FPGA

Post-quantum cryptography (PQC) is a rapidly growing field that aims to develop cryptographic algorithms that can resist attacks by quantum computers. Large-degree polynomial multiplication is an essential mathematical operation for lattice-based post-quantum cryptographic algorithms like CRYSTALS-Kyber, Dilithium, and NTRU. Due to the complexity involved in modular arithmetic, large-degree polynomial multiplication is a critical operation that needs to be accelerated for fast execution of the underlying cryptographic algorithms. In this paper, we aim to provide a comparative case study that focuses on analyzing the effectiveness of Karatsuba, Toom-Cook, and Number theoretic transform (NTT) based multipliers for performing polynomial multiplication in NTRU that uses NTT unfriendly quotient q. Our proposed Karatsuba-based implementation is based on the overlap-free version, and for Toom-Cook, we have used a three-way decomposition. Our result shows that for NTRU, the proposed overlap-free Karatsuba architecture provides a better area delay product than the Toom-cook architecture, even though the Toom-cook algorithm is asymptotically faster than the Karatsuba. Additionally, we have implemented NTRU polynomial multiplication using NTT by performing NTT prime lifting, which exhibits a low area and a compact architecture. This comparative study between different multiplier architectures would enable the hardware designer to choose the suitable multiplier depending on their requirement.
Tamper Resistant Design of Convolutional Neural Network Hardware Accelerator

The globalisation of supply chains and manufacturing processes can lead to loss of control over the manufacturing process and exposure to potentially malicious third parties, thus making the security of Convolutional Neural Network hardware accelerators compromised by emerging attacks (e.g., hardware Trojan(HT) insertion attacks and backdoor attacks from third-party dataset providers). In this paper, a new defence mechanism, called Shuffle and Substitution-Based Defence Mechanism(SSDM), is proposed to effectively defend against attacks launched by attackers from the third-party dataset providers and the Fabrication phase. The new countermeasure proposed in this paper can not only effectively suppress the activation of most existing HTs, but also greatly increase the difficulty for adversaries from third-party dataset providers to successfully execute backdoor attacks. The experimental results show that the new defensive countermeasures are effective in preventing HTs from being activated and significantly increasing the difficulty of backdoor attacks.