Reviewerly/exHarmony huggingface dataset:
Conversational Interfaces in IoT Ecosystems: Where We Are, What Is Still Missing

In the last few years, text and voice-based conversational agents have become more and more popular all over the world as virtual assistants for a variety of tasks. In addition, the deployment on the market of many smart objects connected with these agents has introduced the possibility of controlling and personalising the behaviour of several connected objects using natural language. This has the potential to allow people, also those without a technical background, to effectively control and use the wide variety of connected objects and services. In this paper, we present an analysis of how conversational agents have been used to interact with smart environments (such as smart homes). For this purpose, we have carried out a systematic literature review considering publications selected from the ACM and IEEE digital libraries to investigate the technologies used to design and develop conversational agents for IoT settings, including Artificial Intelligence techniques, the purpose that they have been used for, and the level of user involvement in such studies. The resulting analysis is useful to better understand how this field is evolving and indicate the challenges still open in this area that should be addressed in future research work to allow people to completely benefit from this type of solution.
Predicting Potential Risk: Cerebral Stroke via Regret Minimization

Objective. The data processing of medical test report has always been one of the important contents in biological information domain, especially the process of extracting the effective information from the report so as to assist doctors with the correct medical plan. Usual methods neglect the implicit relationship between features. More features are generally not a better choice because more noise is generated between feature combinations. We propose a practical feature selection strategy RMFS, which aims to select the optimal combination of features. Materials and Methods. Based on the above situation, in this paper, 64 features are extracted from a real medical test report dataset for stroke and feature selection is defined as a reinforcement learning problem to optimize the feature combination by minimizing regret. We select three current mainstream feature selection methods and conduct comparative experiments. Results. We processed and completed a dataset derived from real medical test reports of stroke. We redefine the feature selection problem as a reinforcement learning problem and propose an optimization strategy based on regret minimization and train weight parameters in a DQN network. Experimental results demonstrate that our method can identify feature combinations with higher prediction accuracy. Discussion. RMFS shows a strong robustness to the randomness of the environment and has high computational efficiency and accuracy. Compared with the previous feature selection methods, our method yields superior results. Conclusion. The experimental results demonstrate that our method can obtain a more accurate prediction rate under the same feature scale and we can achieve baseline performance with fewer features.
ECC based Authentication Approach for Secure Communication in IoT Application

Internet of Things (IoT) is an advanced applied science in recent years that enables communication among humans and smart components or among Internet-based components. Besides, IoT provides affiliation of physical and virtual elements that are fully controlled by different kinds of hardware, software, and interaction advancements. Though numerous methods of IoT offer many advantages to our day-to-day routine, it also possesses huge security weaknesses. The traditional methods are vulnerable to a huge range of attacks. Hence, establishing safe and effective security alternatives for the IoT environment remains as a main challenge and the major risk in this security solution is to transfer significant information in a secure manner. To address such limitations, an effective authentication approach named CHEK-based authentication methodology is devised for secure communication in IoT. The proposed authentication scheme consists of four steps and the authentication approach is developed by considering various security operations like hashing, encryption, secret key pairs, passwords, and so on. Moreover, the CHEK-based authentication scheme provides promising solution with minimum computational cost of 16.541 and minimum memory usage of 72.3MB.
A Pilot Study Comparing User Interactions Between Augmented and Virtual Reality

Immersive Analytics (IA) and consumer adoption of augmented reality (AR) and virtual reality (VR) head-mounted displays (HMDs) are both rapidly growing. When used in conjunction, stereoscopic IA environments can offer improved user understanding and engagement; however, it is unclear how the choice of stereoscopic display impacts user interactions within an IA environment. This paper presents a pilot study that examines the impact of stereoscopic display choice on object manipulation and environmental navigation using consumer-available AR and VR HMDs. Our observations indicate that the display can impact how users manipulate virtual content and how they navigate the environment.
Using 2D and 3D Face Representations to Generate Comprehensive Facial Electromyography Intensity Maps

Electromyography (EMG) is a method to measure muscle activity. Physicians also use EMG to study the function of facial muscles through intensity maps (IMs) to support diagnostics and research. However, many existing visualizations neglect anatomical structures and disregard the physical properties of EMG signals. The variance of facial structures between people complicates the generalization of IMs, which is crucial for their correct interpretation. In our work, we overcome these issues by introducing a pipeline to generate anatomically correct IMs for facial muscles. An IM generation algorithm based on a template model incorporates custom surface EMG schemes and combines them with a projection method to highlight the IMs on the patient’s face in 2D and 3D. We evaluate the generated and projected IMs based on their correct projection quality for six base emotions on several subjects. These visualizations deepen the understanding of muscle activity areas and indicate that a holistic view of the face could be necessary to understand facial muscle activity. Medical experts can use our approach to study the function of facial muscles and to support diagnostics and therapy.
ICRB: An improved consensus‐based redactable blockchain considering contextual consistency

