Reviewerly/exHarmony huggingface dataset:
Smart Online Exam Proctoring Assist for Cheating Detection

Online exams are the most preferred mode of exams in online learning environment. This mode of exam has been even more prevalent and a necessity in the event of a forced closure of face-to-face teaching such as the recent Covid-19 pandemic. Naturally, conducting online exams poses much greater challenge to preserving academic integrity compared to conducting on-site face-to-face exams. As there is no human proctor for policing the examinee on site, the chances of cheating are high. Various online exam proctoring tools are being used by educational institutes worldwide, which offer different solutions to reduce the chances of cheating. The most common technique followed by these tools is recording of video and audio of the examinee during the whole duration of exam. These videos can be analyzed later by human examiner to detect possible cheating case. However, viewing hours of exam videos for each student can be impractical for a large class and thus detecting cheating would be next to impossible. Although some AI-based tools are being used by some proctoring software to raise flags, they are not always very useful. In this paper we propose a cheating detection technique that analyzes an exam video to extract four types of event data, which are then fed to a pre-trained classification model for detecting cheating activity. We formulate the cheating detection problem as a multivariate time-series classification problem by transforming each video into a multivariate time-series representing the time-varying event data extracted from each frame of the video. We have developed a real dataset of cheating videos and conduct extensive experiments with varying video lengths, different deep learning and traditional machine learning models and feature sets, achieving prediction accuracy as high as 97.7%.
Hierarchical Group Testing for Byzantine Attack Identification in Distributed Matrix Multiplication

Coded computing has proved its efficiency in handling a straggler issue in distributed computing framework. It uses error correcting codes to mitigate the effect of the stragglers. However, in a coded distributed computing framework, there may exist Byzantine workers who send the wrong computation results to a master in order to contaminate the overall computation output. Therefore, it is essential to identify Byzantine workers from their computation results in coded computing. In this paper, we consider Byzantine attack identification problem in coded computing for distributed matrix multiplication tasks. We propose a new coding scheme which facilitates the efficient Byzantine attack identification, namely locally testable codes. We also suggest a hierarchical group testing method for Byzantine attack identification. We claim the required number of tests for group testing in our scheme, and show that it requires smaller number of tests than the conventional group testing method for the existing coded computing schemes.
Sentiment Analysis of English Text with Multilevel Features

In natural language processing, text sentiment analysis is one of the important branches. It refers to the use of text mining and other technologies to extract attitudes, opinions, and other information from texts containing emotional information for analysis. Traditional sentiment analysis methods can be roughly divided into two categories: one is dictionary-based methods, and the other is machine learning-based methods. The former relies on the quality of the sentiment dictionary, while the latter relies on a large amount of high-quality data, so both have certain limitations. In text sentiment analysis research, word-level and sentence-level sentiment information extraction is a basic research task and has important research value. Through research, it is found that domain knowledge and context are two important factors influencing the extraction of emotional information. To this end, this paper proposes a text sentiment analysis method that integrates multiple features and constructs three features, which are based on the sentiment value feature of the dictionary, the expression feature, and the improved semantic feature, which are combined to build a text sentiment classification model. Aiming at the colloquial, irregular, and diverse features of English social media texts, this paper proposes a multilevel feature representation method. The sentiment classification experiments on English text show that the multilevel features proposed in this paper can effectively improve the F1_macro and accuracy of multiple model classifications. Compared with the existing research, the model in this paper improves the effect the most obvious.
A hybrid steganography technique based on RR, AQVD, and QVC

Pixel value differencing (PVD) steganography techniques suffer with “fall off boundary problem ,” and adaptive PVD (APVD) steganography techniques suffer with “unused blocks” problem. To avoid these problems, this research article describes a new steganography technique based on remainder replacement (RR), adaptive quotient value differencing (AQVD), and quotient value correlation (QVC). It performs embedding and extraction operation on 3-by-3 disjoint pixel blocks. From the 3-by-3 size pixel block two new blocks are derived: (i) the remainder block and (ii) the quotient block. Each remainder in 3-by-3 remainder block is decimal equivalent of two binary bits, so it is substituted by decimal equivalent of two secret bits. Each quotient in 3-by-3 quotient block is decimal equivalent of six binary bits. AQVD procedure is used to conceal data in four corner quotients of the quotient block. In three quotients of the middle row of the 3-by-3 quotient block, QVC embedding procedure is applied to hide the secret bits. The average hiding capacity is 3.21 bits per byte and the average peak signal-to-noise ratio is 35.27dB. Furthermore, regular-singular and pixel difference histogram attacks could not detect this technique.
A Decentralized Marketplace Application based on Ethereum Smart Contract

