Reviewerly/exHarmony huggingface dataset:
Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity

Source code plagiarism is a long-standing issue in tertiary computer science education.Many source code plagiarism detection tools have been proposed to aid in the detection of source code plagiarism.However, existing detection tools are not robust to pervasive plagiarism-hiding transformations and can be inaccurate in the detection of plagiarised source code.This article presents BPlag, a behavioural approach to source code plagiarism detection.BPlag is designed to be both robust to pervasive plagiarism-hiding transformations and accurate in the detection of plagiarised source code.Greater robustness and accuracy is afforded by analyzing the behavior of a program, as behavior is perceived to be the least susceptible aspect of a program impacted upon by plagiarism-hiding transformations.BPlag applies symbolic execution to analyses execution behavior and represents a program in a novel graph-based format.Plagiarism is then detected by comparing these graphs and evaluating similarity scores.BPlag is evaluated for robustness, accuracy and efficiency against five commonly used source code plagiarism detection tools.It is then shown that BPlag is more robust to plagiarism-hiding transformations and more accurate in the detection of plagiarised source code, but is less efficient than the compared tools.
A Robust Radio-Frequency Fingerprint Extraction Scheme for Practical Device Recognition

Radio-frequency fingerprinting (RFF) exploiting hardware characteristics has been employed for device recognition to enhance the overall security. However, the performance unreliability in long-term experiments, channel fading interference, and unauthorized devices verification are three open problems that restrict the development of RFF recognition. To address these issues, a robust RFF extraction scheme based on three corresponding algorithms is studied. For the first problem, a long-term stacking of repetitive symbols (LSRSs) algorithm is proposed to reduce the acquired signal variance, which contributes to the identification accuracy and long-term stability. For the second issue, we propose an artificial noise adding (ANA) algorithm to enhance the recognition robustness through regularization and channel adaptation. For the third issue, a verification algorithm based on the generative Gaussian probabilistic linear discriminant analysis (GPLDA) model is developed to handle unauthorized devices. Our robust RFF extraction scheme is verified in the experiments with 54 CC2530 ZigBee devices. It enables reliable node identification with the accuracy of 99.50% in the short rang line-of-sight (SLOS) scenarios for signals collected over 18 months, and 95.52% in the extensive multipath fading experiments. The equal error rate (EER) of the verification experiments with six authorized devices versus six unseen unauthorized devices is as low as 0.63%.
Infinity Yoga Tutor: Yoga Posture Detection and Correction System

Popularity of yoga is increasing daily. The reason for this is the physical, mental and spiritual benefits that could be obtained by practicing yoga. Many are following this trend and practicing yoga without the training of an expert practitioner. However, following yoga in an improper way or without a proper guidance will lead to bad health issues such as strokes, nerve damage etc. So, following proper yoga postures is an important factor to be considered. In this proposed system, the system is able to identify poses performed by the user and also guide the user visually. This process is required to be completed in real-time in order to be more interactive with the user. In this paper, the yoga posture detection was done in a vision-based approach. The Infinity Yoga Tutor application is able to capture user movements using the mobile camera, which is then streamed at a resolution of 1280 × 720 at 30 frames per second to the detection system. The system consists of two main modules, a pose estimation module which uses OpenPose to identify 25 keypoints in the human body, using the BODY_25 dataset, and a pose detection module which consists of a Deep Learning model, that uses time-distributed Convolutional Neural Networks, Long Short Term Memory and SoftMax regression in order to analyze and predict user pose or asana using a sequence of frames. This module was trained to classify 6 different asanas and the selected model which uses OpenPose for pose estimation has an accuracy of 99.91%. Finally, the system notifies the users on their performance visually in the user interface of the Mobile application.
Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent the whole video, while ignoring visual pattern consistencies and intrinsic temporal relationships across different frames. Furthermore, the semantic correspondence between natural language queries and person-centric actions in videos has not been fully explored. To address these problems, we propose a novel binary representation learning framework, named Semantics-aware Spatial-temporal Binaries ($\text{S}^{2}$ Bin), which simultaneously considers spatial-temporal context and semantic relationships for cross-modal video retrieval. By exploiting the semantic relationships between two modalities, $\text{S}^{2}$ Bin can efficiently and effectively generate binary codes for both videos and texts. In addition, we adopt an iterative optimization scheme to learn deep encoding functions with attribute-guided stochastic training. We evaluate our model on three video datasets and the experimental results demonstrate that $\text{S}^{2}$ Bin outperforms the state-of-the-art methods in terms of various cross-modal video retrieval tasks.
An Efficient Watermarking Approach Based on LL and HH Edges of DWT–SVD

