Reviewerly/exHarmony huggingface dataset:
Examining the Role of Natural Language Processing in Generating Topics from Web Content

Natural Language Processing (NLP) is a major subfield of machine learning that focuses on the interaction between humans and computers through several techniques, including topic modeling, which extracts and generates topics from large amounts of unstructured data on the web. Topic modeling uncovers hidden patterns and contextual meanings in web pages, allowing for the categorization, clustering, recommendation of relevant content, detection of emerging trends, and improved information retrieval. The benefit of topic models relies on their performance to detect and generate topics; however, the evaluation of applying topic modeling on web content data remains lacking in current studies. The absence of examining such performance evaluations hinders understanding performance variability across different web pages, the development of refined models, and the ability to keep pace with emerging patterns in the dynamic online environment. This study presents a comprehensive performance evaluation of the benchmark topic models when applied to web content data. It presents a comparison of topic models using four topic coherence metrics, providing an in-depth performance analysis. The evaluation is conducted on a public dataset consisting of 2 million web pages, providing a substantial basis for assessing the strengths and limitations of these models in the context of web data. Additionally, this study examines the utilization of NLP techniques for topic generation within the context of web content and sheds light on the challenges and issues associated with generating topics from web content data, highlighting potential research prospects and the need for future work in the field of web topic modeling using natural language processing.
A Comprehensive Object Detection Model Empowered with Deep Learning

The innovative image query interface system leverages cutting-edge deep learning techniques to achieve accurate and efficient image categorization. By utilizing meta pseudo labels, the system enhances classification accuracy, yielding notable outcomes of 88.4% on CIFAR-10 and 76.3% on ImageNet datasets. To enhance feature extraction, it combines fine-tuned Decomposition Attention (FDTA) and Divide to Attend (DTA), resulting in 08.3% accuracy for X-ray image detection. For user-friendliness, the system introduces an intuitive interface for quick photo querying. To enhance tiny object identification, it employs combinatorial optimization via the graphical interface DC-YOLOv8. This incorporates a Dual-Context Feature Fusion module with YOLOv8, enhancing the model's capability to discern intricate details and contextual cues. This system demonstrates deep learning's potential in computer vision, offering both accuracy and user-friendly functionality, contributing to the field's advancement.
SLFL: A Server-Less Federated Learning Framework in Software Defined UAV Networks

The unique characteristics of Unmanned Aerial Vehicle (UAV) networks, including limited resources, dynamic topology, and heterogeneity, pose significant challenges for integration with emerging technological scenarios such as Software Defined Network (SDN) and Federated Learning (FL) protocols. In this paper, we propose a consensus-based framework, called Server-Less FL (SLFL), specifically designed for FL in software-defined UAV networks. SLFL utilize real-time sensors and Global Positioning System (GPS) based Three-Dimensional (3D) location to leverage Reliable, Replicated, Redundant, And Fault-Tolerant (Raft) algorithm in distributed to ensure improved consensus among UAVs during the FL processing, allowing them to collaboratively train a global model while preserving data privacy, low overhead and minimizing communication disruptions. We performed model-based evaluation of SLFL and compared with existing solutions. Furthermore, SLFL effectively handles the challenges associated with UAV networks, including limited bandwidth, energy constraints, and mobility. The findings from this research can facilitate the adoption of FL in UAV applications. We concluded our research by highlighting pertinent future research directions for SLFL.
Acquiring Digital Evidence from Health and Safety Devices

Personal health and safety devices are very much used in our modern society today. In the context of investigation, these devices are rich with information that may be useful as digital evidence due to its usage, events, time, location, and motion recording features. For example, smart watches have features such as geolocation, heart-rate monitor, and motion detection that could be relevant in an investigation. However, proper method and the know-how are needed to acquire such digital evidence to be forensically accepted. This knowledge is not known to many inexperienced users, should they need to do so. This research studies selected health and safety devices, locating resources of evidence, and acquiring the digital evidence, accordingly. Three devices were worked on – smart watch with fitness tracker, smart phone with health-related recording, and a dash camera for vehicle safety recording. Methods and tools to acquire potential digital evidence from these devices are discussed and the findings are described in this paper.
Human Activity Recognition Using Ensemble of CNN-Based Transfer Learning Models

