Reviewerly/exHarmony huggingface dataset:
Preface

Extract Trying to write a book about the philosophical implications of deep learning today is a bit like trying to saddle a mustang at full gallop. I started planning this book back in 2015, when it became clear that deep neural networks had fundamentally changed the game in computer vision. Despite having a professional interest in artificial intelligence (AI) and the philosophical implications of artificial neural networks for most of my adult life, I had assumed that we were still decades away from achieving the degree of success that AlexNet and its successors delivered on image labeling tasks. Then just as I’d begun to feel like I understood the basic principles that account for the success of deep convolutional neural networks like AlexNet on computer vision, I realized that I was already several years behind on an entirely different and equally exciting new neural network architecture, generative adversarial networks, that were reaching similarly astounding degrees of performance on image generation tasks. This sense of being perpetually in arrears recurred yet again with the ascendency of transformers on language tasks in 2018. It became clear to me, at least, that deep learning was much more than a one-trick pony, that it would be the workhorse of AI for years to come.
Vulnerability Report Analysis and Vulnerability Reproduction for Web Applications

With the increasing complexity of Web applications, their security issues happen frequently. Vulnerability reports aim to document security issues of Web applications and assist in improving their security and quality. However, vulnerability reports are usually described in highly unstructured natural language, and the descriptions of vulnerabilities vary considerably. So automatically reproducing vulnerabilities from their reports is a challenging task. To this end, this paper proposes an approach to automatically comprehend vulnerability reports and reproduce vulnerabilities in Web applications. In order to automatically parse vulnerability reports of Web applications, a general syntactic dependency pattern is summarized from diverse vulnerability reports to guide the identification and extraction of key information in vulnerability reports. In particular, payloads in vulnerability reports exist mainly in the form of code fragments, unlike natural language. For this reason, a payload extraction rule is further designed. Moreover, considering that the descriptions of vulnerability reports and Web application are different but semantically similar, this paper uses semantic similarity to match the events of web application with the key information of the report, and then generates event sequences and corresponding test scripts to trigger the vulnerability, achieving vulnerability reproduction. To verify the effectiveness of our approach, we collect 400 vulnerability reports from more than 300 Web application projects and summarize syntactic dependency patterns. And 26 real vulnerability reports involving 23 open-source Web applications were used for experiments. The results show that our method can effectively extract critical information from vulnerability reports and generate feasible test scripts to reproduce vulnerabilities, reducing manual operations and improving the efficiency of vulnerability reproduction.
Using the Possibilities of a Virtual Educational Environment to Prepare Stu-dents of Grades 11 for the Unified State Exam in Computer Science

The chapter discusses the specifics of organizing the preparation of 11th grade students for the Unified State Exam (USE) using the capabilities of a virtual educational environment. An example of such an environment created based on cloud storage and automated testing on the Online Test Pad service is considered.
Handling uncertainty issue in software defect prediction utilizing a hybrid of ANFIS and turbulent flow of water optimization algorithm

Abstract During the development cycle of software projects, numerous defects and challenges have been identified, leading to prolonged project durations and escalated costs. As a result, both product delivery and defect tracking have become increasingly complex, expensive, and time-consuming. Recognizing the challenge of identifying every software defect, it is crucial to foresee potential consequences and strive for the production of high-quality products. The goal of software defect prediction (SDP) is to identify problematic locations within software code. This study presents the first experimental investigation utilizing the turbulent flow of water optimization (TFWO) in conjunction with the adaptive neuro-fuzzy inference system (ANFIS) to enhance SDP. The TFWO_ANFIS model is designed to address the uncertainties present in software features and predict defects with feasible accuracy. Data are divided randomly at the beginning of the model into training and testing sets to avoid the local optima and over-fitting issues. By applying the TFWO approach, it adjusts the ANFIS parameters during the SDP process. The proposed model, TFWO_ANFIS, outperforms other optimization algorithms commonly used in SDP, such as particle swarm optimization (PSO), gray wolf optimization (GWO), differential evolution (DE), ant colony optimization (ACO), standard ANFIS, and genetic algorithm (GA). This superiority is demonstrated through various evaluation metrics for four datasets, including standard deviation (SD) scores (0.3307, 0.2885, 0.3205, and 0.2929), mean square error (MSE) scores (0.1091, 0.0770, 0.1026, and 0.0850), root-mean-square error (RMSE) scores (0.3303, 0.2776, 0.3203, and 0.2926), mean bias error (MBE) scores (0.1281, 0.0860, 0.0931, and 0.2310), and accuracy scores (87.3%, 90.2%, 85.8%, and 89.2%), respectively, for the datasets KC2, PC3, KC1, and PC4. These datasets with different instances and features are obtained from an open platform called OPENML. Additionally, multiple evaluation metrics such as precision, sensitivity, confusion matrices, and specificity are employed to assess the model’s performance.
Session Types with Multiple Senders Single Receiver

