Reviewerly/exHarmony huggingface dataset:
Robust Automatic Camera Calibration in Badminton Court Recognition

Badminton has attracted a large number of enthusiasts with its unlimited venues and inexpensive nature, and badminton-related studies have emerged, all of which mainly focus on the analysis of badminton videos. Badminton video analysis requires the foundation of court recognition, and this paper discusses the recognition of badminton courts in images. We propose an automatic camera calibration algorithm that is robust in badminton court recognition, which can recognize badminton courts under a variety of shooting angles and solve the problem of court line mapping in the case that the real court lines fall behind the camera optical center. The main process of our algorithm includes three parts: candidate pixel extraction, court line detection and model fitting, adding line number constraints and 3-dimensional mapping filtering to the traditional camera calibration model to improve the robustness of the algorithm. Our algorithm is tested on a self-built dataset containing 300 images of badminton courts from different angles and has a good performance. Our algorithm can be applied to court recognition of other ball sports and can also be extended to court recognition in videos by modifying the standard model, with a wide range of application scenarios.
On-manifold adversarial attack based on latent space substitute model

Modern image classification networks endure adversarial attacks that deliberately alter the input image by adding small, often imperceptible, perturbations to mislead the network's classification result. From a perspective of manifold theory, most adversarial examples generated by current adversarial attack methods are off-manifold and can be detected by adversarial defense methods based on manifold theory. In this study, we propose a novel adversarial attack method for crafting on-manifold adversarial examples. Specifically, we utilize the adversarial autoencoder (AAE) to obtain the low-dimensional manifold of data, and design a latent substitute model based on the data manifold to ensure that the generated adversarial examples are still on-manifold. Additionally, to overcome the difficulty in limiting the perturbation of adversarial examples when searching in the latent space, we propose a gradient decoding strategy (GDS) and confidence re-ranking strategy (CRS). The results demonstrate the competitive performance of our method compared with that of some of the current attack methods applied to state-of-the-art defense models, especially those based on the manifold theory. Additionally, when using the proposed method for adversarial training, the robustness of the model is improved without reducing the accuracy of classifying the original dataset. We hope that our proposed attack method can serve as a benchmark for evaluating and improving the robustness of networks.
Tight Leakage-Resilient Identity-based Encryption under Multi-challenge Setting

In this work, we present the first leakage-resilient identity-based encryption (LR-IBE) scheme that features (almost) tight security under the multi-challenge setting, in which the adversary could receive multiple challenging ciphertexts from multiple users. Meanwhile, our construction enjoys security against chosen-ciphertext attack (CCA) under the Matrix Decisional Diffie-Hellman (MDDH) assumption. Apart from admitting the continual leakage of secret key, we provide strong security via additionally allowing the continual leakage of the master secret key.
Cyber Threat Intelligence Entity Extraction Based on Deep Learning and Field Knowledge Engineering

The typical domain characteristics of Cyber Threat Intelligence (CTI), such as fuzzy entity boundary, polysemy or a single word corresponding to multiple word expressions and so on, makes the entity recognition result be worse than we expected. In addition, there are many challenges in directly migrating entity recognition models from the general field to CTI field. Therefore, we propose a deep learning entity recognition model with supplementing the domain knowledge engineering, which takes the open-source Cyber Threat Intelligence entity recognition as the research object, covering natural language processing, deep learning and cyber threat intelligence fields. Firstly, we use BERT model to obtain the dynamic word vector, then encode the word sequence by using BiLSTM-CRF, and finally improve the recognition result by using knowledge engineering of the Cyber Threat Intelligence to help increase the accuracy of entity recognition. Besides, we verify the effectiveness of the proposed model by experiments.
Handwriting quality analysis using online-offline models

This work is part of an innovative e-learning project allowing the development of an advanced digital educational tool that provides feedback during the process of learning handwriting for young school children (three to eight years old). In this paper, we describe a new method for children handwriting quality analysis. It automatically detects mistakes, gives real-time on-line feedback for children’s writing, and helps teachers comprehend and evaluate children’s writing skills. The proposed method adjudges five main criteria: shape, direction, stroke order, position respect to the reference lines, and kinematics of the trace. It analyzes the handwriting quality and automatically gives feedback based on the combination of three extracted models: Beta-Elliptic Model (BEM) using similarity detection (SD) and dissimilarity distance (DD) measure, Fourier Descriptor Model (FDM), and perceptive Convolutional Neural Network (CNN) with Support Vector Machine (SVM) comparison engine. The originality of our work lies partly in the system architecture which apprehends complementary dynamic, geometric, and visual representation of the examined handwritten scripts and in the efficient selected features adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbol drawing. The application offers two interactive interfaces respectively dedicated to learners, educators, experts or teachers and allows them to adapt it easily to the specificity of their disciples. The evaluation of our framework is enhanced by a database collected in Tunisia primary school with 400 children. Experimental results show the efficiency and robustness of our suggested framework that helps teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.
Node-Imbalance Learning on Heterogeneous Graph for Pirated Video Website Detection

