Reviewerly/exHarmony huggingface dataset:
Efficient Privacy-Preserving and Secure Authentication for Electric-Vehicle-to-Electric-Vehicle-Charging System Based on ECQV

The use of Electric Vehicles (EVs) is almost inevitable in the near future for the sake of the environment and our plant’s long-term sustainability. The availability of an Electric-Vehicle-Charging Station (EVCS) is the key challenge that owners are worried about. Therefore, we suggest benefiting from individual EVs that have excess energy and are willing to share it with other EVs in order to maximize the availability of EVCSs without the need to rely on the existing charging infrastructure. The Internet of Electric Vehicles (IoEV) is gradually gaining traction, allowing for a more efficient and intelligent transportation system by leveraging these capabilities between EVs. However, the IoEV is considered a trustless environment, with untrustworthy trading partners such as data sellers, buyers, and brokers. Data exchanged between the EV and the Energy AGgregator (EAG) or EV/EV can be used to analyze users’ behavior and compromise their privacy. Thus, a Vehicle-to-Vehicle (V2V)-charging system that is both secure and private must be established. Several V2V-charging systems with security and privacy features have been proposed. However, even if the transmitted communications are entirely anonymous, anonymity alone will not prevent the tracking adversary from reconstructing the target vehicle’s route. These systems frequently fail to find a balance between privacy concerns (e.g., trade traceability to achieve anonymity, and so on) and security measures. In this paper, we propose an efficient privacy-preserving and secure authentication based on Elliptic Curve Qu–Vanstone (ECQV) for a V2V-charging system that fulfils the essential requirements and re-authentication protocol in order to reduce the overhead of future authentication processes. The proposed scheme utilizes the ECQV implicit-certificate mechanism to create credentials and authenticate EVs. The proposed protocols provide efficient security and privacy to EVs, as well as an 88% reduction in computational time through re-authentication, as compared to earlier efforts.
Complex ontology alignment for autonomous systems via the Compact Co-Evolutionary Brain Storm Optimization algorithm

Autonomous Systems (ASs) that work in the open, dynamic environment are required to share their data entities and semantics to implement the co-operations. Typically, AS's data schemas and semantics are described via ontology. Since ASs need to maintain their autonomy and conceptual specificity, their ontologies might define one concept with different terms or in different contexts, which yields the heterogeneity issue and hampers their co-operations. An effective solution is to establish a set of data entity's correspondences through the Ontology Alignment (OA). Sine the simple correspondence of one-to-one style lacks expressiveness and cannot completely cover different types of heterogeneity, ASs' co-operations require using the complex correspondence of one-to-many or many-to-may style. Inspired by the success of applying the Brain Storm Optimization algorithm (BSO) to solve diverse complex optimization problems, this work proposes a Compact Co-Evolutionary BSO (CCBSO) to face the challenge of aligning AS ontologies. In particular, the AS ontology aligning problem is formally defined, a hybrid confidence measure for distinguishing the simple and complex correspondences is proposed, and a problem-specific CCBSO is presented. The experiment tests CCBSO's performance on different AS ontology aligning tasks, which consist of two simple ontology aligning tasks and one complex ontology aligning track. The experimental results show that CCBSO outperforms the state-of-the-art ontology aligning techniques on all simple and complex ontology aligning tasks.
A flexible calibration method based on small planar target for defocused cameras

• A flexible calibration method for defocused cameras is proposed. • The method has not only good calibration accuracy but also high operability. • The image pair is used to realize the linear camera calibration. • The image distance conversion model is built to decouple intermediate parameters. Complex engineering scenes increase the difficulty of camera calibration. High precision and easy operation are the necessary conditions to ensure successful calibration. This paper proposes a flexible calibration method for defocused cameras. Firstly, the initial calibration is completed by traditional Zhang's method. Then, the linear camera calibration based on the image pair is carried out. The image pair is obtained when the lens focuses on the calibration and measuring positions in sequence on the premise of keeping the calibration target stationary. The distortion coefficient is solved independently, the image distance conversion model is established to decouple the intermediate parameters, and finally, the linear solution of the camera parameters is obtained. The proposed method can complete the entire calibration process for a large-range vision measurement system using a small planar target. The number of calibration images required is close to Zhang's method. There is no need to fix the calibration target under multiple attitudes separately, which has strong operability. Experimental results show that the method is effective. The maximum calibration error of intrinsic parameters is 1.29%, and the maximum mean reprojection error is only 0.127 pixels. In addition, this method has good anti-noise performance and has satisfactory calibration accuracy under the action of different influencing factors, which indicates its broad applicability. The method can be applied to calibrating defocused cameras in special scenes such as engineering sites.
Simulating time evolution with fully optimized single-qubit gates on parametrized quantum circuits