Message passing is a fundamental element in software development, ranging from concurrent and mobile computing to distributed services, but it suffers from communication errors such as deadlocks. Session types are a typing discipline for enforcing safe structured interactions between multiple participants. However, each typed interaction is restricted to having one fixed sender and one fixed receiver. In this paper, we extend session types with existential branching types, to handle a common interaction pattern with multiple senders and a single receiver in a synchronized setting, i.e. a receiver is available to receive messages from multiple senders, and which sender actually participates in the interaction cannot be determined till execution. We build the type system with existential branching types, which retain the important properties induced by standard session types: type safety, progress (i.e. deadlock-freedom), and fidelity. We further provide a novel communication type system to guarantee progress of dynamically interleaved multiparty sessions, by abandoning the strong restrictions of existing type systems. Finally, we encode Rust multi-thread primitives in the extended session types to show its expressivity, which can be considered as an attempt to check the deadlock-freedom of Rust multi-thread programs.
String Constraints with Regex-Counting and String-Length Solved More Efficiently

Regular expressions (regex for short) and string-length function are widely used in string-manipulating programs. Counting is a frequently used feature in regexes that counts the number of matchings of sub-patterns. The state-of-the-art string solvers are incapable of solving string constraints with regex-counting and string-length efficiently, especially when the counting and length bounds are large. In this work, we propose an automata-theoretic approach for solving such class of string constraints. The main idea is to symbolically model the counting operators by registers in automata instead of unfolding them explicitly, thus alleviating the state explosion problem. Moreover, the string-length function is modeled by a register as well. As a result, the satisfiability of string constraints with regex-counting and string-length is reduced to the satisfiability of linear integer arithmetic, which the off-the-shelf SMT solvers can then solve. To improve the performance further, we also propose techniques to reduce the sizes of automata. We implement the algorithms and validate our approach on 48,843 benchmark instances. The experimental results show that our approach can solve more instances than the state-of-the-art solvers, at a comparable or faster speed, especially when the counting and length bounds are large.
Cheat-FlipIt: An Approach to Modeling and Perception of a Deceptive Opponent

The modeling of opponent deception in an intelligent game system is not sufficient. However, an opponent agent may launch deceptive actions to consume defense resources, such as feint. We focus on modeling a deceptive opponent. We extend the FlipIt game model and present Cheat-FlipIt model, in which the opponent agent may feint to flip the resources first, and then control the resources after a decay interval. The defense agent models and perceives the cheating behavior of the opponent agent. DQN has some shortcomings such as over-fitting and insufficient exploration, and is not suitable for opponent-deception environment. To address the problems of opponent-deception and non-stationary environment, we present NLD3QN, which incorporates Noisynet, LSTM, Dropout and Dueling Q-Network into the DQN. We further propose a series of cheat strategies of the opponent agent. The defense agent adopts NLD3QN to perceive the cheating behavior of opponents. The proposed approach are evaluated in the Cheat-FlipIt game environments. Experimental results performed on 1 vs. 1 games show that NLD3QN demonstrates superior performance to the baseline DQN. Confronted with a deceptive opponent, the winning rate of NLD3QN is 73.3%, while DQN’s winning rate is 26.67%.
Multi-dimensional Abstraction and Decomposition for Separation of Concerns

Model-driven engineering (MDE) or model-driven architecture (MDA) holds significant appeal for the software industry. Its primary aim is to address software complexity by enabling automated model creation and transformation. Consequently, many software development firms are actively seeking integrated development platforms (IDP) to enhance automation within their software production processes. However, the adoption of MDE and the utilisation of IDPs remain low, with doubts surrounding their success. To tackle this issue, this paper uses the formal refinement of component and object systems (rCOS) as a framework to identify different types of requirements and their relationships, with the goal of supporting MDE. We emphasise the necessity for families of formal languages and transformations among them, as well as the indispensability of architecture modelling and refinement in MDE. Furthermore, to enhance the handling of changes during the development and operation of systems, there is a paramount need for formal methods that facilitate abstractions and decompositions, leading to a multi-dimensional separation of concerns.
Analyzing the Metaverse: Computer Games, Blockchain, and 21st-Century Challenge

