Reviewerly/exHarmony huggingface dataset:
The representation of gendered social actors across five manosphere communities on Reddit

This paper investigates the representation of gendered social actors in a specialised corpus of 10.9 million words, collected from five Reddit communities associated with the so-called ‘manosphere’: incels (involuntary celibates), Men Going Their Own Way (male separatists), pick-up artists, men’s rights activists, and a group dedicated to wider discussions of ‘red pill’ philosophy. Thirty-four gendered social actor terms were identified as key-key-words across the manosphere corpora. Both male and female social actors are referenced using relational terms, while the latter are also referenced using derogatory terms and the former are referenced using terms for kinship and in-group identification. We then analysed the consistent collocates ( Baker et al., 2008 ) of the four most frequent gendered social actor terms ( women, girls, men and guys), to establish the topics, descriptions and actions associated with the social actors across the five groups. Gendered social actors were constructed in essentialist dichotomies, with women and girls, although objectified and passivated in dating/sexual contexts, being represented as violent towards male social actors and as holding a privileged position over men in wider society. The anti-feminist ideology reflected in manosphere discourse can be seen as a more extreme version of mainstream discourse. To the extent that manosphere discourse spreads beyond dedicated forums and websites, its views will be re-imported into the mainstream, leading to a wider radicalisation.
Efficient and Secure Blockchain System for Digital Content Trading

Blockchain is attracting attention as a new solution for problems such as illegal copying, profit distribution, and forgery and falsification in the digital content trading environment, which has become an essential asset in the information age. However, one problem is that it is difficult to propagate digital content to the blockchain network because of a limited capacity to upload to the blockchain. The integrity and transparency of blockchain are also considered as weak points in terms of privacy. In this paper, we propose a new blockchain system, the secret block-based blockchain (SBBC), to address the problems with the blockchain system in the digital content trading environment. SBBC is composed of off-chain and on-chain network components. Off-chain is the part that allows trading digital content through the authentication phase. The digital content that is traded has a digital fingerprint inserted, so if an illegal leak occurs, the destination can be tracked. In addition, the content is encrypted and traded, and only the rightful user can use the digital content, thus ensuring income for the legitimate content author. Next, the on-chain network is licensed to use digital content, and a verification process using a consensus algorithm is performed. The licensed consumer creates a secret block of their transaction and records it only on their ledger. In a private part, secret block creation ensures privacy and solves the network overload that can occur when uploading digital content to the blockchain. Finally, through the verification and agreement of all blockchain participants, a public block is created and recorded in the ledger to finalize the transaction. Consequently, we propose the SBBC system suitable for digital content trading environments and a safe and reliable system through a consensus algorithm in such environments.
RETRACTED ARTICLE: Practical application of wireless communication network multimedia courseware in college basketball teaching

With the acceleration of informatization and the coverage of wireless networks, homes, conferences, schools and other places have a higher pursuit of the wireless transmission capabilities of electronic devices. Wireless screen transmission technology is used more frequently in life, work and study. This article mainly discusses the practical application of network multimedia courseware in college basketball teaching. This article first elaborates the teaching plan of multimedia courseware, including teaching content, teacher guidance, student learning and multimedia courseware. Secondly, the multimedia courseware of basketball tactics basic teaching is completed by using Flash mx2004 plug-in. After that, it specifically introduces the process of how to transmit basketball teaching content through multimedia equipment to the video network for students to learn under the wireless network environment. It emphasizes that the "wireless multimedia communication" course is an important course in the electronic information subject. Finally, through the teaching experiment, the accuracy of the multimedia teaching method was tested, and the validity of the courseware content was tested by the empirical validity evaluation method. At the same time, after the teaching experiment, in order to test the two groups of students' mastery of the basic coordination theory of basketball tactics, the basic coordination theory of basketball tactics was tested. The experimental group had 22 students with a score of 90 or more, accounting for 27.5%, and the control group had 13 students with a score of 90 or more, accounting for 16.5%. The results show that wireless network multimedia computer-assisted teaching has a positive effect on improving students' interest in learning.
Transparent End-to-End Security for Publish/Subscribe Communication in Cyber-Physical Systems