Human activity recognition (HAR) models offer knowledge about human actions that can be used to analyze a person's behavior in a real-world setting. It can be applied in medical assistance, virtual reality, robotics, scene classification, sports analysis, and expert systems. Among various models currently transfer learning based HAR approaches are getting popular among researchers due to the benefits of taking less time and computational resources. In this paper, we propose a neural architecture that exploits the benefits of various convolutional neural network (CNN) based transfer learning models for recognizing human activities. We utilize three pre-trained transfer learning models including ResNet50V2, MobileNetV2, and EfficientNet BO, and fine-tuned them for our human activity recognition task to extract the effective task-specific contextual features. Later, a linear classifier is coupled with each model to get the prediction labels. Finally, we apply a fusion scheme through majority voting to identify the activity label. We conducted our experiments using a human activity detection dataset from Kaggle <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://tinyurl.com/HARDataset. It contains a total of 18000 images with 15 different classes. Experimental results demonstrated the efficacy of our approach over standard baselines. We also provide various insightful findings that might be beneficial for others working in this problem domain.
Bootstrapping Simulation in Determining the Prognostic Factors of Lung Cancer Disease by Parametric Survival Analysis

Big data analytics focuses on getting useful insights, trends and pattern out of complex and large data. Increasing the sample by resampling the data, in biostatistics expertise, can be employed using the bootstrapping techniques. The world of bootstrapping is very large and expanding where it does not only compute the confidence interval but also perform a standard resampling method. Nevertheless, survival analysis study mostly allows the data to be not normally distributed because of the censored observations. Small number of samples also one of the reasons why this study has to perform bootstrapping to overcome the issues of biasness. Bootstrapping method is said to be one of the best methods in handling skewed data. Thus, by considering bootstrapping method, this study aims to find the most significant prognostic factors of lung cancer disease that affect the survival times with the presence of censored observations by using the parametric survival analysis. Therefore, based on 100, 150, 250 and 600 number of sampling sizes, exponential distribution appeared to fit all the assigned sample sizes. Weibull and log-logistic distribution seems to fit the data only for 100 number of samples. Races and two of the interaction terms in the model appeared to be the most significant prognostic factors affecting the survival time of lung cancer.
Application Development for Compatibility of Functional Requirements with Text Information in a User Interface Design

This research focuses on text information for applying two user interface design elements: imagery, control and affordances, because these elements correlate highly in the dataset used. This study uses the SRS object named “Porlan”. In the Porlan user interface, there need to be more user needs that have been determined at the stage of information gathering using the requirement elicitation method. The main objective of this research activity is to develop software regarding the suitability of the user interface through the extraction of functional requirements and design elements obtained through text analysis. The results of the extraction through the text pre-processing will then be compared for suitability with the functional requirements. There are results of application development that carry out the conformity process between functional requirements and user interface design elements in text format. The input text data comes from the requirements elicitation artifacts, functional requirements, and user interfaces. The output of text data processing through this application will produce comparisons between texts. The results of the conformity value obtained for elicitation requirements with functional requirements is 0.89189, which is included in the Cohen Kappa index almost perfect agreement and the conformity value for functional requirements with user interface design elements is 0.74452, which is included in the Cohen Kappa index, substantial agreement. Based on the results of this software development, it is possible to observe examples of compatibility between artifacts in documents D4 and D6 which are included in the “Slight Agreement” category.
An Empirical Study on Power Consumption of Decentralised Identity Management Systems

The rise of decentralized identity management systems has brought concerns about energy consumption and possible implications for sustainability. This paper intends to investigate the energy consumption of three types of identity management systems, namely centralized, decentralized, and federated solutions. Measurements were made through a controlled environment using Docker containers to gather and analyze energy consumption data. The findings reveal that decentralized blockchain-based identity solutions may not inherently consume more energy than alternative approaches. The insights obtained through this study contribute to the discourse on sustainable technologies within identity management and its governance.
Key Competencies Development in the Digital Transformation of Higher Education in Indonesia

