Reviewerly/exHarmony huggingface dataset:
Arduino Based Real-Time Face Recognition And Tracking System

People must remember more passwords and carry more cards these days, as security becomes more important every day. However, such implementations are becoming less safe and feasible, resulting in a surge in interest in biometric systems techniques. Biometrics systems are those that record the physical characteristics of persons in an electronic environment and use that information to recognize them when needed. It works on the idea of identifying a person's physical traits that he cannot change and are distinguishable from others. The designed system aimed to successfully achieve face recognition through real- time detection, comparing the result with pre-recorded face images and tracking the human’s face using a webcam- based system. The system accuracy has been tested in different cases; based on the detection distance, person’s appearance variation, multi-angle posing and multi-people. The system successfully implements face recognition, detection, and tracking with a satisfactory accuracy of 93%
An Analysis of Pairwise Question Matching with Machine Learning

n the realm of Natural Language Processing (NLP) and machine learning, lies the challenging quest to detect duplicate question pairs with semantic precision. Our research endeavors to craft a cutting-edge model capable of discerning whether two questions, despite their divergent phrasing, spelling, or grammatical variations, share a common intent on digital forums or search engines. A paramount facet of this study involves the creation and training of an exemplary model using a meticulously curated dataset of labeled question pairs, each annotated as either duplicates or distinct entities. By leveraging state-of-the-art NLP techniques, we aspire to build an exceptionally accurate model that will revolutionize the user search experience by facilitating the identification of duplicate questions. This pioneering research paves the way for a more refined and enhanced approach to tackle the challenges of semantic similarity in the context of question pairs
Research on Evaluation of Construction Workers’ Job Satisfaction Based on Improved AHP-FCE Method

In the post-epidemic era, the construction industry has been greatly impacted, and the wave of labor shortages in the construction industry has reappeared. The construction industry has frequently faced the problems of “labor shortage” and “difficulty in recruiting workers”. At the same time, with the accelerated development of China’s population aging process, research on the job satisfaction of construction workers, has certain practical significance for improving the employment happiness of construction workers and retaining the labor force in the construction industry. First, through literature research and on-the-spot investigation, based on 6 dimensions: work itself, work environment, public services, rights protection, leaders and colleagues, salary and welfare, 20 indicators were selected to establish a job satisfaction of construction worker evaluation index system; Secondly, the improved AHP-FCE (three-scale analytic hierarchy process-fuzzy comprehensive evaluation) is used to determine the weights of indicators at all levels, and an evaluation model for construction worker’s job satisfaction is built. Finally, the feasibility of the evaluation system is verified through empirical research, and suggestions on improving the job satisfaction of construction workers are put forward in combination with social reality.
Analysis the Success of Using a Project Management Tool in Managing Projects

Using Jira is considered an application with an easy-to-understand interface and customization features for each project. However, in the daily use of Jira, there are still obstacles faced by employees, namely, project status information is still inaccurate, lack of employee knowledge on how to configure Jira so that it takes a long time to meet the needs of each project and features that are still found do not function optimally. Hence, it hinders the process of using Jira in project management. This study aims to determine the success factors of using Jira in managing projects in the Cloud solution provider industry. This study uses a quantitative method to assess the constraints and factors that influence the success of project management tools by distributing questionnaires to employees. The findings in this study are that the socio-cultural variables and technical knowledge do not significantly affect the success of project management tools. However, the variables of personal characteristics and team capabilities are proven to significantly influence the success of project management tools, and the variables of training and learning are proven to have a moderating effect on the variable of team capabilities on the success of project management tools. Personal characteristics and social culture variables have a significant influence on team capability variables.
Cross-Image Disentanglement for Low-Light Enhancement in Real World

Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e.g., real noise. Existing supervised algorithms for low-light image enhancement require a large set of pixel-aligned training image pairs, which are hard to prepare in practice. Though some recent unsupervised methods can alleviate such data challenges, many real world artifacts inevitably get falsely amplified in the enhanced results due to the lack of corresponding supervision. In this paper, instead of using perfectly aligned images for training, we creatively employ the misaligned real world images as the guidance, which are considerably easier to collect. Specifically, we propose a Cross-Image Disentanglement Network (CIDN) with weakly supervised learning, to separately extract cross-image brightness and image-specific content features from low/normal-light images. Based on that, CIDN can simultaneously correct the brightness and suppress image artifacts in the feature domain, which largely increases the robustness of the pixel shifts between training pairs. By considering real world corruptions, we propose a new training dataset with misaligned and noisy image pairs and its corresponding evaluation dataset. Experimental results show that our model achieves state-of-the-art performances on both the newly proposed dataset and other popular low-light datasets. The code implementation is publicly available at: https://github.com/GuoLanqing/CIDN.
A Survey on Neural Data-to-Text Generation

Data-to-text Generation (D2T) aims to generate textual natural language statements that can fluently and precisely describe the structured data such as graphs, tables, and meaning representations (MRs) in the form of key-value pairs. It is a typical and crucial task in natural language generation (NLG). Early D2T systems generated texts with the cost of human engineering in designing domain specific rules and templates, and achieved acceptable performance in coherence, fluency, and fidelity. In recent years, the data-driven D2T systems based on deep learning have reached state-of-the-art (SOTA) performance in more challenging datasets. In this paper, we provide a comprehensive review on existing neural data-to-text generation approaches. We first introduce available D2T resources, including systematically categorized D2T datasets and mainstream evaluation metrics. Next, we survey existing works based on the taxonomy along two axes: neural end-to-end D2T and neural modular D2T. We also discuss the potential applications and the adverse impacts. Finally, we present readers with the challenges faced by neural D2T and outline some potential future directions in this area.
A Framework for QoS-Guaranteed Fast Access Services in Blockchain Radio Access Network

The advent of blockchain technology in wireless networking has spawned a novel decentralized paradigm that engenders the establishment of multi-party trust, secure and efficient resource sharing, and equitable distribution of benefits. However, blockchain often results in lengthy access latencies and can hardly guarantee the quality of off-chain services. The problems stem from the intrinsic flaws of blockchain in dealing with off-chain services such as wireless access. In this study, we propose a unified framework that guarantees service quality and low establishment latency for blockchain-based systems, and incentivizes clients and service providers (SPs) to cooperate without a trusted third party. We use wireless access service in blockchain radio access network (B-RAN) as a typical example and provide a detailed implementation design. We model the service delivery process in our framework and identify the necessary condition for achieving trusting cooperation. Interestingly, we find the necessary condition is also sufficient for cooperation if the service framework is properly designed. Furthermore, we uncover a trade-off between system robustness and transmission efficiency, which offers insightful guidance for framework design in practice. Finally, we present simulation results to illustrate the effectiveness of our proposed wireless access scheme.
DeepIC+: Learning Codes for Interference Channels

A two-user interference channel is a canonical model for multiple one-to-one communications, where two transmitters wish to communicate with their receivers via a shared medium, examples of which include pairs of base stations and handsets near the cell boundary that suffer from interference. Practical codes and the fundamental limit of communications are unknown for interference channels as mathematical analysis becomes intractable. Hence, simple heuristic coding schemes are used in practice to mitigate interference, e.g., time division, treating interference as noise, and successive interference cancellation. These schemes are nearly optimal for extreme cases: when interference is strong or weak. However, there is no optimality guarantee for channels with moderate interference. Here we combine <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep learning</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network information theory</i> to overcome the limitation on the tractability of analysis and construct finite-blocklength coding schemes for channels with various interference levels. We show that carefully designed and trained neural codes using network information theoretic insight can achieve several orders of reliability improvement for channels with moderate interference. Furthermore, we present the interpretation of the learned codes based on the codeword distance and the Centered Kernel Alignment (CKA) analysis.
Self-paced Weight Consolidation for Continual Learning

