Reviewerly/exHarmony huggingface dataset:
An Intelligent Email Classification System

Email is one of the most common methods of official and personal communication to exchange information. For the administration department, dealing with hundreds of emails with the same type of inquiries or requests results in a huge operational overhead. In this study, we explore email classification models. An email classification system should understand the topics in the email content for categorizing emails and indicate if an incoming email should be handled by the mailbox owner. Email categorization based on topics is a multi-label classification task. Most existing email categorization models perform binary classification to identify spam, phishing, or malware attacks. We propose a CNN-BiLSTM model for multiclass email classification. Our experiments show that compared to the two other models that we implemented namely CNN (76.19%) and BiLSTM (61.9%) models, the CNN-BiLSTM (83.33%) and Hierarchical CNN-BiLSTM models (85.33%) have much better performance.
Stop Looking for the Perfect All-Round Tester

Testing of a large-scale and complex software system requires many types of knowledge, skills and personality traits. Contrasting the idea of a perfect all-round tester, this paper presents the Testing Hopscotch model with six complementary profiles, and the key characteristics considered as most relevant for each profile. The model is based on 60 interviews with engineers from three large-scale companies in different industry segments. The Testing Hopscotch model was well received by three focus groups including 22 participants, which further strengthens the validity of the model.
Performance Models for Distributed Deep Learning Training Jobs on Ray

Deep Learning applications are pervasive today, and efficient strategies are designed to reduce the computational time and resource demand of the training process. The Distributed Deep Learning (DDL) paradigm yields a significant speed-up by partitioning the training into multiple, parallel tasks. The Ray framework supports DDL applications exploiting data parallelism by enhancing the scalability with minimal user effort. This work aims at evaluating the performance of DDL training applications, by profiling their execution on a Ray cluster and developing Machine Learning-based models to predict the training time when changing the dataset size, the number of parallel workers and the amount of computational resources. Such performance-prediction models are crucial to forecast computational resources usage and costs in Cloud environments. Experimental results prove that our models achieve average prediction errors between 3 and 15% for both interpolation and extrapolation, thus demonstrating their applicability to unforeseen scenarios.
The Impact of the Type of Fractional Schemes on Image Denoising Algorithms in the Fourier Domain

Fractional anisotropic diffusion in the Fourier domain is a widely accepted model in image denoising. This paper advocates a modification of the classical model regarding the differential operator where central fractional differences on the integer mesh grid are implemented. Convergence analysis of the proposed model allows lower time step, thus requiring less computation time. Numerical experiments conducted with two objective measures are promising especially with respect to lower orders of differentiation. For higher orders results are comparable with classical model, while computation time is significantly reduced.
Securing Social Spaces: Cyberbullying Detection with ML and DL on Social Media Platforms

Genuine dialogue is encouraged in safe social environments. Conversations become more meaningful and real when people feel free to be themselves, voice their perspectives, and share their experiences without filtering or censoring their ideas. With the growth of social media and online communication, cyberbullying on these platforms has become a serious and worrying problem. Cyberbullying is the term for the practice of harassing, intimidating, threatening, or harming people or groups through digital platforms like social media, messaging applications, or other online platforms. Social media posts may include text, photographs, and videos that can be analyzed by artificial intelligence (AI) systems to find potentially offensive or hazardous information. NLP (natural language processing) models can recognize threats, hate speech, and other unpleasant communications. This article provides a comprehensive examination of the pattern identification capabilities of machine learning and deep learning methodologies in the context of identifying instances of cyberbullying on social media platforms at an early stage. Moreover, this study presents a comparative analysis of machine learning and deep learning methodologies in the context of cyberbullying tweets collected from the online social media community. The obtained outcomes are thoroughly examined to demonstrate the importance of each method, as measured by metrics like as test accuracy, train accuracy, precision, and recall.
Load Balancing in Cloud Using Honeybee Optimization