In the modern era, technological advancements are integral to the functioning of organizations, especially within higher education. Digital transformation plays a crucial role in this landscape. This study aims to spotlight a specific digital component, focusing on the critical elements of competency development that contribute to the success of such transformative efforts. The primary concern addressed is the competency gap, particularly in the Indonesian context. The research will provide strategic recommendations across various stages to bridge this gap. Employing a top-down approach, the researcher will draw insights from existing literature and prior research, amalgamating and comparing previous studies' findings. The anticipated outcome of this research is the proposition of essential components that warrant attention for fostering the triumph of digital transformation in higher education. Ideally, this study will culminate in a comprehensive framework to advance the digitalization of education across Indonesia. Ultimately, the objective is to enhance the educational landscape through thoughtful digital tools and practices integration.
Method of Generating the Summary of Discussions in Comments to Web News for Understanding Opinion Trends and Discussion Points

In recent years, there have been increasing opportunities for discussion and exchange of opinions on the internet. In order to clearly understand such discussions, users should be able to fully understand the flow of discussion as well as the conclusions. It is especially important to understand the main points as well as the trends in people's opinions in order to have more than a superficial understanding of the discussion. However, it is often difficult to understand them as the number of comments increases over time and the discussions become complex. In this research, we aim to develop methods for visualizing discussions in comments on online news articles to understand opinion trends and discussion points. This paper describes the methods for generating summaries of discussions and presents an overview of the support system. We also discuss the features of the proposed methods through the results of experiments using a prototype system for visualizing discussions.
Rielkies Nearby Sales Agents Finder Using Haversine Method

With their new culinary products, Rielkies Choco jars, which come in a variety of flavors, the chocolate-based company Rielkies, which was created by one of Malaysia's most well-known actors, has been able to gain popularity in Malaysia. A system that enables Rielkies's customers to locate their preferred and reliable sales agents is crucial given the large number of people registered as their product sales agents in each state throughout Malaysia. Due to privacy and security concerns, many customers steer clear of doing business online. Many customers are also concerned with the sales representatives' delivery strategies because they often favor cash on delivery due to its security. The development of a web-based tool to locate salespeople nearby was suggested in this article. The tool would give customers or users access to the contact information of salespeople as well as information on their location and preferred method of delivery. The Haversine technique is chosen as the most appropriate algorithm to employ for this project to calculate the distance. The system's functions include locating local sales representatives for customers and managing sales representatives for admin to confirm the sales representatives to ensure they are reliable and secure. The result demonstrates that the system's functionality has been attained. To make their transactions more secure and simple than before, the project will assist customers in locating their favorite sales representatives.
Data Imputation via Support Vector Regression with Hybrid Breeding Optimization Algorithm

Missing value is a common problem in real-world datasets and its imputation is crucial for data analyses. A major limitation of the accuracy of missing value imputation model, such as SVR-based model, is the appropriate selection of parameters, which can significantly affect the learning process and prediction accuracy of the model. To address the challenge, an imputation method based on SVR is proposed and the basic idea is to obtain the fittest parameters of SVR by hybrid breeding optimization (HBO) algorithm (SVR-HBO). Experimental results demonstrate that SVR-HBO has superior performance compared to other comparative algorithms.
A hybrid study of DE and ABC algorithms with self-adaptive parameters

In order to make full use of the complementary advantages of artificial bee colony algorithm and differential evolution algorithm to solve various optimization problems, this paper proposes to introduce a difference operator with adaptive parameters into the employed bee and onlooker bee stage of artificial bee colony algorithm, the two algorithms are mixed to form sABChDE algorithm, and the evolution ratio is used to adjust the use probability of both. In order to further enhance the exploitation ability of the algorithm, the roulette strategy is replaced by the improved elite solution strategy in the onlooker bee stage. The results of comparison experiments on CEC2014 benchmark function show that sABChDE algorithm achieves better precision in solving different problems, indicating that the algorithm realizes complementary advantages through adaptive mixing, and improves the universality and robustness of the algorithm.
Research on Intelligent Fusion Method of Equipment Account Data of Pumped Storage Power Station

In order to improve the efficiency of data fusion processing of equipment account of pumped storage power station, and avoid problems such as large energy consumption, data attribute conflict, data null value, poor data redundancy elimination effect caused by too long fusion period, the research on intelligent data fusion method of equipment account of pumped storage power station was carried out. First, collect the equipment account data of pumped storage power station in an all-round and multi-dimensional manner. Second, preprocess the collected equipment account data conflicts to reduce data noise and improve the quality of equipment account data. On this basis, the equipment account data of the pumped storage power station is intelligently fused. Experimental analysis shows that after the application of the new method, the data surplus ratio is always small, the data fusion efficiency has been significantly improved, and the goal of effective fusion of account data attributes and elimination of redundant data has been achieved.
Assessing Word Difficulty: A Mapping Method