Continual learning algorithms which keep the parameters of new tasks close to that of previous tasks, are popular in preventing catastrophic forgetting in sequential task learning settings. However, 1) the performance for the new continual learner will be degraded without distinguishing the contributions of previously learned tasks; 2) the computational cost will be greatly increased with the number of tasks, since most existing algorithms need to regularize all previous tasks when learning new tasks. To address the above challenges, we propose a self-paced Weight Consolidation (spWC) framework to attain robust continual learning via evaluating the discriminative contributions of previous tasks. To be specific, we develop a self-paced regularization to reflect the priorities of past tasks via measuring difficulty based on key performance indicator ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., accuracy). When encountering a new task, all previous tasks are sorted from “difficult” to “easy” based on the priorities. Then the parameters of the new continual learner will be learned via selectively maintaining the knowledge amongst more difficult past tasks, which could well overcome catastrophic forgetting with less computational cost. We adopt an alternative convex search to iteratively update the model parameters and priority weights in the bi-convex formulation. The proposed spWC framework is plug-and-play, which is applicable to most continual learning algorithms ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., EWC, MAS and RCIL) in different directions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., classification and segmentation). Experimental results on several public benchmark datasets demonstrate that our proposed framework can effectively improve performance when compared with other popular continual learning algorithms.
Quantum-Inspired Heuristic Algorithm for Secure Healthcare Prediction using Blockchain Technology

People's health is adversely affected by environmental changes and poor nutritional habits, emphasizing the importance of health awareness. The healthcare system encounters significant challenges, including data insufficiency, threats, errors, and delays. To address these issues and advance medical care, we propose a secure healthcare prediction method, prioritizing patient privacy and data transmission efficiency. The Quantum-inspired heuristic algorithm combined with Kril Herd Optimization (QKHO) is introduced for healthcare prediction, along with a comparison to the Deep Forward Neural Network (DFNN) optimized using Krill Herd Optimization (KHO) and Quantum-inspired heuristic algorithm combined with Kril Herd Optimization. The proposed QKHO model outperforms conventional models and exhibits higher accuracy, precision, recall, and F1-score. Blockchain technology ensures secure data transmission to the server, surpassing the security level of existing RSA and Diffie-Hellman algorithms.
Federated Learning-Assisted Vehicular Edge Computing: Architecture and Research Directions

Recently, realizing machine learning (ML)-based technologies with the aid of mobile edge computing (MEC) in the vehicular network to establish an intelligent transportation system (ITS) has gained considerable interest. To fully utilize the data and onboard units of vehicles, it is possible to implement federated learning (FL), which can locally train the model and centrally aggregate the results, in the vehicular edge computing (VEC) system for a vision of connected and autonomous vehicles. In this article, we review and present the concept of FL and introduce a general architecture of FL-assisted VEC to advance development of FL in the vehicular network. The enabling technologies for designing such a system are discussed and, with a focus on the vehicle selection algorithm, performance evaluations are conducted. Recommendations on future research directions are highlighted as well.
A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.
APPROACHES TO BUILDING KEY DIGITAL COMPETENCES IN SECONDARY SCHOOL ARTIFICIAL INTELLIGENCE LEARNING

Building key digital competencies is one of the main tasks in modern secondary school education. For the realization of this task, packages of strategic documents have been developed at the global, European and national level regarding the study of AI in all degrees and forms of education both in higher and secondary schools. In the EU, the DigComp 2.2 framework was created, in which 5 groups of key competences are defined with four levels of construction defined: fundamental, intermediate, advanced and highly specialized. The report analyzes the accumulated experience and examines some approaches and possibilities for building some of these digital competences through the teaching of Artificial Intelligence in the secondary school.
The Software Defined Vehicle – Technical and Organizational Challenges and Opportunities

Software-based functionalities will become increasingly important in the vehicle of the future and will also have a decisive influence on buyer behavior. The vehicle is evolving into an integral part of the digital life of customers – with corresponding requirements for usability, the reloading of apps and the temporary activation and deactivation of functionalities such as driver assistance functions. Technically and organizationally, this increase in the importance of software in the vehicle represents both a challenge and an opportunity. On the software and E/E side, architectures must be carefully selected and implemented. Virtualization and containerization become standard techniques to ensure maximum encapsulation, independence and security. From an organizational point of view, software development must be accelerated and become more agile – DevOps will become an integral part of automotive development and will help to improve the development speed and quality of the software. In this paper the authors will provide an overview of how FEV.io assesses the challenges of the Software Defined Vehicle and which possible solutions FEV.io has identified in order to solve the technical and organizational challenges successfully.
Impact of Technological Innovations on Bank Performance in Selected West African Countries (1997-2020)

