Reviewerly/exHarmony huggingface dataset:
Argumentation in AI

In this chapter, we focus on abstract argumentation and probabilistic abstract argumentation. In particular, we aim at providing the reader with a proper knowledge of how dialogues and disputes are modeled as abstract argumentation graphs and how analysts reason over them. We illustrate the concepts of arguments and attacks, and the most popular semantics, that are properties defined over graphs.
PRIM Project: What Contributions in the Field of Disabilities?

Through the PRIM project, we aim to give people the power to create Scenagrams (interaction scenarios between a human and digital devices) without the need to be trained in programming or to ask computer scientists for help.In this project, software design follows an unconventional approach, far from classical codes, to embody human thinking (based on interactions) instead of computer logic (based on algorithms).The main idea rests on a new time representation using a PRIM-specific timeline instead of a standardized timeline.We evaluated the acceptability and cognitive compatibility of this new timeline with 50 participants.Results are very promising.In this paper, we will present qualitative evaluation results about the interest of such software in the field of disability.
Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

Suspicious mass traffic constantly evolves, making network behaviour tracing and structure more complex. Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them. They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results. Artificial neural network (ANN) offers optimal solutions in classifying and clustering the various reels of data, and the results obtained purely depend on identifying a problem. In this research work, the design of optimized applications is presented in an organized manner. In addition, this research work examines theoretical approaches to achieving optimized results using ANN. It mainly focuses on designing rules. The optimizing design approach of neural networks analyzes the internal process of the neural networks. Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters. The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues. The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors. The proposed ANN offered optimal results in real-world application problems, and the results were obtained using MATLAB.
The Trade-Off Between Performance and Security of Virtualized Trusted Execution Environment on Android

Nowadays, with the significant growth of the mobile market, security issues on the Android Operation System have also become an urgent matter. Trusted execution environment (TEE) technologies are considered an option for satisfying the inviolable property by taking advantage of hardware security. However, for Android, TEE technologies still contain restrictions and limitations. The first issue is that non-original equipment manufacturer developers have limited access to the functionality of hardware-based TEE. Another issue of hardware-based TEE is the cross-platform problem. Since every mobile device supports different TEE vendors, it becomes an obstacle for developers to migrate their trusted applications to other Android devices. A software-based TEE solution is a potential approach that allows developers to customize, package and deliver the product efficiently. Motivated by that idea, this paper introduces a <b>VTEE</b> model, a software-based TEE solution, on Android devices. This research contributes to the analysis of the feasibility of using a virtualized TEE on Android devices by considering two metrics: computing performance and security. The experiment shows that the <b>VTEE</b> model can host other software-based TEE services and deliver various cryptography TEE functions on the Android environment. The security evaluation shows that adding the <b>VTEE</b> model to the existing Android does not add more security issues to the traditional design. Overall, this paper shows applicable solutions to adjust the balance between computing performance and security.
Machine Learning with Neural Networks

Artificial neural networks provide a distributed computing technology that can be trained to approximate any computable function, and have enabled substantial advances in areas such as computer vision, robotics, speech recognition and natural language processing. This chapter provides an introduction to Artificial Neural Networks, with a review of the early history of perceptron learning. It presents a mathematical notation for multi-layer neural networks and shows how such networks can be iteratively trained by back-propagation of errors using labeled training data. It derives the back-propagation algorithm as a distributed form of gradient descent that can be scaled to train arbitrarily large networks given sufficient data and computing power. Learning Objectives: This chapter provides an introduction to the training and use of Artificial Neural Networks, and prepares students to understand fundamental concepts of deep-learning that are described and used in later chapters.
An Empirical Study of the Intuitive Understanding of a Formal Pattern Language

[Context and motivation] Formal pattern languages with a restricted English grammar, such as the pattern language of Konrad and Cheng, give us the possibility to combine human intuition and the rigour of a machine. [Question/problem] The question arises to what extent the intuitive understanding of such a pattern language is in agreement with its formal semantics. [Principal ideas/results] We present an empirical study to address this question. The existence of a formal semantics allows us to use the machine as an objective judge to decide if the intuitive understanding is correct. The study confirms empirically the practical usefulness of HanforPL in that the intuitive understanding matches the formal semantics in most practically relevant cases. The study reveals that a number of phrases of interest represent critical edge cases where even a prior exposure to formal logic is not a guarantee for the correct intuitive understanding. [Contribution] We show how the alignment of formal and intuitive semantics can be investigated, and that this alignment can not simply be assumed. Nonetheless, results regarding the understandability of HanforPL are favourable with high understandability in commonly used patterns. The results of the study will be the basis of improvements in HanforPL.
Battle Royale Optimization-Based Resource Scheduling Scheme for Cloud Computing Environment

