Reviewerly/exHarmony huggingface dataset:
Determining Metaheuristic Similarity Using Behavioural Analysis

Many nature-inspired metaheuristics have been published, with claims of originality based on the metaphor that inspired the algorithm. Rarely is empirical evidence given to show algorithmic originality. In order to provide an easy and computationally cheap approach to characterise algorithm search behaviour, a suite of 20 behavioural characteristics is proposed. This behavioural characteristic suite allows for the search behaviour of an algorithm to be quantified without manual inspection. By doing so, behavioural novelty of any given algorithm may be determined by comparing the behavioural characteristics to those of well-known metaheuristics. To illustrate this use, and to evaluate whether metaheuristics are behaviourally distinct, a host of metaheuristics is run on various benchmark functions. To evaluate behavioural similarity across all problems, while acknowledging behaviour to be problem dependant, a novel method is proposed. In addition to this method, new behavioural characteristics are also proposed. The behavioural vectors generated for each benchmark function are clustered. The relationships and trends present in the different clusters are summarised by creating a pair-wise matrix for every metaheuristic pair, which tallies the number of times that the pair are found within the same cluster. The tallies are then analysed in order to make inference regarding the distinctness of any metaheuristic’s behaviours, across many different benchmark functions. The analysis finds that the range of unique search behaviours is small and that most metaheuristics share their behaviours with most other metaheuristics. The analysis also identifies both unique algorithms, as well as algorithms which have no unique behaviours.
Evolutionary Instance Selection With Multiple Partial Adaptive Classifiers for Domain Adaptation

Domain adaptation reuses the knowledge learned from an existing (source) domain to classify unlabelled data from another related (target) domain. However, the two domains have different data distributions. Common approaches to bridge the two distributions are selecting/reweighting instances, building domain-invariant feature subspaces, or directly building adaptive classifiers. Recent domain adaptation work has shown that combining the above first two approaches before applying the third approach achieves better performance than performing each approach individually. However, most existing instance selection approaches are based on a ranking mechanism, ignore interdependences between instances, and require a pre-defined number of selected instances. Furthermore, adaptive classifiers are sensitive to their parameters which are challenging to optimise due to the lack of target labelled instances. This paper introduces a novel evolutionary instance selection approach for domain adaptation. We propose a compacted representation and an efficient fitness function for Particle Swarm Optimisation to automatically determine the number of selected instances while considering the interdependencies among instances. This paper also proposes to use multiple partial classifiers to build a more reliable and robust adaptive classifier. The results show that evolutionary instance selection selects better instances than the ranking approach. In cooperation with multiple partial classifiers, the proposed algorithm achieves better performance than nine state-of-the-art and well-known domain adaptation approaches.
FINDGATE: Fine-grained Defect Prediction Based on a Heterogeneous Discrete Code Graph-guided Attention Transformer

Recognizing defects in source code through deep learning methods has become an important research subject for improving software quality. Although Transformer-based models such as CodeBERT have demonstrated impressive performance improvement in defect prediction tasks, models relying on single-structured input data, such as sequences, have limited ability to capture the code's structural features. Treating code simply as text overlooks essential information such as control dependencies, data dependencies, and syntactic structures inherent in the code. This paper proposes the Heterogeneous Discrete Code Graph (HDCG), which assigns structural information to code tokens from multiple perspectives. We also introduce an improved transformer model FINEGATE that leverages HDCG to guide self-attention. The experiments demonstrate that FINEGATE can effectively predict source code defects and perform fine-grained defect localization.
A Verifiable I/O Approach for End-to-end Eligibility Verifiability in Black-box E-Voting Systems

The tension between upholding verifiability and ensuring voter anonymity is a longstanding obstacle in the development of e-voting systems. Although the concept of “end-to-end verifiability” has been rigorously defined for ballot casting, recording, and tallying, its extension to voter eligibility remains inadequately addressed. To fill this void, we introduce a Black Box model coupled with a verifiable output approach, providing a new lens through which to scrutinize how earlier research has successfully achieved various forms of verifiability. Further, we present a groundbreaking set of principles, named “verifiable eligibility conditions,” which not only evaluate the effectiveness of existing eligibility verification methods but also inspire our streamlined “Verifiable I/O Approach” for achieving end-to-end eligibility verifiability. We conclude by showcasing a comprehensive ticket-based e-voting protocol, offering a tangible implementation of our Verifiable I/O framework.
Web-based Face Detection and Recognition using YOLO and Dlib

