Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning in Cloud Elasticity Through Offline Learning and Return Based Scaling

Elastic resource allocation is a desirable feature of cloud environments, and one of the main reasons for their widespread adoption. Resource elasticity allows for adaptive and real-time infrastructure scaling that can follow workload fluctuations in a cost-effective manner without sacrificing performance. Promising Machine Learning (ML) approaches employ Reinforcement Learning (RL) where an agent interacts with the cloud environment by employing actions, observing the reward of their outcome and modifying its strategy accordingly. Nevertheless, one of the main problems of RL in this setup is that to acquire a sufficient initial knowledge of the environment, the agent needs to perform numerous time-consuming and performance-degrading interactions with the cloud. In this work we design and implement RBS-CQL, a Deep-RL Kubernetes agent system to monitor and automatically scale the containers of a NoSQL application according to incoming workload. We combine training optimization techniques from contemporary literature as well as offline RL algorithms to reduce the training time. We provide empirical results that show that RBS-CQL achieves systematic improvement of more than 10% compared to its online equivalent for a given number of experiences and that it is able to extract improved decision-making policies even from data of lower quality.
A Historical Information Based Differential Evolution

Differential evolution is an efficient and robust optimizer. However, DE still has the problems of evolutionary stagnation and inappropriate generated control parameters. In the search behavior of each individual in the past population, some valuable historical information for future evolution may be hidden, which can help the optimizer solve these problems. Based on the above consideration, we propose a historical information based differential evolution (HIDE). In our algorithm, a new mechanism is established to judge whether an individual is in stagnation, and a new mutation strategy based on discarded parent vectors from different periods is proposed to help the stagnant individuals escape from the local optimum. Meanwhile, we designed a new update method for control parameters based on historical information. Compared with the mainstream schemes, the parameters generated by our method are more suitable for the current function. To evaluate the performance of our algorithm, we compared HIDE with eight advanced variants on the CEC 2017 test platform. The experimental results show that the quality of the solution provided by HIDE is better than that of other variants.
Detection of Component Degradation: A Study on Autoencoder-Based Approaches

In the realm of predictive maintenance, the incorporation of artificial intelligence (AI) methods has revolutionized the field by empowering businesses to actively monitor and preemptively address equipment malfunctions. Detecting anomalies plays a crucial role in predictive maintenance as it serves as an early indicator of potential faults or failures. This paper introduces initial findings from the use of autoencoders and their associated vector reconstruction error within the context of the IMOCO4.E project.
Effective Robotic Swarm Shepherding in the Presence of Obstacles

We present a modified planning-assisted swarm shepherding method to effectively control multi-robot (sheepdogs) when herding a swarm of reactive agents (sheep) towards a goal and in environments with obstacles. Given a highly-dispersed sheep swarm, a mission planner based on Ant Colony Optimisation and A * is designed and developed to support the shepherding task. To apply the swarm shepherding method to real robots, a multi-layer environmental modelling method is proposed to construct customised environment maps for sheep and sheepdogs according to their physical characteristics. Then, a lookahead - based sub-goal selection method is presented for herding the sheep swarm to follow the A * optimised reference path. Furthermore, a circle-based method for selecting feasible driving/collecting points while avoiding obstacles is designed. Experiments are conducted in numerical simulation environments to compare the proposed method with the state-of-the-art planning-assisted shepherding method, followed by testing in the robot simulation platform CoppeliaSim to demonstrate the effectiveness of the proposed method.
Behavioural Swarm Optimisation for Stable Slung-Load Aerial Transportation

Collaboration of agents in a natural swarm enables the accomplishment of tasks that would be difficult or impossible for a single agent to complete alone. For example, a swarm of autonomous Unmanned Aerial Vehicles (UAVs) enables the collaborative sensing of bulky loads for transportation over impassable terrains when the load weighs several times more than each UAV. In this work, we propose a hierarchical algorithmic architecture that supports the search and coverage of various unknown payload profiles for subsequent transportation. The grasping formation of UAVs over the payload emerges from the synthetic behaviours in the architecture without any path planning. Experiments show that our proposed design can be successfully applied in searching and coverage of various loads and has been validated in the real world through the use of Crazyflie micro-UAVs. Furthermore, the proposed grasping formation satisfies static equilibrium thereby reducing orientation changes in the load-swarm system during transportation.
Prediction of NOx Emission Concentration Based on Weighted Optimal Combination Model

