Reviewerly/exHarmony huggingface dataset:
CyNER: Information Extraction from Unstructured Text of CTI Sources with Noncontextual IOCs

Cybersecurity threats have been increasing and growing more sophisticated year by year. In such circumstances, gathering Cyber Threat Intelligence (CTI) and following up with up-to-date threat information is crucial. Structured CTI such as Structured Threat Information eXpression (STIX) is particularly useful because it can automate security operations such as updating FW/IDS rules and analyzing attack trends. However, as most CTIs are written in natural language, manual analysis with domain knowledge is required, which becomes quite time-consuming. In this work, we propose CyNER, a method for automatically structuring CTIs and converting them into STIX format. CyNER extracts named entities in the context of CTI and then extracts the relations between named entities and IOCs in order to convert them into STIX. In addition, by using key phrase extraction, CyNER can extract relations between IOCs that lack contextual information, such as those listed at the bottom of a CTI, and named entities. We describe our design and implementation of CyNER and demonstrate that it can extract named entities with the F-measure of 0.80 and extract relations between named entities and IOCs with the maximum accuracy of 81.6%. Our analysis of structured CTI showed that CyNER can extract IOCs that are not included in existing reputation sites, and that it can automatically extract IOCs that have been exploited for a long time and across multiple attack groups. CyNER is thus expected to contribute to the efficiency of CTI analysis.
A dynamic infrared object tracking algorithm by frame differencing

Infrared object tracking is an important research field of computer vision. Existing tracking-by-detection algorithms usually use appearance information to distinguish between the target and the surrounding environment. However, the complex environment and the colorless or textureless infrared target make object tracking difficult, causing existing algorithms to lose the target. In this paper, to mine the correlation between video frames, we propose a simple and effective way to exploit the motion information by frame differences. Specifically, using adjacent frame differencing, we propose a dynamic infrared object tracking framework to assist existing semantic aware tracking algorithms. Experiments on the Anti-UAV infrared datasets, VOT-TIR2015, VOT-TIR2016, PTB-IR and LSOTB-IR datasets show its robustness to the different challenges of the infrared scenes with high efficiency.
How have views on Software Quality differed over time? Research and practice viewpoints

Over the years, there has been debate about what constitutes software quality and how it should be measured. This controversy has caused uncertainty across the software engineering community, affecting levels of commitment to the many potential determinants of quality among developers. An up-to-date catalogue of software quality views could provide developers with contemporary guidelines and templates. In fact, it is necessary to learn about views on the quality of code on frequently used online collaboration platforms (e.g., Stack Overflow), given that the quality of code snippets can affect the quality of software products developed. If quality models are unsuitable for aiding developers because they lack relevance, developers will hold relaxed or inappropriate views of software quality, thereby lacking awareness and commitment to such practices. We aim to explore differences in interest in quality characteristics across research and practice. We also seek to identify quality characteristics practitioners consider important when judging code snippet quality. First, we examine the literature for quality characteristics used frequently for judging software quality, followed by the quality characteristics commonly used by researchers to study code snippet quality. Finally, we investigate quality characteristics used by practitioners to judge the quality of code snippets. We conducted two systematic literature reviews followed by semi-structured interviews of 50 practitioners to address this gap. The outcomes of the semi-structured interviews revealed that most practitioners judged the quality of code snippets using five quality dimensions: Functionality, Readability, Efficiency, Security and Reliability. However, other dimensions were also considered (i.e., Reusability, Maintainability, Usability, Compatibility and Completeness). This outcome differed from how the researchers judged code snippet quality. Practitioners today mainly rely on code snippets from online code resources, and specific models or quality characteristics are emphasised based on their need to address distinct concerns (e.g., mobile vs web vs standalone applications, regular vs machine learning applications, or open vs closed source applications). Consequently, software quality models should be adapted for the domain of consideration and not seen as one-size-fits-all. This study will lead to targeted support for various clusters of the software development community.
A new (w, t, n)-weighted threshold quantum secret sharing scheme based on two-qubit system

The weighted threshold structure of secret sharing has important application values in the hierarchical organization. In order to satisfy the actual needs of participants with different authority, only using single-particle transform of Bell state, we construct a new ( w , t , n )-weighted threshold quantum secret sharing scheme in the two-qubit system based on the Chinese Remainder Theorem. The paper can share not only classical information but also quantum state information. Compared with the existing threshold schemes, the proposed scheme can greatly improve information transmission capacity by employing two-qubit entangled Bell state. Furthermore, the security analysis shows that our scheme can not only detect a series of typical external attacks but resist forgery and collusion attacks.
Colluding RF Fingerprint Impersonation Attack Based on Generative Adversarial Network

