Reviewerly/exHarmony huggingface dataset:
TextRecon for Blind Polluted Text Image Reconstruction

Scene text recognition has attracted great interest with the rapid demand for extracting valuable information from natural scenes. Text reconstruction plays an important role in scene text recognition since text often appears in various degraded forms in natural scenes. However, blind polluted text image reconstruction is still a challenging task due to no clear guidance regarding the specific regions that require restoration. In this work, we present TextRecon for blind polluted text image reconstruction. TextRecon follows the standard image restoration architecture and stacks the proposed Masked Attentional Residual Block (MAResBlock) as the bottleneck. The proposed MAResBlock is an attention-based residual block that generates a mask to indicate the polluted region so as to guide text reconstruction. Hence, TextRecon handles text reconstruction of the specific polluted region without requiring explicit instructions. Experiments show that the proposed method achieves superior performance compared with other state-of-the-art methods.
Vertical Take-Off and Landing System Control Using Deep Reinforcement Learning

This paper introduces the utilization of a controller based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for the Vertical Take-Off and Landing (VTOL) system control. The TD3 algorithm combines deep neural networks and actor-critic reinforcement learning techniques, enabling it to learn optimal policies directly from interactions with the environment. MATLAB and Simulink are used to provide an efficient platform for training and analyzing the performance of the TD3-based controller. This study compares the performance of the TD3-based controller to that of the PID controller subject to step changes and external disturbances. Results obtained by applying the TD3-based controller to one degree of freedom VTOL system demonstrate the effectiveness of the proposed control. The results, also, indicate that the proposed controller performance is better than the PID controller, with no overshoot and approximately four times faster settling time. Also, the proposed controller shows robustness against disturbances under various conditions.
Improving Speech Privacy with Fuzzy Logic-Based Encryption

Speech privacy is a critical concern in today’s interconnected world, where speech processing technologies are becoming increasingly prevalent. Traditional encryption techniques may not be sufficient to protect sensitive information in speech data due to the unique characteristics of speech signals. This research paper explores the application of fuzzy logic-based encryption to enhance speech privacy. Fuzzy logic offers a flexible and adaptive approach to encryption, accommodating the inherent uncertainty and variability present in speech signals. The study investigates the effectiveness of fuzzy logic-based encryption in safeguarding speech data and compares it with conventional encryption methods. Experimental results demonstrate the superiority of fuzzy logic-based encryption in preserving speech privacy, highlighting its potential for securing speech processing systems and applications.
The Impact of Cognitive Dissonance on the Software Development Process

The IT industry is rapidly evolving, and to compete, the development process must be optimized by eliminating and reducing costs. There are many methods to optimize the software development process. In our research, we decided to turn to other areas and, based on their experience, try to identify possible problems in IT and ways to solve them. We have built much of our research around the book “Black Box Thinking” [25]. From this book, we transferred the idea of the impact of cognitive dissonance on the software development process. To substantiate our point of view, we surveyed more than 100 employees in the field. We conclude that this problem exists in the IT sphere based on the data obtained and describe each existing factor. Furthermore, the research presents solutions based on other studies that deal with the problem of cognitive dissonance.
A Dynamic Multipath Load Balancing Algorithm Based on Particle Swarm Optimization in DCN

Aiming at effectively coping with the elephant flow conflicts in the data center network(DCN), this paper proposes a dynamic multipath load balancing algorithm based on particle swarm optimization(DMLBP) in DCN. Experimental results show that compared with the traditional ECMP and SA algorithms, the DMLBP algorithm improves bandwidth utilization and network throughput, while reducing the network propagation delay.
Adaboost Based Risk Level Prediction Model For Maternal Health

Most of the pregnant women are ignorant about the risks factors and die from complications of pregnancy. Therefore, the health data of pregnant women should be constantly monitored. Risk levels should be determined. In this study, machine learning techniques were used to automatically determine the risk levels of pregnant women in order to reduce the workload of doctors. Health data of pregnant women were analyzed to determine the level of risk intensity. Classification approaches according to the intensity of risk were used for the analysis of risk factors. Decision tree and Adaboost algorithms were used in the study. Among the decision tree algorithms, Random Forest, Rep Tree, Random Tree and SimleCart algorithms were preferred. The data were first classified using only decision trees. Then, the Adaboost algorithm applied data was classified using decision tree algorithms. The results were compared. As a result, it was seen that Adaboost algorithm increased the classification success. The highest accuracy value was obtained when Adaboost and Random tree algorithm were used together. This value is 86.09%. For this reason, a model in which Adaboost and Random tree algorithms are used together is proposed.
Evaluation Usability and User Experience (UX) of Bstation Mobile Applications