The electronic-based government system (SPBE) is an effort initiated by the Indonesian government to create a clean and trustworthy governance and public service system. However, the government’s public services involve sensitive data owned by Indonesian citizens. An identity verification system is needed to ensure the security and integrity of the system and prevent system abuse. A face recognition system is arguably the most accessible biometric verification method available. This paper used YOLOv5-Face and YOLOv7-Face pre-trained models for face detection. The models were then integrated with the Dlib library to identify faces based on their identities. Testing was performed by determining a confusion matrix for each weight used. The best-performing face recognition system was then implemented as a web application. The yolov7-lite-s weight achieved an accuracy of 94.5% with an average processing time of 237.45 ms per image on a private dataset.
Investigating Execution Trace Embedding for Test Case Prioritization

Most automated software testing tasks, such as test case generation, selection, and prioritization, can benefit from an abstract representation of test cases. Although test case representation usually is not explicitly discussed in software literature, but traditionally test cases are mostly represented based on what they cover in source code (e.g., which statements or branches), which is in fact an abstract representation. In this paper, we hypothesize that execution traces of test cases, as representations of their behaviour, can be leveraged to better encode test cases compared to code-based coverage information, for automated testing tasks. To validate this hypothesis, we propose an embedding approach, Test2Vec, based on an state-of-the-art neural program embedding (CodeBert), where the encoder maps test execution traces, i.e. sequences of method calls with their inputs and return values, to fixed-length, numerical vectors. We evaluate this representation in automated test case prioritization (TP) task. Our TP method is a classifier trained on the passing and failing vectors of historical test cases, in regression testing. We compare our embedding with multiple baselines and related work including CodeBert itself. The empirical study is based on 250 real faults and 703,353 seeded faults (mutants) over 250 revisions of 10 open-source Java projects from Defects4J, with a total of over 1,407,206 execution traces. Results show that our approach improves all alternatives, significantly, with respect to studied metrics. We also show that both inputs and outputs of a method are important elements of the execution-based embedding.
Comparison of Dempster Shafer and Certainty Factor for Anxiety Disorder Detection System

In the current millennial era, many people, especially teenagers, experience mental disorders. Based on research, more than 8% of adolescents have mental and psychological disorders, namely anxiety disorders. This disease has characteristics such as sufferers experiencing anxiety, social disorders and anxiety. This disease cangene be detected by a technological approach that applies an expert-based knowledge system. Methods suitable for this technological approach are certainty factors and dempster shafer which can help detect uncertainty in diagnosing mental illness. In this study, the Certainty Factor and Dempster Shafer methods were used to detect anxiety disorders and compared with the results from experts. This research gives the results of validation sampling accuracy with experts of 93.33 % for the certainty factor method and the dempster shafer method has an accuracy of 80%. So, the certainty factor method is more recommended for detecting anxiety disorders.
Web-Based Gender Classification ML Application Development for e-KYC

e-KYC is used to recognize the user of the system and validate if they are real and has the authority to access the service. The process can be automated using machine learning models. One of the elements of e-KYC is gender classification. The implementation consists of a face detection model and a gender classification model. Two web services, frontend and backend, are used to connect the user to the machine learning models. The product is created to fit in larger e-KYC systems, such as SPBE (Sistem Pemerintahan Berbasis Elektronik/electronic-based government system). The system can capture the users’ image on a website using the devices’ webcams, processes it using two machine learning models, and shows the result in the form of labeled bounding boxes representing the gender and confidence score for every detected face.
Development of Controllable LED Light Source for Retinomorphic Sensor Measurement Test

In the digital age, the advancement of autonomous vehicles is currently extremely quick. The invention and development of retinomorphic sensors lend credence to this. This sensor’s ability to function similarly to the human eye makes it handy for detecting the presence of objects in front of the sensor. It is necessary to use a tool known as an LED controller to test this sensor. To evaluate the voltage regulation on the LED controller both mechanically and digitally, this research will explore, design, and construct an LED controller. The LED Controller has voltage adjustment capabilities between 1.2 and 25 volts. However, the highest power the LED can withstand is somewhere between 15 and 16 volts. As a result, 16 Volts should be set as the input limit value in the microcontroller program.
Design and Implementation of An Employee Data Processing System for SMEs Using Marvel Apps and Javascript