Radio frequency fingerprint (RFF) is an effective way to improve the security of wireless communications. Existing research mainly focused on the classification capability and the robustness of RFFs but overlooked malicious attacks. In this paper, a colluding impersonation attack framework is proposed to emulate the RFF of legitimate users. A colluding attacker is introduced to observe the signal features of the impersonation attacker and the legitimate user and compare their difference. The difference is fed back to the impersonation attacker to help improve its RFF impersonation method. With this idea, the impersonation attack is realized by the Generative Adversarial Network (GAN) structure. The RFF impersonation is formulated as the generator whose objective is to output the signal with RFF similar to the legitimate user, viewed from the colluding attacker’s perspective. Simulation results show that the proposed method can effectively impersonate the legitimate user’s RFF under the dynamic block fading channel.
Trustworthy AI Explanations as an Interface in Medical Diagnostic Systems

We propose "Trustworthy AI Explanations as an Interface" framework to evaluate the AI systems by comparing the explanations generated by the system with the reasoning provided by the domain experts based on their experience and expertise. The proposed framework is based on the Trustworthy Explainability Acceptance metric and helps calibrate the trust and acceptance of the system accordingly. This framework can be easily implemented to any explainable AI system and can be used by standardization organizations to test and evaluate the AI systems. We illustrate the proposed framework using an interpretable medical diagnostic system for Breast Cancer Prediction.
Dating Phish: An Analysis of the Life Cycles of Phishing Attacks and Campaigns

Phishing attacks are still a general and world-wide threat to users of the Internet. In the past, several approaches to detect phishing websites earlier and shorten the time frame between their creation and inclusion in a blocklist have been proposed. Understanding the life cycle of phishing attacks, in particular the time of their creation and the time span from the first to last attack in a campaign, provides additional insights into the potential success of these methods. In this paper, we present an analysis of the life cycles of 133,667 phishing websites based on the publicly available information from certificates, whois, as well as images and resources on the phishing websites themselves. While we confirm the findings from previous work, that many websites have short lifetimes of only several days, we also note that the timing information from phishing websites using public hosting or compromised infrastructure is far less accurate in dating the creation of the websites. We further cluster the phishing websites into campaigns based on patterns in their domain names, and find that the detected campaigns often take place over several weeks, with an average duration of almost 12 days. Our results showcase advantages and limitations for the early detection of phishing websites, in particular regarding the time span between the creation of a website and its inclusion in a blocklist, and how patterns in domain names remain the same over a period of up to several weeks in the phishing campaigns analyzed in this paper.
Stress reduction interventions: A scoping review to explore progress toward use of haptic feedback in virtual reality

With the objective of providing scientific guidance for the development of a multisensory virtual reality (VR) relaxation device using haptic stimulation, the present review focuses on analysis of existing traditional and VR-based stress reduction interventions as well as their relevant measures. Two primary methods of stress reduction are explored: relaxation techniques and meditation techniques. Relaxation techniques enable the practitioner to achieve a tension-free state through control of and reduction in physiological activity. Meditation techniques also induce a relaxation response, but can additionally increase sustained attention to the present moment, to one’s own bodily sensations, emotions, tensions, thoughts, etc. , or to an object, without judgement or adherence to a particular perspective. The limitations of traditional techniques are also noted, including the time required for training or mastery and the need for visualization efforts, and the benefits of VR-based relaxation techniques for the user are explored: these include the reduction of negative emotions, stress, anxiety, depression, and pain, as well as improved relaxation and positive affect. Particular attention is paid to the multisensorial approach made possible by VR. However, while it has been known for decades that tactile stimulation is very efficient to relax users, reduce stress, and induce positive emotions, tactile stimuli are currently under-exploited in VR-based stress reduction interventions. This review focuses specifically on touch and its beneficial effects on stress and affect. Finally, we discuss and provide forward-looking perspectives on the present and future use of tactile stimulation as a component of VR tools designed to reduce stress.
Awareness of electronic crimes related to E-learning among students at the University of Jordan