Cloud computing (CC) is developing as a powerful and flexible computational structure for providing ubiquitous service to users. It receives interrelated software and hardware resources in an integrated manner distinct from the classical computational environment. The variation of software and hardware resources were combined and composed as a resource pool. The software no more resided in the single hardware environment, it can be executed on the schedule of resource pools to optimize resource consumption. Optimizing energy consumption in CC environments is the question that allows utilizing several energy conservation approaches for effective resource allocation. This study introduces a Battle Royale Optimization-based Resource Scheduling Scheme for Cloud Computing Environment (BRORSS-CCE) technique. The presented BRORSS-CCE technique majorly schedules the available resources for maximum utilization and effectual makespan. In the BRORSS-CCE technique, the BRO is a population-based algorithm where all the individuals are denoted by a soldier/player who likes to go towards the optimal place and ultimate survival. The BRORSS-CCE technique can be employed to balance the load, distribute resources based on demand and assure services to all requests. The experimental validation of the BRORSS-CCE technique is tested under distinct aspects. The experimental outcomes indicated the enhancements of the BRORSS-CCE technique over other models.
Dynamic GPU Scheduling with Multi-resource Awareness and Live Migration Support

In clouds and data centers, GPU servers with multiple GPUs are widely deployed. Current state-of-the-art GPU scheduling policies are “static” in assigning applications to different GPUs. These policies usually ignore the dynamics of the GPU utilization and are often inaccurate in estimating resource demand before assigning/running applications, so there is a large opportunity to further balance the loads and improve GPU utilization. Based on CUDA (Compute Unified Device Architecture), we develop a runtime system called DCUDA which supports <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“dynamic”</i> scheduling of running applications between multiple GPUs. In particular, DCUDA takes into consideration multidimensional resources, including computing cores, memory usage, and energy consumption. It first provides a real-time and lightweight method to accurately monitor the resource demand of applications and GPU utilization. Furthermore, it provides a universal migration facility to migrate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“running applications”</i> between GPUs with negligible overhead. More importantly, DCUDA transparently supports all CUDA applications without changing their source code. Experiments with our prototype system show that DCUDA can reduce 78.3% of overloaded time of GPUs on average. As a result, for different workloads consisting of a wide range of applications we studied, DCUDA can reduce the average execution time of general applications by up to 42.1%, and even up to 67% for memory-intensive tasks. Besides, DCUDA also reduces 13.3% of energy in light-load scenarios.
Bringing Stakeholders Along for the Ride: Towards Supporting Intentional Decisions in Software Evolution

[Context and Motivation] During elicitation, in addition to collecting requirements, analysts also collect stakeholders' goals and the present and historical interests that motivate their goals. This information can guide the resolution of requirements conflicts, support the evolution of requirements when changes occur (e.g., environmental constraints), and inform decisions in software design. [Problem] Unfortunately, this information is rarely explicitly represented and maintained. When a stakeholder is modeled in the literature, the captured information is only part of that stakeholder's intention (i.e., the goals and the present and historical interests that motivate those goals) and not other requirements documents. In addition, such representations of a stakeholder are not traced and kept aligned with the design and, thus, cannot be used during iterative development and in case of changes. [Principal Idea] To support engineers in making informed decisions during the design, development, and evolution of a system, we propose a framework to collect and maintain intentionality in an efficient and effortless way. [Contributions] To define intentionality, disambiguate it from its use in literature, and position it in relation to similar concepts (i.e., rationale and goals), we conduct a literature review. Based on our derived definition, we present our framework to appropriately include intentionality throughout the stages of a project and the research agenda to realize such a framework.
Ontology-Based Automatic Reasoning and NLP for Tracing Software Requirements into Models with the OntoTrace Tool