This research purpose is to develop a web-based employee data management system specifically for SMEs. With the problems that occur in the employee management data in SMEs that still use manual methods such as writing on paper and using manual spreadsheets, this approach is often inefficient, prone to errors, difficult to control, and does not provide easy access to needed information; it is essential to build a computerized system. The employee data processing information system is built to facilitate the proper management of employee data and is accurately accompanied by management or control over web-based computerized systems for SMEs. This will support the smooth running of business processes in SMEs, such as storing employee personal data, inputting attendance, and calculating salaries. This research aims to build an employee data processing information system using the prototyping method with Marvel Apps as a prototyping tool and JavaScript as the primary programming language. The results of this research are expected to provide significant benefits for SMEs in managing personnel data, increasing efficiency, reducing administrative errors, expediting payroll processes, and providing easy access and exchange of information by using the system application.
Predicting sentiment analysis for Web users with a deep learning approach

In the last decade, Sentiment analysis has been an interesting research topic in natural language processing (NLP) and data mining fields. We have noticed that deep neural network (DNN) models are always applied in sentiment analysis researches to obtain good results. Many neural architectures are applied for sentiment analysis. Among these architectures, Long Short- Term Memory (LSTM) models are the most used ones. In this contribution, we collected our dataset from our developed web application. The obtained dataset contains different types of features; Quality of Service (QoS) metrics, Web Quality of Experience (QoE) metrics, user engagement metrics, comments to videos that are included in the application, and the Mean Opinion Score (MOS) expressed by users. After that, we trained our dataset on our proposed model LSTM-CNN-RNN to predict the MOS. As a conclusion, we obtained a good accuracy and a low loss rate.
Car Detection over Network using Yolov8 in Forza Horizon 4

In the realm of autonomous vehicles, object detection holds a pivotal role in enabling accurate perception and safe navigation within complex environments. This study incorporates these domains by proposing a vehicle detection system employing YOLO v8 within the popular game Forza Horizon 4. YOLO v8’s real-time, accurate object detection prowess aligns seamlessly with the game’s dynamics, enriching the user experience through improved vehicle tracking. The research encompasses YOLO v8 model training on an expansive vehicle dataset, adaptation for Forza Horizon 4, and rigorous evaluation, comparing accuracy, speed, and efficiency against existing methods. The methodology entails dataset preparation, YOLO v8 configuration, object detection, result evaluation, optional fine-tuning, and iterative testing and optimization. By scrutinizing two data transmission protocols, RTMP and HTTPS, the research accentuates RTMP’s superiority in real-time image data transmission for object detection, surpassing HTTPS in processing speed. While RTMP excels in low-latency scenarios, HTTPS augments security through encrypted data transmission. The findings prove RTMP’s speed in image data transmission for real-time object detection, whereas HTTPS’s secure communication advantages.
Performance Comparison of String Matching Methods for Consumer Data Retrieval

Searching for data or information has become a necessity in today’s digital era. One type of data search that is often used is string matching or patterns in a text. The speed of an application in the data search process, especially applications that have many users and large data on a server is a challenge, especially for digital business companies. Internet connection, powerful servers, effective and efficient data search algorithms are some of the determining factors. In general, string matching uses the SQL Query Like method. In this research, a comparison of several algorithm methods that can be used in string search, including Boyer-Moore, Naive String Matching, Knuth-Morris-Pratt and Rabin-Karp Algorithm to find the best algorithm in the data search process. From the tests that have been carried out, the results of the Boyer More algorithms are faster than other algorithms or 45.8% faster based on performance testing with 250,000 data. In the memory usage testing process, the average memory used by SQL is more than other algorithms.
An Intelligent Clustering Approach For Analyzing A Multivariate Time Series Dataset, Case Study COVID-19 Outbreak in Indonesia

The coronavirus disease 2019 (COVID-19) became a pandemic across the world in 2020 - 2022. Indonesia is one of the countries affected by the COVID-19 outbreak. As an archipelago country, the spreading of COVID-19 in one and other provinces varies. This study is conducted to cluster the provinces in Indonesia according to the daily new cases, deaths, and recoveries of COVID-19 patients. It implements Intelligent Kernel K-Means and Intelligent K-Means to cluster multivariate time series data without determining the number of clusters. Intelligent Kernel K-Means finds two clusters and obtains the best average Silhouette score of 0.81. It also reveals that the severity of COVID-19 in some areas correlated to the variables population density, population, general hospitals, inpatient health centers, not inpatient health centers, urban areas with water pollution, urban areas with land pollution, urban areas with air pollution, and urban areas without pollution.
BTM: Black-Box Testing for DNN Based on Meta-Learning

