Reviewerly/exHarmony huggingface dataset:
A Survey on Group Signatures and Ring Signatures: Traceability vs. Anonymity

This survey reviews the two most prominent group-oriented anonymous signature schemes and analyzes the existing approaches for their problem: balancing anonymity against traceability. Group signatures and ring signatures are the two leading competitive signature schemes with a rich body of research. Both group and ring signatures enable user anonymity with group settings. Any group user can produce a signature while hiding his identity in a group. Although group signatures have predefined group settings, ring signatures allow users to form ad-hoc groups. Preserving user identities provided an advantage for group and ring signatures. Thus, presently many applications utilize them. However, standard group signatures enable an authority to freely revoke signers’ anonymity. Thus, the authority might weaken the anonymity of innocent users. On the other hand, traditional ring signatures maintain permanent user anonymity, allowing space for malicious user activities; thus achieving the requirements of privacy-preserved traceability in group signatures and controlled anonymity in ring signatures has become desirable. This paper reviews group and ring signatures and explores the existing approaches that address the identification of malicious user activities. We selected many papers that discuss balancing user tracing and anonymity in group and ring signatures. Since this paper scrutinizes both signatures from their basic idea to obstacles including tracing users, it provides readers a broad synthesis of information about two signature schemes with the knowledge of current approaches to balance excessive traceability in group signatures and extreme anonymity in ring signatures. This paper will also shape the future research directions of two critical signature schemes that require more awareness.
Virtual reality assessments (VRAs): Exploring the reliability and validity of evaluations in VR

Abstract Virtual reality (VR) is a potential assessment format for constructs dependent on certain perceptual characteristics (e.g., realistic environment and immersive experience). The purpose of this series of studies was to explore methods of evaluating reliability and validity evidence for virtual reality assessments (VRAs) when compared with traditional assessments. We intended to provide the basis of a framework on how to evaluate VR assessments given that there are important fundamental differences to VR assessments compared with traditional assessment formats. Two commercial off‐the‐shelf (COTS) games (i.e., Project M and Richie's Plank Experience ) were used in Studies 1 and 2, while a game‐based assessment (GBA; Balloon Pop , designed for assessment) was used in Study 3. Studies 1 and 2 provided limited evidence for the reliability and validity of the VRAs. However, no meaningful constructs were measured by the VRA in Study 3. Findings demonstrate limited evidence for these VRAs as viable assessment options through the validity and reliability methods utilized in the present studies, which in turn emphasize the importance of aligning the assessment purpose to the unique advantages of a VR environment.
Obstacle Avoidance onboard MAVs using a FMCW Radar

Micro Air Vehicles (MAVs) are increasingly being used for complex or hazardous tasks in enclosed and cluttered environments such as surveillance or search and rescue. With this comes the necessity for sensors that can operate in poor visibility conditions to facilitate with navigation and avoidance of objects or people. Radar sensors in particular can provide more robust sensing of the environment when traditional sensors such as cameras fail in the presence of dust, fog or smoke. While extensively used in autonomous driving, miniature FMCW radars on MAVs have been relatively unexplored. This study aims to investigate to what extent this sensor is of use in these environments by employing traditional signal processing such as multi-target tracking and velocity obstacles. The viability of the solution is evaluated with an implementation on board a MAV by running trial tests in an indoor environment containing obstacles and by comparison with a human pilot, demonstrating the potential for the sensor to provide a more robust sense and avoid function in fully autonomous MAVs.
Adversarial CAPTCHAs

Following the principle of to set one's own spear against one's own shield, we study how to design adversarial completely automated public turing test to tell computers and humans apart (CAPTCHA) in this article. We first identify the similarity and difference between adversarial CAPTCHA generation and existing hot adversarial example (image) generation research. Then, we propose a framework for text-based and image-based adversarial CAPTCHA generation on top of state-of-the-art adversarial image generation techniques. Finally, we design and implement an adversarial CAPTCHA generation and evaluation system, called aCAPTCHA, which integrates 12 image preprocessing techniques, nine CAPTCHA attacks, four baseline adversarial CAPTCHA generation methods, and eight new adversarial CAPTCHA generation methods. To examine the performance of aCAPTCHA, extensive security and usability evaluations are conducted. The results demonstrate that the generated adversarial CAPTCHAs can significantly improve the security of normal CAPTCHAs while maintaining similar usability. To facilitate the CAPTCHA security research, we also open source the aCAPTCHA system, including the source code, trained models, datasets, and the usability evaluation interfaces.
Evaluating user susceptibility to phishing attacks