Digital watermarking is playing a vital role in the improvement of authentication, security, and copyright protection in today’s digital transformation. The performance of this technique is shown to be impressive around the globe. Text, audio, video, and image data are acted as watermarks in the digital platform. In this article, a hybrid watermarking scheme is proposed to furnish the robustness and protection of digital data. This hybrid scheme is a form of discrete wavelet transform (DWT) and singular value decomposition (SVD). The embedding and extracting features are carried out through multi-level operations of DWT and SVD. Various attacks are added to the proposed method to justify the robustness of the watermark. In the end, the suggested approach is contrasted with existing methods to confirm the supremacy.
An efficient framework for zero-shot sketch-based image retrieval

Zero-shot sketch-based image retrieval (ZS-SBIR) has recently attracted the attention of the computer vision community due to its real-world applications, and the more realistic and challenging setting that it presents over SBIR. ZS-SBIR inherits the main challenges of multiple computer vision problems including content-based Image Retrieval (CBIR), zero-shot learning and domain adaptation. The majority of previous studies using deep neural networks have achieved improved results by either projecting sketch and images into a common low-dimensional space, or transferring knowledge from seen to unseen classes. However, those approaches are trained with complex frameworks composed of multiple deep convolutional neural networks (CNNs) and are dependent on category-level word labels. This increases the requirements for training resources and datasets. In comparison, we propose a simple and efficient framework that does not require high computational training resources, and learns the semantic embedding space from a vision model rather than a language model, as is done by related studies. Furthermore, at training and inference stages our method only uses a single CNN. In this work, a pre-trained ImageNet CNN (i.e., ResNet50) is fine-tuned with three proposed learning objects: domain-balanced quadruplet loss, semantic classification loss, and semantic knowledge preservation loss. The domain-balanced quadruplet and semantic classification losses are introduced to learn discriminative, semantic and domain invariant features by considering ZS-SBIR as an object detection and verification problem. To preserve semantic knowledge learned with ImageNet and exploit it for unseen categories, the semantic knowledge preservation loss is proposed. To reduce computational cost and increase the accuracy of the semantic knowledge distillation process, ground-truth semantic knowledge is prepared in a class-oriented fashion prior to training. Extensive experiments are conducted on three challenging ZS-SBIR datasets: Sketchy Extended, TU-Berlin Extended and QuickDraw Extended. The proposed method achieves state-of-the-art results, and outperforms the majority of related works by a substantial margin.
Industry Digitalization Consepts with 5G-based IoT

Recent developments in wireless technology and computing have given rise to two new and promising perspectives: the fifth generation (5G) wireless network and Internet of Things (IoT). If we take a closer look at these technologies, they together play an important role in the digitalization of industry and the design of smart industries. This article provides information on the key potentials and concepts of the 5G-based digitization industry, as well as the relevant usage conditions and requirements. In addition, the article presents some of the key blocks of 5G and some important issues that have not yet been resolved and their solutions.
Collaborative filtering with a deep adversarial and attention network for cross-domain recommendation

Cross-domain recommendation can alleviate the data sparsity problem in the target domain and has become a promising research area. Recently, various models have been proposed to provide recommendation across domains. Some models successfully embedded adversarial learning to reduce domain discrepancies between the source and target domains; however, these models only focuse on extracting the domain-shared features among multiple domains. In this paper, we devise a novel framework that considers both domain-shared and domain-specific knowledge across domains, namely, DAAN. Specifically, we tightly couple matrix factorization-based collaborative filtering with deep adversarial domain adaptation via an attention network. In this framework, we first learn the domain-specific representations for each user and each item from the source and target user-item interaction matrices. Then, we capture the domain-shared features between two domains with common user (or item) embeddings in a domain-adversarial paradigm. Additionally, an attention network is used to adjust the degree of importance between domain-shared and domain-specific knowledge. Extensive experiments on six tasks demonstrate that our method outperforms various baselines in terms of two ranking metrics. To the best of our knowledge, our proposed approach is the first deep model that considers both domain-shared and domain-specific knowledge across domains within a domain-adversarial training paradigm for cross-domain recommendation.
Asynchronous Multithreading Reinforcement-Learning-Based Path Planning and Tracking for Unmanned Underwater Vehicle

