Reviewerly/exHarmony huggingface dataset:
Hyper Hoare Logic: (Dis-)Proving Program Hyperproperties (extended version)

Hoare logics are proof systems that allow one to formally establish properties of computer programs. Traditional Hoare logics prove properties of individual program executions (so-called trace properties, such as functional correctness). Hoare logic has been generalized to prove also properties of multiple executions of a program (so-called hyperproperties, such as determinism or non-interference). These program logics prove the absence of (bad combinations of) executions. On the other hand, program logics similar to Hoare logic have been proposed to disprove program properties (e.g., Incorrectness Logic), by proving the existence of (bad combinations of) executions. All of these logics have in common that they specify program properties using assertions over a fixed number of states, for instance, a single pre- and post-state for functional properties or pairs of pre- and post-states for non-interference. In this paper, we present Hyper Hoare Logic, a generalization of Hoare logic that lifts assertions to properties of arbitrary sets of states. The resulting logic is simple yet expressive: its judgments can express arbitrary trace- and hyperproperties over the terminating executions of a program. By allowing assertions to reason about sets of states, Hyper Hoare Logic can reason about both the absence and the existence of (combinations of) executions, and, thereby, supports both proving and disproving program (hyper-)properties within the same logic, including (hyper-)properties that no existing Hoare logic can express. We prove that Hyper Hoare Logic is sound and complete, and demonstrate that it captures important proof principles naturally. All our technical results have been proved in Isabelle/HOL.
Federated Learning with Clustering-Based Participant Selection for IoT Applications

Modern Internet of Things (IoT) systems are highly complex due to its mobile, ad-hoc and geographically distributed nature. Very often, an edge-cloud infrastructure is established to offer intelligent services in modern IoT systems. However, IoT edge devices are typically resource-constrained and can not perform sophisticated machine learning algorithm on board. Data sharing with a central server is a common approach of crowdsourcing, but also brings privacy and security concerns. The emerging federated learning offers a promising pathway to achieve an accurate model through distributed machine learning while ensuring data privacy. The existing federated learning process is not tailored to the mobile and adhoc nature of IoT systems where devices are of varying data and system qualities and may not be able to participate the entire training process. Therefore, in this paper, a new federated learning framework is proposed to support asynchronous model fusion with clustering-based participant selection. The proposed framework aims to accommodate the ad-hoc nature of IoT devices, and at the same time avoiding low quality or even malicious data from its participants to ensure model convergence and performance.
Explainable AI (XAI) and its Applications in Building Trust and Understanding in AI Decision Making

In recent years, there has been a growing need for Explainable AI (XAI) to build trust and understanding in AI decision making. XAI is a field of AI research that focuses on developing algorithms and models that can be easily understood and interpreted by humans. The goal of XAI is to make the inner workings of AI systems transparent and explainable, which can help people to understand the reasoning behind the decisions made by AI and make better decisions. In this paper, we will explore the various applications of XAI in different domains such as healthcare, finance, autonomous vehicles, and legal and government decisions. We will also discuss the different techniques used in XAI such as feature importance analysis, model interpretability, and natural language explanations. Finally, we will examine the challenges and future directions of XAI research. This paper aims to provide an overview of the current state of XAI research and its potential impact on building trust and understanding in AI decision making.
Development of RBL-STEM Learning Tools to Improve Students’ Computational Thinking Skills Solving Rainbow Antimagic Coloring Problems and Their Application to Traffic Flow Problems with Spatial Temporal Graph Neural Network

Computational thinking is thinking process that is needed in formulating problems and solutions, so that these solutions can be effective information processing agents in solving problems. Indicators of computational thinking consist of problem decomposition, algorithmic thinking, pattern recognition, abstraction and generalization. To improve higher-order thinking skills, we apply RBL learning integrated with STEM approach. To improve students’ thinking skills, it is necessary to develop tools that support the success of learning activities. The learning tools that have been developed meet the criteria of valid, practical, and effective. The validity scores obtained by each device are 3.5 for the face-to-face plan, 3.41 for the student worksheet, and 3.56 for the learning outcomes test. The observation result of the learning implementation score was 3.8 with a percentage of 95%. There were 23 students who completed or around 88,46%, percentage of average score of student activities was 94.17%, and as many as 94.47% of students gave a positive response.
ACMEOLOGICAL APPROACH - AS THE BASIS OF IMPROVING THE PROFESSIONAL TRAINING OF THE EDUCATORS IN HIGHER EDUCATION