With the rapid development of video streaming, the problem of copyright infringement has become increasingly severe. Despite its explicit illegality in many countries, a large variety of pirated video websites are still active, causing huge damage to copyright holders and security risks to users. Traditional methods for detecting malicious websites, such as blacklists or feature-based classifiers, can be easily bypassed by evading approaches like Domain-Flux. Some researchers recently proposed sophisticated graph-based methods to utilize various relations between websites and convert the detection task into node representation learning. However, the node imbalance issue impairs their performance on real-world datasets. In this paper, given the limitations of the above methods, we propose a model named Heterogeneous Graph Node Re-weighting (HGNR) to detect pirated video websites. We construct a heterogeneous graph with diverse meta relations and design a weight adjustment mechanism to deal with node imbalance issue. The experiments with different imbalance ratios show that HGNR outperforms state-of-the-art graph-based methods. Furthermore, we analyze the best-performed meta relation and disclose how video pirates gain profits, which can help the security community thwart video piracy.
Real Time Machine Translation System between Indian Languages

Language understanding is one of those perpetual challenges which has dogged research from many decades. As a means of connecting with others, but communication is not limited to a single language. India contains around 121 different languages. As a result, there is a linguistic barrier. Natural Language Processing is the process of developing a communicational interface between machines and humans. A model that turns the supplied source language text (Marathi) into the desired language text (Gujarati) using Text to Text Translation has been developed. Because LSTMs allow us with a vast variety of parameters like as learning rates, input and output biases, and there is no need for existence, this model is constructed by adding multilingual features to the LSTM Model based on deep learning Principles. The goal of the proposed is to create a deep learning-based translation system. The suggested system uses a variety of models to perform text-to-text translation, after which the input text will be translated into the target language (text).
Digital Technography: A Methodology for Interrogating Emerging Digital Technologies and Their Futures

This article introduces “digital technography” as a methodology to interrogate and voice emerging digital technologies and their anticipated futures. I demonstrate, with reference to recent research on wearable self-tracking devices, digital food technologies, and platforms for work automation, how one can gain an understanding of these technologies by attending to the materials in which they are promoted; and actively engaging with them imaginatively and self-reflexively as a social scientist. This article outlines a digital technographic methodology centered around the three conceptual anchors of specification, valorization, and anticipation, all of which pertain to how a digital technology aims and perhaps even aspires to become a part of everyday life.
Secure-Computation-Friendly Private Set Intersection from Oblivious Compact Graph Evaluation

Private set intersection (PSI) is a secure two-party computation ($2$PC) protocol that reveals only the intersection of two private sets. Driven by different applications, various works devise specific protocols for private computation on the intersection (PCI), e.g., summation of the values labeled with each element in the intersection. Upgrading a PSI protocol to PCI for generic computations while maintaining efficiency and preventing leakage is known to be not straightforward (e.g., the intersection set size could be leaked).
An efficient encrypted deduplication scheme with security-enhanced proof of ownership in edge computing

With the rapid expansion of Internet of Things (IoT), relevant files are stored and transmitted at the network edge by employing data deduplication to eliminate redundant data for the best accessibility. Although deduplication improves storage and network efficiency, it decreases security strength and performance. Existing schemes usually adopt message-locked encryption (MLE) to encrypt data, which is vulnerable to brute-force attacks. Meanwhile, these schemes utilize proof-of-ownership (PoW) to prevent duplicate-faking attacks, while they suffer from replay attacks or incur large computation overheads. This paper proposes SE-PoW, an efficient and location-aware hybrid encrypted deduplication scheme with a dual-level security-enhanced Proof-of-Ownership in edge computing. Specifically, SE-PoW firstly encrypts files with an inter-edge server-aided randomized convergent encryption (RCE) method and then protects blocks with an intra-edge edge-aided MLE method to balance security and system efficiency. To resist duplicate-faking attacks and replay attacks, SE-PoW performs the dual-level PoW algorithm. Then it combines the verification of a cuckoo filter and the homomorphism of algebraic signatures in sequence to enhance security and improve ownership checking efficiency. Security analysis demonstrates that SE-PoW ensures data security and resists the mentioned attacks. Evaluation results show that SE-PoW reduces up to 61.9% upload time overheads compared with the state-of-the-art schemes.
Area-Time Efficient Hardware Architecture for CRYSTALS-Kyber

