Reviewerly/exHarmony huggingface dataset:
Consumers’ Acceptance of the Retail Service Robots: A Humanoid Perspective

This study focused on explaining the influence of retail service robot (RSR) personality traits on consumers’ acceptance of retail service robot (RSR); this study also examined moderating impact of anxiety toward robots. Based on the extensive literature review, this study formulated eight hypotheses to support the arguments. Quantitative methodology with a survey strategy was undertaken, which had an effective sample size of 259 young consumers. Furthermore, analysis was carried out using Smart partial least squares (PLS)-structural equation modelling. The study finds a significant direct relationship between the RSR personality traits (intelligence, sincere, and creative) and consumers’ perceived control. Other than that, it also finds significant relationships between consumers’ perceived control, anticipated service quality and RSR acceptance. It was further revealed that anxiety toward Robots significantly moderates impact on RSR personality traits on consumers’ perceived control. The findings shed the light on improving retail service quality with humanoid based applications.
Interpretation of Sri Lankan Sign Language: A Wearable Sensor-based Approach

Hearing-impaired and speech-impaired people communicate not only with themselves but also with ordinary people using visual languages. Sri Lankan Sign Language (SSL) is the standard visual language used in Sri Lanka. Like other sign languages, the SSL relies on a distinct combination of hand gestures, body movements, and facial expressions for communication. As a result, SSL is more challenging for individuals without knowledge of SSL to understand. On the other hand, the steep learning curve associated with SSL makes it even more difficult to acquire. Thus, the interpretation of SSL has become a need. However, Sri Lanka is suffering from a severe dearth in the availability of SSL interpreters. This justifies the need to use either vision-based or sensor-based technological approaches to help the interpretation of SSL. However, vision-based approaches are susceptible to conditions such as skin tone, background color, ambient light intensity, and real-time constraints, whilst the sensor-based solutions are generally better in gesture recognition. Further, there is no attempt has been made on developing a cost-effective, portable, and real-time solution to accurately interpret the hand gestures of SSL. In this paper, we, therefore, present a novel, wearable, sensor-based, real-time gesture recognition glove, and a machine-learning Long Short-Term Memory (LSTM) model to recognize the hand and finger positions in three-dimensional space for classification and interpretation of SSL. The proposed approach has achieved 320ms of lowest inference time while showing a promising result of 83% for categorical accuracy. Our aim is to help the interpretation of SSL with an affordable, portable as well as a real-time solution.
Process Improvement Framework for DevOps Adoption in Software Development

DevOps is welcomed by software development companies in recent years as a novel approach attached to the Agile software development methodology. Yet, they are in trouble with implementing DevOps because it doesn’t just concentrate on technological changes. It alters the software development process more broadly. To assist this challenging process, DevOps maturity models have been established by a few scholars in recent years. Nevertheless, those models consist variety of drawbacks as; the majority of them have not been properly evaluated and published. This research aimed to provide a critical evaluation of the data available in existing studies on the DevOps maturity models and to propose a DevOps adoption process improvement framework that is validated by industry practitioners. To accomplish this target, a systematic literature review was applied and studied the available DevOps maturity models, weaknesses, and strengths of those models. A new framework for DevOps process improvement is developed by monitoring and contrasting the available data. Furthermore, it was assessed by an interview survey to strengthen the research’s overall goal. The study presents a verified DevOps process improvement model which consists of four main DevOps success areas; DevOps practices, DevOps team, DevOps culture, and DevOps measurement. Each area follows five maturity levels starting with beginning to expert. This framework assists software development companies in obtaining benefits while reducing the difficulties associated with DevOps adoption.
A Static CFG Extraction Scheme for RISC-V Runtime CFI

The Control Flow Graph represents all possible paths a program may take during execution. Control Flow Integrity is a critical concern in embedded system security, and the CFG can be integrated with hardware circuit design to validate CFI. In this paper, we present a static CFG extraction scheme for RISC-V program based on the executable files. During the construction of CFGs, information such as instructions in each basic blocks, basic block length, transfer instructions of basic blocks, and transfer targets can be extracted. Additionally, data such as hash values for basic blocks can be calculated. Those metadata elements are sensitive information for the subsequent runtime CFI check. We use this extraction scheme to perform static analysis on several benchmark programs, and experimental results demonstrate that this scheme can generate correct CFGs and key sensitive information for runtime CFI verification.
A Novel Method for Medical Semantic Word Sense Disambiguation by Using Graph Neural Network

