Reviewerly/exHarmony huggingface dataset:
Evaluating Software Quality in DevOps Practices in Pakistan: A Survey Approach

Quality is one of the main pressures that any software development process has to deal with. Ensuring software quality is an essential aspect of DevOps, as it helps to identify and resolve defects early in the development process, leading to faster and more reliable software releases. This paper explores the relationship between various DevOps practices and their impact on software quality. For that purpose, we have designed an industrial survey to examine the current practices of DevOps in different software firms in Pakistan. After a thorough literature study, four hypotheses based on four main practices of DevOps (Communication and collaboration, Automation, Continuous Integration & Continuous Deployment, and Measuring & Monitoring) were developed. DevOps processes are the independent variables whereas Software Quality is the dependent variable. We assessed the correlation between these variables using the Pearson Correlation coefficient. The results revealed that there is a positive and significant correlation between the study variables. We used ordinary least squares (OLS) multiple linear regression to test the proposed hypotheses. Our results showed that automation in DevOps practices has the strongest impact on software quality. In contrast, Continuous Integration & deployment don't show that significant impact on software quality. Based on the respondent's feedback and our hypothesis assessment with multiple regression analysis, the authors have recommended the use of DevOps practices for achieving better quality software.
Comparative Performance Analysis of Deep Neural Networks for Human Activity Recognition

Human activity recognition is a challenging computer vision problem, which involves identifying human activities using artificial intelligence methods, from the data collected from various sensors. In this paper, existing deep neural networks are studied and a custom deep architecture is proposed for human pose and activity recognition. The proposed architecture is mainly based on the approach of localizing human body joints in a 3D space on a single 2D image. The fundamental problem is the wide variations in appearance, caused by various camera angles which include, clothing, body shapes, and self-occlusion, and complications in background variations. The Human 3.6M and patient MoCap datasets are used as a benchmark and our proposed architecture shows competitive performance compared to the state-of-the-art methods.
Diffusion Model in Causal Inference with Unmeasured Confounders

We study how to extend the use of the diffusion model to answer the causal question from the observational data under the existence of unmeasured confounders. In Pearl's framework of using a Directed Acyclic Graph (DAG) to capture the causal intervention, a Diffusion-based Causal Model (DCM) was proposed incorporating the diffusion model to answer the causal questions more accurately, assuming that all of the confounders are observed. However, unmeasured confounders in practice exist, which hinders DCM from being applicable. To alleviate this limitation of DCM, we propose an extended model called Backdoor Criterion based DCM (BDCM), whose idea is rooted in the Backdoor criterion to find the variables in DAG to be included in the decoding process of the diffusion model so that we can extend DCM to the case with unmeasured confounders. Synthetic data experiment demonstrates that our proposed model captures the counterfactual distribution more precisely than DCM under the unmeasured confounders.
Dimensionality Reduction for Smart Meter Data Using Variational Autoencoders

The growth of smart grid technology has led to the generation of exceptionally high-dimensional energy datasets, owing to the increased frequency of measurements. Moreover, utilizing a year-long dataset is imperative, as relying solely on monthly or quarterly data may lead into wrong conclusions due to variations in seasonal dynamics and customer behavior. In order for this vast amount of information to be usable, it must be summarized into a low-dimensional representation. In this paper, we propose a Long Short Term Memory-based Variational Autoencoder (LSTM-VAE) to generate an encoded representation of the original complex multivariate data. By utilizing this method, a 8760-dimensional dataset can be condensed into a 10-dimensional dataset, which can be subsequently further reduced by Principal Component Analysis (PCA) to a 2D representation for visualization purposes. In addition to visually inspecting the 2D representation of the data, the effectiveness of this method is assessed by a subsequent clustering algorithm using the Adjusted Rand Index (ARI). Both visual inspection and the ARI index demonstrate that the proposed method outperforms other dimensionality reduction techniques by a substantial margin.
RoBERTa for URL Classification: Enhancing Web Security and Content Filtering

The rising occurrence of malicious URLs on the internet is a major concern for users and their devices. To combat this problem, there is a need to develop effective methods to protect against these threats. The use of artificial intelligence (AI) presents an opportunity to leverage this technology to tackle this issue. This study proposes a URL classification model that uses RoBERTa transformer, an AI-based natural language processing technique, to classify URLs based on their intent. The model’s performance has been evaluated using various metrics, showcasing the potential of AI to assist in the creation of robust web security and online content filtering tools.
Fourier U-Shaped Network for Multi-Variate Time Series Forecasting