Streaming anime has become a popular pastime for Japanese animation lovers around the world. One anime streaming platform that can be used is the Bstation Mobile Application. The Bstation Mobile application is a streaming service that provides various kinds of anime series. To create a good user experience, it is necessary to evaluate the usability of the Bstation Mobile application to determine user satisfaction and identify problems experienced by users such as the translation often goes missing in the middle of videos, the translation that appears delayed, and other bugs found by users. Therefore, a usability evaluation and user experience evaluation was carried out to find out feedback from users using the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) methods. Testing was carried out with 100 responses from Bstation Mobile users. Based on the test results, the average SUS score is 75.1%. Testing on UEQ shows positive assessment results for aspects of Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and neutral ratings for aspects of Novelty. This indicates that the Bstation Mobile application is quite good, but still need an improvements to made the application is more optimal for use. The recommendations are given as guidance for the app's development and improvement based on the testing results to address any identified issues.
A Novel Approach for Migrating Relational Databases into Graph Databases

Relational database (RDB) systems are widely used to store traditional data for many organizations despite their challenges when dealing with non-traditional data required by contemporary applications. These systems should be able to handle transactions and domains that require storing large amounts of data. Recently, data has been growing faster than ever before, as extracting valuable information from big data is a complicated task using traditional database systems. Therefore, most organizations are trending to use NoSQL systems to store and manipulate their data. Converting existing SQL-based data into NoSQL structures is a very complex task due to the need to ensure data equivalence and accuracy with better performance. This paper aims to present a new approach to migrating data from an RDB to a graph database based on a conceptual data model. Algorithms are developed to generate the target database according to a set of migration rules to translate all RDB constructs into a graph schema, based on which the RDB data is converted to the new environment. The proposed solution has proved effective and accurate after a prototype system has been implemented and the results empirically tested and evaluated.
A Hybrid CNN-LSTM Framework for Unsupervised Anomaly Detection in Water Distribution Plant

To reduce potential threats to public health and economic losses, water distribution systems, which are critical parts of industrial control systems, require accurate and effective anomaly detection. The successful incorporation of machine learning models into anomaly detection procedures has been made possible by recent developments in computer science. In this research, a hybrid CNN-LSTM framework for anomaly detection in water distribution systems is presented using Python programming language in the JupyterLab environment. The performance of the proposed CNN-LSTM network model is carefully evaluated on the publicly available WADI dataset, which contains several types of attacks for a water distribution testbed, using a statistical window-based anomaly detection approach. The experimental results show acceptable precision, recall and F1 scores of 0.8619, 0.7041, and 0.775, respectively, demonstrating the efficiency of the framework in detecting anomalies in the water distribution system.
A Named Entity Recognition Method Based on ALBERT's Multi-Headed Attention Mechanism with Word Fusion

To address the shortcomings of Chinese named entity recognition in terms of sub-word ambiguity and performance improvement, we propose a named entity recognition method based on ALBERT's multi-headed attention mechanism and word fusion. The method first uses the pretrained language model ALBERT to obtain the dynamic word vector of the text and fully extract the information of the text. After that, the features of the character vectors are extracted using CNN in the word fusion CAW layer, and the contextual semantic features of each word are extracted using BiLSTM, and the output results of both are stitched and fused. Then the output of CAW layer is fed into BiLSTM to obtain deep features. After that the tightness of the connections between elements in the sentence is captured using a multi-headed attention mechanism. Finally, the most likely label is obtained by CRF as the identification result. The model was experimented on the People's Daily dataset and achieved an F1 value of 97.14%, which is better than the comparison model, indicating the effectiveness of the model.
Deep Residual Neural Network for Aggressive Physical Activity Recognition Using Surface Electromyography Sensors

