Reviewerly/exHarmony huggingface dataset:
Computing Smallest MUSes of Quantified Boolean Formulas

Computing small (subset-minimal or smallest) explanations is a computationally challenging task for various logics and non-monotonic formalisms. Arguably the most progress in practical algorithms for computing explanations has been made for propositional logic in terms of minimal unsatisfiable subsets (MUSes) of conjunctive normal form formulas. In this work, we propose an approach to computing smallest MUSes of quantified Boolean formulas (QBFs), building on the so-called implicit hitting set approach and modern QBF solving techniques. Connecting to non-monotonic formalisms, our approach finds applications in the realm of abstract argumentation in computing smallest strong explanations of acceptance and rejection. Justifying our approach, we pinpoint the complexity of deciding the existence of small MUSes for QBFs with any fixed number of quantifier alternations. We empirically evaluate the approach on computing strong explanations in abstract argumentation frameworks as well as benchmarks from recent QBF Evaluations.
Tunas - Fishing for Diverse Answer Sets: A Multi-shot Trade up Strategy

Answer set programming (ASP) solvers have advanced in recent years, with a variety of different specialisation and overall development. Thus, even more complex and detailed programs can be solved. A side effect of this development are growing solution spaces and the problem of how to find those answer sets one is interested in. One general approach is to give an overview in form of a small number of highly diverse answer sets. By choosing a favourite and repeating the process, the user is able to leap through the solution space. But finding highly diverse answer sets is computationally expensive. In this paper we introduce a new approach called Tunas for Trade Up Navigation for Answer Sets to find diverse answer sets by reworking existing solution collections. The core idea is to collect diverse answer sets one after another by iteratively solving and updating the program. Once no more answer sets can be added to the collection, the program is allowed to trade answer sets from the collection for different answer sets, as long as the collection grows and stays diverse. Elaboration of the approach is possible in three variations, which we implemented and compared to established methods in an empirical evaluation. The evaluation shows that the Tunas approach is competitive with existing methods, and that efficiency of the approach is highly connected to the underlying logic program.
IASCAR: Incremental Answer Set Counting by Anytime Refinement

Answer set programming (ASP) is a popular declarative programming paradigm with various applications. Programs can easily have so many answer sets that they cannot be enumerated in practice, but counting still allows to quantify solution spaces. If one counts under assumptions on literals, one obtains a tool to comprehend parts of the solution space, so called answer set navigation. But navigating through parts of the solution space requires counting many times, which is expensive in theory. There, knowledge compilation compiles instances into representations on which counting works in polynomial time. However, these techniques exist only for CNF formulas and compiling ASP programs into CNF formulas can introduce an exponential overhead. In this paper, we introduce a technique to iteratively count answer sets under assumptions on knowledge compilations of CNFs that encode supported models. Our anytime technique uses the principle of inclusion-exclusion to systematically improve bounds by over- and undercounting. In a preliminary empirical analysis we demonstrate promising results. After compiling the input (offline phase) our approach quickly (re)counts.
Optimising Business Process Discovery Using Answer Set Programming

Declarative business process discovery aims at identifying sets of constraints, from a given formal language, that characterise a workflow by using pre-recorded activity logs. Since the provided logs represent a fraction of all the consistent evolution of a process, and the fact that many sets of constraints covering those examples can be selected, empirical criteria should be employed to identify the "best" candidates. In our work we frame the process discovery as an optimisation problem, where we want to identify optimal sets of constraints according to preference criteria. Declarative constraints for processes are usually characterised via temporal logics, so different solutions can be semantically equivalent. For this reason, it is difficult to use an arbitrary finite domain constraints solvers for the optimisation. The use of Answer Set Programming enables the combination of deduction rules within the optimisation algorithm, in order to take into account not only the user preferences but also the implicit semantics of the formal language. In this paper we show how we encoded the process discovery problem using the ASPrin framework for qualitative and quantitative optimisation in ASP, and the results of our experiments.
Comments or Issues: Where to Document Technical Debt

Self-admitted technical debt (SATD) is a form of TD where developers document the debt using source code comments (SATD-C) or issues (SATD-I). We survey authors of both SATD-C and SATD-I and provide 13 guidelines to help developers decide which to use.
Improved SinGAN for Single-Sample Airport Runway Destruction Image Generation