We propose a method to sequentially optimize arbitrary single-qubit gates in parametrized quantum circuits for simulating real- and imaginary-time evolution. The method utilizes full degrees of freedom of single-qubit gates and therefore can potentially obtain better performance. Specifically, it simultaneously optimizes both the axis and the angle of a single-qubit gate, while the known methods either optimize the angle with the axis fixed, or vice versa. It generalizes the known methods and utilizes sinusoidal cost functions parametrized by the axis and angle of rotation. Furthermore, we demonstrate how it can be extended to optimize a set of parametrized two-qubit gates with excitation-conservation constraints, which includes the HOP and the reconfigurable beam-splitter gates. We perform numerical experiments showing the power of the proposed method to find ground states of typical Hamiltonians with quantum imaginary-time evolution using parametrized quantum circuits. In addition, we show the method can be applied to real-time evolution and discuss the tradeoff between its simulation accuracy and hardware efficiency.
IoT forensics: What kind of personal data can be found on discarded, recycled, or re-sold IoT devices

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.
Movement Guidance using a Mixed Reality Mirror

Mirror reflections offer an intuitive and realistic Mixed Reality (MR) experience comparable to other MR interfaces. Their high visual fidelity, and the sensorimotor contingency from the reflected moving body, make the mirror an ideal instrument for MR movement guidance. The translucent two-way mirror display enables users to follow a virtual humanoid instructor's movement accurately by visually matching it with their reflections. In this work, we conduct the first formal evaluation of movement acquisition performance with simple motor tasks, using visual guidance from an MR mirror and a humanoid virtual instructor. Our results of performance and subjective ratings indicate that, comparing with simulated virtual mirror and with traditional screen-based movement guidance, the real MR mirror yields better acquisition performance and stronger sense of embodiment with the reflection, for upper-body movement. But the benefits diminish with larger-range head movements. We provide design guidelines for future mirror movement guidance interfaces and MR mirror experiences at large.
Urban Seismic Network Based on MEMS Sensors: The Experience of the Seismic Observatory in Camerino (Marche, Italy)

Urban seismic networks are considered very useful tools for the management of seismic emergencies. In this work, a study of the first urban seismic network in central Italy is presented. The urban seismic network, built using MEMS sensors, was implemented in the urban district of Camerino, one of the cities in central Italy with the greatest seismic vulnerability. The technological choices adopted in developing this system as well as the implemented algorithms are shown in the context of their application to the first seismic event recorded by this innovative monitoring infrastructure. This monitoring network is innovative because it implements a distributed computing and statistical earthquake detection algorithm. As such, it is not based on the traces received by the stations from the central server; rather, each station carries out the necessary checks on the signal in real time, sending brief reports to the server in case of anomalies. This approach attempts to shorten the time between event detection and alert, effectively removing the dead times in the systems currently used in the Italian national network. The only limit for an instant alarm is the latency in the tcp/ip packages used to send the short reports to the server. The presented work shows the infrastructure created; however, there is not enough data to draw conclusions on this new early warning approach in the field, as it is currently in the data collection phase.
A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequences

Point cloud is one of the most widely used digital formats of 3D models, the visual quality of which is quite sensitive to distortions such as downsampling, noise, and compression. To tackle the challenge of point cloud quality assessment (PCQA) in scenarios where reference is not available, we propose a no-reference quality assessment metric for colored point cloud based on captured video sequences. Specifically, three video sequences are obtained by rotating the camera around the point cloud through three specific orbits. The video sequences not only contain the static views but also include the multi-frame temporal information, which greatly helps understand the human perception of the point clouds. Then we modify the ResNet3D as the feature extraction model to learn the correlation between the capture videos and corresponding subjective quality scores. The experimental results show that our method outperforms most of the state-of-the-art full-reference and no-reference PCQA metrics, which validates the effectiveness of the proposed method.
Feature space transformation of user-clicks and deep transfer learning framework for fraudulent publisher detection in online advertising