The underwater unmanned vehicle (UUV) is widely used in various marine operations, in which path planning and trajectory tracking are the critical technologies to achieve autonomous motion planning. Unlike previous research methods, this article proposes the asynchronous multithreading proximal policy optimization-based path planning (AMPPO-PP) and trajectory tracking (AMPPO-TT) algorithms and applies these two methods to different task scenarios of UUVs. Taking advantage of the AMPPO, the expensive online computational procedure is converted to an offline training process. The proposed algorithms enable the UUV to learn autonomous planning, tracking, and emergency obstacle avoiding. Besides, the algorithm architecture of the AMPPO-PP and the AMPPO-TT is described in detail. By refining the reward in each timestep and utilizing the reward-shaping trick, the reward sparsity is avoided. The goal-distance heuristic reward function is used to make the UUV explore more directionally. Various simulation environments are developed from simple to complex, along with multiple comparative experiments to verify the effectiveness of the proposed algorithms.
IoT Registration and Authentication in Smart City Applications with Blockchain

The advent of 5G will bring a massive adoption of IoT devices across our society. IoT Applications (IoT Apps) will be the primary data collection base. This scenario leads to unprecedented scalability and security challenges, with one of the first areas for these applications being Smart Cities (SC). IoT devices in new network paradigms, such as Edge Computing and Fog Computing, will collect data from urban environments, providing real-time management information. One of these challenges is ensuring that the data sent from Edge Computing are reliable. Blockchain has been a technology that has gained the spotlight in recent years, due to its robust security in fintech and cryptocurrencies. Its strong encryption and distributed and decentralized network make it potential for this challenge. Using Blockchain with IoT makes it possible for SC applications to have security information distributed, which makes it possible to shield against Distributed Denial of Service (DDOS). IoT devices in an SC can have a long life, which increases the chance of having security holes caused by outdated firmware. Adding a layer of identification and verification of attributes and signature of messages coming from IoT devices by Smart Contracts can bring confidence in the content. SC Apps that extract data from legacy and outdated appliances, installed in inaccessible, unknown, and often untrusted urban environments can benefit from this work. Our work's main contribution is the development of API Gateways to be used in IoT devices and network gateway to sign, identify, and authorize messages. For this, keys and essential characteristics of the devices previously registered in Blockchain are used. We will discuss the importance of this implementation while considering the SC and present a testbed that is composed of Blockchain Ethereum and real IoT devices. We analyze the transfer time, memory, and CPU impacts during the sending and processing of these messages. The messages are signed, identified, and validated by our API Gateways and only then collected for an IoT data management application.
Heterogeneous Hypergraph Variational Autoencoder for Link Prediction

Link prediction aims at inferring missing links or predicting future ones based on the currently observed network. This topic is important for many applications such as social media, bioinformatics and recommendation systems. Most existing methods focus on homogeneous settings and consider only low-order pairwise relations while ignoring either the heterogeneity or high-order complex relations among different types of nodes, which tends to lead to a sub-optimal embedding result. This paper presents a method named Heterogeneous Hypergraph Variational Autoencoder (HeteHG-VAE) for link prediction in heterogeneous information networks (HINs). It first maps a conventional HIN to a heterogeneous hypergraph with a certain kind of semantics to capture both the high-order semantics and complex relations among nodes, while preserving the low-order pairwise topology information of the original HIN. Then, deep latent representations of nodes and hyperedges are learned by a Bayesian deep generative framework from the heterogeneous hypergraph in an unsupervised manner. Moreover, a hyperedge attention module is designed to learn the importance of different types of nodes in each hyperedge. The major merit of HeteHG-VAE lies in its ability of modeling multi-level relations in heterogeneous settings. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed method.
Emotions in leader’s crisis communication: Twitter sentiment analysis during COVID-19 outbreak

Adoption of social media by political leaders for information dissemination, campaigns, and elections has been proliferating since 2008 and in 2020, 23% growth in Twitter users is observed. Past studies revealed, content and tone as the vital elements of social media communication. Therefore, current study aimed to explore on how leaders portray emotions in communication on Twitter, particularly amid crisis like COVID-19 when establishing trust is crucial for smooth dissemination of knowledge to public. Total 12128 tweets were mined from official Twitter handles of 29 Indian political leaders, from the day of COVID-19 pandemic declaration to Indian Lockdown 2.0. For analysis, NRC-based sentiment analysis of NLP is employed using R Software. Results exhibit that amongst Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Trust, Negative, Positive; positive and trust are the most common emotion found whenever the authorities issued or communicated the new guidelines. Insights from results will be helpful for leaders to improve communication on virtual world considering emotions.
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models