The spread of e-learning as an alternative to traditional or face-to-face education has faced many problems and challenges in general and ethical and legal challenges in particular. This study aims to measure students' awareness of the safe use of technology and its tools in e-learning that is consistent with ethical and legal standards. The study attempts to reveal the degree of awareness of students of the University of Jordan about electronic crimes related to e-learning and the legal procedures and penalties related to electronic crimes in e-learning. Quantitative research methods were used. A questionnaire was established and distributed to students enrolled in the following online courses: Ethics and Human Values, Communication Skills, and National Culture. Analysis of the data revealed that students had a high awareness about cybercrime due to the widespread use of the internet by students as it became an integral part of their daily lives. The degree of awareness of student about legal procedures and penalties related to electronic crimes in e-learning was medium. This indicates students' lack of awareness of the effectiveness of procedures and penalties for electronic crimes that can be applied in e-learning due to the rapid transition in the learning process at the University of Jordan from traditional learning to distance e-learning that was imposed during the Corona pandemic. Based on these findings, the study presented a set of recommendations that could be implemented to increase awareness and maximize the benefit of using e-learning.
Mining Popular Topics from the Media

As we are living in an uncertain world, the uncertainty may have significant and/or direct implications on various aspects of the computer industry. Hence., innovations of computers, software and applications have emerged as a pressing need. Innovative ideas can be inspired by technology., entertainment, and/or design posted in the media. In this paper, we present a solution to mine popular topics from the media. In particular., we focus on mining online talks for interesting patterns-such as popular topics, their trends, high viewership, high amounts of interaction in the form of comments, impact tags on popularity. Evaluation on real-life data from TED Talks data (e.g., collections of transcripts, viewership statistics, and classifier tags) demonstrates the practicality of our solution in mining popular topics from the media.
MPC: Multi-node Payment Channel for Off-chain Transactions

Payment channel (PC) greatly improves blockchain scalability by allowing an unlimited number of off-chain transactions instead of committing every transaction to the blockchain. However, one PC just confines to two nodes. Thus, for a node, like Cafe, who frequently receives money from multiple nodes, massive PCs need to be created, which leads to massively and repeatedly information addition of channels to the blockchain and costs much fees. In this paper, we introduce a multi-node payment channel (MPC) method to solve the problem above. MPC suits the scenario that one node frequently receives money from multiple nodes, such as Cafe, shop, etc. Compared to PC, MPC can contain nearly unlimited number of nodes, thus avoiding repetitive channel information addition to the blockchain and meanwhile reducing the fee. MPC supports the same transaction logic of PC and cooperates well with existing PC. The security, economy and efficiency of MPC are proved. We implement MPC’s smart contract in Ethereum and experimental results show that MPC outperforms the existing PC.
LBPNet: Exploiting texture descriptor for deepfake detection

Recent AI advancements made it significantly easier to generate high-quality synthesized faces, referred as deepfakes. It can make people saying and doing things that never happened actually. Owing to the vast usage of social media, manipulated content is susceptible to spread unrest in the society. With the continuous evolvement of AI-enabled deepfake generators, the research on deepake detection has started progressing in the last few years. From analyzing eye-blinking in the previous generation deepfakes to deep learning based models for advanced deepfakes, the existing deepfake detectors have analyzed numerous artefacts. This paper proposes a novel detection approach, LBPNet, to distinguish deepfaked faces from genuine ones by means of exploiting inconsistencies in texture information. In particular, Local Binary Pattern (LBP) of deepfaked and pristine faces have been investigated through a CNN based model. Moreover, the proposed LBPNet technique is evaluated on more advanced and diverse deepfaked datasets such as Celeb-DF, DFDC, and DeeperForensics, which provided a detection accuracy of 92.38%, 80% and 86% respectively. Comprehensive analysis on different benchmark datasets and comparison with state-of-art endorse the superior performance of the proposed method. Thorough experimentations also reveal the robustness of LBPNet against different compression levels and tampering types.
End-to-End Video Violence Detection with Transformer

Human action recognition is a widely investigated field in computer vision. Violence automatic detection is a subset of action recognition, which deserves special attention because of its wide applicability in unmanned security monitoring systems. This paper presents an end-to-end model, which introduces Transformer for human pose estimation and 3d convolutional neural network to capture motion present in spatial-temporal dimension. We train a 3d convolutional neural network to learn spatial-temporal features of human keypoint sequences which are the outputs of Transformer block. Our proposed model achieves an accuracy of 89% on the test set of large-scale video database RWF-2000, and obtains an accuracy of 93% on our own school violent video database. Our experiment result shows that transformer-based approach can be used in video violence detection.
Adding Commonsense to Robotic Application Using Ontology-Based Model Retraining