This article discusses the need to improve the professional training of future teachers based on the acmeological approach and the factors of their use of the acmeological approach. Intellectual, personal, moral characteristics and possibilities of organization and implementation of professional-pedagogical activities are highlighted. Through this, the need to form new professional competencies that meet the requirements of the labor market is considered.
An Ensemble Learning Model for Predicting Social Engineering Pharming Attacks

Pharming attack has a broad scope as social engineers can masquerade as anyone, particularly during the COVID-19 pandemic from health authorities or even organization executives getting in touch with their personnel. The study aims to develop an ensemble model for predicting social engineering-based pharming attacks from the client-side pharming attack. The target population for the study includes 1781 URLs, which are secondary and readily available on Kaggle having been compiled by Manu Siddhartha. The study focused on identifying URLs that facilitate Pharming attacks, a cybersecurity threat. Malicious URLs miss the protocol segmentation, certificate of authorization, and bait targets into becoming victims of pharming attacks. The research instrument used for data process and building a pharming attack model was CSV and Jupyter notebook. Data was collected from secondary data sources. A model for predictive pharming attacks was built utilizing Logic regression, Random Forest, and gradient boost as the model for boosting algorithms to reduce pharming malware attacks.
Digital technologies in intellectual and moral development of personality

The purpose of the chapter is to analyze guided by the humanistic personality-oriented educational paradigm, digitalization of the modern education system. Design/methodology/approach: The authors used such method as method of studying literary sources, method of concrete historical analysis, modeling method. The use of these methods of scientific and pedagogical research made it possible to use library funds, catalogs, bibliographic indexes and descriptions, relying on comparison, generalization, systematization, search arguments for modeling the process of forming the information culture of a student's personality and determining factors closely related to it. Findings: It shown that the structural model of formation of the information culture of the student depends on personal characteristics of students and information and communication technologies. Originality/value: A perspective method of solving the determined problem is use of modeling method and on the base of culturological approach formation of the information culture of the future teacher.
Professional standard "Digital tutor" as the guidance for the specialist of a new profile

In this article the following notions are characterized: the concept, structural and substantive features of the professional standard "Consultant in the development of digital competence of the population (Digital tutor)," which regulates the activities of representatives of a new breakthrough profession in ensuring digital literacy of the population. It was pointed that the development and implementation of this professional standard meets the requirements of time and the need of the population to use information and communication technologies in the transition to the fifth technological order, for which the competencies of the digital economy are urgently needed. Based on the interpretation of the results of the content analytical study, it was proved that the professional standard is focused on two groups of competencies – IT (Information technology) and pedagogical. They are presented in three clusters: "Labor actions," "Necessary skills" and "Necessary knowledge." The content of these clusters determines the training requirements for future specialists. There is a bias towards the requirements for information technology training to the detriment of pedagogical training, despite the fact that the pedagogical activity aiming to educate the population of various typological groups should dominate. A loss of logical sequence was established when designing the content of the pedagogical unit, the lack of accounting for the initial level of pedagogical awareness at the stage of selecting potential tutors. Thematic gaps were identified in determining the content of pedagogical training between the requirements for specialists of the third and fifth qualification groups.
PENGARUH PENGGUNAAN GAME EDUKASI WORDWALL TERHADAP MOTIVASI DAN HASIL BELAJAR MATEMATIKA PESERTA DIDIK

This research is motivated by the lack of student motivation to learn, causing student learning outcomes to be low. Alternative media that can be used to increase students' motivation and learning outcomes is wordwall educational game media. The purpose of this study was to determine whether there was a significant effect of the use of wordwall educational games on the motivation and learning outcomes of seventh grade students in the quadrilateral material. This research is a quantitative research with an experimental model. The data collection technique for motivation is a questionnaire, while the data collection for learning outcomes is pretest and posttest. The data analysis technique was carried out using SPSS 19.0 for windows with t-test. The results of this study indicate that the use of wordwall educational games in mathematics learning activities has a significant effect on students' motivation and learning outcomes. It can be seen from the percentage of learning motivation in the experimental class is higher than the control class and the average value of the experimental class is higher than the control class.
Embodying digital spaces in a clinical encounter

