Reviewerly/exHarmony huggingface dataset:
The Not-So-Obvious Harm of Cyberhate: Source Magnification of Hate Tweets, Unhealthy Food Choice, and the Moderating Role of Group Identification

Guided by research demonstrating the impact of social identity threat on physical health and the source magnification framework, we investigated how hate tweets from multiple sources, relative to hate tweets from a single source, influence target group members' health-related behavior-specifically, food choice. We conducted an online experiment with a sample of Asian Twitter users in the United States. Participants were randomly assigned to view an identical set of anti-Asian hate tweets either from a single individual or from multiple individuals. Subsequently, in an ostensibly unrelated task masked as a marketing survey, participants customized a pizza by selecting toppings from a list presenting an equal number of healthy and unhealthy toppings. We found that participants exposed to multiple-source hate tweets selected a greater number of unhealthy toppings than those exposed to single-source hate tweets. We also found that group identification was a significant moderator, with those lower in group identification more likely to choose unhealthy toppings when exposed to multiple-source hate tweets. Our findings suggest that exposure to multiple-source hate messages, relative to single-source hate messages, may exert a detrimental influence on target group members' health to a greater extent, while highlighting a possible buffering role of group identification.
Continuous learning with random memory for object detection in robotic applications

If the robot is to interact with environment designed for humans it has to be able to cope with new objects in it’s surrounding, and not only to classify but also effectively localize objects in its reach based on visual sensing. We cannot predict all objects the robot will face. So it needs to learn them while operating, without an external update of software. One of the promising approaches to handle this task is using detectors based on convolution neural networks (CNN) with continuous learning setup. However, there is a well-known problem of catastrophic forgetting (CF) that negatively affects accuracy of neural networks, the main algorithms in object recognition nowadays. Moreover, we need to reach a trade off between training time (because it is unacceptable if the robot trains for hours) and good acccuracy of detection. In this paper we review some of the latest approaches in the field and adapt one of SOtA methods in classification task to detection task. Particularly, we adapt YOLOv5 to work in scenario with continuous learning by adding random memory mechanism from [1] and applying layer freezing in order to increase training speed. We benchmark the proposed method using iCubWorld Transformations dataset and report results with significantly improved metrics and inference speed.
Research on Adaptive Dynamic Access Control Model Based on Blockchain and Token

Abstract The new power system presents new features such as massive, decentralized, multiple types of equipment and user access, more complex interactions, and faster response speed. The zero-trust security model still has problems such as the single-point failure risk of permission control and the inability to balance dynamic fine-grained permission adjustment and real-time response. Based on this, this paper proposes an adaptive dynamic access control model based on blockchain and token. First, build a decentralized authorization management architecture based on blockchain smart contracts to provide users with reliable and intelligent authorization services. Secondly, an authorization technology based on short-term tokens is proposed, which realizes adaptive dynamic access authority adjustment through user trust evaluation and smart contract automatic discrimination. Finally, an intelligent identity authentication mechanism based on user behavior data analysis is designed to support the adaptive update of tokens. Experiments have verified that this model has certain advantages in accurate and adaptive dynamic authorization, which can provide a reference for the improvement of the zero-trust model.
Node-Polysemy Aware Recommendation by Matrix Completion with Side Information

Matrix completion is a well-known approach for recommender systems. It predicts the values of the missing entries in a sparse user-item interaction matrix, based on the low-rank structure of the rating matrix. However, existing matrix completion methods do not take node polysemy and side information of social relationships into consideration, which can otherwise further improve the performance. In this paper, we propose a novel matrix completion method that employs both users’ friendships and rating entries to predict the missing values in a user-item matrix. Our approach adopts a graph-based modeling where nodes are users and items, and two types of edges are considered: user friendships and user-item interactions. Polysemy-aware node features are extracted from this heterogeneous graph through a graph convolution network by considering the multifaceted factors for edge formation, which are then connected to a hybrid loss function with two heads: (1) a social-homophily head to address node polysemy, and (2) an error head for user-item rating regression. The latter is formulated on all matrix entries to combat the sensitivity of negative sampling of the vast majority of missing entries during training, with a smart technique to reduce the time complexity. Extensive experiments over real datasets verify that our model outperforms the state-of-the-art matrix completion methods by a significant margin.
Cybersecurity Awareness and Its Impact on Protecting Children in Cyberspace