Deep learning is widely used in security fields like autonomous driving, but testing deep learning models poses challenges due to low generation efficiency and limited error detection. Current white-box test case generation methods rely on neuron coverage, but black-box testing is crucial when model details cannot be accessed. Prior methods also needed more consideration for error diversity under lower time resources. In this paper, we propose a novel approach based on the intuition that two models trained on the same classification task learn similar rules at a coarse-grained level. We employ meta-learning to train universal meta perturbation on a surrogate model, which increases neuron coverage on the surrogate model while inducing misclassifications in the original model (thus enhancing the adequacy of the initial tests). Subsequently, we apply data initialization to discover a wider range of errors faster. To accelerate test case generation and reduce resource consumption, we introduce a set of acceleration techniques based on image prediction probabilities, minimizing the time spent exploring irrelevant regions in images. Finally, we evaluate our approach on a well-known dataset and several well-known models, demonstrating its effectiveness.
Analysis of Decision Tree and Random Forest Algorithms for Nutrient Deficiency Classification in Citrus Leaves through Image Processing

The research presents a hybrid approach to identify and categorise nutritional deficiency syndrome in citrus leaves using image processing and machine learning. The method includes processing images, segmenting images and extracting color-texture information. The Decision Tree and the Random Forest algorithms use various statistical measures for classification, including mean, skewness, variance, entropy, maximum probability, angular second moment, contrast, correlation, dissimilarity, energy, and homogeneity. The test results show a range of performance levels, with an accuracy of 80.28% being attained by utilising an 80:20 data ratio in the Decision Tree approach. The Random Forest method’s highest accuracy, 91.25%, was reached using an 80:20 ratio. In conclusion, the accuracy of the Random Forest approach regularly beats that of the Decision Tree method.
Exploring the Impact of Speech AI: A Comparative Analysis of ML Models on Arabic Dataset

Recent advancements in artificial intelligence and high-performance computing have revolutionized various facets of human interaction. Speech synthesis (text-to-speech) and automatic speech recognition (speech-to-text) technologies have become ubiquitous in smartphones, smart homes, websites, and automotive systems, transforming how we communicate with technology. This paper resembles a showcase of the efficacy of end-to-end text-to-speech (TTS) models trained specifically on Arabic data. The study presents an in-depth analysis of various TTS system architectures mainly Tacotron2 and FastSpeech2, highlighting their performance in generating high-quality synthesized speech from written Arabic text. Through a beginner’s lens, the article sheds light on the exciting domain of audio deep learning, providing valuable insights for researchers and practitioners interested in TTS technology. By demonstrating the capabilities and potential challenges of employing Arabic data in TTS model training, this paper contributes to the advancement of multilingual speech synthesis and inspires further exploration in the field of audio deep learning.
Sentiment Analysis of Air Quality to Identify Public Perspectives in Indonesia Based on Machine Learning

The worsening air quality in Indonesia, especially in DKI Jakarta, has become a serious concern. In this context, sentiment analysis can provide new information about people’s views and responses to this environmental issue. In this study, we collected data from social media, i.e., the YouTube platform, which reflects people’s views on air quality. We implemented various ML algorithms such as Random Forest, Decision Tree, Naive Bayes, Extra Trees, and Hard Voting Ensemble to analyze the sentiment in the retrieved YouTube comments. We evaluate the performance of each algorithm based on the accuracy in classifying the sentiments. Through sentiment analysis, we identified the most common patterns of views appearing in the analyzed data. The novelty of the research is the application of Machine Learning techniques in analyzing sentiments related to air quality in Indonesia. As a result, all ML algorithms showed accuracy above 90%, except the NB algorithm with 76.14% accuracy. This shows that ML algorithms have good sensitivity to the dataset containing YouTube comments used in this study.
The Power of Two Matrices in Spectral Algorithms for Community Recovery

Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms have been successfully used for graph partitioning, hidden clique recovery and graph coloring. In this paper, we study the power of spectral algorithms using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two matrices</i> in a graph partitioning problem. We use two different matrices resulting from two different encodings of the same graph and then combine the spectral information coming from these two matrices. We analyze a two-matrix spectral algorithm for the problem of identifying latent community structure in large random graphs. In particular, we consider the problem of recovering community assignments <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exactly</i> in the censored stochastic block model, where each edge status is revealed independently with some probability. We show that spectral algorithms based on two matrices are optimal and succeed in recovering communities up to the information theoretic threshold. Further, we show that for most choices of the parameters, any spectral algorithm based on one matrix is suboptimal. The latter observation is in contrast to our prior works (2022a, 2022b) which showed that for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">symmetric</i> Stochastic Block Model and the Planted Dense Subgraph problem, a spectral algorithm based on one matrix achieves the information theoretic threshold. We additionally provide more general geometric conditions for the (sub)-optimality of spectral algorithms.