Abstract Blockchain is a distributed ledger proposed by Satoshi Nakamoto. It has become one of the most important networks for humans in the world. With the rapid development of blockchain technology, an increasing number of criminals are engaging in fraudulent activities, extortion and other financial crimes on the blockchain. Due to the immutability of blockchain, it is difficult for users to recover their losses. Some scholars have proposed the concept of the redactable blockchain. However, most redactable blockchain schemes fail to consider the impact of modification on the contextual economic relationships of the blockchain. We propose an improved redactable blockchain considering contextual consistency. Additionally, we utilize a security deposit mechanism to guarantee financial contextual consistency for the modified blockchain. We distribute users' security deposit account private keys to random users on the blockchain using Verifiable Secret Sharing, Verifiable Random Functions and other cryptographic primitives to achieve decentralized key management and modification. Experiments show that the performance of our scheme is acceptable in realistic situations.
Augmented Reality’s Role in Highlighting Historical Heritage / Kirkuk Citadel

Augmented Reality (AR) is one of the technological developments in recent years that uses the real world as a basic pillar and enhances it by overlaying virtual objects created by computers. The study provides an overview of the use of augmented reality technology in meeting one of the most important requirements of cultural tourism: highlighting cultural and historical heritage. Accordingly, the research problem concentrated on the lack of local literature associated with the use of augmented reality in cultural tourism. It aimed to fill this gap by building a knowledge base and demonstrating the potential of augmented reality in this field. Hence, the importance of research in drawing the attention of tourism practitioners to the importance of augmented reality and discovering its potential. The research discusses the topics of cultural heritage, cultural tourism, augmented reality, and the study of its potential and applications in tourism. Augmented reality capabilities from the theoretical framework have been applied to Kirkuk Citadel -as a case of study- through the application of a proposal by the researcher for Kirkuk Citadel. The research concludes that augmented reality is an interactive scientific guide that supports the tourist, meets his cultural needs, and positively impacts the quality of tourism and tourist attractions. The study is part of an ongoing research project aimed at developing the tourism of historic city centers through augmented reality technology.
Elevating Android Privacy: A Blockchain-Powered Paradigm for Secure Data Management

The significance of medical test records in diagnos-ing and treating illnesses cannot be overstated. These records serve as the foundation upon which medical professionals craft precise treatment strategies tailored to a patient’s unique health condition and ailment. However, in several developing nations, such as Vietnam, a concerning trend persists: medical test records predominantly exist in vulnerable paper format, entrusted to patients for safekeeping. When patients transition between health-care facilities, the responsibility of carrying these paper-based medical histories rests with them, introducing a significant risk factor due to the inherent fragility of paper documents, which can be easily damaged by fire or water. The loss of these crucial records can lead to severe disruptions in the diagnostic and therapeutic journey of patients, potentially compromising their well-being. Despite the emergence of various alternatives to address this vulnerability, Vietnam faces multifaceted challenges. These challenges encompass low technological literacy among patients and substantial infrastructural limitations. In response to these pressing issues, this study endeavors to harness the transformative potential of blockchain technology, smart con-tracts, and Non-Fungible Tokens (NFTs) to effectively mitigate the drawbacks associated with paper-based medical test records. Our comprehensive approach includes meticulous cataloging of current hospital practices, the introduction of a purpose-built blueprint for decentralized record sharing, the proposal of an innovative NFT-backed authentication model, the development of a practical proof-of-concept, and comprehensive platform testing. Through these efforts, we aim to revolutionize the management of medical test records in Vietnam, enhancing accessibility, security, and reliability for both patients and healthcare providers.
A Particle Filter based Visual Object Tracking: A Systematic Review of Current Trends and Research Challenges

Visual object tracking is a crucial research area in computer vision because it can simulate a dynamic environment with non-linear motions and multi-modal non-Gaussian noises. However, This paper presents an overview of the recent devel-opments in particle filter-based visual object tracking algorithms and discusses the pros and cons of particle filters, respectively. There are presentations of many different methodologies and algorithms in the research literature. The majority of visual object tracking research at present is on particle filters. In addition, the most advanced technique for visual object tracking has also been developed by combining the convolutional neural network (CNN) and the particle filter. The advantage of particle filters is that they can handle nonlinear models and non-Gaussian advancements, sequentially concentrating on the areas of the state space with higher densities, primarily parallelization, and simplicity of implementation. Despite this, it offers a robust framework for visual object tracking because it incorporates uncertainty and outperforms other filters like the Kalman filter, Kernelized correlation filter, optical filter, mean shift filter, and extended Kalman filter in recognition tests. In contrast, this study provided information on various particle filter features and classifiers.
Identification of Air-Writing Tamil Alphabetical Vowel Characters