Aims: To solve the problem of difficult acquisition of airport runway destruction image data. Objectives: This paper introduces SinGAN, a single-sample generative adversarial network algorithm. Methods: To address the shortcomings of SinGAN in image realism and diversity generation, an improved algorithm based on the combination of Gaussian error linear unit GELU and efficient channel attention mechanism ECANet is proposed Results: Experiments show that its generated image results are subjectively better than SinGAN and its lightweight algorithm ConSinGAN, and the model can obtain an effective balance in both quality and diversity of image generation. Conclusion: The algorithm effect is also verified using three objective evaluation metrics, and the results show that the method in this paper effectively improves the generation effect compared with SinGAN, in which the SIFID metric is reduced by 46.67%.
Emotionally Toned Online Discussions Evoke Subjectively Experienced Emotional Responses

Abstract. Emotions are an important element of human interactions, including those in social media. Despite the prominence of text-based messages in online communication, little is known about how the emotional tone of the messages affects the emotions of the recipients. With three experiments, the current study investigated these effects in the context of online news discussions. Participants first read news discussion threads with a negative, neutral, and positive tone, and then rated their subjective emotional state in terms of valence and arousal. Results showed that negatively toned discussions induced more negative valence and higher arousal ratings than the other conditions. Positive discussions had an opposite effect. Emotionally toned online comments evidently affect the quality and the intensity of the readers’ subjective emotions. We discuss implications for future research and emotion mitigation strategies to improve online discussion quality.
Human Gesture Recognition in Computer Vision Research

Human gesture recognition is a popular issue in the studies of computer vision, since it provides technological expertise required to advance the interaction between people and computers, virtual environments, smart surveillance, motion tracking, as well as other domains. Extraction of the human skeleton is a rather typical gesture recognition approach using existing technologies based on two-dimensional human gesture detection. Likewise, I t cannot be overlooked that objects in the surrounding environment give some information about human gestures. To semantically recognize the posture of the human body, the logic system presented in this research integrates the components recognized in the visual environment alongside the human skeletal position. In principle, it can improve the precision of recognizing postures and semantically represent peoples’ actions. As such, the paper suggests a potential and notion for recognizing human gestures, as well as increasing the quantity of information offered through analysis of images to enhance interaction between humans and computers.
Displacement monitoring of crossbeams in an airport runway extension using digital image correlation

The structure of the Madeira Airport runway extension was built above the sea and is composed by a slab supported by frames at heights above 60 m. When landing in the E-W direction, aircraft touch this structure, which results in bending of the support beams. A vision system was installed under the runway in order to evaluate the deflection of the two support beams more directly involved with impact loads upon landing, but otherwise also involved in take-off or taxiing operations. Each system consists of a camera mounted on a beam, directed at the midspan of the following beam, whose displacement is set to be measured. The section captured by the camera has been prepared with a 2 m by 1 m speckle pattern target for displacement tracking using digital image correlation (DIC). A trigger mechanism was developed in order to save only the images obtained upon clear operational events. The camera acquires images continuously onto a circular buffer and compares them with a reference using DIC. When a displacement is detected, the images on the buffer, along with the frames taken in the next few seconds, are saved for posterior image processing. This system was successful at obtaining measurements for the monitored areas’ displacement fields and the evolution of deflection through time for each event. Significant values for strain are yet to be obtained, as they were, most likely, too small for the system’s resolution, for the events captured to this date.
Email Filtering Using Hybrid Feature Selection Model

Undoubtedly, spam is a serious problem, and the number of spam emails is increased rapidly.Besides, the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used for spam filtering.Also, some emergent spam detection techniques use machine learning methods and feature extraction.Some methods and algorithms have been introduced for spam detecting and filtering.This research proposes two models for spam detection and feature selection.The first model is evaluated with the email spam classification dataset, which is based on reducing the number of keywords to its minimum.The results of this model are promising and highly acceptable.The second proposed model is based on creating features for spam detection as a first stage.Then, the number of features is reduced using three well-known metaheuristic algorithms at the second stage.The algorithms used in the second model are Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), and these three algorithms are adapted to fit the proposed model.Also, the authors give it the names AABC, AACO, and APSO, respectively.The dataset used for the evaluation of this model is Enron.Finally, well-known criteria are used for the evaluation purposes of this model, such as true positive, false positive, false negative, precision, recall, and F-Measure.The outcomes of the second proposed model are highly significant compared to the first one.
Separation Inference: A Unified Framework for Word Segmentation in East Asian Languages