This paper presents a novel area-time efficient hardware architecture of the lattice-based CRYSTALS-Kyber, which has entered the third round of the post-quantum cryptography standardization competition hosted by the National Institute of Standards and Technology. By developing a dual-path delay feedback number theoretic transform multiplier dedicating for Kyber parameter set and deploying this multiplier in the Kyber architecture, the key generation, encryption, and decryption operations are accelerated substantially. Furthermore, the proposed architecture offers the best value of area-time product in comparison with existing approaches. The implementation results on Xilinx Vivado targeted for Virtex-7 FPGA board demonstrate that the proposed Kyber cryptoprocessor completes encryption and decryption operations in approximately 57.5 μs at the highest frequency of 226 MHz. Furthermore, the area-time product value when using the proposed Kyber architecture is improved by at least twofold compared with existing architectures.
Referring expression grounding by multi-context reasoning

Referring expression grounding plays a fundamental role in vision-language understanding, which aims at locating a certain target region in an image described by a natural language expression. It needs to understand high-level semantic correlations between objects in the image according to the referred expression for the task. Thus, it inherently requires reasoning the context information, i.e., appearance context and relationship context. While most existing approaches either ignore to explore the appearance details of the target region or rely on a manually designed reasoning structure and treat the context information of each neighboring object equivalently, inflexible to the scenario where referring expressions are complicated. In this paper, we put forward Multi-context Reasoning Network (MCRN) for referring expression grounding task, which can apply appearance context reasoning and relationship context reasoning simultaneously. Methodologically, for appearance context reasoning, we propose a local node attention to obtain local representation of the target object, which gives a more focus on its appearance details. For relationship context reasoning, we approach it as a language-guided multi-step reasoning problem and design a multi-step graph reasoning module to capture intra-context and inter-context between the target region of its intra-class and inter-class neighboring objects in an iterative way, which makes the reasoning process more reliable and interpretable. Our method demonstrates superiority based on extensive experimental outputs on three popular benchmark datasets.
Using Locality-Sensitive Hashing for SVM Classification of Large Data Sets

We propose a novel method using Locality-Sensitive Hashing (LSH) for solving the optimization problem that arises in the training stage of support vector machines for large data sets, possibly in high dimensions. LSH was introduced as an efficient way to look for neighbors in high dimensional spaces. Random projections-based LSH functions create bins so that when great probability points belonging to the same bin are close, the points that are far will not be in the same bin. Based on these bins, it is not necessary to consider the whole original set but representatives in each one of them, thus reducing the effective size of the data set. A key of our proposal is that we work with the feature space and use only the projections to search for closeness in this space. Moreover, instead of choosing the projection directions at random, we sample a small subset and solve the associated SVM problem. Projections in this direction allows for a more precise sample in many cases and an approximation of the solution of the large problem is found in a fraction of the running time with small degradation of the classification error. We present two algorithms, theoretical support, and numerical experiments showing their performances on real life problems taken from the LIBSVM data base.
The Ethics of Digital Technology Usage among Secondary Stage Students

This study aimed to identify the degree of practice among secondary stage students in Kuwait regarding the ethics of digital technology use from their perspectives.Also, it investigates the degree of practice's relationship with the variables of gender, specialization, and grade.The researchers used the descriptive survey method to answer the study questions.The study sample was 771 male and female students of the secondary stage of Al-Ahmadi Educational Zone in Kuwait for the academic year of 2018/2019, where they were selected by random stratified method from different schools.The study instrument (survey) was developed, and its validity and reliability were verified.The results showed that the degree of the practice of secondary stage students in Kuwait of the ethics of using digital technology was high.In addition, results revealed that there is a statistical difference attributed to the variables of gender, specialization, and grade.Several recommendations were discussed.
Semantically accurate super-resolution Generative Adversarial Networks