The field of medicine has experienced rapid advancements, accumulating a vast quantity of medical literature and clinical notes. However, a common challenge in automated medical language processing arises from multiple expressions for many medical terms, resulting in either multiple meanings assigned to a single term or multiple terms referring to a single meaning. Addressing this challenge, therefore, requires the development of efficient models for the normalization of specialized terms. In this research paper, we propose a novel method of graph neural network (GNN) in conjunction with a recommendation algorithm to explore the intricate relationships among words and sentences. This combined approach aims to enhance the effectiveness of clinical terminology normalization and resolve the issue of polysemy. Specifically, we incorporate GraphSAGE with a recommendation algorithm to tackle the task of word sense disambiguation. Our experiments demonstrate that integrating a graph neural network and recommendation algorithm for word sense disambiguation yields a noteworthy average Micro F1 score of 64.6%, representing a significant improvement compared to other classical models.
YOLO-CBD: A Recognition and Detection Method for Cigarette Box Based on YOLOv5

In recent years, cigarette box recognition has be-come a significant aspect of object detection that has applications in many areas, such as the retail industry and health care. This paper proposes a real-time YOLOv5-based cigarette box detection method (YOLO-CBD). Specifically, the approach involves training YOLOv5 to detect and label the location of cigarette boxes in an image, then using the ArcFace algorithm to extract the feature of cigarette boxes. We evaluate the YOLO-CBD method on the cigarette box datasets. The results show that our method achieves a high MAP of 95.1% in complex scenarios, which is better than the state-of-the-art methods. In conclusion, the YOLO-CBD demonstrates high accuracy and stability in detecting cigarette boxes. It is a promising approach for practical applications in the tobacco industry.
Trellis Coded Quantization for JPEG Compression

JPEG (Joint Photographic Experts Group) compression is widely used for the efficient storage and transmission of digital images. However, improving compression performance while maintaining image quality remains a challenge. This paper presents a study on the application of Trellis Coded Quantization (TCQ) techniques which are used in many video coding such as VVC and H.264 to enhance JPEG compression. Trellis coded quantization(TCQ), a robust quantization technique, reduces errors and enhances compression efficiency. This research implements TCQ in different stages of the JPEG compression pipeline, including Discrete Cosine Transform (DCT) coefficient quantization and entropy coding. The proposed method achieves progressive coding by adaptively allocating bit budgets to different frequency bands during quantization, in which way we can get better quantization indexes for coding. The trellis quantization algorithm optimizes the quantization process by considering both the rate-distortion performance and quantization noise shaping. Experimental results demonstrate that the TCQ-based approach outperforms conventional JPEG compression by 27.3% to 34% in terms of compression efficiency and image quality preservation in presented datasets with a little more compression time cost. Furthermore, the study investigates the impact of different parameters and configurations on the performance of TCQ. It explores trade-offs between compression efficiency, visual quality, and computational complexity. The findings indicate that TCQ can significantly enhance the compression performance of JPEG while maintaining competitive image quality. The research also discusses the limitations and potential extensions of TCQ in JPEG compression. Future work may focus on exploring adaptive strategies for bit allocation and trellis quantization optimization, as well as investigating the integration of TCQ with other advanced compression techniques. In conclusion, this paper presents a comprehensive investigation of Trellis Coded Quantization for JPEG compression. The results demonstrate the effectiveness of TCQ in improving compression efficiency and image quality, thereby contributing to the advancement of JPEG compression techniques.
Home Care Assistance Solution based on a Software Multi-Agents System

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. Three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. The paper also compares results obtained by running five machine learning algorithms. The agents function in accordance with predefined dataset describing residents ’ age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home’s residents. The proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies.
EEG Multi-Objective Feature Selection using a Genetic Procedure with Hybrid Mutation Operator