Wearable sensors and machine learning have opened new doors for innovative automated systems. Smart wearables like smartwatches and wristbands can efficiently record human movements due to their integrated, compact sensors. This data collection is vital for understanding human behavior, a field known as human activity recognition (HAR), dedicated to classifying actions. Deep learning methods, which autonomously extract intricate features, have shown promise in HAR. Sensor-based HAR is extensively studied in academia and applied across domains like wellness tracking, medical diagnosis, and mobility analysis. However, challenges persist in detecting complex activities. Our study focuses on recognizing aggressive behaviors through surface electromyography (sEMG) sensor data analysis. We introduce ResNeXt, a novel deep neural network, to improve recognition accuracy. Through a series of tests, we evaluate ResNeXt's recognition capabilities and compare its results to other deep learning models. Our experiments reveal that ResNeXt outperforms other models, achieving an impressive 94.06% overall accuracy and an exceptional 97.68% accuracy in distinguishing normal from aggressive actions.
Binary Classification with Variational Quantum Circuit

Quantum Computing (QC) is an emerging paradigm offering fundamentally a new and more effective way of computation based on the properties of quantum mechanics, such as superposition, entanglement, and quantum parallelism. The intersection of QC and Machine Learning (ML) fields has given rise to a new research area, Quantum Machine Learning (QML). With the computational power of quantum computers, it proposes using quantum computers to process classical data for learning. Therefore, QML can be an efficient means of classification for computationally intensive tasks. In this paper, we perform an experimental binary classification task with our three qubit Ansatz/Variational Quantum Circuit (VQC). The dataset used in this study, Maternal Health Risk Data Set (MHRD), is publicly available and collected from different hospitals and clinics by means of Internet of Things (IoT) systems. We use amplitude embedding to encode feature vector to the state of qubits after preprocessing and normalization of the data. The operations of cost value calculation and parameter tuning are carried out in a classical way. We have tested our proposal with PennyLane library, and the experimental results show that the proposed VQC classifies the data with 92% accuracy.
Exploring Effective Techniques and Approaches for Requirement Elicitation in Software Development: A Critical Review

This research paper critically reviews different requirement elicitation techniques in the software development process. The study examines how selection methods are linked to project characteristics, significantly impacting the requirement elicitation technique selection process. This study aims to enhance selection techniques in software development by assisting system analysts and developers, particularly those involved in the system development team during the requirement elicitation phase. The research seeks to expand knowledge on various selection techniques that meet stakeholders needs by facilitating the collection of accurate requirements. Given the complexities inherent in requirement elicitation in software development, a systematic literature review will be employed to critically investigate the appropriate requirement elicitation techniques for the software development process.
Handling Imbalanced Data for Improved Classification Performance: Methods and Challenges

Imbalanced data significantly impacts the efficacy of machine learning models. In cases where one class greatly outweighs the other in terms of sample count, models might develop a bias towards the majority class, thereby undermining the performance of the minority class. Imbalanced data act to increase the risk of overfitting, as the model may memorize the majority of class samples instead of learning underlying patterns. This paper addresses these challenges in the classification field by exploring various solutions, including under-sampling, oversampling, SMOTE, cost-sensitive learning, and ensemble deep learning methods. The evaluate the performance of these methods on different datasets and provide insights into their strengths and limitations. The paper presents a taxonomy of strategies for imbalanced binary and multi-class classification problems, including resampling, algorithmic, and hybrid methods. Ultimately, the paper furnishes guidelines to facilitate the selection of the most pertinent method for mitigating imbalanced data challenges within a specific classification context.
Secured Cloud APIs Using Activity Analyzer

Application programming interfaces (APIs) are a framework provided by cloud computing providers for managing and accessing cloud services. Security risks might be caused by these APIs. For access to cloud resources and services, developers using APIs require authentication credentials. If an unauthorized user obtains credentials, a serious threat can arise. The likelihood of engaging in risky behavior is therefore very high. To safeguard the cloud environment, numerous studies of security models have been carried out. Building a model for safety in identifying and measuring abnormal (unsafe) activities remains difficult and scalable due to behavioral complexity. This paper proposed an automated model for analyzing API activity for detecting and measuring any unsafe behavior of API based on various metrics. The analyzer takes an appropriate response when detecting any abnormal API activity. According to several measures, the experiments showed that the proposed model improved cloud security and blocked unsafe activities.
Comparative Analysis of Cyberbullying Detection: A case study for Turkish and English

In this study, we have implemented a significant number of experiments using machine learning (ML), deep learning (DL), and feature extraction techniques to detect cyberbullying in both Turkish and English languages. The results showed that our model performed well using DL with FastText word embedding on both English and Turkish datasets. However, the pre-trained variants of BERT (a language model) performed exceptionally well, achieving an average F-score of 0.9775 for the Turkish dataset and 0.9892 for the English dataset. These findings highlight the effectiveness of using advanced natural language processing techniques for cyberbullying detection in different languages.
360 Virtual Tour for Online Tourism Promotion: A Study of Visual Indonesian Virtual Tour of Surabaya