Existing methods consider Word Segmentation (WS) as sequence tagging. Each tag indicates the position of the current character in a segment. The exactness of the position for any non-boundaries character is unnecessary. Any incorrect inner prediction reduces model performance. The position information restricts tag-to-tag transition. Thereby, extra context information and the Conditional Random Field (CRF) network are desired to control unreasonable tag transition. To steer away from the implicit restriction, we propose the Separation(Sp)-Adhesion(Ad), which targets straight on the essential character-to-character connections, to tackle the WS task directly. Merely bigram that is specially tailored for &#x201C;Sp-Ad&#x201D; is required and considered as the processing unit to identify the connection states of every two adjacent characters. The elimination of the position restriction makes the model independent of the CRF layer which is widely adopted to revise unreasonable tags. Therefore, CRF can then be substituted with a classification network. We construct the Separation Inference (SpIn) framework based on the bigram features and softmax classification network to tackle the WS task. SpIn significantly reduces the inference complexity, dispels extra context information, and boosts the accuracy of the WS task. Besides its effectiveness in Chinese Word Segmentation, performance boosts on Japanese and Korean Word Segmentation further prove SpIn is universal for East Asian Languages. Moreover, our extensive experiments also verify the cross-domain effectiveness of SpIn by attaining state-of-the-art performances in the benchmark tests of in-domain and cross-domain Chinese Word Segmentation.
Towards Optimal Triage and Mitigation of Context-Sensitive Cyber Vulnerabilities

Cyber vulnerabilities are security deficiencies in computer and network systems of organizations, which can be exploited by an adversary to cause significant damage. The technology and security personnel resources currently available in organizations to mitigate the vulnerabilities are highly inadequate. As a result, systems routinely remain unpatched, thus making them vulnerable to security breaches from the adversaries. The potential consequences of an exploited vulnerability depend upon the context as well as the severity of the vulnerability, which may differ among networks and organizations. Furthermore, security personnel tend to have varying levels of expertise and technical proficiencies associated with different computer and network devices. There exists a critical need to develop a resource-constrained approach for effectively identifying and mitigating important context-sensitive cyber vulnerabilities. In this article, we develop an advanced analytics and optimization framework to address this need and compare our approach with rule-based methods employed in real-world cybersecurity operations centers, as well as a vulnerability prioritization method from recent literature. First, we propose a machine learning-based vulnerability priority scoring system (VPSS) to calculate the priority scores for each of the vulnerabilities found in an organization’s network and quantify organizational context-based vulnerability exposure. Next, we propose a decision-support system, which consists of a two-step sequential optimization approach. The first model selects the high priority vulnerability instances from the dense report subject to resource constraints, and the second model then optimally allocates them to the security personnel with matching skill types for mitigation. Experiment results conducted using a real-world vulnerability data set show that our approach 1) outperforms both the rule-based methods and the vulnerability prioritization method from literature in prioritizing context-sensitive vulnerabilities, which are found across highly susceptible organizationally relevant host machines, and 2) maximizes the pairs of vulnerability instance type and the respective security analyst skill type for optimal mitigation.
Service-Splitting-Based Privacy Protection Mechanism for Proximity Detection Supporting High Utility

Proximity detection is one of the most popular location-based applications in social networks when users intend to find their nearby friends. However, the existing proximity detection has access to precise and real-time location information of users, raising serious privacy concerns for millions of users. A number of privacy-preserving models have taken shape over the past decade, but they almost universally rely on syntactic privacy models such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -anonymity and location perturbation, which are proved to waver in the balance of privacy and availability requirements. To solve this problem, we introduce a novel location privacy-preserving mechanism for proximity detection to support user-defined range queries while guaranteeing a certain level of privacy. It divides the proximity detection service into two independent subservices and ensures that each subservice provider can only access part of the user’s location information, which is encoded by Geohash and divided into two parts (i.e., prefix and suffix). By adjusting the length of location encoding, we can make a good trade-off between accuracy and system overhead. The privacy requirements of users are not implicated in the quality of service, and it achieves the balance of privacy and utility. The analysis results through an extensive simulation indicate that our scheme successfully ensures that neither each server in the system nor an external attacker can obtain the real location of the user. Moreover, it demonstrates the effectiveness of the proposed scheme.
Development of an Embedding Framework for Clustering Scientific Papers