Objective: Our proposed study investigates the impact of cybersecurity awareness among children in terms of protecting them in cyberspace. The prevalence of cybercrime among children at the global and national levels imposes serious concerns. Cybercrimes penetrated largely during the digital transformation of education that facilitated children to access cyberspace unconditionally. Methodology: The research is based on reviewing the academic literature relevant to cybersecurity awareness’s and its impact on protecting children in cyberspace. Main Findings: The findings highlight the significant role of cybersecurity awareness in reducing the impact of cyberattacks against children. Awareness of cybersecurity and the protection of children in cyberspace are interlinked with each other. Comprehensive cybersecurity awareness programs for children can significantly reduce the spread. Widespread awareness programs in the educational curriculum while utilizing all the required safety criteria to guarantee that the children are Trustworthy, secure, and ensuring their data safety is crucial step to consider. Social Implications: The article highlights the cybersecurity awareness measures that should be considered for children to provide them safe cyberspace platforms. Secure cyberspace for children to get education during the global spread of the Corona pandemic is the dire current social need. Originality/Novelty of the Study: The global spread of the Coronavirus has considerably affected people’s daily lives. Due to increased dependability on online platforms during pandemic, we can name this situation as cyber-pandemic. Data breaches and cyberattacks rises dramatically due to a lack of cybersecurity awareness among the citizens.
Sequential Indifferentiability of Confusion-Diffusion Networks

A large proportion of modern symmetric cryptographic building blocks are designed using the Substitution-Permutation Networks (SPNs), or more generally, Shannon's confusion-diffusion paradigm. To justify its theoretical soundness, Dodis et al. (EUROCRYPT 2016) recently introduced the theoretical model of confusion-diffusion networks, which may be viewed as keyless SPNs using random permutations as S-boxes and combinatorial primitives as permutation layers, and established provable security in the plain indifferentiability framework of Maurer, Renner, and Holenstein (TCC 2004).
Robust Watermarking of Screen-Photography Based on JND

With the popularity of smartphones, it is often easy to maliciously leak important information by taking pictures of the phone. Robust watermarking that can resist screen photography can achieve the protection of information. Since the screen photo process can cause some irreversible distortion, the currently available screen photo watermarks do not consider the image content well and the visual quality is not very high. Therefore, this paper proposes a new screen-photography robust watermark. In terms of embedding region selection, the intensity-based Scale-invariant feature transform (SIFT) algorithm used for the construction of feature regions based on the density of feature points, which can make it more focused on the key content of the image; in terms of embedding strength, the Just noticeable difference (JND) model is applied to limit the intensity of the watermark embedding according to the luminance and texture of the picture to balance robustness and invisibility; after embedding watermark, the coefficients in the neighborhood are again adjusted with optimal constraints to improve the accuracy of watermark extraction. After experiments, it is shown that the method we proposed can improve the correct rate of watermark extraction, the quality of the visual aspect of the watermarked picture is also improved.
Graphical State Space Model

In this paper, a new framework, named as graphical state space model, is proposed for the real time optimal estimation of one kind of nonlinear state space model. By discretizing this kind of system model as an equation which can not be solved by Extended Kalman filter, factor graph optimization can outperform Extended Kalman filter in some cases. A simple nonlinear example is given to demonstrate the efficiency of this framework.
A Multipath Payment Scheme Supporting Proof of Payment

Blockchain technology has always been plagued by performance problems. Given this problem, many scaling schemes have been put forward. A layer 2 network is a technology that solves the performance problem of blockchain. Connected parties in this network can set up channels to send digital currency to each other. Since the interaction with the blockchain is only required when the channel is established and closed, a large number of transactions do not need to be recorded on the blockchain, thus reducing the blockchain capacity. Due to the special structure of the payment channel, the distribution of funds in the channel is often unbalanced, which limits the route payment to a certain extent. This paper improves the original payment method in the second layer network by introducing new scripts. The new payment scheme supports proof of payment which is integral to the nature of the lightning network and divides the payment into several subpayments, so the large payment can be divided into relatively small payments. Due to the capacity limitation of the payment channel, theoretically, the success rate of the micropayment route is higher. This paper tests the new payment scheme on the simulated network and validates the nature of this solution to have a high routing success rate while supporting proof of payment.
Digital Evidence Data Collection: Cloud Challenges