Computer science developments in medical data processing increase modeling accuracy, speed up data analysis, and obtain automated diagnostics. As a result, the role of machine learning algorithms becomes significant, as well as the need to implement faster and more accurate systems. For example, classification is a common component in computer-aided diagnostics, and choosing the proper set of predictors is critical for training a classifier model. Usually, medical datasets are highly dimensional, and their characteristical data correlations require reducing the set of features used for classification. Besides supervised grouping algorithms, genetic optimization procedures are well known for their robust verification of competing attributes by directly estimating their usefulness on a specific classifier. This paper discusses the assessment of Electroencephalogram data samples acquired during working memory load n-back scenarios using a new embedded feature selection procedure based on Multi-Objective Optimization via Genetic Algorithms enhanced with a hybrid mutation operator. Experimental results focus on both the hybrid mutation operator performances, and obtaining overall higher Support Vector Machine classification accuracy while reducing the number of selected features.
Multi-Objective Decision Making System Based on Spatial -Temporal Logics

Multi-objective decision making systems based on Spatial-Temporal logics presents a class of computational systems based on Artificial Intelligence in which spatial location and time evolution of processes (events) are taken into account. Multi-objective decision making systems showcase a distributed computing architecture based on Agents (Computational Nodes) that form a network for data processing. The functionality of the decision making system is based on events that are spatially and temporally localized. The set of Agents, based on the events, calculates its influence coefficient on the decision taken by the Agent. An Agent can generate both decisions, which lead to personal qualitative evolution, and events that will be processed by other Agents. This work elaborates the following: the multi-objective decision system diagram, the communication model between Agents, the set of operators for spatial-temporal logic, and the model for optimal solution search in event space.
Privacy risk analysis and metrics in capturing and storing network traffic

Network traffic analysis is a process of paramount importance to monitor network availability and operational activity, identify anomalies, maximize performance, find threats, and detect attacks. Due to this fact, in everyday work network managers need to capture, analyze and store a tremendous amount of data which can definitely be classified as ”Big Data”. On the other side, it is crucial to point out that the captured network traffic has significant privacy implications, in particular in the territorial scope of GDPR or other similar regulations because, according to GDPR, not only the payload but also the IP address of the sender and the receiver of packets have to be considered personal data. This paper deals with the privacy issues related to network traffic capture/processing/storage, the risks, and the associated mitigation techniques. As a conclusion of the work, a privacy risk analysis using PIA, together with the methodology developed by the French Data Protection Authority (CNIL) is discussed. The analysis performed highlights the effect of the use of some well-known anonymization and pseudonymization techniques on the severity and likelihood of risk.
Automated Neural Network Design using Genetic Algorithms

With their ability to analyse and learn from complex data, neural networks have emerged as an ubiquitous machine learning tool with applications in many industries. Despite the success of neural networks in various tasks, obtaining the optimal structure for a specific problem is both time-consuming and difficult because of the large number of hyperparameters that need to be properly selected. To address this issue, this work proposes an approach for automating the design of neural networks through the use of genetic algorithms (GAs). More specifically, the proposed GA-based technique aims to find the optimal Convolutional Neural Network (CNN) architecture and learning-related hyperparameters for the problem of image classification. The approach is evaluated on the MNIST dataset, a widely used benchmark for image classification tasks, on which our top performing architecture obtains an accuracy of 99.35%. These results demonstrate the effectiveness of the proposed approach in terms of finding architectures that outperform those designed by human experts and are competitive with state-of-the-art models. The evaluation results are accompanied by a comprehensive analysis of the evolution of hyperparameters within generations and the changes in network architecture over time, providing insights into how the GA explores the search space. The proposed approach is a step forward in the direction of automating the process of neural network design and its results indicate that it can be a valuable tool for researchers.
Pose orientation analysis for fall detection using non-invasive technologies

Older adults are more susceptible to falling which can cause serious health problems due to their frail bodies, dizziness, and other common health problems common for this age group. Fall detection systems can have a significant impact on this issue, potentially saving lives, mainly due to the underlying fall detection algorithm which is indispensable for the system’s primary function. In this article, we propose a threshold-based fall detection algorithm that is the crux of a finite automaton intended for non-invasive sensor-based fall detection systems, especially radar-type sensors. Furthermore, we will be discussing similar algorithms to get a better understanding of the performance of our algorithm.
Pneumonia Detection Using Federated Learning