Purpose: Technological innovations are understood as new or improved processes, products or services, the technical characteristics of which are significantly different from the previous ones. Technological innovations in the banking sector have undoubtedly improved the functionality and performance of banks across the globe including West African countries such as Nigeria, Ghana and Coted’Ivoire. It has redefined the way and regime in banking operation in West Africa. It enables banks to improve the quality of service delivery to their numerous customers and makes it easy for customers to access banking services at the lowest cost possible. This study, therefore, focused on examining the impact of technological innovations on bank performance in West Africa. Method: A set of annual time series covering the period 1997 to 2020 and a multiple regression analysis including an autoregressive distributed lag (ARDL) model, a fully modified OLS (FMOLS) model and a dynamic OLS (DOLS) model were used. Bank performance was measured using bank return on assets (ROA) and bank return on equity (ROE), while technological innovation was measured using indicators such as Internet Banking (INB), Automated Teller Machines (ATM), Mobile Banking (MBN) and Point of Sale (POS) whose control variables are inflation rate (INFR) and exchange rate (EXR). Results and conclusion: Findings from the ARDL panel results show that both positive and negative long-term relationships exist between technological innovation and bank performance in West Africa. We thoroughly verified the results from the ARDL model with FMOLS and DOLS and the findings show that technological innovation has a positive and negative long-run relationship with bank performance in West Africa and the results were the same for Nigeria, Ghana and Ivory Coast. Originality/Value: This study, therefore, recommends that improving the quality of technologically innovative tools of banks such as internet banking, ATMs, POS and mobile banking with quality apparatus can lead to improved bank performance. Banks should also invest in cyber security to ensure funds deposited in banks are safe which will boost investor and customer confidence, acceptance and lead to increased bank performance.
Design and Development of an Identification System for Gauge Meter Value

In many factories in Taiwan, there are still analog instruments used to monitor machine data. Traditionally, patrol personnel were required to walk around and manually record the data. However, this approach often led to discrepancies between the instrument data on the manufacturing site and the data returned by the instrument, or errors caused by personnel negligence in recording the data. In this study, we propose an automated recognition system for instruments. With this system, patrol personnel only need to use our inspection camera to capture images, and the data can be recognized in real-time and transmitted to the database. First, we use YOLOv5 object detection to outline the features of the instrument, then the second stage of YOLOv5 outlines whether the pointer is leaning to the right or left, and the last, Hough Transform is used to draw the angle of the pointer and convert it into instrument data using a formula. Our experimental data shows an accuracy rate of over 98%.
CarGameAR: An Integrated AR Car Game Authoring Interface for Custom-Built Car Programed on Arduino Board

In this paper, we present CarGameAR: An Integrated AR Car Game Authoring Interface for Custom-Built Car Programed on Arduino Board. The car consists of an Arduino board, an H-bridge, and motors. The objective of the project is to create a system that can move a car in different directions using a computer application. The system uses Unity software to create a virtual environment where the user can control the car using keyboard commands. The car's motion is achieved by sending signals from the computer to the Arduino board, which then drives the motors through the H-bridge. The project provides a cost-effective and efficient way to build a car, which can be used for educational purposes, such as teaching programming. Moreover, this project is not limited to the control of the car through keyboard commands in a virtual environment. The system can be adapted to support augmented reality (AR) technology, providing an even more immersive and engaging user experience. By integrating the car with AR, the user can control the car's motion using physical gestures and movements, adding an extra layer of interactivity to the system. This makes the car an ideal platform for game development in AR, allowing the user to create driving games that blend the physical and virtual worlds seamlessly. Additionally, the car's affordability and ease of construction make it an accessible and valuable tool for teaching programming and principles in a fun and interactive way. Overall, this project demonstrates the versatility and potential of the car system, highlighting the various applications and possibilities it offers for both education and entertainment.