Cloud computing has become ubiquitous in the modern world and has offered a number of promising and transformative technological opportunities. However, organizations that use cloud platforms are also concerned about cloud security and new threats that arise due to cloud adoption. Digital forensic investigations (DFI) are undertaken when a security incident (i.e., successful attack) has been identified. Forensics data collection is an integral part of DFIs. This paper presents results from a survey of existing literature on challenges related to forensics data collection in cloud. A taxonomy of major challenges was developed to help organizations understand and thus better prepare for forensics data collection.
Lightweight and Privacy-preserving Search over Encryption Blockchain

With the development of cloud computing, a growing number of users use the cloud to store their sensitive data. To protect privacy, users often encrypt their data before outsourcing. Searchable Symmetric Encryption (SSE) enables users to retrieve their encrypted data. Most prior SSE schemes did not focus on malicious servers, and users could not confirm the correctness of the search results. Blockchain-based SSE schemes show the potential to solve this problem. However, the expensive nature of storage overhead on the blockchain presents an obstacle to the implementation of these schemes. In this paper, we propose a lightweight blockchain-based searchable symmetric encryption scheme that reduces the space cost in the scheme by improving the data structure of the encrypted index and ensuring efficient data retrieval. Experiment results demonstrate the practicability of our scheme.
Uncertainty-Aware Sequence Labeling

Conditional random fields (CRFs) have been widely used for sequence labeling tasks in the field of natural language processing. However, how to model both local and global dependencies among labels is not well solved yet. In this study, we introduce a novel two-stage label decoding method to better model the short- and long-term label dependencies, while being much more computationally efficient with the use of graphics processing units (GPUs). A base model is first used to propose draft labels, and then a novel two-stream self-attention model makes refinements on these draft predictions based on long-range label dependencies. Besides, in order to mitigate the side effects of incorrect draft labels, Bayesian neural networks are used to indicate the labels with high probabilities of being wrong, which helps to mitigate the error propagation. Not only can our method model sentence-level label dependencies, but it is also easily extended to document-level sequence labeling by querying and storing a key-value memory matrix with label co-occurrence relationships. The experimental results on both sentence-level and document-level sequence labeling benchmarks show that the proposed method outperforms existing label decoding methods while taking advantage of parallel computations on GPUs.
Threat Modeling in Agile Software Development

Threat modeling is a way to get an overview of possible attacks against your systems. The advantages of threat modeling include tackling security problems early, improved risk assessments, and more effective security testing. There will always be limited resources available for security, and threat modeling will allow you to focus on the most important areas first. There is no one single “correct” way of doing threat modeling, and “agile” is no excuse for not doing it. This chapter describes the authors' experiences with doing threat modeling with agile development organizations, outlining challenges to be faced and pitfalls to be avoided.
Bibliometric Analysis on Blockchain Technology in Healthcare

The key objective of these works is to deliver a summary of the research that has been done in this field. As the primary objective of this paper, we will look at a recent study carried out during the healthcare sector, in which blockchain technology was used to ensure patient information security and privacy in healthcare records. The information was gathered from a pool of more than 480 participants in the poll and was gathered from the PubMed database from the 2012 year to the present year (2021). Beginning with the retrieval of articles from PubMed that were published between 2012 and the present year, an analysis of these documents regarding blockchain technology in healthcare is carried out. The VOS viewer tool (version 1.6.16) is being used in the following step to analysis the data set in different components, like co-authorship, keywords, and so forth. During the survey, we were able to retrieve a total of 480 publications from the PubMed database that were published between 2012 and the present year and that were about healthcare blockchain technology. As a result of using network analysis and mathematical, users may infer that there is a large amount of potential for working on blockchain in order to ensure higher private information, safety, and honesty.
State-of-the-Art Survey on Web Search

This paper presents a state-of-the-art survey of scientific research related to web search. Essential pillars, such as content discovery and natural language processing, are elaborated in front of a review of the structure of the World Wide Web (WWW) and a taxonomy of search, elaborated from literature. From this, a classification of the Web with respect to structure and search is derived.
Selective Classification of Danmaku Comments Using Distributed Representations