Pneumonia is a major cause of illness and death among older adults, and hospitalization rates for pneumonia in patients aged 65 and older have been increasing. However, detecting pneumonia automatically is challenging because it requires large amounts of data from many patients, making traditional machine learning solutions impractical in real life, despite the good results they can achieve. To address this issue, our paper focuses on using federated learning, a new method for applying machine learning techniques in a distributed environment while maintaining patients’ privacy and reducing communication and storage overhead at the processing servers. We propose a federated learning system that can analyze X-rays of patients with and without pneumonia, present our approach, and demonstrate promising results.
Analysis of design solutions for autonomous drones

Drones as a particular case of robots were recently regulated by the EU as a guideline for all member states, raising several barriers in the use of drones in industrial applications for the future. Because of safety risks and privacy concerns, from the legal point of view, drones have now a limited applicability outside the visual line of sight of the human pilot therefore a limited economical applicability to some areas accessible by the pilot and outside urban areas. In this paper, we identify the key paths autonomous drone technology should progress towards, to establish a strong basis for a future certification of operating at human tolerable safety risks, and we explore the existing options to simulate in 3D environments the behavior of drones and use simulator rendered data to capture sensor data and images for constructing a set of training and validation data. Moreover, we report on the abilities of simulators to integrate software and hardware in the loop autopilots of drones, explore ways of using custom network architectures on drones, and test the ability of low power computing chips to run custom design neural network architectures. We explore the existing options to simulate in 3D environments the behavior of drones and use simulator rendered data to capture sensor data and images for constructing a set of training and validation data. We also test the abilities of simulators to integrate software and hardware in the loop autopilots of drones and possibility to integrate well known programming languages. To further explore the possibility of using custom network architectures on drones, we test the ability of low power computing chips to run custom design neural network architectures, and we identify some of the challenges ahead.
A case study for emerging technologies and the potential applications of AR in higher education

The modernization of educational processes and the transition from traditional to modern methods using emerging technologies, represent significant challenges and contributions to education all over the world. This shift in paradigm began to take shape at an accelerated pace with the recent transition from classic courses to online or hybrid ones, requiring adaptation from both teachers and students. This paper presents a review of current research and applications that focus on emerging technologies, highlighting the benefits offered from an educational perspective. A solution based on a mobile application with huge potential in gamification is proposed. The application is hosted on a platform intended for a varied and quite extensive audience, being used from self-guided tours to outdoor games. With such tools, learning environments can take education to another level, captivating and motivating students to discover and complete the tasks created in the application. To be on track with the latest technologies and be aligned with the emerging trends in the education domain, it is required to design and develop robust architectures with user-friendly interfaces, easy to use by the end-users and with concrete impact on the learning process.
Enhanced Design and Functionalities for the Virtual Patient Educational Platform

Within a partnership between the Carol Davila University of Medicine and Pharmacy in Bucharest and the University Politehnica of Bucharest, an educational web-based platform is being developed. The platform enables medical students to virtually reproduce the interactions that they usually have with patients in hospitals, through an avatar in clinically valid scenarios. Students can interact with virtual patients through text and speech interactions. They can access the health records of the virtual patients, perform objective exams, request and analyze medical investigations, elaborate treatment plans and provide diagnoses. They can receive feedback from a professor in real time or after the end of the session. The platform integrates various tools that professors can use to provide feedback, generate scenarios, manage the medical data on the platform and annotate the data. In the current stage of the implementation, the platform supports English and Romanian languages, it incorporates some customizable features, a user ranking system and a scoring system that evaluates and scores each session of the student and rewards the student with points. The scoring system takes into consideration the feedback provided by the professor, who may manually adjust the final score (if he/she desires).
Experiments with Semi-Supervised Learning: from Cityscapes to Medical Images

Deep neural networks have proved exceptional performance in supervised learning tasks, particularly in image segmentation and classification, when trained on large sets of labeled data, like ImageNet. However, creating such extensive datasets requires significant effort, which may not be feasible in many real-world settings. This limitation slows down the process of adopting a solution based on deep learning in various contexts. To address this challenge and develop more data-efficient deep learning methods, there is an increasing interest in semi-supervised learning, which aims to reduce the amount of labeled data required. In this paper, we discuss different methods of data augmentation to be used in semi-supervised learning and report on experiments with Generative Adversarial Networks (GANs) and Domain Adaptation. We apply a supervised method on two datasets from two different domains, to obtain a better perspective on the performance of the approach, and then test how we can improve it using fewer real data through procedures such as synthetic data generation, augmentation, and domain adaptation.