What is it like to interact in a clinical setting when a technological device is participating? This inquiry was conducted in a primary healthcare setting, with the aim of shedding light on clinicians’ and patients’ experiences regarding the use of a tablet-with-app, intended for a more systematic assessment, as well as electronic registration and storing of patient data. In this paper, we present an account of four experiential exemplars of adopting an eTool in a clinical setting. The “faciality” of the digital device seems to be important to both patients and clinicians, as well as the interaction between them. The “face” can be used for engaging in conversation, addressing awkward topics, communicating, or inviting involvement. The face can also be used for just resting the eyes or lowering the gaze to maintain a low profile during the clinical encounter. Concurrently, the size, the shape, and the backside of the eTool’s face can mediate distance. We expand the notion of “screen sharing” and suggest that humans’ ability to move from one mode to another and embody digital spaces in the clinical encounter seems enhanced by their ability to include the eTool’s face in their interaction. This knowledge can be used in the development of digital tools for teaching, as well as for health professions.
Influence of National Home-Grown School Feeding Programme on primary school pupils attendance and academic performance in Makurdi, Benue State, Nigeria

This study examined influence of National Home-Grown School Feeding Programme on primary school pupils’ attendance and academic performance in Makurdi, Benue State, Nigeria. Two specific objectives with corresponding research questions and hypotheses were raised and formulated respectively to guide the study. The study adopted survey research design and was carried out in Makurdi Local Government Area of Benue State, Nigeria. The population of this study is 1870 teachers from 79 public primary schools in Makurdi Local Government Area of Benue State. The sample size for this study is 330 teachers. A structured questionnaire titled “National Home-Grown School Feeding Programme and Pupils Attendance and Performance in Public Primary Schools Questionnaire" was developed and used for the study. Cronbach Alpha Statistic was used to obtain an overall reliability coefficient of 0.89 for the instrument. The descriptive statistics of mean and standard deviation were used to answer the two research questions. Chi-square statistics was used to test the two null hypotheses at 0.05 level of significance. From analysis of data, the following major findings were made: provision of meals to pupils improve their daily regular attendance, improves enrolment in primary schools, enhances their punctuality, and also improves their co-curricular activities. Based on the findings of the study, the following recommendations are made; the State Government should try and replicate the blue print of the Federal Government's National Home-Grown School Feeding Programme to every Local Government in the State so as to ensure sustainability in the provision of meals to pupils and as well as improve their daily regular attendance as well as enhancing their punctuality. Also, the Universal Basic Education Commission (UBEC) and other education stakeholders should initiate school feeding integrated intervention programmes that improves co-curricular activities through provision of meals to pupils this will influence their ability to understand the practicality of things during learning and also improve their ability to engage with digital technologies.
A Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure (TRACI)

Cybercrime against critical infrastructures such as nuclear reactors, power plants, and dams has been increasing in frequency and severity.Recent literature regarding these types of attacks has been extensive but due to the sensitive nature of this field, there is very little empirical data.We address these issues by integrating Routine Activity Theory and Rational Choice Theory, and we create a classification tool called TRACI (Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure).We take a Design Science Research approach to develop, evaluate, and refine the proposed artifact.We use mixed methods to demonstrate that our taxonomy can successfully capture the characteristics of various cyberattacks against critical infrastructure.TRACI consists of three dimensions, and each dimension contains its own subdimensions.The first dimension comprises hacker motivation, which can be financial, socio-cultural, thrill-seeking, and/or economic.The second dimension represents the assets such as cyber, physical, and/or cyber-physical components.The third dimension is related to threats, vulnerabilities, and controls that are fundamental to establishing and maintaining an information security posture and overall cyber resilience.Our work is among the first to utilize criminological theories and Design Science to create an empirically validated artifact for improving critical infrastructure risk management.
Editing Language Model-based Knowledge Graph Embeddings

Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, making them difficult to modify post-deployment without re-training after deployment. To address this issue, we propose a new task of editing language model-based KG embeddings in this paper. This task is designed to facilitate rapid, data-efficient updates to KG embeddings without compromising the performance of other aspects. We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR, and evaluate several knowledge editing baselines demonstrating the limited ability of previous models to handle the proposed challenging task. We further propose a simple yet strong baseline dubbed KGEditor, which utilizes additional parametric layers of the hypernetwork to edit/add facts. Our comprehensive experimental results reveal that KGEditor excels in updating specific facts without impacting the overall performance, even when faced with limited training resources. Code and datasets are available in https://github.com/zjunlp/PromptKG/tree/main/deltaKG.
Cross-lingual Argument Mining in the Medical Domain