This work addresses the problems of semantic segmentation and image super-resolution by jointly considering the performance of both in training a Generative Adversarial Network (GAN). We propose a novel architecture and domain-specific feature loss, allowing super-resolution to operate as a pre-processing step to increase the performance of downstream computer vision tasks, specifically semantic segmentation. We demonstrate this approach using Nearmap’s aerial imagery dataset which covers hundreds of urban areas at 5–7 cm per pixel resolution. We show the proposed approach improves perceived image quality as well as quantitative segmentation accuracy across all prediction classes, yielding an average accuracy improvement of 11.8% and 108% at 4× and 32× super-resolution, compared with state-of-the art single-network methods. This work demonstrates that jointly considering image-based and task-specific losses can improve the performance of both, and advances the state-of-the-art in semantic-aware super-resolution of aerial imagery.
Residential Security System based on Facial Recognition

Two most critical universal rights and factors of life are Privacy and Security. Today facial recognition is one of the most popular technologies in the field of security. A human being can remember a finite number of faces. Whereas in a computer system, due to no limits on storage and hardware capability, they can be used where huge databases of facial records are needed. In recent years, there has been an increase in demand for better and more con venient household security; computers have come as our savior. Due to high computing power, large memory storages, and their precision, considerable development has been made in face recognition. This technology is ideally suited to overcome many security issues and attain flexibility to intelligent security control. However, these components come at a price that is too costly for people to invest in for a good performance that can guarantee results. Hence, after many years of research, state-of-the-art models have been developed which work on smaller IoT/mobile devices with high accuracy. This paper presents an implementation of a facial recognition security system android app that works in real-time to identify residents of a residential complex. This system also keeps track of the entry details of individual residents, which can be easily requested in case of some criminal offense. To make it convenient for the residents, the whole system is made synchronous. Using the resident app, the residents can change their faces quickly whenever required from the comfort of their homes.
Online handwritten signature verification via network analysis

In this paper, a model for online handwritten signature verification via networks analysis is proposed, in which centrality metrics are used as predictive characteristics. Supervised learning methods were used to classify the veracity of the signatures. The study uses the MCYT-100 and SVC2004 task2 signature databases, which are widely used in signature verification problems. In the framework, signatures were considered as nodes of a complex network in which the existing connection relationship between two signatures is measured through Pearson correlations between time series of signature coordinates that exceed a threshold of comparison. The attributes for the model were topological measures of centralities of the networks and the entropy of the time series signature. Some metrics are used to measure classification errors, including the False Rejection Rate (FRR) which identifies the percentage of true signatures that are rejected by the model, the False Acceptance Rate (FAR) as the percentage of false signatures that are accepted by the learning engine and the Average Error Rate (AER) as the average error. In this framework, we obtained a false positive rate of 6.19%, a false negative rate of 6.39% and an average error of approximately 6.28%. This result is close to ones obtained by state-of-the-art models for this problem, showing that the use of complex networks for online handwritten signature verification is a promising venue of research.
Traffic Sign Recognition using YOLOv4

Traffic sign recognition can provide road information assessment and real-time safety early warning for safe driving of vehicles. The YOLOv4 model was adopted for traffic sign detection and recognition, and the K-means clustering algorithm is used to improve YOLOv4. A test was performed with the CCTSDB dataset. We achieved a 25.3fps and 90.42% mAP on the GTSDB dataset.
Multi-View Graph Clustering by Adaptive Manifold Learning

Graph-oriented methods have been widely adopted in multi-view clustering because of their efficiency in learning heterogeneous relationships and complex structures hidden in data. However, existing methods are typically investigated based on a Euclidean structure instead of a more suitable manifold topological structure. Hence, it is expected that a more suitable manifold topological structure will be adopted to carry out intrinsic similarity learning. In this paper, we explore the implied adaptive manifold for multi-view graph clustering. Specifically, our model seamlessly integrates multiple adaptive graphs into a consensus graph with the manifold topological structure considered. We further manipulate the consensus graph with a useful rank constraint so that its connected components precisely correspond to distinct clusters. As a result, our model is able to directly achieve a discrete clustering result without any post-processing. In terms of the clustering results, our method achieves the best performance in 22 out of 24 cases in terms of four evaluation metrics on six datasets, which demonstrates the effectiveness of the proposed model. In terms of computational performance, our optimization algorithm is generally faster or in line with other state-of-the-art algorithms, which validates the efficiency of the proposed algorithm.