This paper explores the rapidly evolving Metaverse, particularly its relationship with computer games and blockchain technology, where the most development is happening, and the consequential ethical and societal implications. As we will observe, interaction is crucial for developing the Metaverse. Using a hybrid research methodology, incorporating structured review and extensive bibliometric analysis, we map the intricate dynamics of this emerging field, revealing an accelerated global adoption of the Metaverse and its integration with Blockchain. The year 2022 marks a turning point in the development of the Metaverse. Plus, we discuss the transformation spurred by this convergence, evaluating emerging ethical issues like ownership rights, privacy concerns, potential exploitation, and susceptibility to manipulation. Particular emphasis is placed on the rising presence of AI in gaming, investigating its role in addictive gaming mechanisms, potential propagation of harmful behaviours, and cultural bias reinforcement. We underline the Metaverse's unceasing growth, emphasizing the need for future research to develop creative interaction strategies that respect social perspectives and prioritize ethical safeguards, enabling the Metaverse's responsible evolution for all stakeholders' benefit. Finally, we want to highlight many other potential applications of Metaverse and Blockchain, such as copyright management, economic disputes, traceability, or contract transparency.
On the sustainability of deep learning projects: Maintainers' perspective

Abstract Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers' workloads and workloads growth in DL projects, and compare them with traditional open‐source software (OSS) projects. In this regard, we first investigate how DL projects grow, then, understand maintainers' workload in DL projects, and explore the workload growth of maintainers as DL projects evolve. After that, we mine the relationships between maintainers' activities and the sustainability of DL projects. Eventually, we compare it with traditional OSS projects. Our study unveils that although DL projects show increasing trends in most activities, maintainers' workloads present a decreasing trend. Meanwhile, the proportion of workload maintainers conducted in DL projects is significantly lower than in traditional OSS projects. Moreover, there are positive and moderate correlations between the sustainability of DL projects and the number of maintainers' releases, pushes, and merged pull requests. Our findings shed lights that help understand maintainers' workload and growth trends in DL and traditional OSS projects and also highlight actionable directions for organizations, maintainers, and researchers.
Spatial-temporal episodic memory modeling for ADLs: encoding, retrieval, and prediction

Abstract Activities of daily living (ADLs) relate to people’s daily self-care activities, which reflect their living habits and lifestyle. A prior study presented a neural network model called STADLART for ADL routine learning. In this paper, we propose a cognitive model named Spatial-Temporal Episodic Memory for ADL (STEM-ADL), which extends STADLART to encode event sequences in the form of distributed episodic memory patterns. Specifically, STEM-ADL encodes each ADL and its associated contextual information as an event pattern and encodes all events in a day as an episode pattern. By explicitly encoding the temporal characteristics of events as activity gradient patterns, STEM-ADL can be suitably employed for activity prediction tasks. In addition, STEM-ADL can predict both the ADL type and starting time of the subsequent event in one shot. A series of experiments are carried out on two real-world ADL data sets: Orange4Home and OrdonezB, to estimate the efficacy of STEM-ADL. The experimental results indicate that STEM-ADL is remarkably robust in event retrieval using incomplete or noisy retrieval cues. Moreover, STEM-ADL outperforms STADLART and other state-of-the-art models in ADL retrieval and subsequent event prediction tasks. STEM-ADL thus offers a vast potential to be deployed in real-life healthcare applications for ADL monitoring and lifestyle recommendation.
Out of Doors and Outdoors

Abstract This study examines STEAM learning outside the classroom: necessary preparations, how students cope with ‘mobile learning’, and how motivation changes while working on problem-based STEAM tasks. Further, the authors provide examples of tasks without and with technology support, and compare two mobile applications, Actionbound and MathCityMap, supporting outdoor math trails. The study started in 2017 by collecting data based on observations about students’ motivation and views on outdoor mathematics and has been carried on ever since by collecting data mainly from lower secondary school classes involving 90 students. As the authors also gave workshops on STEAM to pre-service teachers, they questioned the participants (a group of 13) about their experiences with STEAM , technology, and outdoor mathematics. The authors found that ‘mobile learning’ and STEAM tasks enhance students’ motivations, however, pre-service teachers have little experience and may need more support during their studies to implement STEAM and technology, hence the authors aim to design such courses.
STEAM Trails in Outdoor Learning Environments around the Egyptian Pyramids Complex