In classical computer vision, rectification is an integral part of multi-view depth estimation. It typically includes epipolar rectification and lens distortion correction. This process simplifies the depth estimation significantly, and thus it has been adopted in CNN approaches. However, rectification has several side effects, including a reduced field of view (FOV), resampling distortion, and sensitivity to calibration errors. The effects are particularly pronounced in case of significant distortion (e.g., wide-angle fisheye cameras). In this paper, we propose a generic scale-aware self-supervised pipeline for estimating depth, euclidean distance, and visual odometry from unrectified monocular videos. We demonstrate a similar level of precision on the unrectified KITTI dataset with barrel distortion comparable to the rectified KITTI dataset. The intuition being that the rectification step can be implicitly absorbed within the CNN model, which learns the distortion model without increasing complexity. Our approach does not suffer from a reduced field of view and avoids computational costs for rectification at inference time. To further illustrate the general applicability of the proposed framework, we apply it to wide-angle fisheye cameras with 190° horizontal field of view. The training framework UnRectDepthNet takes in the camera distortion model as an argument and adapts projection and unprojection functions accordingly. The proposed algorithm is evaluated further on the KITTI rectified dataset, and we achieve state-of-the-art results that improve upon our previous work FisheyeDistanceNet [1]. Qualitative results on a distorted test scene video sequence indicate excellent performance <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Privacy-preserving in smart contracts using blockchain and artificial intelligence for cyber risk measurements

Blockchain application and cyber-physical systems play a crucial role to modernize the traditional industrial process, technical procedures, and business models. It uses frame resilient and smart contracts to reduce the complexities of service costs. The blockchain applications associate the key features such as self-verification, and self-integrity to eliminate the role of trusted third parties access. It evolves scientific and industrial progress to reform as Industry 4.0 that uses Artificial Intelligence (AI) to process and extract the significant information of the real-time systems. Moreover, it applies digital analytics to link the data with blockchain and cloud repositories to improve system efficiencies. However, the security and privacy issues are still challenging to investigate the AI techniques and tools. Thus, this paper proposes a privacy-preserving in smart contracts using blockchain and artificial intelligence (PPSC-BCAI) framework that simplifies human interaction, system activities, service alerts, security risks, and fraudulent claims. To analyze the data transaction and sharing, an extreme gradient boosting (XGBoost) is applied. It analyses the transaction service to optimize the network load that reveals whether the transmission rate changes over the connectivity of unreliable networks.
Data Management for Future Wireless Networks: Architecture, Privacy Preservation, and Regulation

Next-generation wireless networks (NGWN) aim to support diversified smart applications that require frequent data exchanges and collaborative data processing among multiple stakeholders. Data management (DM), including data collection, storage, sharing, and computation, plays an essential role in empowering NGWN. However, DM for NGWN faces two significant challenges: stakeholders' data cannot be easily managed across different trust domains under a distributed network architecture; and privacy preservation requirements of personal data become more rigorous under new privacy regulations. To explore possible solutions to address the challenges, we first investigate the state-of-the-art architecture designs for DM and emphasize advantages of a blockchain-based DM architecture. Then we summarize existing privacy-preserving techniques in terms of advantages and challenges when being applied to DM. In addition, we review recent privacy regulations with their impacts on DM and discuss the existing solutions with privacy regulation compliance based on blockchain. Finally, we identify further research directions for achieving DM with privacy preservation.
Mining Key Classes in Java Projects by Examining a Very Small Number of Classes: A Complex Network-Based Approach

Key classes have become excellent starting points for developers to understand unknown software systems.Up to now, a variety of approaches have been proposed to mine key classes in a software project.Many of them are based on a network representation (namely, software networks) of the software projects.However, the software networks they used are usually un-weighted and un-directed, which is not consistent with the reality in a real software project where the coupling actually has direction and strength.Worse still, the number of key class candidates returned by existing approaches is usually very large.Thus, it is usually infeasible for developers to start the comprehension process from these classes, especially when there are tight time and resource constraints.To tackle these problems, in this paper, we propose an approach named MinClass, to Mine key Classes in Java projects by examining a very small number of classes.First, the software structure at the class level is represented by a weighted directed software network, which considers both the coupling strength and direction between every pair of classes.Second, we propose a new metric, OSE (One-order Structural Entropy), and use it to calculate the importance of each class in the system.Finally, we sort classes in descending order according to their OSE values, and a small number of top-ranked classes are treated as the key class candidates identified by MinClass.Experiments are performed on six open-source Java projects, and comparison studies with other eight state-of-the-art approaches are also performed.Results show that, although no one method performs best in all software systems, MinClass is the most promising one.It performs best in the whole set of software systems according to the average ranking of the Friedman test.Thus, MinClass is a valuable technique that can be used to mine the key classes.