Context and motivation. Traceability is an essential part of quality assurance tasks for software maintainability, validation, and verification. However, the effort required to create and maintain traces is still high compared to their benefits. Problem. Some authors have proposed traceability tools to address this challenge, yet some of those tools require historical traceability data to generate traces, representing an entry barrier to software development teams that do not do traceability. Another common requirement of existing traceability tools is the scope of artefacts to be traced, hindering the adaptability of traceability tools in practice. Principal ideas. Motivated by the mentioned challenges, in this paper we propose OntoTraceV2.0: a tool for supporting trace generation of arbitrary software artefacts without depending on historical traceability data. The architecture of OntoTraceV2.0 integrates ontology-based automatic reasoning to facilitate adaptability for tracing arbitrary artefacts and natural language processing for discovering traces based on text-based similarity between artefacts. We conducted a quasi-experiment with 36 subjects to validate OntoTraceV2.0 in terms of efficiency, effectiveness, and satisfaction. Contribution. We found that OntoTraceV2.0 positively affects the subjects' efficiency and satisfaction during trace generation compared to a manual approach. Although the subjects' average effectiveness is higher using OntoTraceV2.0, we observe no statistical difference with the manual trace generation approach. Even though such results are promising, further replications are needed to avoid certain threats to validity. We conclude the paper by analysing the experimental results and limitations we found, drawing on future challenges, and proposing the next research endeavours.
Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

Natural Language Processing (NLP) for the Arabic language has gained much significance in recent years. The most commonly-utilized NLP task is the ‘Text Classification’ process. Its main intention is to apply the Machine Learning (ML) approaches for automatically classifying the textual files into one or more pre-defined categories. In ML approaches, the first and foremost crucial step is identifying an appropriate large dataset to test and train the method. One of the trending ML techniques, i.e., Deep Learning (DL) technique needs huge volumes of different types of datasets for training to yield the best outcomes. The current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machine-based Arabic Corpus Classification (DODHBM-ACC) model in this background. The presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding process. For Arabic text classification, the DHBM technique is utilized. This technique is a hybrid version of the Deep Boltzmann Machine (DBM) and Deep Belief Network (DBN). It has the advantage of learning the decisive intention of the classification process. To adjust the hyperparameters of the DHBM technique, the Dice Optimization Algorithm (DOA) is exploited in this study. The experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC model. The outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.
Flight Control Method Using Neural Network in Prediction for Suppressing Ship Airwake Impact in Carrier Landing

Ship airwake is an important factor affecting the accurate carrier landing of carrier-based aircraft. To suppress the effects of airwake, this article presents a flight control method using neural network prediction. Different from most studies focusing on improving the adaptability of the flight control methods, the emphasis of the research is placed on modeling the effects of the ship airwake in the carrier landing environment pertinently and reflecting the effects in the control model. From a large amount of carrier landing flight data, the impact of airwake on the landing control can be extracted and summarized into models based on neural network algorithm. Then the airwake impact in the next period of time can be predicted. A flight control law is designed combining the predicting model of airwake impact with model predictive control. The simulation demonstrated that the presented method can reduce the trajectory deviation influenced by airwake and decrease the touchdown dispersions.
Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

The field of sentiment analysis (SA) has grown in tandem with the aid of social networking platforms to exchange opinions and ideas. Many people share their views and ideas around the world through social media like Facebook and Twitter. The goal of opinion mining, commonly referred to as sentiment analysis, is to categorise and forecast a target’s opinion. Depending on if they provide a positive or negative perspective on a given topic, text documents or sentences can be classified. When compared to sentiment analysis, text categorization may appear to be a simple process, but number of challenges have prompted numerous studies in this area. A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron (MLP) techniques has been proposed as a way to automate sentiment analysis (SA). In this study, online product reviews can be enhanced by integrating classification and feature election. The firefly (FF) algorithm was used to extract features from online product reviews, and a multi-layer perceptron was used to classify sentiment (MLP). The experiment employs two datasets, and the results are assessed using a variety of criteria. On account of these tests, it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon (98% accuracy) and iPod (99% accuracy).
MULTIMODAL COMMUNICATION OF BRITISH MUSEUMS ON FACEBOOK