Purpose Phishing is a well-known cybersecurity attack that has rapidly increased in recent years. It poses risks to businesses, government agencies and all users due to sensitive data breaches and subsequent financial losses. To study the user side, this paper aims to conduct a literature review and user study. Design/methodology/approach To investigate phishing attacks, the authors provide a detailed overview of previous research on phishing techniques by conducting a systematic literature review of n = 367 peer-reviewed academic papers published in ACM Digital Library. Also, the authors report on an evaluation of a high school community. The authors engaged 57 high school students and faculty members (12 high school students, 45 staff members) as participants in research using signal detection theory (SDT). Findings Through the literature review which goes back to as early as 2004, the authors found that only 13.9% of papers focused on user studies. In the user study, through scenario-based analysis, participants were tasked with distinguishing phishing e-mails from authentic e-mails. The results revealed an overconfidence bias in self-detection from the participants, regardless of their technical background. Originality/value The authors conducted a literature review with a focus on user study which is a first in this field as far the authors know. Additionally, the authors conducted a detailed user study with high school students and faculty using SDT which is also an understudied area and population.
Remote Monitoring System Architectures in Healthcare

The healthcare system is one of the rapidly developing areas where technical and technological innovations are introduced in the first place. Remote monitoring systems for patients, people requiring special care, and people who want to control their health are gaining more and more popularity, especially in recent years. Remote monitoring systems are diverse; in this paper, we have combined all possible systems and classified the architecture of the structure of these systems from the point of view of healthcare informatics. Remote monitoring systems are classified according to the nodes' interaction features, to the distribution of system functions, as well as according to the algorithms for the functioning of the system.
Optical Character Recognition of Indian Language Manuscripts using Convolutional Neural Networks

India's constitution has 22 languages written in 17 different scripts. These materials have a limited lifespan, and as generations pass, these materials deteriorate, and the vital knowledge is lost. This work uses digital texts to convey information to future generations. Optical Character Recognition (OCR) helps extract information from scanned manuscripts (printed text). This paper proposes a simple and effective solution of optical character recognition (OCR) Sanskrit Character from text document images using long short-term memory (LSTM) and neural networks of Sanskrit Characters. Existing methods focuses only upon the single touching characters. But our main focus is to design a robust method using Bidirectional Long Short-Term Memory (BLSTM) architecture for overlapping lines, touching characters in middle and upper zone and half character which would increase the accuracy of the present OCR system for recognition of poorly maintained Sanskrit literature.
DS2PM: A Data-Sharing Privacy Protection Model Based on Blockchain and Federated Learning

With the development of big data and blockchain, an increasing number of scholars have begun to study blockchain for data sharing. By studying data sharing models that are based on blockchain, we find that almost all of them have the following problems: 1) it is difficult to protect the privacy and integrity of users’ data, along with users’ data ownership; 2) the storage burden of blockchain is heavy, and blockchain lacks a mechanism for dealing with data with diverse types and inconsistent formats; and 3) the consensus mechanism has low fairness or low efficiency. Therefore, we propose a data-sharing privacy protection model (DS2PM) that is based on blockchain and a federated learning mechanism for solving these problems. The safety analysis and experimental results show that the DS2PM outperforms the previously established schemes.
Fault-Prototypical Adapted Network for Cross-Domain Industrial Intelligent Diagnosis

Despite rapid advances in machine learning based fault diagnosis, their identical distribution assumption of the training (source domain) and testing data (target domain) is generally challenged in industrial applications due to the variation of working conditions. In this article, a fault-prototypical adapted network (FPAN) is proposed, which enables cross-domain industrial intelligent fault diagnosis aided by deep transfer learning. First, a similarity learning-based discrimination module is designed to learn fault prototypes (FPs) that are representative for each fault and discriminative across different faults. Then, a fault prototypical-adaptation module is developed, which adapts the multiple FPs to the target dataset and enables more precise category-wise domain invariance. The two modules are trained simultaneously to extract transferrable and discriminative FPs, by which the cross-domain intelligent diagnosis can be readily achieved. Experimental results on two industrial cases illustrate that the proposed approach learns transferable feature representations that better reduce domain discrepancy, and provides improved diagnosis performance on target data. Note to Practitioners—As the distribution of source and target domain data may differ due to the varying working conditions, cross-domain intelligent fault diagnosis plays an increasingly important role in practical industrial cases. This work presents the FPAN model, in which the fault discrimination and the category-wise adaptation are naturally connected and unified by exploring representative virtual FP for each category, irrespective of which domain that the category comes from. Connected by the adaptive FPs, the similarity learning-based discrimination and the fault-prototypical adaptation modules can benefit from each other to boost the target learning. Finally, the cross-domain fault diagnosis can be performed based on the similarities with different FP representations. The proposed method is readily applicable to various intelligent fault diagnosis problems, including the mechanical cases and the industrial processes.
Extraction and analysis of text patterns from NSFW adult content in Reddit