The presence of increasingly advanced technology has affected positively the advertising industry. The presence of a virtual tour as an alternative to virtual tourism, for instance, has offered a new space for the travel experience as users can feel how it is to be in a certain place without having to go to that place directly. In addition, virtual tours are able to provide an initial description of how a tourist object is reviewed before traveling and be used as a virtual promotional medium. As used as a means of digital promotion, the visual depiction in the virtual tour should comply with the rules of the elements and design concepts of the virtual tour. Using content analysis through explanatory approach, the results of visual observations of virtual tour objects in Indonesian Virtual Tour are Surabaya virtual tourism objects which are described even though they have fulfilled several elements of the design concept but still need to take a more selling point of view to the visitors in the case of their visit to be used as a means of tourism promotion.
Switch-Based Quorum Coordination for Low Tail Latency in Replicated Storage

Modern distributed storage requires microsecond-scale tail latency, but the current coordinator-based quorum coordination causes a burdensome latency overhead. This paper presents Archon, a new quorum coordination architecture that supports low tail latency for microsecond-scale replicated storage. The key idea of Archon is to perform the quorum coordination in the network switch by leveraging the flexibility and capability of emerging programmable switch ASICs. Our in-network quorum coordination is based on the observation that the modern programmable switch provides nanosecond-scale processing delay and high flexibility simultaneously. To realize the idea, we design a custom switch data plane. We implement a Archon prototype on an Intel Tofino switch and conduct a series of testbed experiments. Our experimental results show that Archon can provide lower tail latency than the coordinator-based solution.
Context-Clues-Based-Strategy for Better Reading Comprehension Skill

In this study, eighth graders at an Islamic Junior High School in Pamekasan were taught reading comprehension utilizing a context-clues-based strategy. The pre-experimental design was employed, while the instrument used to collect the data was a reading test. The pre-test and post-test were two separate tests. The eighth graders (VIII-C) class served as the study's sample. The results showed that using the context-clues-based strategy led to an improvement. To determine whether the hypothesis was accepted or rejected, the researcher conducted a t-test. The outcome revealed that the Ha is accepted, while the Ho is rejected, or t-score &gt; t-table or 7.405 &gt; 2.048. Students' reading comprehension skills are considerably improved by using a context-clues-based strategy.
A DFT and IWT-DCT Based Image Watermarking Scheme for Industry

Until today, digital image watermarking has not been large-scale used in the industry. The first reason is that the watermarking efficiency is low and the real-time performance cannot be satisfied. The second reason is that the watermarking scheme cannot cope with various attacks. To solve above problems, this paper presents a multi-domain based digital image watermarking scheme, where a fast DFT (Discrete Fourier Transform) based watermarking method is proposed for synchronization correction and an IWT-DCT (Integer Wavelet Transform-Discrete Cosine Transform) based watermarking method is proposed for information embedding. The proposed scheme has high efficiency during embedding and extraction. Compared with five existing schemes, the robustness of our scheme is very strong and our scheme can cope with many common attacks and compound attacks, and thus can be used in wide application scenarios.
Empirical Analysis of Methods for Evaluating Faithfulness of Explanations by Feature Attribution

Many high-performance machine learning models in the real world exhibit the black box problem. This issue is widely recognized as needing output reliability and model transparency. XAI (Explainable AI) represents a research field that addresses this issue. Within XAI, feature attribution methods, which clarify the importance of features irrespective of the task or model type, have become a central focus. Evaluating their efficacy based on empirical evidence is essential when proposing new methods. However, extensive debate exists regarding the properties that importance should be possessed, and a consensus on specific evaluation methods remains elusive. Given this context, many existing studies adopt their evaluation techniques, leading to fragmented discussions. This study aims to "evaluate the evaluation methods," focusing mainly on the faithfulness metric, deemed especially significant in evaluation criteria. We conducted empirical experiments related to existing evaluation techniques. The experiments approached the topic from two angles: correlation-based comparative evaluations and property verification using random sequences. In the former experiment, we investigated the correlation between faithfulness evaluation tests using numerous models and feature attribution methods. As a result, we found that very few test combinations exhibited high correlation, and many combinations showed low or no correlation. In the latter experiment, we observed that the measured faithfulness varied depending on the model and dataset by using random sequences instead of feature attribution methods to verify the properties of the faithfulness tests.