In this era, research and development are becoming a continuous and accelerating process because technology changes rapidly with a short lifecycle. As a result, various methodologies are being developed to monitor these rapidly changing research trends; In particular, clustering method-related studies in science and technology documents are being developed with a variety of approaches. However, previous studies on document clustering methods focus on a specific field or language but do not take into consideration certain important pieces of information in science and technology documents. Therefore, this study proposes an embedding methodology that uses important content from scientific and technical documents. We took into consideration the importance of information containing core structures in science and technology documents and proposed a clustering methodology that analyzes structured and unstructured data, such as textual information, author information, and citation information. The proposed method combines both textual and structural data from the paper, using a method that focuses on screening important information by sections in science and technology documents. Then, Girvan-Newman clustering and Louvain clustering models are applied to generate embedding vectors and show evaluation results through the clustering indices. As a practical example, we applied the proposed methodology using paper data from the field of hydrogen cell vehicles. The results of this study will be effective in identifying gaps in technology for new technological development, identifying technology trends, and presenting directional information for future technology development.
Residuality Theory, random simulation, and attractor networks

This article reviews earlier articles on the topic of residuality theory and places residuality theory in the context of the complexity sciences, relating the major concepts of residuality theory in terms of Kauffman Networks, complex networks, random simulations, and attractors. This paper aims to outline the logic of the theory and to expand on the use of matrices for encouraging emergent component decomposition, at the same time as proposing a related theory of software engineering that allows comparison between residuality theory and other approaches.
Data-Enhancement-Aided Protocol-Agnostic Transmitter Recognition for Open-Set in IoT

Radio-frequency fingerprinting identification (RFFI), based on hardware inherent imperfections, is an effective technique for physical authentication in the Internet of Things (IoT). However, due to the nonstationary and open factors in realistic scenes, problems, such as inaccurate fingerprinting extractions and unreliable transmitter identifications, often occur. Specifically, in actual scenarios, there are challenges, such as varying channel status, variable transmission content, insufficient data set acquisition, and the unknown number of rogue devices. In this article, a robust and protocol-agnostic RFFI system is proposed for open-set recognition, which is achieved by a slicing-enhanced preprocessing with noise augmentation and a well-designed bone network with a modified open layer. Moreover, in order to verify the effectiveness and transferability of the designed RFFI system in applications of IoT, comparative experiments are conducted using a real-world data set of automatic-dependent surveillance-broadcast (ADS-B) signals and the open-source FIT/CorteXlab data set. The performance of the designed extractor and classifier, under varying channel and openness conditions, verifies the channel feasibility and openness generalization of our designed system. The results of the ablation experiment indicate that the designed RFFI system can achieve an accuracy of 96.28% in the classification of 50 registered IoT transmitters and 87.52% in open-set recognition of mixed 50 registered and 10 unregistered IoT transmitters.
Tmo-Det: Deep Tone-Mapping Optimized with and for Object Detection

Detecting objects in challenging illumination conditions is critical for autonomous driving. Existing solutions detect objects with standard or tone-mapped Low Dynamic Range (LDR) images. In this paper, we propose a novel Generative Adversarial approach to jointly optimize deep-learning-based tone-mapping operators (mapping High Dynamic Range (HDR) to LDR) and object detection networks. We analyse different ways to combine the feedback from tone-mapping quality and object detection quality during training. We show our deep tone-mapping operator jointly trained with an object detector achieves the best tone-mapping quality and detection performance compared to conventional approaches.
Exploring the Impacts of Artificial Intelligence (AI) Implementation at Individual and Team Levels: A Case Study in the UAE Government Sector

Despite the growth of Artificial Intelligence (AI) implementation in operational workplace processes, there is limited understanding of its effects at the micro-individual and meso-team levels in government sector organisations. This study examines the impacts of implementing AI within a single case study of a governmental entity in the United Arab Emirates (UAE). Qualitative data was collected through semi-structured interviews with 15 participants and analysed using thematic analysis. The findings identify AI implementation as an incremental process requiring organisational leaders' adequate understanding of change management. AI has a considerable influence on the individual as it improves the ability of employees to carry out their work and increases their autonomy, competence, relatedness and ultimately productivity. It also enhanced team performance through efficient team communication and cooperative decision-making. The findings add to our understanding of the effects of AI implementation at the individual and team levels and signal the need for organisations to embrace change management approaches to support the transition to AI-enabled operations.