Centralized marketplaces run on a process where a single authority has control over all activity. They suffer from limitation such as- mandatory fees to be paid to the platform for listing and selling items, lack of privacy and control over account, improper transaction security and so on. This paper presents a decentralized application aiming to reduce these drawbacks using Ethereum blockchain. It was implemented using truffle evolvement framework with solidity language. Ethereum smart contracts contain all functions of this application. Client-side application was created through an API called web3.js. Analytical analyses of this application using Kovan test network show least transactional fees reported in literature till date. The application was shown to have average transaction fee of 0.1524472 (eth) with average run time of 3.5 seconds and average gas consumption of 4.6 gwei, these are the best results obtained till date for such system ensuring cost effectiveness and reducing time complexity. The contract making time was less than a second. Comparison with well-known centralized online marketplaces revealed that proposed system was more economical than current centralized online applications in terms of profit margin.
Category supervised cross-modal hashing retrieval for chest X-ray and radiology reports

With the wide application of radiography, massive data of chest X-ray and the associated radiology reports have been accumulated. Cross-modal retrieval between the chest X-ray and radiology reports is useful for the level of practicing radiologists and substantial other medical settings. However, existing cross-modal retrieval methods, which are mainly designed for the natural images, are not suitable for the chest X-ray images and radiology reports. In this paper, we propose a category supervised cross-modal hashing retrieval between chest X-ray and radiology reports, which learns the cross-modal similarity using a category supervised hashing network and union hashing network. Specifically, we design a category hashing network to learn hash code for each category, then use the learned category hash code as supervised information to guide the learning of images modality hash, texts modality hash. On the other hand, we propose the union hashing network to learn the correlation between two different modalities. Comprehensive experiments have been done on the public dataset MIMIC-CXR and the results show that the proposed method is on average 6.62% better than the traditional shallow method, achieved an increase of 0.57% compared to the deep cross-modal hashing (DCMH) in term of mAP. The ablation study is also implemented and the results demonstrates effectiveness of the proposed method.
Enabling Regulatory Compliance and Enforcement in Decentralized Anonymous Payment

Decentralized anonymous payment (DAP) enables users to directly transfer cryptocurrencies privately without passing through a central authority. Anonymous cryptocurrencies have been proposed to improve the privacy degree of DAP systems, such as Zerocash and Monero. However, the strong degree of privacy may cause new regulatory concerns, i.e., the anonymity of transactions can be used for illegal activities, such as money laundering. In this paper, we propose a novel DAP scheme that supports regulatory compliance and enforcement. We first introduce regulators into the system, who define regulatory policies for anonymous payment, and the policies are enforced through commitments and non-interactive zero-knowledge proofs for compostable statements. By doing so, users can prove that transactions are valid and comply with regulations. A tracing mechanism is embedded in the scheme to allow regulators to recover the real identities of users when suspicious transactions are detected. The formal security model and proof are provided to demonstrate that the proposed scheme can achieve desired security properties, and the performance evaluation shows its high efficiency.
Demonstration of Complete Information Trade-Off in Quantum Measurement

While an information-disturbance trade-off in quantum measurement has been at the core of foundational quantum physics and constitutes a basis of secure quantum information processing, recently verified reversibility of a quantum measurement requires to refine it toward a complete version of information trade-off in quantum measurement. Here we experimentally demonstrate a trade-off relation among all information contents, i.e., information gain, disturbance, and reversibility in quantum measurement. By exploring quantum measurements applied on a photonic qutrit, we observe that the information of a quantum state is split into three distinct parts accounting for the extracted, disturbed, and reversible information. We verify that such different parts of information are in trade-off relations not only pairwise but also triplewise all at once, and find that the triplewise relation is tighter than any of the pairwise relations. Finally, we realize optimal quantum measurements that inherently preserve quantum information without loss of information, which offer wider applications in measurement-based quantum information processing.
Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training

