Reviewerly/exHarmony huggingface dataset:
HIGH IMPACT CYBERSECURITY CAPACITY BUILDING

The shift of our societies towards automation and connectivity is accompanied by growing vulnerabilities that can be exploited to cause effects ranging from nuisance to large-scale breaches of sensitive personal data, terrorism and destabilisation of democratic processes . To achieve preparedness and resilience, cybersecurity skills need to be continuously advanced at all levels of ICT and security personnel, in a constant learning process, to address complex demands of individual and organizational level capacity building through trainings and exercises. In this article authors describe a proposal for a common cybersecurity training framework enabling practical, operational and hands-on trainings that are adaptable to changing threat conditions, and a methodology to transform user needs into interoperable training specifications to be used by cyber range and cybersecurity training providers and that can take advantage of simulations and knowledge transfer solutions. Such a framework improves cybersecurity training capabilities by leveraging dynamic simulation environments for delivery of realistic training scenarios enabling participants and organisations to prepare for current and future threats. The framework provides the foundation for systematic development of cybersecurity skills at the individual and organizational levels. It consists of a multidisciplinary cybersecurity training methodology; a model that describes the various skills required to perform cybersecurity duties, as well as the levels of proficiencies; and the training and skills evaluation methodology. The innovation potential of the proposed training framework comes from a holistic approach to understanding capacity building in terms of translating end-user needs into training scenarios and applying a human-centred and organisational approach to cybersecurity, taking into consideration behavioural aspects of training.
Knowledge-Based Techniques for Document Fraud Detection: A Comprehensive Study

Due to the availability of cost-effective scanners, printers, and image processing software, document fraud detection is, unfortunately, quite common nowadays. The main challenges of this task are the lack of freely available annotated data and the overflow of mainly computer vision approaches. We consider that relying on the textual content of forged documents could provide a different view on their detection by exploring semantic inconsistencies with the aid of specialized knowledge bases. We, thus, perform an exhaustive study of existing state-of-the-art methods based on knowledge-graph embeddings (KGE) using a synthetically forged, yet realistic, receipt dataset. We also explore additional knowledge base incremental data enrichments, in order to analyze the impact of the richness of the knowledge base on each KGE method. The reported results prove that the performance of the methods varies considerably depending on the type of approach. Also, as expected, the size of the data enrichment is directly proportional to the rise in performance. Finally, we conclude that, while exploring the semantics of documents is promising, document forgery detection still poses a challenge for KGE methods.
Application of the Promethee II Method for Determining Road Improvement Priorities

The road is an infrastructure that is often passed by the community. If there is damage, it will significantly disrupt community activities; for example, road conditions become jammed. This is necessary for the Public Works and Spatial Planning Office of the City of Cirebon to handle the road repairs, but in carrying out repair planning, there are obstacles, one of which is the difficulty in determining priority for road repairs with limited funds from the center. This research decision support system using the Promethee II method aims to find accurate results that are useful in determining priority road repairs that will be selected for repair. The criteria used in this study are Section Length, Road Width, Damage Condition, Traffic Volume (LHR), and Road Access. The results of this study selected Jalan A1 as an alternative that had the highest rating level of 0.613333333, and this indicated that this road was a road that had to be prioritized for repair. The test results show that the Promethee II method can produce priority recommendations for road improvements based on the required criteria.
Detecting Cyberbullying from Tweets Through Machine Learning Techniques with Sentiment Analysis

Technology advancement has resulted in a serious problem called cyberbullying. Bullying someone online, typically by sending ominous or threatening messages, is known as cyberbullying. On social networking sites, Twitter in particular is evolving into a venue for this kind of bullying. Machine learning (ML) algorithms have been widely used to detect cyberbullying by using particular language patterns that bullies use to attack their victims. Text Sentiment Analysis (SA) can provide beneficial features for identifying harmful or abusive content. The goal of this study is to create and refine an efficient method that utilizes SA and language models to detect cyberbullying from tweets. Various machine learning algorithms are analyzed and compared over two datasets of tweets. In this research, we have employed two different datasets of different sizes of tweets in our investigations. On both datasets, Convolutional Neural Network classifiers that are based on higher n-grams language models have outperformed other ML classifiers; namely, Decision Trees, Random Forest, Naïve Bayes, and Support-Vector Machines.
A Bibliometric Analysis of Research on the Convergence of Artificial Intelligence and Blockchain in Smart Cities