Danmaku commenting has become popular for co-viewing on video-sharing platforms. However, there are usually a large number of irrelevant comments, that contaminate the quality of the information provided by videos. To address this problem, this paper presents a novel approach of classifying Danmaku comments into video categories. Specifically, we use BERT as the backbone architecture to extract semantic features from comments. We introduce a loss function that has an abstention option, which enables the detection of comments that do not fall into any predefined category. The experiments that we conducted using Nicovideo data demonstrated that our selective classification approach effectively discarded those that were irrelevant to a video’s content. We also present a method for subdividing the existing video categories based on the results of Danmaku comment classification. This entails a potential application of our method in hierarchical video clustering.
Generating and Numbering Permutations With the Use of Chaotic Maps

Finite set permutations play an essential role in many engineering applications. In the professional literature, you can find many algorithms that allow you to generate and number permutations. This article proposes a new algorithm for generating and numbering permutations using an adequately constructed chaotic mapping. This algorithm can be considered as a non-algebraic version of the well-known Fisher-Yates algorithm. A numerical example illustrates its operation.
Using Linked Open Data for automating digital public administration reporting

This paper aims to showcase how the Open Data directive and the Linked Open Data principles have been endorsed by public administrations for the publication and interlinking of public administration reporting data. It focuses on the endeavour of publishing the Digital Public Administration Factsheets, which reflect the most recent developments of public administrations in EU member states, as Linked Open Data to support the European Interoperability Framework principles of openness, transparency, and reusability. The paper provides a short introduction to Linked Open Data principles for the interested reader and then it mainly focuses on how these principles have been applied for automating digital public administration reporting. It highlights the transformation process of the Digital Public Administration Factsheets from word documents to HTML and RDF formats, using relevant metadata from existing models and vocabularies to annotate key document information and therefore ensure semantic interoperability. The publication of public sector data as Linked Open Data facilitates its reuse and improves transparency towards citizens.
An Enhanced Binary Cuckoo Search Algorithm Using Crossover Operators for Features Selection

One of the most important preprocessing steps is the determination of the most relevant subset of features in any dataset. This step is called “Feature Selection”, which is considered an NP-Hard optimization problem. Cuckoo Search Algorithm (CSA) is a popular Nature-Inspired Meta-heuristic algorithm, which is used for handling continuous and discrete optimization problems. Although CSA has a great performance on many optimization problems, however, it lacks the balancing between the exploration and exploitation abilities. In this research, a binary cuckoo search algorithm with different types of crossover operators to improve is proposed. The crossover operators help the nest to discover different unexplored regions and avoid the issue of trapping in the local optima. All of the proposed versions have been applied on several datasets, and the results in terms of the classification accuracy and number of selected features were recorded. The experimental results demonstrated the effectiveness of the proposed algorithms in comparison to the original binary cuckoo search algorithm and different classifications.
Convergence and Optimality Analysis of Low-Dimensional Generative Adversarial Networks Using Error Function Integrals

Due to their success at synthesising highly realistic images, many claims have been made about optimality and convergence in generative adversarial networks (GANs).But what of vanishing gradients, saturation, and other numerical problems noted by AI practitioners?Attempts to explain these phenomena have so far been based on purely empirical studies or differential equations, valid only in the limit.We take a fresh look at these questions using explicit, low-dimensional models.We revisit the well known optimal discriminator result and, by construction of a counterexample, show that it is not valid in the case of practical interest: when the dimension of the latent variable is less than that of the data: dim(z) < dim(x).To examine convergence issues, we consider a 1-D least squares (LS) GAN with exponentially distributed data, a Rayleigh distributed latent variable, a square law generator and a discriminator of the form D(x) = (1 + erf (x))/2 where erf is the error function.We obtain explicit representations of the cost (or loss) function and its derivatives.The representation is exact down to the evaluation of a well-behaved 1-D integral.We present analytical numerical examples of 2D and 4D parameter trajectories for gradient-based minimax optimisation.Although the cost function has no saddle points, it generally has a minimum, maximum and plateaux areas.The gradient algorithms typically converge to a plateau, where the gradients vanish and the cost function saturates.This is an undesirable setting with no implications of optimality for either the generator or discriminator.The analytical method is compared with stochastic gradient optimisation and proven to be a very accurate predictor of the latter's performance.The quasi-deterministic framework we develop is a powerful analytical tool for understanding convergence behaviour of low-dimensional GANs based on least-squares cost criteria.