The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges. One such security challenge is adversarial attacks. Although there has been much work demonstrating the susceptibility of DL-based classification tasks to adversarial attacks, regression-based problems in the context of a wireless system have not been studied so far from an attack perspective. The aim of this paper is twofold: (i) we consider a regression problem in a wireless setting and show that adversarial attacks can break the DL-based approach and (ii) we analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly. Specifically, the wireless application considered in this paper is the DL-based power allocation in the downlink of a multicell massive multi-input-multi-output system, where the goal of the attack is to yield an infeasible solution by the DL model. We extend the gradient-based adversarial attacks: fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent method to analyze the susceptibility of the considered wireless application with and without adversarial training. We analyze the deep neural network (DNN) models performance against these attacks, where the adversarial perturbations are crafted using both the white-box and black-box attacks.
Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data

Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN) architecture is used to predict the SPA's tip position in 3 axes. Initially, data from actual 3D printed SPAs is obtained to build a training dataset for the network. Irregular-intervals pressure inputs are used to drive the SPA in different actuation sequences. The network is then iteratively trained and optimized. The demonstrated method is shown to successfully model the complex non-linear behavior of the SPA, using only the control input without any feedback sensory data as additional input to the network. In addition, the ability of the network to estimate the kinematics of SPAs with different orientation angles θ is achieved. The ESN is compared to a Long Short-Term Memory (LSTM) network that is trained on the interpolated experimental data. Both networks are then tested on Finite Element Analysis (FEA) data for other θ angle SPAs not included in the training data. This methodology could offer a general approach to modeling SPAs with varying design parameters.
QoE-Driven Adaptive Streaming for Point Clouds

With increasing popularity of virtual reality and augmented reality, application of point clouds is in critical demand as it enables users to freely navigate in an immersive scene with six degrees of freedom. However, point clouds usually comprise large amounts of data, and are thus difficult to stream in bandwidth-constrained networks. It is therefore important, yet challenging, to efficiently stream the resource-intensive point clouds, such that the user’s quality of experience (QoE) is guaranteed on a high-level but with a low bandwidth consumption. To this end, we propose a QoE-driven adaptive streaming approach for the tile-based point cloud transmission, to maximize the user’s QoE while reducing the transmission redundancy. By exploiting the perspective projection, we specifically model the QoE of a 3D tile as a function of the bitrate of its representation, user’s view frustum and spatial position, occlusion between tiles, and the resolution of rendering device. Based on this QoE model, we then formulate the QoE-optimized rate adaptation problem as a multiple-choice knapsack problem, which allocates bitrates for different tiles under a given transmission capacity. It is equivalently converted to a submodular function maximization problem subject to knapsack constraints, and solved by a practical greedy-based algorithm with a theoretical worst-case performance guarantee. The proposed algorithm is able to achieve a near-optimal performance, but with a very low computational complexity. Experimental results further demonstrate superiority of the proposed rate adaptation algorithm over existing schemes, in terms of both user’s visual quality and transmission efficiency.
Improved Secure and Efficient Chebyshev Chaotic Map-Based User Authentication Scheme

A Chebyshev chaotic map is a method for implementing efficient authentication. Recently, Chatterjee <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed an authentication scheme for multi-server/multi-client environments using a Chebyshev chaotic map. However, we found four critical vulnerabilities in the proposed authentication method. To begin with, the method is open to user as well as server impersonation attacks. User revocation is also infeasible. Finally, there is a possibility of critical information leakage. We propose a new scheme that overcomes the four weaknesses listed previously using a symmetric Chebyshev chaotic map. We tested our proposed scheme using ProVerif and AVISPA, and compared its performance with that of the conventional authentication method. Results indicate that our scheme is 44.025 times more efficient than the conventional method.
The Role of Digital Technologies to Promote Collaborative Creativity in Language Education