Multi-variate time series forecasting plays a crucial role in addressing key tasks across various domains, such as early warning, pre-planning, resource scheduling, and other critical tasks. Thus, accurate multi-variate time series forecasting is of significant importance in guiding practical applications and facilitating these essential tasks. Recently, Transformer-based multi-variate time series forecasting models have demonstrated tremendous potential due to their outstanding performance in long-term time predictions. However, Transformer-based models for multi-variate time series forecasting often come with high time complexity and computational costs. Therefore, we propose a low time complexity model called Fourier U-shaped Network (F-UNet) for multi-variate time series forecasting, which is non-Transformer based. Specifically, F-UNet is composed of low time complexity neural network components, such as Fourier neural operator and feed-forward neural network, arranged in a U-shaped architecture. F-UNet conducts channel and temporal modeling separately for the multi-variate time series. The U-Net constructed based on Fourier neural operators is employed to achieve channel interactions, while linear layers are used to realize temporal interactions. Experimental results on several real-world datasets demonstrate that F-UNet outperforms existing Transformer-based models with higher efficiency in multi-variate time series forecasting.
Arabic Offensive Language Classification: Leveraging Transformer, LSTM, and SVM

Social media platforms have become indispensable parts of our lives, offering avenues to share news, thoughts, and updates, as well as connect with new friends and explore various fields of knowledge. However, these platforms also harbor challenges, as they can inadvertently propagate hate speech and offensive content. Arabic, being the sixth most spoken language globally and widely used in over 22 countries, requires special attention to control and prevent the spread of hate speech. The core objective of this study is to develop an improved Arabic model for classifying offensive content, achieved by merging multiple Arabic hate and offensive datasets, including Iraqi offensive samples. Three distinct strategies were used: support vector machine (SVM), long short-term memory (LSTM), and the AraBERT transformer model. The used models were evaluated using recall, precision, F1-score, and accuracy metrics. Notably, the transformer model consistently outperformed the others across all metrics, showcasing its superior performance. Moreover, each dataset underwent assessment using the three models, consistently revealing the transformer’s heightened efficiency.
UPoS Tagger for Low Resource Assamese Language: LSTM and BiLSTM based Modelling

Part-of-Speech (PoS) tagging is a crucial component of natural language processing, enabling syntactic and semantic analysis. This paper presents the development and evaluation of a Neural PoS tagger tailored for the Assamese language using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) architectures. Assamese, a low resource language, with its rich morphological structure, presents unique challenges for PoS tagging. Leveraging deep learning, we aim to achieve state-of-the-art performance in Universal PoS (UPoS) tagging, contributing to multilingual NLP research. In the laboratory setup, an Intel Xeon-based CPU and an NVIDIA Quadro P1000 GPU powered a robust system configuration. TensorFlow, Keras, scikit-learn, and numpy were employed for model development, feature extraction, and data preprocessing. The Assamese corpus, comprising 30,000 sequences from the GUIT Assamese Corpus, was meticulously cleaned, creating the gold standard dataset of sequences with a total of 420320 tokens. The UPoS tagset, designed for cross-lingual NLP, consists of 17 universal PoS categories. Evaluation metrics included precision, recall, F1-score, and overall accuracy. The BiLSTM model outperformed LSTM, with accuracy, precision, recall, and F1-score exceeding 97%. Comparison with a GRU-based baseline model demonstrated the superiority of the BiLSTM model, with improvements of up to 3.77% across performance metrics. The trained models lay the foundation for building high-quality tagged corpora for this low resource language. This is a groundbreaking approach to UPoS tagging in Assamese using LSTM and BiLSTM techniques. The use of a smaller UPoS tagset showcases the impressive capabilities of the trained models, paving the way for larger, low-error tagged corpora. This research contributes to the development of NLP tools tailored for the Assamese language, marking a significant step forward in the field.
Image Size Reduction by Controlled Amount of Band Limitation According to Coding Difficulty

Preprocessing of high-compression video coding was developed for image size reduction. A blocking artifact for video coding is likely to form in a pixel region having high coding difficulty. An example of such a pixel region includes a region with a high-powered spatial high-frequency component in the original image. In such a region, the coding is difficult because aliasing is more likely to form in a reduced-size image. In addition, even if an ideal low-pass filter is applied corresponding to the reduced image size, the coding difficulty is high because high-power spatial high-frequency components are included in the reduced-size image. To prevent blocking artifacts in video coding of the reduced-size image, the proposed method adaptively controls the amount of band limitation of spatial frequency in an iterative process using the spectrum power information of the original image and a quantization parameter in video coding of its reduced-size image. When iterative processing is not allowed, only the spectrum power information of the original image from encoding information calculated previously by the video codec is used to estimate a quantization parameter. Experiments showed that coding in the proposed method is less difficult in comparison with the traditional bicubic and conventional wavelet methods to prevent blocking artifact formation. These experimental results and ideas for future improvement of the method are discussed.
Comparative Analysis of Two K-means-based Methods for Color Quantization