Reddit is one of the few social networks that handles Not Safe For Work (NSFW) content in an explicit and well-structured way. Despite this, in the past literature on Reddit, there are very few researches concerning this topic. In particular, a study on the text of NSFW comments and posts published in this social medium is missing. In this paper, we aim at contributing to fill this gap by proposing an approach for extracting and analyzing text patterns from NSFW adult content in Reddit. Some peculiarities of our approach are the following: (i) text patterns are extracted based not only on frequency but also, and mostly, on several utility measures; (ii) extracted patterns contribute to the definition of social networks whose analysis allows us to extract several useful information about the users publishing and/or accessing NSFW content and the language adopted by them; (iii) our approach is not only descriptive but also predictive, because, in addition to identifying already existing user communities, it is able to propose new ones; these are made up of users who do not yet know each other but share the same interests and the same language.
Trajectory Prediction of Hypersonic Glide Vehicle Based on Empirical Wavelet Transform and Attention Convolutional Long Short-Term Memory Network

Hypersonic glide vehicle (HGV) has brought severe challenges to the existing radar system. Its trajectory prediction is a new problem affecting aerospace security, but it has not attracted extensive attention. The existing approaches provide unsatisfactory accuracy of HGV trajectory prediction due to the neglect of noise processing and the single prediction method. Here, we propose a HGV trajectory prediction method based on empirical wavelet transform (EWT) and attention convolutional long short-term memory (AConvLSTM) network to solve this problem. Firstly, aerodynamic acceleration components of HGV are selected as the prediction parameters, and the dynamic tracking model of HGV is established to estimate the prediction parameters. Secondly, we decompose and reconstruct the prediction parameters of HGV by the EWT and grey correlation degree to reduce noise interference to the prediction model. Finally, the prediction parameters after denoising are used to train the AConvLSTM network to predict the trajectory of HGV. Simulations show that the proposed method can effectively predict the trajectory of HGV using radar tracking data and achieve better prediction accuracy than the existing approaches.
Quantum circuit compilation by genetic algorithm for quantum approximate optimization algorithm applied to MaxCut problem

The Quantum Circuit Compilation Problem (QCCP) is challenging to the Artificial Intelligence community. It was already tackled with temporal planning, constraint programming, greedy heuristics and other techniques. In this paper, QCCP is formulated as a scheduling problem and solved by a genetic algorithm. We focus on QCCP for Quantum Approximation Optimization Algorithms (QAOA) applied to the MaxCut problem and consider Noisy Intermediate Scale Quantum (NISQ) hardware architectures. Based on the fact that these algorithms apply a set of basic quantum operations repeatedly over a number of rounds, we propose a genetic algorithm approach, termed Decomposition Based Genetic Algorithm (DBGA), that in each round extends the partial solutions obtained for the previous ones. DBGA is compared to the state of the art across a set of conventional instances. The results of the experimental study provided interesting insight in the problem structure and showed that DBGA is quite competitive with the state of the art. In particular, DBGA outperformed the best current method on the largest instances and provided new best solutions to most of them.
Evaluating the Adequacy of Emoji Use in Positive and Negative Messages from Close and Distant Senders