In cloud computing, load balancing distributes traffic and workloads to make sure that no one server or computer is idle, overburdened, or underloaded. In order to increase total cloud performance, load balancing optimizes a number of limited factors, including execution time, response time, and system stability. There are numerous alternative load balancing algorithms that each have their own benefits and drawbacks. Users may experience performance issues when a server is overcrowded. This can be avoided by load balancing, which distributes the workload among several servers. There are several different load balancing methods that can be applied. One such method is to employ a round-robin DNS. Each request is delivered to a different server in a round-robin method with round-robin DNS. This guarantees that the amount of traffic reaching each server is equal. Honeybee optimization (HBO) is one of the most promising new load balancing methods. A bio-inspired algorithm called HBO is based on how honeybees behave. The capacity of honeybees to determine the quickest route between their colony and a food source is well recognized. To discover the best load balancing option, HBO employs a similar capability. A metaheuristic algorithm called Honeybee Optimization (HBO) is based on the feeding habits of honey bees. Honey bees use a well developed algorithm to locate food sources. This paper introduces the improved method for honey bee optimization.
How Agile Are you? Discussing Maturity Levels of Agile Maturity Models

With the emergence of agile software development methods, new approaches for determining agile maturity have become necessary. Other than for traditional maturity and capability models like CMMI and ISO/IEC 15504, the field of agile maturity models is not yet settled. Even worse, a common understanding regarding agility in general and the levels of agility in particular is missing. The paper at hand aims to shed light on the field of agile maturity models with a particular focus on maturity levels, their definition, and their evaluation and computation. We conducted a systematic literature review to extract maturity levels and provide an initial harmonization of the levels found. Our findings from analyzing 19 agile maturity models show that there is yet no agreement with regard to the maturity levels. In total, 69 maturity levels have been analyzed for harmonization opportunities. Two major dimensions of maturity levels of agile maturity models could be identified: (1) team-related and (2) general maturity, which is comparable to standard approaches. However, the procedures to assess organizations and processes, if at all present, are to a large extent focused on persons and their personal opinion, which paves the way for future research, e.g., in terms of developing measurement systems for assessing agile maturity.
On potential improvements in the analysis of the evolution of themes in code review comments

Context: The modern code review process is considered an essential quality assurance step in software development. The code review comments generated can provide insights regarding source code quality and development practices. However, the large number of code review comments makes it challenging to identify interesting patterns manually. In a recent study, Wen et al. used traditional topic modeling to analyze the evolution of code review comments. Their approach could identify interesting patterns that may lead to improved development practices.Objective: In this study, we investigate potential improvements to Wen et al.’s state-of-the-art approach to analyze the evolution of code review comments.Method: We used 209,166 code review comments from three open-source systems to explore and empirically analyze alternative design and implementation choices and demonstrate their impact.Results: We identified the following potential improvements to the current state-of-the-art as described by Wen et al.: 1) utilize a topic modeling method that is optimized for short texts, 2) a refined approach for identifying a suitable number of topics, and 3) a more elaborate approach for analyzing topic evolution. Our results indicate that the proposed changes have quantitatively different results than the current approach. The qualitative interpretation of the topics generated with our changes indicates their usefulness.Conclusions: Our results indicate the potential usefulness of changes to state-of-the-art approaches to analyzing the evolution of code review comments, with practical implications for researchers and practitioners. However, further research is required to compare the effectiveness of both approaches.
Detecting Architecture Debt in Micro-Service Open-Source Projects

A micro-service architecture emphasizes the use of subsystems that are small enough for changing them on the fly. Such architecture supports the continuous evolution of the system because individual services can be updated at different times, making system maintenance flexible. Consequently, the architecturally important properties of micro-services are constituted by service APIs that must be well maintained, with experimental, static, and deprecated versions clearly indicated. Like any software, micro-services can induce technical debt (TD) problems in service API, architecture and source code, if their quality and maintainability have not been asserted beforehand. This paper explores the relationship between TD and micro-services. Specifically, we investigate the role of architectural smells (AS) in open-source micro-service projects, where the architectural debt is principally recognized through the detection of architectural smells in the projects. As tools for this investigation, we used Arcan and Designite. The empirical data for the work is constituted by 20 open-source projects where we analyze the relationship between architecture smells and micro-services.
Dimensionality Reduction-Integrated Analog-to-Digital Converters for Efficient Data Acquisition in High-Dimensional Signal Processing