Smart cities aim to enhance the quality of life for citizens by integrating information technology in various aspects of daily life. This paper focuses on recent innovations in the integration of two prominent technologies, artificial intelligence (AI) and blockchain, to manage complex interactions between smart connected devices, individuals, government agencies, and the private sector. By conducting a systematic scientometric analysis and visualization of 505 articles published between 2017 and 2023, we uncover the social, conceptual, and intellectual structures of the literature in this field through co-authorship, co-word, and co-citation networks. Our analysis identifies key insights, research hotspots, specialties, and emerging trends by examining important nodes in the bibliometric networks. The findings of this study can be of interest to both academics and practitioners working in the fields of AI, blockchain, and smart cities.
Exploration the Effect Factors in Applying the Knowledge Sharing System (Kss) in Food Industries.Co

Purpose: This study aims to identify a number of the most important individual and organizational factors that affect the application of the Knowledge- Sharing System (KSS) used by individuals working in the food industry. Co Theoretical framework: Recent literature reported that the application of KSS is still a very new practice, and difficult to apply and measure, so for the application of KSS, it is necessary to review this development from the other study in this field to confirm the application of (KSS) in our study of the food industry. Design/methodology/approach: This study uses The descriptive analytical approach within the methodology of a questionnaire for 83 respondents in Food Industries. co to collect the data, to provide a description of the relationship between the variables of (factors) used to Apply the (KSS). Findings: The findings of this study indicated The significance Identified all of the individual factors and organizational factors to the (KSS), that are applied variable percentage, also The ﬁndings Indicate the individual who has responded to this questionnaire have positive correlations towards Apply The knowledge-Sharing System. Research, Practical &amp; Social implications: Future studies can consider other indicators of applying knowledge sharing and factors model, In order to know the assess their influence on the application of the (KSS) in other non-food institutions. Originality/value: This study enhancing the understanding of the literature related to the applied knowledge sharing system by descriptive analysis, and provides an applied conception of the role played to The factors that influence the application (KSS)
Machine Translation

This chapter provides a history of machine translation from the pioneering research and the early operational systems (1950s and 1960s) to the dominance of rule-based systems (1967 to 1989) and finally to the emergence of corpus-based systems (in particular statistical approaches), translation memories, evaluation methods, and current applications.
IoT Data Sharing Platform in Web 3.0 Using Blockchain Technology

As Internet of Things (IoT)-based systems become more prevalent in the era of data-driven intelligence, they are prone to some unprecedented challenges in terms of data security and systems scalability in an era of context-sensitive data. The current advances in IoT-driven data sensing and sharing rely on third-party sources of information (TTPs) that gather data from one party, then transmit it to the other. As a result of TTPs’ involvement, such IoT systems suffer from many issues including but not limited to security, transparency, trust, and immutability as a result of the involvement of the company. Moreover, a multitude of technical impediments, such as the computation and storage poverty of IoTs, privacy concerns, and energy efficiency, enhances the challenges for IoTs. To address these issues of IoT security, we propose a blockchain-enabled open IoT data-sharing framework based on the potential of the interplanetary file system (IPFS). We have used a case study-based approach to evaluate the proposed solution. It is submitted that the proposed scenario is implemented by building smart contracts in Solidity and deploying them on the local Ethereum test network, using the Solidity programming language. With the implementation of smart contracts on the blockchain for access roles in IoT data sensing, the proposed solution advocates for a blockchain-based approach to data security for IoT systems that makes use of smart contracts for access roles.
Mechanical Search on Shelves with Efficient Stacking and Destacking of Objects

Although stacking objects increases shelves storage efficiency, the lack of visibility and accessibility makes the mechanical search problem of revealing and extracting a target object difficult for robots. In this paper, we extend the lateral-access mechanical search problem to shelves with stacked items and introduce two novel policies—Distribution Area Reduction for Stacked Scenes (DARSS) and Monte Carlo Tree Search for Stacked Scenes (MCTSSS)—that use destacking and restacking actions. MCTSSS improves on prior lookahead policies by considering three future states after each potential action. Experiments with 3600 simulated and 18 physical trials with a Fetch robot equipped with a blade and suction cup suggest that these policies can reveal the target object with 82–100% success in simulation outperforming the baseline by up to 66%, and can achieve 67–100% success in physical experiments. DARSS outperforms MCTSSS on median number of steps to reveal the target, but MCTSSS has a higher success rate in physical experiments, suggesting its robustness to perception noise. See https://sites.google.com/berkeley.edu/stax-ray for supplementary material.
Toward Intraoperative Margin Assessment Using a Deep Learning-Based Approach for Automatic Tumor Segmentation in Breast Lumpectomy Ultrasound Images