The ongoing digitization of industrial manufacturing leads to a decisive change in industrial communication paradigms. Moving from traditional one-to-one to many-to-many communication, publish/subscribe systems promise a more dynamic and efficient exchange of data. However, the resulting significantly more complex communication relationships render traditional end-to-end security futile for sufficiently protecting the sensitive and safety-critical data transmitted in industrial systems. Most notably, the central message brokers inherent in publish/subscribe systems introduce a designated weak spot for security as they can access all communication messages. To address this issue, we propose ENTRUST, a novel solution for key server-based end-to-end security in publish/subscribe systems. ENTRUST transparently realizes confidentiality, integrity, and authentication for publish/subscribe systems without any modification of the underlying protocol. We exemplarily implement ENTRUST on top of MQTT, the de-facto standard for machine-to-machine communication, showing that ENTRUST can integrate seamlessly into existing publish/subscribe systems.
Fuzzy Adaptive Charged System Search for global optimization

This study proposes a new fuzzy adaptive Charged System Search (CSS) for global optimization. The suggested algorithm includes a parameter tuning process based on fuzzy logic with the aim of improving its performance. In this regard, four linguistic variables are defined which configures a fuzzy system for parameter identification of the standard CSS algorithm. This process provides a focus for the algorithm on higher levels of global searching in the initial iterations while the local search is considered in the last iterations. Twenty mathematical benchmark functions, the Competitions on Evolutionary Computation (CEC) regarding CEC 2020 benchmark, three well-known constrained, and two engineering problems are utilized to validate the new algorithm. Moreover, the performance of the new algorithm is compared and contrasted with other metaheuristic algorithms. The obtained results reveal the superiority of the proposed approach in dealing with different unconstraint, constrained, and engineering design problems.
Evaluation of deep learning algorithms for semantic segmentation of car parts

Abstract Evaluation of car damages from an accident is one of the most important processes in the car insurance business. Currently, it still needs a manual examination of every basic part. It is expected that a smart device will be able to do this evaluation more efficiently in the future. In this study, we evaluated and compared five deep learning algorithms for semantic segmentation of car parts. The baseline reference algorithm was Mask R-CNN, and the other algorithms were HTC, CBNet, PANet, and GCNet. Runs of instance segmentation were conducted with those five algorithms. HTC with ResNet-50 was the best algorithm for instance segmentation on various kinds of cars such as sedans, trucks, and SUVs. It achieved a mean average precision at 55.2 on our original data set, that assigned different labels to the left and right sides and 59.1 when a single label was assigned to both sides. In addition, the models from every algorithm were tested for robustness, by running them on images of parts, in a real environment with various weather conditions, including snow, frost, fog and various lighting conditions. GCNet was the most robust; it achieved a mean performance under corruption, mPC = 35.2, and a relative degradation of performance on corrupted data, compared to clean data (rPC), of 64.4%, when left and right sides were assigned different labels, and mPC = 38.1 and rPC = $$69.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>69.6</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> when left- and right-side parts were considered the same part. The findings from this study may directly benefit developers of automated car damage evaluation system in their quest for the best design.
Optimum design of steel building structures using migration-based vibrating particles system

Vibrating Particles System (VPS) is developed based on some principles of physics in which the free vibration of a system with single degree of freedom including viscous damping is concerned. In this algorithm, each possible solution or vibrating particle seeks its equilibrium position in the search space. Despite of a relatively good exploration ability of the VPS algorithm, it is poor at exploitation and the convergence speed of this algorithm is also an issue in some cases. In this paper, the VPS algorithm is hybridized with the Migration-Based Local Search (MBLS) mechanism of the Biogeography-Based Optimization (BBO) algorithm with the strong local search capability to concentrate the search process around promising vibrating particles and locate the optimum solution more precisely. Three hybrid algorithms are developed based on how to use the VPS and MBLS methods as parallel, series and mixed series–parallel schemes. In order to evaluate the capability of the proposed hybrid methods in dealing with difficult structural optimization problems, a 24-story benchmark frame problem, a 10-story steel structure with 1026 structural members alongside a 20-story steel structure with 3860 members are optimized using presented algorithms. The findings affirm the robustness and efficiency of the proposed hybrid methods over the standard existing relevant approaches for optimum design of steel building structures.
Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes With Semantic Consistency and Attention Mechanism

Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc. In this paper, we investigate cross-modal retrieval between food images and cooking recipes. The goal is to learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. Two major challenges in addressing this problem are 1) large intra-variance and small inter-variance across cross-modal food data; and 2) difficulties in obtaining discriminative recipe representations. To address these two problems, we propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities through aligning output semantic probabilities. Besides, we exploit a self-attention mechanism to improve the embedding of recipes. We evaluate the performance of the proposed method on the large-scale Recipe1M dataset, and show that we can outperform several state-of-the-art cross-modal retrieval strategies for food images and cooking recipes by a significant margin.
Arabic Sentiment Analysis Using Deep Learning and Ensemble Methods

With the outbreak of social networks, blogs, and forums, classifying subjective text influenced by personal feelings and opinions has become an interesting research area. Many techniques have been proposed to solve the problem of analyzing and classifying sentiments held in those reviews and recommendations. Recently, deep learning models showed promising outcomes in many fields, including sentiment analysis. Therefore in this study, we propose a sentiment analysis deep learning-based model to predict the polarity of opinions and sentiments. Two types of recurrent neural networks are leveraged to learn higher-level representations. Then to mitigate the data dependency problem and to increase the model robustness, three distinct classification algorithms were utilized to produce the final output. Experimental results proved that our model prevailed in all the selected datasets with an accuracy ranging between 81.11 and 94.32%. Moreover, the model reduced the relative classification error rate by up to 26% compared to state-of-the-art models.
TSRGAN: Real-world text image super-resolution based on adversarial learning and triplet attention

The text in a low-resolution (LR) image is usually hard to read. Super-resolution (SR) is an intuitive solution to this issue. Existing single image super-resolution (SISR) models are mainly trained on synthetic datasets whose LR images are obtained by performing bicubic interpolation or gaussian blur on high-resolution (HR) images. However, these models can hardly generalize to practical scenarios because real-world LR images are more difficult to super-resolve. The newly proposed TextZoom dataset is the first dataset for real-world text image super-resolution. We propose a new model termed TSRGAN trained on this dataset. First, a discriminator is designed to prevent the SR network from generating over-smoothed images. Second, we introduce triplet attention into the SR network for better representational ability. Moreover, besides L2 loss and adversarial loss, wavelet loss is incorporated to help reconstruct sharper character edges. Since TextZoom provides text labels, the recognition accuracy of scene text recognition (STR) model can be used to evaluate the quality of SR images. It can reflect the performance of text image SR models better than traditional SR evaluation metrics such as PSNR and SSIM. Comprehensive experiments show the superiority of our TSRGAN. Compared with the state-of-the-art method, the proposed TSRGAN improves the average recognition accuracy of ASTER, MORAN and CRNN by 0.8%, 1.5% and 3.2% on TextZoom respectively.
Biventricular Surface Reconstruction From Cine Mri Contours Using Point Completion Networks

Many important cardiac biomarkers used in clinical practice describe cardiac anatomy and function in three dimensions (3D). However, common cardiac magnetic resonance imaging (MRI) protocols often only generate two-dimensional (2D) image slices of the underlying 3D anatomy and are susceptible to various types of motion artifacts causing slice misalignment. In this paper, we propose a deep learning method acting directly on point clouds to reconstruct a dense 3D biventricular heart model from misaligned 2D cardiac MR image contours. The method is able to reduce mild, medium, and strong slice misalignments (mean translation $\sim 3.5$ mm; mean rotation $\sim 2.5^{\circ})$ to a Chamfer distance below image resolution (1.25 mm) with high robustness (standard deviation 0.18 mm) on a statistical shape model dataset. It also manages to reconstruct smooth 3D shapes with accurate left ventricular volumes from cine MR images of the UK Biobank study.
Incorporating explicit syntactic dependency for aspect level sentiment classification

Aspect level sentiment classification aims to extract fine-grained sentiment expressed towards specific aspects from a sentence. The key to this task lies in connecting aspects and their respective sentiment contexts. Existing methods measure the dependency weights between aspects and context words via either the semantic similarity between words captured by attention mechanism or the structural proximity between words in syntactic structures. However, methods in both groups fail to fully exploit explicit syntactic dependency, which we argue should be critical to identify sentiment contexts. In this paper, we propose a novel syntactic-dependency-based attention network (SDATT) to incorporate explicit syntactic dependency for aspect level sentiment classification. SDATT first models the dependency path between each word and the aspect to characterize aspect-oriented syntactic representation of each word. The generated syntactic representations are later fed into the attention layer to help infer the dependency weights for sentiment prediction. Experimental results on five benchmark datasets show the superior performance of the proposed model over state-of-the-art baselines.
Adversarial Attacks and Defenses

Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. Techniques to protect models against adversarial input are called adversarial defense methods. Although many approaches have been proposed to study adversarial attacks and defenses in different scenarios, an intriguing and crucial challenge remains that how to really understand model vulnerability? Inspired by the saying that "if you know yourself and your enemy, you need not fear the battles", we may tackle the challenge above after interpreting machine learning models to open the black-boxes. The goal of model interpretation, or interpretable machine learning, is to extract human-understandable terms for the working mechanism of models. Recently, some approaches start incorporating interpretation into the exploration of adversarial attacks and defenses. Meanwhile, we also observe that many existing methods of adversarial attacks and defenses, although not explicitly claimed, can be understood from the perspective of interpretation. In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation. We categorize interpretation into two types, feature-level interpretation, and model-level interpretation. For each type of interpretation, we elaborate on how it could be used for adversarial attacks and defenses. We then briefly illustrate additional correlations between interpretation and adversaries. Finally, we discuss the challenges and future directions for tackling adversary issues with interpretation.
A privacy‐preserving framework for location recommendation using decentralized collaborative machine learning

Abstract The nowadays ubiquitous location‐aware mobile devices have contributed to the rapid growth of individual‐level location data. Such data are usually collected by location‐based service platforms as training data to improve their predictive models' performance, but the collection of such data may raise public concerns about privacy issues. In this study, we introduce a privacy‐preserving location recommendation framework based on a decentralized collaborative machine learning approach: federated learning. Compared with traditional centralized learning frameworks, we keep users' data on their own devices and train the model locally so that their data remain private. The local model parameters are aggregated and updated through secure multiple‐party computation to achieve collaborative learning among users while preserving privacy. Our framework also integrates information about transportation infrastructure, place safety, and flow‐based spatial interaction to further improve recommendation accuracy. We further design two attack cases to examine the privacy protection effectiveness and robustness of the framework. The results show that our framework achieves a better balance on the privacy–utility trade‐off compared with traditional centralized learning methods. The results and ensuing discussion offer new insights into privacy‐preserving geospatial artificial intelligence and promote geoprivacy in location‐based services.
A Novel Attack on Monochrome and Greyscale Devanagari CAPTCHAs

The use of computer programs in breaching web site security is common today. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) and human interaction proofs are the cost-effective solution to these kinds of computer attacks on web sites. These CAPTCHAs are available in many forms, such as those based on text, images and audio. A CAPTCHA must be secure enough that it cannot be broken by a computer program, and it must be usable enough that humans can easily understand it. The most popular is the text-based scheme. Most text-based CAPTCHAs are based on the English language and are not usable by the native people of India. Research has proven that native people are more comfortable with native language–based CAPTCHA. Devanagari-based CAPTCHAs are also available, but the security aspect has not been tested. Unfortunately, English language–based CAPTCHAs are successfully broken. Therefore, it is important to test the security of Devanagari script-based CAPTCHAs. We picked five unique monochrome CAPTCHAs and five unique greyscale CAPTCHAs for testing security. We achieved 88.13% to 97.6% segmentation rates on these schemes and generated six types of features for these segmented characters, such as raw pixels, zoning, projection, Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Oriented Fast and Rotated BRIEF (ORB). For classification, we used three classifiers for comparative analyses. Using k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Random Forest, we achieved high recognition on monochrome and greyscale schemes. For monochrome Devanagari CAPTCHAs, the recognition rate of k-NN ranges from 64.78% to 82.39%, SVM ranges from 76.46% to 91.34% and Random Forest ranges from 80.34% to 91.28%. For greyscale Devanagari CAPTCHAs, the recognition rate of k-NN ranges from 67.52% to 85.47%, SVM ranges from 76.9% to 91.71% and Random Forest ranges from 83.07% to 92.13%. We achieved a breaking rate for monochrome schemes of 66% to 85% and for greyscale schemes of 73% to 93%.