Humans struggle to comprehend and visualize data in more than three dimensions. Dimensionality reduction techniques allow us to project high-dimensional data into lower-dimensional spaces (such as 2D or 3D), enabling easier visualization and interpretation of patterns and relationships in the data. This research study proposes a novel approach to revolutionize high-dimensional signal processing by seamlessly integrating adaptive dimensionality reduction algorithms with analog-to-digital converters (ADCs). The focal point is the development of a system that not only efficiently acquires high-dimensional data but dynamically tailors its processing strategy based on the inherent characteristics of incoming signals. The adaptive dimensionality reduction algorithms embedded in the system facilitate real-time adjustments to the processing pipeline, ensuring optimal feature selection in response to varying signal patterns. This adaptability is especially crucial in dynamic environments where signal characteristics may change dynamically. Emphasizing low-power design, the integrated ADCs are optimized to operate efficiently in energy-constrained settings, making the system well-suited for deployment in resource-sensitive applications, including IoT devices and wearables.
Evaluation of Image Cryptography by Using Secret Session Key and SF Algorithm

In the unreliable domain of data communication, safeguarding information from unauthorized access is imperative. Given the widespread application of images across various fields, ensuring the confidentiality of image data holds paramount importance. This study centers on the session keys concept, addressing the challenge of key exchange between communicating parties through the development of a random-number generator based on the Linear Feedback Shift Register. Both encryption and decryption hinge on the Secure Force algorithm, supported by a generator. The proposed system outlined in this paper focuses on three key aspects. First, it addresses the generation of secure and randomly generated symmetric encryption keys. Second, it involves the ciphering of the secret image using the SF algorithm. Last, it deals with the extraction of the image by deciphering its encrypted version. The system’s performance is evaluated using image quality metrics, including histograms, peak signal-to-noise ratio, mean square error, normalized correlation, and normalized absolute error (NAE). These metrics provide insights into both encrypted and decrypted images, analyzing the extent to which the system preserves image quality. This assessment underscores the system’s capability to safeguard and maintain the confidentiality of images during data transmission.
Online Neural-Detection of False Data Injection Attacks on Financial Time Series

False data injection detection is a topic of interest because systems are prone to cyberattacks which can manipulate the state estimation process by injecting malicious data into the measurements, bypassing the detection of the security system. Causing the results of the state estimation to deviate from the safe values. This work proposes a false data injection detection methodology based on deep neural networks using sliding windows to generate online error vectors in order to detect and classify malicious data from measurement data. Two multilayer perceptron deep neural networks and the convolutional neural network were used in this work. In order to verify the feasibility of the proposed methodology, it is tested on data daily closing prices of the S&P 500 Index, pulled from Yahoo Finance for the years 2013–2022 to which false data were injected via software. The results show that the convolutional neural network presents the best results, with an accuracy above 93% and an F1-score of 0.91. It is shown that deep neural networks are a powerful tool in the detection of false data in data obtained through measurements.
Appearance-Based Gaze Estimation Enhanced with Synthetic Images Using Deep Neural Networks

Human eye gaze estimation is an important cognitive ingredient for successful human-robot interaction, enabling the robot to read and predict human behavior. We approach this problem using artificial neural networks and build a modular system estimating gaze from separately cropped eyes, taking advantage of existing well-functioning components for face detection (RetinaFace) and head pose estimation (6DRepNet). Our proposed method does not require any special hardware or infrared filters but uses a standard notebook-builtin RGB camera, as often approached with appearance-based methods. Using the MetaHuman tool, we also generated a large synthetic dataset of more than 57,000 human faces and made it publicly available. The inclusion of this dataset (with eye gaze and head pose information) on top of the standard Columbia Gaze dataset into training the model led to better accuracy with a mean average error below two degrees in eye pitch and yaw directions, which compares favourably to related methods. We also verified the feasibility of our model by its preliminary testing in real-world setting using the builtin 4K camera in NICO semi-humanoid robot's eye.
Optimal Production Scheduling by Integer Form of Population-Based Incremental Learning with Initial Probability Matrix Setting Methods and a Practical Production Simulator