In terms of the level of technological capability in the world today, the use of automated robotics is common in various fields. There are large projects going on in many industries that collaborate between robots and other robots, as well as humans and robots. In hospital environments, care for people with medical needs and their needs and used to make appropriate suggestions to their problems. Robots can also be found in certain areas that can respond quickly as an emergency rescue agent. Furthermore, robots, which can be seen in the hotel industry as waiters and as farm assistants in agriculture, have a great tendency to be used as multi-tasking agents in many fields. In each of these areas, robots must co-operate with humans. In that situation, the importance of the exchange of mutual knowledge between robots-robots and between humans-robots comes into the picture. What matters here is not only the quantitative vastness of knowledge but also the ability to understand each other in the same medium. Although the common sense that people need in their day-to-day work is completely obvious to humans, the commonsense knowledge domain needs to be implanted in robots. Whatever concept is defined for adding commonsense to robotics, it should be a consistent concept that can be logically constructed so that it can be understood by a machine. As will be discussed later in the paper, different methods have been used in various related works to add a different kind of domain knowledge to robotics. The objective of this paper is to provide an improved retrained model for robotics in order to give them the ability to act more human-like when performing tasks. By using the proposed model robots are able to answer the incomplete command or inquiries related to a given context. One of the objectives of this work is to use the ontology-based, commonsense-support existing knowledge base as a mechanism to retrain and build a new model.
Multi-view Unet for Automated GI Tract Segmentation

Gastrointestinal cancer has shown a high incidence trend in recent years, affecting the health of millions of people. During the treatment of gastrointestinal cancer, radiation oncologists have to deliver x-ray beams pointed toward the tumor and meanwhile avoid the stomach and intestines. In this paper, we propose an automated process for gastro-intestinal tract (GI Tract) image segmentation. The 2D segmentation methods are time-saving while not good at modeling the multidimensional information of medical images. The 3D methods take advantage of both temporal and spatial information. However, they are relatively time-consuming and dependent on computing resources. To combine the advantages of the above methods, we propose an improved 2.5D method for GI Tract image segmentation. In order to effectively utilize the association of adjacent images, we explore as well as fuse different 2.5D data generation approaches and propose a 2.5D feature fusion method with adjacent weighting. To make up for the impact of low spatial-temporal resolution of traditional methods on the results, we propose a method for fusing 2.5D and 3D results. Our method fuses plenty of representation processes by embedding multi-dimensional convolutions deeply into basic modules, making the selections of representation processes flexible, thus balancing representation for sparse inter-slice information and dense intra-slice information adaptively. It also takes the advantages of the high segmentation accuracy of the easily recognized regions in 2.5D view as well as the high smoothness of 3D organ contour representations, thus obtaining better explanatory and more accurate modeling for target regions. Comprehensive experiments are performed on a public GI Tract dataset, experiments show that the 2.5D fusion method improves 0.36% on dice and 0.12% on Jaccard compared with the 2.5D method without feature fusion. The results fusion method improves 0.007 scores compared with 2.5D and 0.009 scores compared with 3D on the kaggle open test set.
A Vital Improvement? Relating Google's Core Web Vitals to Actual Web QoE

Providing sophisticated web Quality of Experience (QoE) has become paramount for web service providers and network operators alike.Due to advances in web technologies (HTML5, responsive design, etc.), traditional web QoE models focusing mainly on loading times have to be refined and improved.In this work, we relate Google's Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of web QoE.To this end, we first perform objective measurements in the web using Google's Lighthouse.To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies.In these studies, we use CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals.Our results suggest that the Core Web Vitals have less predictive value for web QoE than expected and that page loading times remain the main influence factor in this context.
Quality of Experience in the Metaverse: An Initial Analysis on Quality Dimensions and Assessment

The Metaverse provides a novel experience to the user, by opening the doors to social-based multiuser environments merging physical reality with digital virtuality. In this paper, we present an initial analysis of the Quality of Experience (QoE) in the Metaverse. We first consider traditional influence factors (human, system, and context). Then, we introduce the social and economic dimensions of the Metaverse as additional factors to be considered for QoE assessment. Finally, we discuss what QoE assessment methods can be more suitable for Metaverse applications, with a particular focus on implicit assessment methods (e.g., physiological, human cognitive, affective behaviour).