In recent years, there has been a lot of focus on gesture recognition because of its potential as a means of communication for cutting-edge gadgets. As a special category of gesture recognition, air-writing is the practice of forming letters or words in the air using one’s fingers or the move-ments of one’s hands. The primary objective of this study is to propose a classification framework with feature extraction techniques to enhance the recognition of vowel characters in the Tamil language. The data collection and classification procedure involved a set of 12 distinct letters. A methodology has been developed to facilitate the analysis of various configurations for the purpose of evaluation. To get useful features from the 2-second time window data segments, this study uses a one-dimensional convolutional neural network (1D CNN). In our approach, we employ five machine-learning methods to conduct our evaluation. These methods include Naive Bayes, Random Forest, K-Nearest Neighbor, Support Vector Machine, and Decision Tree. The classification algorithms are considered to be superior based on the results obtained from our dataset in this experiment. The results of the tests show that the suggested K-nearest neighbors (KNN) algorithm works very well when used with a k-1 and 0.6:0.4 split ratio for training and testing. Specifically, the KNN model achieved an accuracy rate of 91.67%. The present study builds upon previous research by utilizing applications that have been employed in prior studies. However, a unique aspect of our system is the integration of cutting-edge technology, which utilizes collected sensor data to classify the characters. The examination of the window size has the potential to enhance accuracy and performance.
TGC: Transaction Graph Contrast Network for Ethereum Phishing Scam Detection

Phishing scams have become the most serious type of crime involved in Ethereum. However, existing methods ignore the natural camouflage and sparse distribution of phishing scams in Ethereum leading to unsatisfactory performance, and they are also limited by the data scale which cannot be applied to real-world dynamic scenarios. In this paper, we propose a Transaction Graph Contrast network (TGC) to enhance phishing scam detection performance on Ethereum. TGC inputs subgraphs instead of the entire graph for training, which eases the model’s requirements for machine configuration and data connectivity. Motivated by phishing nodes are surrounded by normal nodes, we design the comparison between node-level to help phishing nodes learn the unique properties of themselves different from their neighbors. Observing the small number and sparse distribution of phishing nodes, we narrow the distance between phishing nodes by comparing node context-level structures, so as to learn universal transaction patterns. We further combine the obtained features with common statistics to identify phishing addresses. Evaluated on real-world Ethereum phishing scams datasets, our TGC outperforms the state-of-the-art methods in detecting phishing addresses and has obvious advantages in large-scale and dynamic scenarios.
Efektivitas Model Cooperative Integrated Reading and Composition dalam Pembelajaran Mengubah Teks Wawancara Menjadi Karangan Narasi

Writing is one of the language skills that students must have. This research examines the effectiveness of using the Cooperative Integrated Reading and Composition model in learning to convert interview text into narrative for class VII students at SMP Negeri 4 Sengkang. The type of research used is experimental research. This research uses a quasi-experimental design. The population in this study was all class VII students, totaling 85 students. The research sample was class VII B as the experimental class, totaling 21 students and class VII A as the control class, totaling 21 students. The collected data was then analyzed using descriptive and inferential statistical analysis. The research results show that the Cooperative Integrated Reading and Composition model is effectively applied in learning to convert interview text into narrative for class VII students at SMP Negeri 4 Sengkang. Implementation of this model can provide the basis for more interactive and supportive cooperative learning, with a focus on developing language skills
CTL AND BLENDED LEARNING MODELS IN IMPROVING LEARNING OUTCOMES

Contextual Teaching And Learning (CTL) and Blended Learning models are learning models that both improve student learning outcomes. The purpose of this study was to analyze the effect of the Contextual Teaching And Learning (CTL) Learning Model on the learning outcomes of students in Class 2022 A of the Economic Education Study Program, Faculty of Economics and Business, Universitas PGRI Sumatera Barat, to analyze the effect of the Blended Learning Model on the learning outcomes of students in Class 2022 B of the Economic Education Study Program, Faculty of Economics and Business, Universitas PGRI Sumatera Barat, and to see the difference in the effect of the Contextual Teaching And Learning (CTL) Learning Model with the Blended Learning Model on the learning outcomes of students in Class 2022 of the Economic Education Study Program, Faculty of Economics and Business, Universitas PGRI Sumatera Barat. The method in this study uses the Quasy Experiment method, with the population being 108 batch students with the sample being batch 2022 A and 2022 B with a total of 50 people. The hypothesis test used is using factorial analysis which is modified by a different test. From the results of the study obtained the results that: there is an effect of the Contextual Teaching And Learning Model (CTL) on the learning outcomes of students in Class 2022 A of the Economic Education Study Program, Faculty of Economics and Business, Universitas PGRI West Sumatra, there is an effect of the Blended Learning Model on the learning outcomes of students in Class 2022 B of the Economic Education Study Program, Faculty of Economics and Business, Universitas PGRI West Sumatra, and there is a difference in the effect of the Contextual Teaching And Learning Model (CTL) with the Blended Learning Model on the learning outcomes of students in Class 2022 of the Economic Education Study Program, Faculty of Economics and Business, Universitas PGRI Sumatera Barat.
Optimizing Machine Learning Models in Human Activity Recognition with the WISDM Dataset