There is an unmet clinical need for an accurate, rapid and reliable tool for margin assessment during breast-conserving surgeries. Ultrasound offers the potential for a rapid, reproducible, and non-invasive method to assess margins. However, it is challenged by certain drawbacks, including a low signal-to-noise ratio, artifacts, and the need for experience with the acquirement and interpretation of images. A possible solution might be computer-aided ultrasound evaluation. In this study, we have developed new ensemble approaches for automated breast tumor segmentation. The ensemble approaches to predict positive and close margins (distance from tumor to margin ≤ 2.0 mm) in the ultrasound images were based on 8 pre-trained deep neural networks. The best optimum ensemble approach for segmentation attained a median Dice score of 0.88 on our data set. Furthermore, utilizing the segmentation results we were able to achieve a sensitivity of 96% and a specificity of 76% for predicting a close margin when compared to histology results. The promising results demonstrate the capability of AI-based ultrasound imaging as an intraoperative surgical margin assessment tool during breast-conserving surgery.
Automated Grading of Automata with ACL2s

Almost all Computer Science programs require students to take a course on the Theory of Computation (ToC) which covers various models of computation such as finite automata, push-down automata and Turing machines.ToC courses tend to give assignments that require paper-and-pencil solutions.Grading such assignments takes time, so students typically receive feedback for their solutions more than a week after they complete them.We present the Automatic Automata Checker ( A2C), an open source library that enables one to construct executable automata using definitions that mimic those found in standard textbooks [28].Such constructions are easy to reason about using semantic equivalence checks, properties and test cases.Instructors can conveniently specify solutions in the form of their own constructions.A2C can check for semantic equivalence between student and instructor solutions and can immediately generate actionable feedback, which helps students better understand the material.A2C can be downloaded and used locally by students as well as integrated into Learning Management Systems (LMS) like Gradescope to automatically grade student submissions and generate feedback.A2C is based on the ACL2s interactive theorem prover, which provides advanced methods for stating, proving and disproving properties.Since feedback is automatic, A2C can be deployed at scale and integrated into massively open online courses.
Full-Scale Selective Transformer for Semantic Segmentation

In this paper, we rethink the multi-scale feature fusion from two perspectives (scale-level and spatial-level) and propose a full-scale selective fusion strategy for semantic segmentation. Based on such strategy, we design a novel segmentation network, named Full-scale Selective Transformer (FSFormer). Specifically, our FSFormer adaptively selects partial tokens from all tokens at all scales to construct a token subset of interest for each scale. Therefore, each token only interacts with the tokens within its corresponding token subset of interest. The proposed full-scale selective fusion strategy can not only filter out the noisy information propagation but also reduce the computational costs to some extent. We evaluate our FSFormer on four challenging semantic segmentation benchmarks, including PASCAL Context, ADE20K, COCO-Stuff 10K, and Cityscapes, outperforming the state-of-the-art methods. We evaluate our FSFormer on four challenging semantic segmentation benchmarks, including PASCAL Context, ADE20K, COCO-Stuff 10K, and Cityscapes, outperforming the state-of-the-art methods.
Boundary-Aware Network for Kidney Parsing

Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable tumor sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Kidney PArsing (KiPA) Challenge dataset and achieved an average Dice score of 89.65 $$\%$$ for kidney structures segmentation on CTA scans using 4-fold cross-validation. The results demonstrate the effectiveness of the BA-Net. Code and pre-trained models are available at https://github.com/ShishuaiHu/BA-Net .
Resume Classification using Elite Bag-of-Words Approach

As technology is advancing day by day, new trends are booming up, like automation, where traditional libraries are being automated to digital libraries. Therefore, instead of manually screening the resumes of all candidates, algoiithms and models are being employed to screen resumes in job and career portals. This complete process of mapping resumes to their corresponding job profiles could be efficiently accomplished by making use of various machine learning and Natural Language Processing (NLP) tools. This article utilizes a recently introduced text vectorization technique called Elite bag-of-wo rds for the vectorization of resumes. To implement this method, words in each dass are ranked based on their occurring frequency, and then applied maximum entropy partitioning (MEP) to derive the top-ranked significant keywords in each class. These keywords, defined as the Elite keywords, were extracted from each class, and concatenated without redundancy, for predicting the resume type. This research study presents an expeiimental comparison of the proposed method with existing bag-of-words approaches. This paper implements four vectorization techniques and it isproved that the Elite bag-of-words approach outperforms the othermethods for resume classification.
COVID-19 case prediction using emotion trends via Twitter emoji analysis: A case study in Japan