In order to achieve accurate prediction of NOx emissions concentration from coal-fired boiler outlets, this paper proposes a nitrogen oxide emission concentration prediction model based on a weighted optimization combination model. Firstly, in order to extract the depth time-frequency information in the data, the RLMD decomposition is used to decompose the input data. Secondly, considering the existence of redundant information in the input data, the ReliefF feature selection is used to select the strongly correlated feature variables as the modeling input. Finally, in order to improve the accuracy of the prediction model, Extreme Learning Machine (ELM), Echo State Network (ESN), Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) are combined, and the weights of the four models are optimized using the ant colony algorithm to construct the NOx emission concentration prediction model. Experimental results demonstrate that the proposed algorithm has a prediction error of less than 2% for NOx emission concentration under different operating conditions, and can achieve accurate prediction of NOx emission concentration from coal-fired boiler outlets.
Explainable AI-Based Clinical Decision Support System for Obesity Comorbidity Analysis

This paper presents a novel Clinical Decision Support System based on eXplainable Artificial Intelligence (XAI-CDSS) as a comprehensive structured tool consisting of three main parts: predictive models, XAI interpretation, and a graph-based visualization of non-communicable pathologies. Machine learning models are proposed to predict the risk factors related to the direct association between obesity and comorbidities such as cardiovascular, heart disease, and diabetes. Multilayer perceptron and extreme gradient boosting are chosen among different machine learning algorithms as the best performing for the risk factors prediction of the selected comorbidities. They perform prediction with an accuracy of 0.72 for diabetes, and 0.73 for cardiovascular and heart disease. The intuitive XAI interface gives the end-user insight into the machine learning decision process, while the graph visualization links such co-occurrent pathologies to many other non-communicable diseases and provides a global view to healthcare professionals, usable for obesity and associated pathologies prevention and long-term treatment and care.
A Novel Diversity-Aware Inertia Weight and Velocity Control for Particle Swarm Optimization

Particle Swarm Optimization (PSO) has efficiently solved several real-world applications and optimization problems. However, it has shortcomings, such as premature convergence and stagnation at local minima. Inertia weight is a parameter of this algorithm that controls the global and local exploration and exploitation capability by determining the influence of the previous velocity on its current motion. Therefore, this article proposes a PSO with a Diversity-aware Inertia and Velocity Control (PSOIVC) algorithm to improve the PSO performance. The PSOIVC employs a novel diversity-aware inertia weight and velocity control approach to tune the parameters to produce a trade-off between exploration and exploitation of the algorithm using the dimension-wise diversity. The PSOIVC algorithm is compared with eight algorithms, including variants of the PSO, on a set of 30 benchmark functions for a single objective real parameter in 30 and 50 dimensions. Based on the results, the proposal presents significant outcomes according to the average values obtained for both comparisons; because it performed similarly or better than the other algorithms in 23/30 and 16/30 for 30 and 50 dimensions, respectively.
RepoGraph: A Novel Semantic Code Exploration Tool for Python Repositories Based on Knowledge Graphs and Deep Learning

This work presents RepoGraph, an integrated semantic code exploration web tool that combines information extraction, knowledge graphs, and deep learning models. It offers new capabilities for software developers (from academia and industry) to represent and query Python repositories. Unlike existing tools, RepoGraph not only provides a novel search interface powered by deep learning techniques but also exposes the underlying features and representations of repositories to users. Additionally, it offers several interactive visualizations. We also introduce RepoPyOnto, a new ontology that captures the features of Python code repositories and is used by RepoGraph for representing the captured knowledge. Finally, we successfully evaluate RepoGraph against several criteria, including function summarization performance, the correctness and relevance of search results, as well as the processing time for constructing graphs of various sizes.
Performance Characterization of Multi-Container Deployment Schemes for Online Learning Inference

Online machine learning (ML) inference services provide users with an interactive way to request for predictions in realtime. To meet the notable computational requirements of such services, they are increasingly being deployed in the Cloud. In this context, the efficient provisioning and optimization of ML inference services in the Cloud is critical to achieve the required performance and meet the dynamic queries by end-users. Existing provisioning solutions focus on framework parameter tuning and infrastructure resources scaling, without considering deployments based on containerization technologies. The latter promises reproducibility and portability features for ML inferences services. There is limited knowledge about the impact of distinct deployment schemes at the container-level on the performance of online ML inference services, particularly on how to exploit multi-container deployments and its relation with processor and memory affinity. In light of this, in this paper we investigate experimentally the containerization of ML inference services and analyze the performance of multi-container deployments that partition the threads belonging to an online learning application into multiple containers in each node. This paper shares the findings and lessons learned from conducting realistic client patterns on an image classification model across numerous deployment configurations, especially including the impact of container granularity and its potential to exploit processor and memory affinity. Our results indicate that fine-grained multi-container deployments and affinity are useful for improving performance (both throughput and latency). In particular, our experiments on single-node and four-node clusters show up to 69% and 87% performance improvement compared to the single-container deployment, respectively.
IRIS: Interference and Resource Aware Predictive Orchestration for ML Inference Serving