Abstract During the last years, the arrival of new technologies facilitated STEAM integration in real-world learning situations, e.g., GPS -based software and mixed realities allow us to combine the virtual and real world to increase our understanding of complex knowledge. Based on these technologies, the authors designed several teaching practices by creating STEAM trails in an outdoor learning environment, integrating architecture in place-based learning approaches that are likely to occur with architecture connected to arts, culture, history and mathematics. However, the complexity of STEAM -integrated teacher practices within real-world situations is challenging. In this conceptual article, the authors will present and discuss the designed teaching practices, stress possible hindrances and chances for their integration into teaching and discuss further research opportunities. This study may be of interest to educators, curriculum developers and policy makers to promote outdoor teaching practices in integrated STEAM trails.
Estimating the Impact of “Humanizing” AI Assistants

More and more product designers are adopting anthropomorphic design strategies to facilitate the widespread use of AI assistants. However, existing studies show that the ideal boundary of the degree of anthropomorphism is still unclear. Therefore, we design two scenario experimental studies to explore the influence of different degrees of anthropomorphism on human–AI interaction quality. We also consider the difference in impact between two usage contexts: the hedonic and utilitarian contexts. The results show that different degrees of anthropomorphism significantly affect human–AI interaction quality in different ways; when AI assistants have a medium-level degree of anthropomorphism, the positive effect is pronounced. Furthermore, the positive relationship between anthropomorphism and human–AI interaction quality is more robust in the hedonic usage context; this positive effect disappears in the utilitarian usage context. We expect this study to provide practical guidance for AI product designers to achieve their products’ long-term feasibility and sustainability.
Temporally extended goal recognition in fully observable non-deterministic domain models

Goal Recognition is the task of discerning the intended goal that an agent aims to achieve, given a set of goal hypotheses, a domain model, and a sequence of observations (i.e., a sample of the plan executed in the environment). Existing approaches assume that goal hypotheses comprise a single conjunctive formula over a single final state and that the environment dynamics are deterministic, preventing the recognition of temporally extended goals in more complex settings. In this paper, we expand goal recognition to temporally extended goals in Fully Observable Non-Deterministic (fond) planning domain models, focusing on goals on finite traces expressed in Linear Temporal Logic (ltlf) and Pure-Past Linear Temporal Logic (ppltl). We develop the first approach capable of recognizing goals in such settings and evaluate it using different ltlf and ppltl goals over six fond planning domain models. Empirical results show that our approach is accurate in recognizing temporally extended goals in different recognition settings.
Annotazioni di storia della lessicografia veneziana. Un testo inedito di Paolo Zolli

The article aims to present an unpublished study by Paolo Zolli preserved in the Archive “Carte del contemporaneo” of the Centro Interuniversitario di Studi Veneti (Università Ca’ Foscari Venezia). The study reviews the fundamental stages of the Venetian lexicography starting from its origins (with attention to the relationship between Venetian and Tuscan) up to the Raccolta of F.Z. Muazzo, on which Zolli had already given an initial assessment when the manuscript re-emerged in the Archivio di Stato of Venice. Thanks to internal data, the drafting text’s dating is placed in the beginning of 1970’s: it is therefore pioneering in a field of study – that of dialectal lexicography – that today has a great interest for historical linguistics.
Computers’ Interpretations of Knowledge Representation Using Pre-Conceptual Schemas: An Approach Based on the BERT and Llama 2-Chat Models

Pre-conceptual schemas are a straightforward way to represent knowledge using controlled language regardless of context. Despite the benefits of using pre-conceptual schemas by humans, they present challenges when interpreted by computers. We propose an approach to making computers able to interpret the basic pre-conceptual schemas made by humans. To do that, the construction of a linguistic corpus is required to work with large language models—LLM. The linguistic corpus was mainly fed using Master’s and doctoral theses from the digital repository of the University of Nariño to produce a training dataset for re-training the BERT model; in addition, we complement this by explaining the elicited sentences in triads from the pre-conceptual schemas using one of the cutting-edge large language models in natural language processing: Llama 2-Chat by Meta AI. The diverse topics covered in these theses allowed us to expand the spectrum of linguistic use in the BERT model and empower the generative capabilities using the fine-tuned Llama 2-Chat model and the proposed solution. As a result, the first version of a computational solution was built to consume the language models based on BERT and Llama 2-Chat and thus automatically interpret pre-conceptual schemas by computers via natural language processing, adding, at the same time, generative capabilities. The validation of the computational solution was performed in two phases: the first one for detecting sentences and interacting with pre-conceptual schemas with students in the Formal Languages and Automata Theory course—the seventh semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. The second phase was for exploring the generative capabilities based on pre-conceptual schemas; this second phase was performed with students in the Object-oriented Design course—the second semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. This validation yielded favorable results in implementing natural language processing using the BERT and Llama 2-Chat models. In this way, some bases were laid for future developments related to this research topic.