Nowadays the medical domain is receiving more and more attention in applications involving Artificial Intelligence as clinicians decision-making is increasingly dependent on dealing with enormous amounts of unstructured textual data. In this context, Argument Mining (AM) helps to meaningfully structure textual data by identifying the argumentative components in the text and classifying the relations between them. However, as it is the case for man tasks in Natural Language Processing in general and in medical text processing in particular, the large majority of the work on computational argumentation has been focusing only on the English language. In this paper, we investigate several strategies to perform AM in medical texts for a language such as Spanish, for which no annotated data is available. Our work shows that automatically translating and projecting annotations (data-transfer) from English to a given target language is an effective way to generate annotated data without costly manual intervention. Furthermore, and contrary to conclusions from previous work for other sequence labelling tasks, our experiments demonstrate that data-transfer outperforms methods based on the crosslingual transfer capabilities of multilingual pre-trained language models (model-transfer). Finally, we show how the automatically generated data in Spanish can also be used to improve results in the original English monolingual setting, providing thus a fully automatic data augmentation strategy.
Development and Application of Interactive Teaching Systems for Online Design Courses

Digital education has recently become a mainstream education model. Despite digital education's increasing popularity, there remain issues when it comes to teacher-student interactions in digital space, which have made it impossible for this model to achieve the same teaching quality as traditional in-person education. Compared with other academic subjects, online courses in design practice have even more severe problems with teacher-student interaction. This study proposes a new teaching interaction assistance model in online design practice courses. The model uses the TAPs method to establish evaluation codes based on students' design thinking as well as through feedback-thinking related codes. This teaching interaction assistance model is established by combining these thinking codes and the LSTM model. This study takes an online design practice course as a case study, and the results have shown that this model can help teachers and students communicate more effectively and improve the teaching quality of online design practice courses.
Clueless: A Tool Characterising Values Leaking as Addresses

Clueless is a binary instrumentation tool that characterises explicit cache side channel vulnerabilities of programs. It detects the transformation of data values into addresses by tracking dynamic instruction dependencies. Clueless tags data values in memory if it discovers that they are used in address calculations to further access other data. Clueless can report on the amount of data that are used as addresses at each point during execution. It can also be specifically instructed to track certain data in memory (e.g., a password) to see if they are turned into addresses at any point during execution. It returns a trace on how the tracked data are turned into addresses, if they do. We demonstrate Clueless on SPEC 2006 and characterise, for the first time, the amount of data values that are turned into addresses in these programs. We further demonstrate Clueless on a micro benchmark and on a case study. The case study is the different implementations of AES in OpenSSL: T-table, Vector Permutation AES (VPAES), and Intel Advanced Encryption Standard New Instructions (AES-NI). Clueless shows how the encryption key is transformed into addresses in the T-table implementation, while explicit cache side channel vulnerabilities are note detected in the other implementations.
Probing Taxonomic and Thematic Embeddings for Taxonomic Information

Modelling taxonomic and thematic relatedness is important for building AI with comprehensive natural language understanding. The goal of this paper is to learn more about how taxonomic information is structurally encoded in embeddings. To do this, we design a new hypernym-hyponym probing task and perform a comparative probing study of taxonomic and thematic SGNS and GloVe embeddings. Our experiments indicate that both types of embeddings encode some taxonomic information, but the amount, as well as the geometric properties of the encodings, are independently related to both the encoder architecture, as well as the embedding training data. Specifically, we find that only taxonomic embeddings carry taxonomic information in their norm, which is determined by the underlying distribution in the data.
k-plus Anticlustering: An Improved k-means Criterion for Maximizing Between-Group Similarity

Anticlustering refers to the process of partitioning elements into disjoint groups with the goal of obtaining high between-group similarity and high within-group heterogeneity. Anticlustering thereby reverses the logic of its better known twin—cluster analysis—and is usually approached by maximizing instead of minimizing a clustering objective function. This paper presents k-plus, an extension of the classical k-means objective to maximize between-group similarity in anticlustering applications. K-plus represents between-group similarity as discrepancy in distribution moments (means, variance, and higher order moments), whereas the k-means criterion only reflects groups differences with regard to means. While constituting a new criterion for anticlustering, it is shown that k-plus anticlustering can be implemented by optimizing the original k-means criterion after the input data has been augmented with additional variables. A computer simulation and practical examples show that k-plus anticlustering achieves high between-group similarity with regard to multiple objectives. In particular, optimizing between-group similarity with regard to variances usually does not compromise similarity with regard to means; the k-plus extension is therefore generally preferred over classical k-means anticlustering. Examples are given on how k-plus anticlustering can be applied to real norming data using the open source R package anticlust, which is freely available via CRAN.