Computer-mediated communication allows people to communicate across several contexts (e.g., friends, professional settings) by using video-based or text-based channels. In the latter case, communication lacks nonverbal cues (e.g., tone of voice) that are critical to message interpretation. Including emoji can help express emotion and reinforce or clarify the meaning of a message. However, the benefits of using emoji are likely to depend on the context (e.g., the relationship between interlocutors) and the messages' features (e.g., the valence of the message). To date, studies have not systematically and empirically examined how the use of emoji is perceived across different communication scenarios. In the current study, we asked 175 participants (49.5 percent women; Mage = 36.32, SD = 12.22) to imagine receiving either a negative or a positive message from 22 senders (e.g., friend; bank manager) and to indicate, for each case, how much they would like to receive an emoji and how useful and appropriate they considered the use of emoji. These ratings were combined into a single index of emoji use adequacy. Based on factor analysis, the 22 scenarios were aggregated in distant and close scenarios. Overall, results showed that participants considered emoji use more inadequate in distant (vs. close) scenarios and for negative (vs. positive) messages. These findings suggest that the perceived benefits of emoji use for text-based communication may not be generalized to all text-based communication contexts, such that relationship proximity and message valence should not be overlooked. Implications for the fields of consumer psychology and communication are discussed.
Toward fine‐grained access control and privacy protection for video sharing in media convergence environment

Video streaming applications are rapidly proliferating, allowing users to browse, download, and share videos through platforms such as YouTube, Netflix, and Amazon. Media content providers are required to register copyrights on digital platforms. With the existence of blockchain technology, users can manage their data using a smart contract, which allows them to define access and search policies and use an instant payment system without a third party in a decentralized network. We propose a secure and reliable video sharing scheme based on blockchain, using cryptographic primitives to secure the information of streamers and viewers. The use of blockchain ensures reliability and prevents the manipulating or forging of multimedia content. A smart contract system is implemented to represent contractual actions once a given access condition is satisfied. Analysis of the scheme's security and performance demonstrates that it is reliable and efficient.
Potential Requirements and Opportunities of Blockchain-Based Industrial IoT in Supply Chain: A Survey

The integration of the industrial Internet of Things (IoT) and blockchain technology is changing the business and management model of the supply chain. Much work is focused on how to promote the application of blockchain-based industrial IoT in the supply chain from both academic research and industrial practice. However, due to the different industrial requirements in industrial scenes, the gap between technology researches and industrial applications is still large. Therefore, the article adopts the mixed method of enterprise survey and literature review to identify the actual industrial requirements in different supply chain scenes. Also, the characteristics and applicable scenarios of industrial IoT and blockchain have been analyzed. Then, the potential application opportunities of blockchain-based industrial IoT in nine scenes are discussed in detail. This study reveals the technical challenges and practical challenges of these applications, which potentially guides research on applying industrial IoT and blockchain technology in the supply chain.
History of Abstract Interpretation

We trace the roots of abstract interpretation and its role as a foundational principle to understand and design static program analysis and verification methods. Starting from the historical roots of formal methods and static program analysis, we show how abstract interpretation evolved and influenced the way we reason about program correctness in different programming languages and how this method shaped the literature and the practice in program analysis in the last 45 years.
A simple and precise calibration method for binocular vision

Abstract Binocular vision is an important part of machine vision measurement. Calibration accuracy is crucial for binocular vision. As for the determination of the structure parameters of the two cameras, the existing approaches usually obtain the initial values and optimize them according to the image-space errors, object-space errors or a combination of these. In the optimization process, constructing the objective function only through the image-space errors or object-space errors is not enough. Moreover, the image-space and object-space errors can form a variety of combinations to construct the objective function. Therefore, it is hard to choose the error criterion. An inadequate error criterion may lead to over-optimized or local minima (ambiguity solution). To solve this problem, this paper proposes a simple and precise calibration method for binocular vision based on the points distance constraints and image-space errors. The process of determining the structure parameters was divided into noniterative and iterative parts. We calculated the structure parameters of the two cameras according to the distance constraints of every two feature points noniteratively. The results obtained in this step were set as the initial value and refined through minimizing the reprojection errors using the Levenberg–Marquardt method. Because the results obtained in the noniterative step are accurate enough, only one iteration is needed. In this way, we finish the calibration avoiding the need to choose the error criterion. Furthermore, our method reduces the number of iterations to improve the calibration efficiency on the premise of guaranteeing the calibration accuracy. The experimental results show the superiority of this calibration method compared with other calibration methods. Using the calibration results of our method, in the measurement range of −45°∼ 45°, the rotation angle measurement error was less than ±0.032°. In the measurement range of 0 ∼ 39 mm, the displacement measurement error was less than ±0.047 mm. As for the length measurement of a 300 × 225 mm target, the length measurement error was less than ±0.039 mm.