This paper presents a word difficulty mapping method that utilizes three indicators: Average Number of Times, Word Frequency Score, and Word Difficulty Score, to map the difficulty of words. The effectiveness of this method is demonstrated using the EERIE as a case study. To predict the Average Number of Times, a Refined Simulated Annealing Model, which combines K-Dimension Tree, is employed. Subsequently, word difficulty is mapped using K-Means cluster analysis. The results indicate a strong correlation between the Average Number of Times, Frequency Score of The Word, Difficulty Score of The Word and word difficulty, highlighting the efficacy of the proposed word difficulty mapping method.
Unsupervised Learning Exploration for Duplicate Bug Report Detection

Bugs are inevitable as software complexity increases and bug reports are crucial for program maintenance. Due to the variety of language expressions, there will be duplicate bug reports where different reports correspond to the same bug. Existing methods for detecting duplicate bug reports are supervised, but manually labeling data is time-consuming and difficult because it requires expertise to identify the specific bug in the report. To reduce the dependence of current duplicate bug report detection methods on data labels, we expect to explore an unsupervised approach for detecting duplicate bug reports based on GCN. Firstly, since the bug report dataset has no prior graph information, we utilize the estimated distribution of connectivity between two disjoint sets in a bipartite graph to build a graph. Then, anchor points are introduced to address the problem that traditional graph autoencoders are not suitable for large-scale datasets. Finally, we conduct extensive experiments to evaluate the performance of our unsupervised approach. Our work shows that unsupervised approaches are feasible for duplicate bug report detection.
Overview of Game Decision Intelligence

Survival of the fittest is the law of nature. Game decision-making is ubiquitous in human society, ranging from personal salary negotiations and stock investments to national-level financial policies, international cooperation, and even national security, all comprising a game process. Given the broad application prospects of game decision-making, more and more researchers are devoting themselves to this field of study. Game decision intelligence belongs to the cognitive intelligence field of artificial intelligence. Game decision intelligence has always played an essential role in the development of artificial intelligence. Some milestones in the history of artificial intelligence are closely related to the study of game decision intelligence. The emergence of game decision-making artificial intelligence such as AlphaGO, AlphaZero, AlphaStar, DeepStack, Libratus, and Pluribus, which have surpassed human performance, has attracted numerous researchers to engage in research in this field, making it a gradually popular research hotspot. Research on game decision intelligence will have significant implications for our deep understanding of human decision-making behavior in society and for promoting the development of intelligent systems. This study will summarize the development process of game decision intelligence, analyze the advantages and disadvantages of various methods, and explore its potential application value.
WNRoom Dataset: White Noise Dataset for Detecting the Status of Room

This paper presents a dataset for detecting whether a room door or window is closed with white noise. Detecting whether doors and Windows are closed is a common smart home task. Most of the existing methods rely on various sensor devices, which require high cost and difficult deployment. In this paper, a new approach is proposed to determine whether a room is closed by white noise, which does not rely on sensors and is suitable for edge computing compared to previous schemes. In order to promote the development of this idea, this paper proposes a white noise dataset, which includes five different types of white noise data of 16 rooms with different window opening states, and conducts a series of baseline experiments based on this dataset, which confirms the feasibility of the idea and the validity of the dataset.
Staging Emotions in Stock Market Data Using Natural Language Processing with Transformers

The stock market is complex and difficult to analyze and predict. One of the important factors is investors’ sentiments. In recent years, investors frequently post about the market on Social Networking Service (SNS). It is conceivable that using Nature Language Processing (NLP) to stage wmotions in SNS posts could be a feature of market analysis and forecasting. In this study, Long Short-Term Memory (LSTM), Transformer, and Transformer with sparsemax are used in the sentiment NLP task. The posts are staged in to five levels: too negative, negative, neutral, positive, and too positive. The results showed that LSTM model was superior in terms of training time and Transformer model was superior in terms of accuracy. Too positive, too negative, and neutral can be labeled with high accuracy. Therefore, the models are considered to be able to identify strong trends. The emotional labels obtained in this study could be useful in analyzing and predicting the stock market.