This paper presents two color image quantization methods, namely RKI-CIQ and RK2-CIQ. These are population-based methods that use the k-means algorithm. Both color quantization methods require only a few control parameters. In this paper, a comparative analysis of the two color image quantization methods is presented. The experimental analysis is based on four test images and different color palette sizes. The obtained results demonstrate the successful application of these color quantization methods.
Carbon Monoxide Emission Prediction Based on Concept Drift Detection Using KPCA for Municipal Solid Waste Incineration Processes

Municipal solid waste incineration (MSWI) technology has developed rapidly worldwide. Carbon monoxide (CO) is one of the to be controlled key operating index of such processes. CO emission concentration prediction is a challenge problem duo to its large fluctuation range. A new CO emission concentration prediction method based on concept drift detection using kernel principal component analysis (KPCA) is proposed. The proposed approach includes off-line model construction module, on-line concept drift detection prediction and updating module. First, we construct the LSTM-based CO prediction model using historical data and KPCA-based concept drift detection model for calculating the evaluation index. Then, recursive KPCA is used to adaptive monitor the concept drift of the time-varying process. Finally, based on continuous updating of the historical LSTM mode with the concept drift samples, we achieve higher prediction accuracy. The rationality and validity are verified with the actual data of MSWI processes.
Multilevel Thresholding Image Segmentation Based on Multi-swarm Particle Swarm optimization with a Dynamic Learning Strategy and Kapur’s entropy

In this paper, the multilevel thresholding method based on Kapur’s entropy and the recently proposed multi-swarm particle swarm optimization with a dynamic learning strategy is considered. The multilevel thresholding method is extensively evaluated on ten benchmark images. The experimental results, which include the mean and standard deviation of Kapur’s entropy, obtained from forty independent executions of the thresholding method for each test image and the considered total number of thresholds, demonstrate that multi-swarm particle swarm optimization with dynamic learning strategy can be successfully applied to solve the multilevel thresholding problem.
Towards more flexible human-machine speech communication

The research presented in the paper addresses challenges related to the development of more flexible systems for speech communication between humans and machines. Specifically, the paper presents the main results of the speech technology research group at the Faculty of Technical Sciences, University of Novi Sad, Serbia, in the development of a multilingual human-machine communication system. The approach, which fully exploits recent advances in the area of machine learning and artificial intelligence, extends the basic functionality of a text-to-speech system by increasing its flexibility with respect to the speaking style, speaker identity and even language, by means of neural network embedding. At the same time, the performance of automatic speech recognition is improved in terms of its adaptability to different channels and speakers based on machine learning algorithms originally used in image processing. Domain transfer, as well as creation of dynamic dictionaries have played a crucial role in most recent developments in the area of speech recognition. The focus of the research presented in the paper is on the cases when the available quantity of adaptation data is very small, which corresponds to an increased practical usability of proposed approaches in many real world scenarios.
Multi-Robot System Architecture Focusing on Plan Recovery for Dynamic Environments

The complexity of multi-robot systems (MRS) involves the challenging task of robot coordination to achieve the system's goal. That indicates the necessity to integrate automated planning to mitigate disruptions and continually adjust the behavior of robots in the presence of failures. Adequate architectures to integrate MRS with automated planning present a gap in the literature, indicating the necessity for further research. To address this gap, we present the Multi-Robot System Architecture with Plan (MuRoSA-Plan) for mission coordination of heterogeneous robots illustrated with a healthcare service case. This work contribution is the MuRoSA-Plan architecture to MRS domain applications focusing on plan recovery. The experimental results show that MuRoSA-Plan generates runtime-adapted plans satisfying the goals of the multi-robot coordination case mitigating mission disruptions.
Developing Elementary Federated Learning Algorithms Leveraging the ChatGPT

The Python Testbed for Federated Learning Algorithms is a simple Python FL framework easy to use by ML&AI developers who do not need to be professional programmers, and this paper shows that it is also amenable to emerging AI tools. In this paper, we successfully developed three elementary FL algorithms using the development process of transforming the given sequential code into the target FL code through the following three steps: (i) specify context comprising the sequential code, API description, and the function main, (ii) ask ChatGPT to complete server and clients’ callback functions, and (iii) verify the generated code. We conducted a series of experiments with the goal of creating minimal contexts based on the feedback from ChatGPT through its answers to a questionnaire given at the end of each experiment.
Pornography Image Detection in Digital Forensics