The concept drift is a challenge in click fraud detection wherein frequent changes in the actual status label of publishers complicate the identification of publishers’ fraudulent behavior. However, using transfer learning by leveraging the knowledge from previously learned domains to newer domains can make these differences more accessible while saving training time and improving the model’s performance. But the absence of other user-click datasets available publicly poses complexity in using transfer learning. Therefore, to use transfer learning towards predicting the publisher’s conduct concerning change in their labels, this work aims to transform 1D user-click non-image features into a 2D graphical image. The work proposes a deep convolution neural network-based transfer learning (DCNNTr) framework that utilizes different pre-trained Deep Convolutional Neural Network (DCNN) models as powerful feature extractors that leverage prior learnings to avert learning from scratch. The robust features extracted by the feature extractors help identify the conduct of publishers and classify them as fraudulent or non-fraudulent from 2D graphical images using machine learning models. By leveraging the weighted layers in extracting features, DCNN models utilize their special properties of being computationally efficient and locally focused. We evaluated the designed model on the FDMA2012 user-click dataset using precision, recall, F1-score, and AUC. Current work uniquely transforms the time series user-click non-image data into an image form. The experimental results demonstrate that features extracted with DenseNet121 followed by GTB have identified the fraudulent publishers with an average precision score of 79.8%.
Arabic Hate Speech Detection Using Deep Recurrent Neural Networks

With the vast number of comments posted daily on social media and other platforms, manually monitoring internet activity for possible national security risks or cyberbullying is an impossible task. However, with recent advances in machine learning (ML), the automatic monitoring of such posts for possible national security risks and cyberbullying becomes feasible. There is still the issue of privacy on the internet; however, in this study, only the technical aspects of designing an automated system that could monitor and detect hate speech in the Arabic language were targeted, which many companies, such as Facebook, Twitter, and others, could use to prevent hate speech and cyberbullying. For this task, a unique dataset consisting of 4203 comments classified into seven categories, including content against religion, racist content, content against gender equality, violent content, offensive content, insulting/bullying content, normal positive comments, and normal negative comments, was designed. The dataset was extensively preprocessed and labeled, and its features were extracted. In addition, the use of deep recurrent neural networks (RNNs) was proposed for the classification and detection of hate speech. The proposed RNN architecture, called DRNN-2, consisted of 10 layers with 32 batch sizes and 50 iterations for the classification task. Another model consisting of five hidden layers, called DRNN-1, was used only for binary classification. Using the proposed models, a recognition rate of 99.73% was achieved for binary classification, 95.38% for the three classes of Arabic comments, and 84.14% for the seven classes of Arabic comments. This accuracy was high for the classification of a complex language, such as Arabic, into seven different classes. The achieved accuracy was higher than that of similar methods reported in the recent literature, whether for binary classification, three-class classification, or seven-class classification, as discussed in the literature review section.
Blockchain-based AMI framework for data security and privacy protection

As a key component of smart grid (SG), advanced metering infrastructure (AMI) realizes two-way communication between users and power companies. Considering that AMI involves a large number of important data, which are related to the measurement of users’ electricity bills and the decision-making of power suppliers. Therefore, it is necessary to strengthen the protection of AMI data security and privacy to avoid economic losses caused by data leakage and tampering. Aiming at the problems of trust and single point of failure in the existing protection schemes, this paper proposes an AMI security framework based on blockchain. By combining the characteristics of blockchain such as decentration, tamper proof, traceability, the architecture of AMI data management system is reshaped. The technologies of peer-to-peer (P2P) transmission, asymmetric encryption, consensus algorithm and smart contract of blockchain are used to solve the problems of secure storage, transmission and application of AMI data. Through theoretical analysis and experiments, the feasibility of introducing blockchain technology into AMI is verified. The results show that the proposed framework is secure and reliable, which provides a strong guarantee for AMI data security and privacy protection.
Diagnosis of endometrium hyperplasia and screening of endometrial intraepithelial neoplasia in histopathological images using a global-to-local multi-scale convolutional neural network