The article is devoted to social media activity of leading British museums, content strategies they implement to share the news and information via multimodal discourse. Museums social media accounts became a significant component of modern online landscape, in particular, during COVID-19 pandemic when millions of people worldwide started looking for some extracurricular recreation, cultural and aesthetic impressions. The pandemic was both, a challenge and an impetus for many museums to increase online presence and intensify their activity on social media. Having faced the closure during the lockdown many museums had to transform their communication on websites and Facebook, Twitter, Instagram to keep in touch with their followers, visitors and members, to inform, educate and entertain the digital audience. Modern technologies helped museums to diversify modes and media, to make social media posts and virtual communication with followers multimodal, to disseminate knowledge in new ways. Leading British museums turned out to be highly innovative and creative and set multimodality. standards in museums communication. Facebook remains the most popular social media platform for museums so the content of Facebook accounts of Madame Tussauds Museum, National Gallery, National Portrait Gallery, Natural History Museum, Victoria and Albert Museum in 2021-2022 was analyzed in terms of multimodality, communication strategies, edutainment, use of elements characteristic of online communication such as hashtags, emoji, conversation style, dialogic principles, user-generated content. Сontent strategies of British museums in Facebook vary depending on their profile. Edutainment, games, puzzles, quizes, light-hearted communication provoke more response, more emotional reaction of followers as well as their engagement into discussions and dialogues, interactive experience.
Towards a Cross-Country Analysis of Software-Related Tweets

[Context and motivation] Twitter is one of the most widely used micro-blogging platforms. Globally distributed developers and software companies use Twitter to communicate about software updates, bugs and other type of information related to the software. End-users from diverse geographical regions also use Twitter to give feedback about the software they use. Previous research has shown that this feedback is valuable for requirements engineering, containing information such as feature requests and usage scenarios. However, the effect of the country of origin on software-related tweets has not been studied so far. [Question] In this paper, we investigate to what extent people from various countries provide distinct feedback regarding certain characteristics on Twitter. [Principal ideas/results] We collected 70,759 tweets (Original: 17,940, Replies: 52,819) from popular Twitter support accounts of ten software applications for two months. In the subsequent analysis, we selected the tweets originating from the eight most popular countries and analyzed a sample of 1,813 tweets with the help of automatic and manual content analysis. Results show that out of three characteristics (content, sentiment and text length); content, and sentiment differ significantly at the country level in some cases. These characteristics are used in algorithms automatically processing user feedback. Such algorithms are commonly used for requirements engineering tasks. [Contributions] Our findings show the importance of considering software-related user feedback on Twitter from a diverse audience during the design, testing, and validation of feedback processing algorithms to minimize bias concerning different countries of origin.
Cognitive Load Measurement Based on EEG Signals

Measurement of the cognitive load should be advantageous in designing an intelligent navigation system for visually impaired people (VIPs) when navigating unfamiliar indoor environments. Electroencephalogram (EEG) can offer neurophysiological indicators of the perceptive process indicated by changes in brain rhythmic activity. To support the cognitive load measurement by means of EEG signals, the complexity of the tasks of the VIPs during navigating unfamiliar indoor environments is quantified considering diverse factors of well-established signal processing and machine learning methods. This chapter describes the measurement of cognitive load based on EEG signals analysis with its existing literatures, background, scopes, features, and machine learning techniques.
Requirements Classification Using FastText and BETO in Spanish Documents

Context and motivation: Machine Learning (ML) algorithms and Natural Language Processing (NLP) techniques have effectively supported the automatic software requirements classification. The emergence of pre-trained language models, like BERT, provides promising results in several downstream NLP tasks, such as text classification. Question/problem: Most ML/DL approaches on requirements classification show a lack of analysis for requirements written in the Spanish language. Moreover, there has not been much research on pre-trained language models, like fastText and BETO (BERT for the Spanish language), neither in the validation of the generalization of the models. Principal ideas/results: We aim to investigate the classification performance and generalization of fastText and BETO classifiers in comparison with other ML/DL algorithms. The findings show that Shallow ML algorithms outperformed fastText and BETO when training and testing in the same dataset, but BETO outperformed other classifiers on prediction performance in a dataset with different origins. Contribution: Our evaluation provides a quantitative analysis of the classification performance of fastTest and BETO in comparison with ML/DL algorithms, the external validity of trained models on another Spanish dataset, and the translation of the PROMISE NFR dataset in Spanish.