The worldwide COVID-19 pandemic, which began in December 2019 and has lasted for almost 3 years now, has undergone many changes and has changed public perceptions and attitudes. Various systems for predicting the progression of the pandemic have been developed to help assess the risk of COVID-19 spreading. In a case study in Japan, we attempt to determine whether the trend of emotions toward COVID-19 expressed on social media, specifically Twitter, can be used to enhance COVID-19 case prediction system performance.We use emoji as a proxy to shallowly capture the trend in emotion expression on Twitter. Two aspects of emoji are studied: the surface trend in emoji usage by using the tweet count and the structural interaction of emoji by using an anomalous score.Our experimental results show that utilizing emoji improved system performance in the majority of evaluations.
Co-design of Human-centered, Explainable AI for Clinical Decision Support

eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique and its explanation user interface for clinical Decision Support Systems (DSS). We first present an XAI technique that meets the technical requirements of the healthcare domain: sequential, ontology-linked patient data, and multi-label classification tasks. We demonstrate its applicability to explain a clinical DSS, and we design a first prototype of an explanation user interface. Next, we test such a prototype with healthcare providers and collect their feedback with a two-fold outcome: First, we obtain evidence that explanations increase users’ trust in the XAI system, and second, we obtain useful insights on the perceived deficiencies of their interaction with the system, so we can re-design a better, more human-centered explanation interface.
Kick-start your scientific journey into the metaverse

The term “metaverse” first appeared in Neal Stephenson’s 1992 science fiction novel Snow Crash. It was defined as a virtual environment shared through the Internet in which avatars represent people in three dimensions, and users can build what they wish. Since the publication of Stephenson’s novel, technological advances have enabled the creation of several metaverses for widespread usages, such as virtual world platforms like World of Warcraft and Second Life. This paper aims to review studies on the metaverse using bibliometric analysis. The findings include a summary of the most important scientific articles and journals in the subject area and the most prolific and prominent authors, organisations, and nations. Furthermore, keyword co-occurrence analysis revealed the core research clusters and their sub-themes, allowing for more pertinent debates and viewpoints on crucial areas for future research. Finally, the implications for theory and practice have been addressed, offering a comprehensive overview of expected metaverse impacts on industry and society.
CVIK: A Matlab-based cluster validity index toolbox for automatic data clustering

We present CVIK, a Matlab-based toolbox for assisting the process of cluster analysis applications. This toolbox aims to implement 28 cluster validity indices (CVIs) for measuring clustering quality available to data scientists, researchers, and practitioners. CVIK facilitates implementing the entire pipeline of automatic clustering in two approaches: (i) evaluating candidate clustering solutions from classical algorithms, in which the number of clusters increases gradually, and (ii) assessing potential solutions in evolutionary clustering algorithms using single- and multi-objective optimization methods. This toolbox also implements distinct proximity measures to estimate data similarity, and the CVIs are capable of processing both feature data and relational data. The source code and examples can be found in this GitHub repository: https://github.com/adanjoga/cvik-toolbox.
CLEF 2023 SimpleText Track

The general public tends to avoid reliable sources such as scientific literature due to their complex language and lacking background knowledge. Instead, they rely on shallow and derived sources on the web and in social media – often published for commercial or political incentives, rather than the informational value. Can text simplification help to remove some of these access barriers? This paper presents the CLEF 2023 SimpleText track tackling technical and evaluation challenges of scientific information access for a general audience. We provide appropriate reusable data and benchmarks for scientific text simplification, and promote novel research to reduce barriers in understanding complex texts. Our overall use-case is to create a simplified summary of multiple scientific documents based on a popular science query which provides a user with an accessible overview on this specific topic. The track has the following three concrete tasks. Task 1 (What is in, or out?): Selecting passages to include in a simplified summary. Task 2 (What is unclear?): Difficult concept identification and explanation. Task 3 (Rewrite this!): Text simplification - rewriting scientific text. The three tasks together form a pipeline of a scientific text simplification system.