Endometrial hyperplasia (EH), a uterine pathology characterized by an increased gland-to-stroma ratio compared to normal endometrium (NE), may precede the development of endometrial cancer (EC). Particularly, atypical EH also known as endometrial intraepithelial neoplasia (EIN), has been proven to be a precursor of EC. Thus, diagnosing different EH (EIN, hyperplasia without atypia (HwA) and NE) and screening EIN from non-EIN are crucial for the health of female reproductive system. Computer-aided-diagnosis (CAD) was used to diagnose endometrial histological images based on machine learning and deep learning. However, these studies perform single-scale image analysis and thus can only characterize partial endometrial features. Empirically, both global (cytological changes relative to background) and local features (gland-to-stromal ratio and lesion dimension) are helpful in identifying endometrial lesions.We proposed a global-to-local multi-scale convolutional neural network (G2LNet) to diagnose different EH and to screen EIN in endometrial histological images stained by hematoxylin and eosin (H&E). The G2LNet first used a supervised model in the global part to extract contextual features of endometrial lesions, and simultaneously deployed multi-instance learning in the local part to obtain textural features from multiple image patches. The contextual and textural features were used together to diagnose different endometrial lesions after fusion by a convolutional block attention module. In addition, we visualized the salient regions on both the global image and local images to investigate the interpretability of the model in endometrial diagnosis.In the five-fold cross validation on 7812 H&E images from 467 endometrial specimens, G2LNet achieved an accuracy of 97.01% for EH diagnosis and an area-under-the-curve (AUC) of 0.9902 for EIN screening, significantly higher than state-of-the-arts. In external validation on 1631 H&E images from 135 specimens, G2LNet achieved an accuracy of 95.34% for EH diagnosis, which was comparable to that of a mid-level pathologist (95.71%). Specifically, G2LNet had advantages in diagnosing EIN, while humans performed better in identifying NE and HwA.The developed G2LNet that integrated both the global (contextual) and local (textural) features may help pathologists diagnose endometrial lesions in clinical practices, especially to improve the accuracy and efficiency of screening for precancerous lesions.
Redirected Walking for Exploring Immersive Virtual Spaces With HMD: A Comprehensive Review and Recent Advances

Real walking techniques can provide the user with a more natural, highly immersive walking experience compared to the experience of other locomotion techniques. In contrast to the direct mapping between the virtual space and an equal-sized physical space that can be simply realized, the nonequivalent mapping that enables the user to explore a large virtual space by real walking within a confined physical space is complex. To address this issue, the redirected walking (RDW) technique is proposed by many works to adjust the user's virtual and physical movements based on some redirection manipulations. In this manner, subtle or overt motion deviations can be injected between the user's virtual and physical movements, allowing the user to undertake real walking in large virtual spaces by using different redirection controller methods. In this paper, we present a brief review to describe major concepts and methodologies in the field of redirected walking. First, we provide the fundamentals and basic criteria of RDW, and then we describe the redirection manipulations that can be applied to adjust the user's movements during virtual exploration. Furthermore, we clarify the redirection controller methods that properly adopt strategies for combining different redirection manipulations and present a classification of these methods by several categories. Finally, we summarize several experimental metrics to evaluate the performance of redirection controller methods and discuss current challenges and future work. Our study systematically classifies the relevant theories, concepts, and methods of RDW, and provides assistance to the newcomers in understanding and implementing the RDW technique.
A mobile learning framework for higher education in resource constrained environments

It is well documented that learning oppourtunities afforded by mobile technology (m-learning) holds great potential to enhance technology-enhanced learning in countries and communities with low socio-economic conditions where web-based e-learning has failed because of limited infrastructure and resources. Despite the potential for m-learning, its actual uptake has been low. The extant literature in this sphere provides some theoretical insight, with evidence of limited on-the-ground practical studies that often do not progress beyond the pilot phase. Failure to embed sustainable learning opportunities has been attributed to the absence of a contextual framework suitable for the heterogeneous nature of many developing countries. This paper thus presents an m-learning framework that considers the sociocultural and socio-economic contexts of low-income economies. The framework is based on a range of studies conducted over four years, including the outcome of two empirical studies conducted in a Nigerian university. Documenting the research underpinning the design provides practitioners and policymakers with a framework for a potentially sustainable strategy for long-term mainstream m-learning integration in higher education in low-income countries.