Digital forensic experts often encounter the challenge of managing a significant volume of data during their investigations, which can lead to time and efficiency constraints. To address this issue, a tool has been developed to analyze provided images and specifically target the identification of pornographic content, with a particular emphasis on detecting child pornography as a future work, which is a criminal offense in many countries. This tool incorporates various optimization techniques to achieve its objective. First of all, it examines each image to determine the presence of a face or a person in order to extract the corresponding color palette. Subsequently, it conducts an in-depth analysis of the entire image for detecting skin regions by using a precise chromatic spectrum of pixels and extracting the relevant color palette. Finally, a comparison is performed between the two extracted palettes to identify common and contiguous areas. The proposed tool allows the user to set a specific threshold, the system provides a prompt response indicating the presence or absence of pornography. This ensures an efficient workflow for digital forensic professionals who are focused on combating child pornography and contribute to the global official activities by judicial authorities against this heinous crime.
Enhanced Generalization Through Prioritization and Diversity in Self-Imitation Reinforcement Learning Over Procedural Environments with Sparse Rewards

Exploration poses a fundamental challenge in Reinforcement Learning (RL) with sparse rewards, limiting an agent's ability to learn optimal decision-making due to a lack of informative feedback signals. Self-Imitation Learning (self-IL) has emerged as a promising approach for exploration, leveraging a replay buffer to store and reproduce successful behaviors. However, traditional self-IL methods, which rely on high-return transitions and assume singleton environments, face challenges in generalization, especially in procedurally-generated (PCG) environments. Therefore, new self-IL methods have been proposed to rank which experiences to persist, but they replay transitions uniformly regardless of their significance, and do not address the diversity of the stored demonstrations. In this work, we propose tailored self-IL sampling strategies by prioritizing transitions in different ways and extending prioritization techniques to PCG environments. We also address diversity loss through modifications to counteract the impact of generalization requirements and bias introduced by prioritization techniques. Our experimental analysis, conducted over three PCG sparse reward environments, including MiniGrid and ProcGen, highlights the benefits of our proposed modifications, achieving a new state-of-the-art performance in the MiniGrid-MultiRoom-N12-S10 environment.
Adversarial Attention for Human Motion Synthesis

Analysing human motions is a core topic of interest for many disciplines, from Human-Computer Interaction, to entertainment, Virtual Reality and healthcare. Deep learning has achieved impressive results in capturing human pose in real-time. Acquiring human motion datasets is highly time-consuming, challenging, and expensive. Hence, human motion synthesis is a crucial research problem within deep learning and computer vision. We present a novel method for controllable human motion synthesis by applying attention-based probabilistic deep adversarial models with end-to-end training. We show that we can generate synthetic human motion over both short- and long-time horizons through the use of adversarial attention.
Cross-chain General Message Passing Protocol via Eternal Bridge

This paper introduces the Eternal General Message Passing Bridge design, addressing the increasing need for interconnection among blockchain-based applications. Security challenges inherent to individual blockchains are compounded in cross-chain communication protocols. We analyze existing bridge protocols, combining their strengths for secure General Message Passing between diverse blockchains. The paper outlines the high-level architecture, key components, purposes, and data flow within connected chains, providing an innovative cross-chain solution.
Innovative Approaches to Neurosurgical Planning: Virtual Reality Integration in Honduran Secondary Care

Physicians from different specialties often have complications when carrying out their surgical planning prior to venturing into a surgical intervention. This may be the result of different reasons, such as the impediments in the understanding and analysis of medical images by the medical specialist due to the complexity of the anatomical structure to be studied and this is not clearly represented in the medical images traditional two-dimensional. This research will study the feasibility of being able to implement virtual reality for surgical planning of neurosurgery in Honduras, emulating the work carried out in foreign countries. This type of technique will not only provide a much more efficient tool for the specialist doctor, since it allows him to visualize structures and plan surgical strategies through images that are not visible in 2D images; but also, promote the development of medical technology in Honduras through evidence with cases of real patients, especially in complex medical specialties such as neurosurgery. The development of the investigation determined that the implementation of virtual reality for surgical planning offers new and important tools to medical specialists to be able to visualize the real anatomy of the patient who is going to intervene in the operating room, and also established the possible investment that a health center should run to establish this technology as conveniently as possible.
Learning Control Policies for Variable Objectives from Offline Data

Offline reinforcement learning provides a viable approach to obtain advanced control strategies for dynamical systems, in particular when direct interaction with the environment is not available. In this paper, we introduce a conceptual extension for model-based policy search methods, called variable objective policy (VOP). With this approach, policies are trained to generalize efficiently over a variety of objectives, which parameterize the reward function. We demonstrate that by altering the objectives passed as input to the policy, users gain the freedom to adjust its behavior or re-balance optimization targets at runtime, without need for collecting additional observation batches or re-training.