The importance of cultivating creativity in language education has been widely acknowledged in the academic literature. In this respect, digital technologies can play a key role in achieving this endeavour. The socio-cultural conceptualization of creativity stresses the role of communication, collaboration and dialogical interaction of creative expression in language education. The objective of this paper is to study the literature focusing on cases of collaborative creativity and technology embedded in language education. To this end, we carry out a systematic revision of state-of-the-art literature consisting of 26 blind peer-reviewed empirical studies selected from several databases that address our main research question, namely, which specific roles and forms of digital technology can be identified in the existing literature that support collaborative creativity in language education. Results show that the features of digital technology unfold a range of learning opportunities in language education and can play three different roles in promoting collaborative creativity: (1) as a tutoring device that guides the implementation of key co-creation skills; (2) as a tool that enables and shapes the development of co-creative thinking skills; and (3) as a medium that creates rich and resourceful environments to stimulate the emergence of collective creative processes. The paper also reveals that these three roles can be performed using a wide range of interactive technologies that encourage students to participate in a rich, co-creative language learning experience and equip learners with key competences to approach complex problems in a globalised and hyper-connected world. Finally, this paper may contribute to developing future language technology-enhanced learning projects capable of promoting key collaborative and creative processes.
Vehicle Image Detection Method Using Deep Learning in UAV Video

Traditional machine learning algorithms are susceptible to objective factors such as video quality and weather environment in the vehicle detection of Unmanned Aerial Vehicle (UAV) videos, resulting in poor detection results. A vehicle image detection method using deep learning in UAV video is proposed. The algorithm in this paper treats surveillance video as many frames of images for vehicle detection in the image. First, perform HSV (Hue-Saturation-Value) spatial brightness translation operation on the original sample to increase the adaptability to different light conditions and sample diversity. Then, the Single Shot MultiBox Detector (SSD) model framework is used as the basis for vehicle detection. In order to obtain a better feature extraction effect, focus loss is added to the basic SSD for optimization. Finally, the trained network model is used to analyze the UAV video, and the detection performance is analyzed experimentally. The results show that the vehicle detection rate of this algorithm has reached 96.49%. It can ensure that the vehicle is accurately detected from the drone video.
UBIS: Unigram Bigram Importance Score for Feature Selection from Short Text

A huge amount of data has been generated over the internet since few decades which is increasing exponentially. It has become difficult to manually classify the online and offline short textual documents. In this context, two major feature extraction techniques are used in existing literature, namely, TFIDF vectorizer and Count vectorizer. The major challenge in the existing feature extraction techniques is the number of textual features extracted. The textual feature reduction techniques are associated with the use of features and its correlation with resulting value or category. However, it is interesting to note that the importance of uni-grams and bi-grams may contribute more efficiently in determining the feature space vector. In this research work, the Graph of Words (GoW) based selective feature extraction technique is proposed as Uni-gram Bi-gram Importance Score (UBIS) as obtained from node score and edge score in Graph of Words. The experimental results show the effectiveness of the UBIS over TFIDF vectorizer and Count Vectorizer which are hybridized with feature selection techniques. To test and validate the experiments, logistic regression with gradient descent is used as the linear classification model over three different binary text classification dataset.
A Smart Governance diffusion model for blockchain as an anti-corruption tool in Smart Cities

Government corruption in Latin America prevents the region’s sustainable development and constitutes a failure of current systems. Governments have started to leverage new technologies to increase transparency and promote integrity within their processes. Among these, blockchain technology has gained traction as a promising tool for Smart Governance due to its unique properties in terms of immutability, trust, coordination, security, and transparency. The use of blockchain within Smart City initiatives is complicated by a lack of diffusion models. This paper develops a diffusion model for blockchain technology after analyzing three case studies and conducting a survey of thirty blockchain start-up leads across Latin America. The final model, named Smart Governance BT diffusion model, conceptualizes the diffusion of blockchain as a gradual process including inputs, a dynamic throughput, and outputs. Relevant for academics and government representatives alike, the model aims at improving the conceptual understanding of technology diffusion in Smart Cities and highlights the potential of blockchain as a mitigator of vulnerabilities to corruption. As initial conditions matter for its successful adoption and diffusion, it is recommended for Latin American governments to (1) redesign and digitalize their processes and legislation, (2) conduct a thorough cost-benefit analysis for specific use cases, and (3) reduce inefficiencies in areas with relatively medium to low levels of historical corruption in a first instance. The model can be tested and refined in future research and can be applied to any Smart City initiative requiring conceptualization.