Over the last years, the ever-growing number of Machine Learning(ML) and Artificial Intelligence(AI) applications deployed in the Cloud has led to high demands on the computing resources required for efficient processing. Multiple users deploy multiple applications on the same server node to maximize Quality of Service(QoS); however, this leads to increased interference. In addition, Cloud providers aim to minimize their operating costs by efficiently utilizing the available resources. These conflicting optimization goals form a complex paradigm where efficient scheduling is required. In this work, we present IRIS, an interference- and resource-aware predictive inference scheduling framework for ML inference serving in the cloud. We target the multi-objective problem of QoS maximization with effective CPU utilization based on Queries per Second(QPS) predictions by proposing a modelless ML-based solution and integrating it into the Kubernetes platform. Our approach is evaluated over real hardware infrastructure and a set of ML applications. Our experimental analysis shows that under various QoS constraints, the model specific interference-aware scheduler violates QoS constraints less frequently by achieving 1.8x fewer violations, on average, compared to over-provisioning and 3.1 x fewer violations compared to under-provisioning, through efficient exploitation of available CPU resources. The model-less feature is able to cause, on average, 1.5x fewer violations compared to the model-specific scheduler, while further reducing the average CPU utilization by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 30{\%}$</tex> .
Modeling dynamics of legal relations with dynamic logic

Abstract The fundamental relations in private law are claims and duties. These legal relations can be changed by agents with the appropriate legal powers. We use propositional dynamic logic and ideas about propositional control from the agency literature to formalize these changes in legal relations. Our models are sets of states with functions specifying atomic facts, agents’ abilities to change atomic facts, legal relations between agents concerning changing atomic facts and agents’ powers. We present a formal language that allows us to describe models and changes of models caused by two kinds of actions: actions that change atomic facts and actions that change legal relations. Next, we present a sound and complete calculus for this language. The paper demonstrates that the perspective on actions borrowed from computer science can be used to shed interesting light on the dynamics of legal relations.
DoME: An Architecture for Domain Model Evolution at Runtime Using NLP

In traditional information systems, domain models are represented as database tables with attributes and relationships. Changes in the domain models exist due to system evolution and the emergence of new requirements. In these applications, domain models evolve using CRUD operations requested by users. However, it is necessary to support changes in domain models during the applications’ runtime when new (unforeseen) situations may occur. This work presents an architecture called DoME, which relies on natural language processing (NLP) to allow users to trigger changes in the domain models and self-adaptation techniques to update the models at runtime. It is instantiated in a concrete architecture using a chatbot in Telegram and Transformers Libraries for NLP. The architecture has been preliminary evaluated regarding its assertiveness and user satisfaction, resulting in an 82.55% hit rate and confirming that NL provides good usability and facilitates data manipulation.
A Thematic Synthesis on Empathy in Software Engineering based on the Practitioners' Perspective

Context: Empathy is the ability to understand and share the emotions of others. Despite its relevance for research and practice in software engineering, it is still an under-researched topic. Aims: To investigate the meaning, importance, practices, and effects of empathy from the perspective of software practitioners. Method: We apply a thematic synthesis of grey literature. We analyzed 22 articles from DEV, an online community used by software developers. Results: We found that empathy has different meanings for software practitioners. The word is used to express understanding, compassion, and perspective-taking, among other meanings. Practitioners consider empathy important, undervalued, needed, and wanted. The study points out 19 empathetic practices in SE, such as adopting good programming practices, understanding others, being compassionate, and being mindful. It also lists 28 effects of these practices, including quality improvement, better products, and build trust. Conclusion: We organize this body of knowledge in a framework supporting new research efforts. The framework may also support software professionals to develop empathetic skills in SE.
Analyzing the Impact of CI Sub-practices on Continuous Code Quality in Open-Source Projects: An Empirical Study