Human Activity Recognition (HAR) is a technology with significant potential, utilizing data from various devices like smartphones and cameras. It finds applications in daily activities such as driving, cleaning, and gaming, involving fundamental movements like standing, sitting, jogging, and typing. Accurate identification of these actions is crucial for effective human-computer interaction systems. This work incorporates a HAR module to extract valuable insights from data signals. It employs Machine Learning (ML) models to automatically detect human activities using raw data from Internet of Things wearable sensors, including innovative combinations like Adagrad and ELU within the MLP algorithm. The ML models' performance is evaluated using statistical metrics such as accuracy, precision, recall, and f1-score, and comparisons are made with existing models.
Penerapan Algoritme K-Means Dalam Mengelompokkan Data Pengangguran Terbuka Di Provinsi Jawa Barat

Unemployment is a major social problem in many regions, including West Java province in Indonesia. West Java province is one of the most populous regions with a high level of urbanization. With population growth and urbanization, the challenge of creating enough jobs becomes more difficult. Therefore, the purpose of this study is to cluster open unemployment data in West Java communities classified by the number of unemployed people by district or city. This research uses CRISP-DM method with K-Means clustering algorithm. The result of this research is 10 regencies/cities that have low level of unemployment, then there are 15 regencies/cities that have medium level of unemployment and there are 2 regencies/cities that have high level of unemployment. The result of the test using Davies Bouldin Index cluster = 3 has the best cluster quality, because the value of the Davies Bouldin Index test result with c = 3 is the smallest value of 0.28, which is the lower, the better the cluster.
Analisis Sentimen terhadap Penyelenggaraan Sea Games 2023 Kamboja pada Twitter Menggunakan Algoritma Naive Bayes

Southeast Asian Games or SEA Games is a Southeast Asian sporting event held every 2 years, where the participants are 11 member countries of the Association of Southeast Asian Nation (ASEAN). Cambodia was chosen as the host for the 2023 SEA Games. The implementation of the SEA Games in Cambodia experienced many controversies ranging from the inverted Indonesian flag to leaking lodging rooms for athletes. Social media Twitter became one of the places for netizens to express their opinions about the implementation of the SEA Games in Cambodia. This study aims to determine the level of tendency of positive, negative and neutral opinions through the sentiment analysis process. The sentiment analysis process is carried out using the Naive Bayes method, through five main stages, namely Data Selection, Preprocessing, Transformation, Data Mining, and Evaluation. The data used comes from Twitter users who use the hashtag "SEA Games Cambodia" then obtained data as many as 1595 tweets. The results of this study describe the results of Naive Bayes implementation and performance testing using confusion matrix obtained accuracy 66%, precision 70%, recall 66%, and f1-score 61%. and also obtained the results of the tendency of public opinion sentiment on Twitter with positive results as much as 49%, then negative results as much as 40% and neutral results as much as 11%.
Developing on the Quality Evaluation Mechanism of Innovative Talents in International Education through Stakeholder Theory

With the growing trend of international education, universities, and higher education institutions have gradually begun to pay attention to cultivating innovative talents. Among them, the evaluation mechanism of the quality of innovative talents is the path of feedback on innovative education in universities, and it has a vital guiding role in the reform of innovative education. Stakeholder theory is a theoretical guidance to coordinate the development of the overall situation from the manager's point of view. Analyzing the quality evaluation mechanism of innovative talents in international education from the perspective of stakeholders is based on the interests and needs of stakeholders in innovative education, considering the requirements for the quality of innovative talents in highly relevant groups, and timely revising the dynamics of the talent training program mechanism. In this study, fully considering the interests and needs of high-related stakeholders, that is, the social demand for innovative talents, puts forward better revision suggestions for the quality evaluation mechanism of innovative talents in international education and finally gives a simple mechanism feedback model.