This paper proposes an optimal production scheduling method using a practical production simulator and integer form of population-based incremental learning (IF-PBIL) with two initial probability matrix setting methods. There are three parameters for decision variables in a target factory. It is necessary to optimize these three parameters at the same time in order to evaluate them. Moreover, IF-PBIL is one of the cooperative metaheuristics and determines integer values based on probability values and generates solutions. Initial integer values are determined by equal probability values, and various solutions are generated. Hence, there is a possibility that it is difficult to search high-quality solutions from initial stages of the search. Furthermore, since the production simulator requires long execution time, the execution number of the production simulator should be reduced as much as possible. In order to tackle the challenge, the proposed method applies two initial probability matrix setting methods. It is confirmed that the proposed method can search high-quality solutions from initial stages of the search and can reduce the production costs with the fewer execution number of the production simulator using actual factory data of a polishing process of an assembly processing factory.
Features and Classes Drift Detector to Deal with Imbalanced Data Streams

Data streams, due to their dynamic nature, tend to impose a number of constraints on the functioning of the learning models used to extract knowledge from these environments. In this context, concept drift is an emerging research area, as they negatively affect the performance of classifiers: after they have been trained with a specific concept, they tend to lose accuracy in the presence of a new concept. Additionally, this problem is often worsened in environments with imbalanced classes, because the identification of changes in the distributions of examples belonging to minority classes is usually more complex, due to their lack of representativeness in the data stream. This work proposes the Features and Classes Drift Detector (FCDD), a new method specially designed to deal with the problem of concept drifts in imbalanced data streams, aiming to maintain the accuracy of detections in minority classes and, in addition, to avoid discarding the knowledge inherent to the classes unaffected by drifts. Experiments conducted in an imbalanced scenario with partial concept drift demonstrated the effectiveness of the proposed method when compared to the current state of the art detectors.
Improving Natural Language Inference in Arabic Using Transformer Models and Linguistically Informed Pre-Training

This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contradiction Detection (CD). Arabic is considered a resource-poor language, meaning that there are few data sets available, which leads to limited availability of NLP methods. To overcome this limitation, we create a dedicated data set from publicly available resources. Subsequently, transformer-based machine learning models are being trained and evaluated. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches, when we apply linguistically informed pretraining methods such as Named Entity Recognition (NER). To our knowledge, this is the first large-scale evaluation for this task in Arabic, as well as the first application of multitask pretraining in this context.
Advancing Smart Cities Through Novel Social Media Text Analysis: A Case Study of Calgary

In numerous cities, population expansion and technological advancements necessitate proactive modernization and integration of technology. However, the existing bureaucratic structure often hinders local officials' efforts to effectively address and monitor residents' needs and enhance the city accordingly. Understanding what people find important and useful can be inferred from their posts on social media. Twitter, as one of the most popular social media platforms, provides us with valuable data that, with the right tools and analysis, can provide insights into the performance of urban services and residents' perception of them. In this study, we used the city of Calgary as an exemplar to gather tweets and analyze topics relating to city development, urban planning, and minorities. Natural language processing (NLP) techniques were used and developed to preprocess stored tweets, classify the emotions, and identify the topics present in the dataset to eventually provide a set of topics with the prevalent emotion in that topic. We utilized a variety of methods to analyze the collected data. BERTopic for topic modeling and few-shot learning using Setfit for emotion analysis outperformed the others. Hence, we identify issues related to city development, senior citizens, taxes, and unemployment using these methods, and we demonstrate how delving into these analyses can improve urban planning.
EXPERIMENTAL ANALYSIS OF USING EXAMPLE-NONEXAMPLE METHOD IN INCREASING STUDENTS' READING SKILLS

This research aims to enhance the reading ability of students at MIN 21 Meulaboh by implementing the Examples and Non-Examples method. It is a quasi-experimental study with a one-group pre-test and post-test design. The population of this study consists of 16 students in the second grade of MIN 21 Meulaboh. Data collection techniques include observation sheets for student activities, teacher observations, and pre-test and post-test assessments. The data analysis results before applying the Examples and Non-Examples method showed that students' reading ability obtained an average score of 79.68. After applying the Examples and Non-Examples model, the average score increased to 92.81. The hypothesis testing results for the second-grade students showed a calculated t-value greater than the critical t-value (24.75 &gt; 1.75), indicating the rejection of the null hypothesis (Ho) and the acceptance of the alternative hypothesis (Ha). These findings demonstrate the influence of implementing the Examples and Non-Examples model in improving students' reading ability at MIN 21 West Aceh