Continuous Integration (CI) is a widely adopted practice that automates and enhances the frequency of code integration. Previous research has explored the relationship between CI sub-practices (such as frequent commit activity and high test coverage) and software quality. However, limited knowledge exists regarding the impact of specific CI sub-practices on the Continuous Code Quality (CCQ) inspection outcomes of software projects, such as technical debts, bug density, duplicated lines, and code smells. This paper aims to analyze the extent to which the adoption of CI sub-practices improves CCQ outcome metrics in software projects. First, we investigate the association between Travis CI adoption, Travis CI usage maturity, and improved quality inspection outcomes in a set of 75 popular open-source projects from GitHub. Additionally, we use data from other 97 open-source projects to identify specific CI sub-practices, such as maintaining shorter build durations, that exhibit a strong correlation with enhanced quality inspection outcomes. Our findings reveal that the quality inspection outcomes are improved in projects with higher test coverage and shorter build duration. Merely adopting a CI service is not a guarantee for improved quality outcomes. Therefore, it is essential for developers to consistently adhere to recommended CI sub-practices. By doing so, they can effectively harness the benefits of CI in their projects and achieve improved quality outcomes.
Evolution of Teamwork Quality Instruments in Agile Software Development: A Systematic Literature Review

[Context] Multiple models (or instruments) for measuring Teamwork Quality (TWQ) and Teamwork Effectiveness (TWE) for Agile Software Development (ASD) have been created. However, such models have different constructs and measures, with a limited understanding of how they are related and have evolved. [Goal] Our goal is to identify all ASD instruments to gain insights into the evolution of specific instruments for ASD. [Method] We performed a systematic review methodology using a search string and a forward snowballing approach to identify the specific instruments that assess TWQ and TWE. Later, we conducted a frequency analysis of the factors measured by these ASD instruments. [Results] We provided a comprehensive view of the evolution of teamwork instruments in ASD and classified them into Generic teamwork instruments and Agile-based teamwork instruments. We found that these instruments have evolved with the more specialized factors specific to the agile context. In addition, they have semantically similar factors with different factor names, pointing to the need for terminology standardization. [Conclusion] A conceptual framework integrating the instrument factors within the agile context is needed. We advocate further studies on this topic, aiming to develop a unified taxonomy to be taken as a reference for constructing new teamwork instruments.
High performance multi-image encryption based on temporal-spatial double random ghost coding

Abstract A high performance multi-image encryption method is presented by using the temporal-spatial double random ghost coding (TSDRGC). As an original idea in optical encryption, TSDRGC employs the chaotic lasers as light sources instead of conventional lasers. The random fluctuations of the chaotic laser can be seen as the new temporal security keys introduced into the cryptosystem, except the random illumination patterns used usually in the conventional ghost imaging as a set of spatial security keys. In the encryption process, all the images can be optically encoded in parallel and the time consumed by the encryption process does not increase compared to encrypting an image alone. Meanwhile, a decryption algorithm is proposed to achieve high quality decryption of each image independently while reducing the bit-width requirement. The simulations demonstrate the excellent performance of the scheme in terms of security, capability and robustness.
An adaptive framework for predicting breast cancer at an early stage

Breast cancer is one of the leading medical problems in the healthcare field among women. The cancer-related death rate is a major global health problem, particularly in developing countries. Early diagnosis of breast cancer is the only effective way to deal with this mortality factor. Although there are many methods for preventing cancer, there are still some types that have unknown cures. Breast cancer is extremely common and can be effectively treated if caught in its early stages. A correct diagnosis of breast cancer is a vital first step in treatment. Predicting the subtype of breast cancer is an active area of study. In this article, an attempt has been made to present a framework to predict breast cancer using machine learning techniques at an early stage to get the best treatment for the patient. The proposed method uses the machine learning classifiers Logistic Regression and Support Vector Machine to classify breast cancer patients into benign (Non Cancerous Tumor) and salignant (Cancerous Tumor) categories. The proposed mechanism is implemented using Python and Jupyter Notebook on the real dataset, which is generated through a sensing device and collected from the UCI repository. The performance is analyzed using performance metrics such as accuracy, precision, F-measure, etc. The Logistic Regression (LR) model achieves an accuracy of 97.14%, whereas, in the SVM model, the obtained accuracy is 96%. The performance of the proposed framework was evaluated and compared with the other algorithms, and the results indicated that the proposed framework achieved better performance than other models.