Reviewerly/exHarmony huggingface dataset:
Second-Order Spatial-Temporal Correlation Filters for Visual Tracking

Discriminative correlation filters (DCFs) have been widely used in visual object tracking, but often suffer from two problems: the boundary effect and temporal filtering degradation. To deal with these issues, many DCF-based variants have been proposed and have improved the accuracy of visual object tracking. However, these trackers only adopt first-order data-fitting information and have difficulty maintaining robust tracking in unconstrained scenarios, especially in the case of complex appearance variations. In this paper, by introducing a second-order data-fitting term to the DCF, we propose a second-order spatial–temporal correlation filter (SSCF) learning model. To be specific, the SSCF tracker both incorporates the first-order and second-order data-fitting terms into the DCF framework and makes the learned correlation filter more discriminative. Meanwhile, the spatial–temporal regularization was integrated to develop a robust model in tracking with complex appearance variations. Extensive experiments were conducted on the benchmarking databases CVPR2013, OTB100, DTB70, UAV123, and UAVDT-M. The results demonstrated that our SSCF can achieve competitive performance compared to the state-of-the-art trackers. When penalty parameter λ was set to 10−5, our SSCF gained DP scores of 0.882, 0.868, 0.706, 0.676, and 0.928 on the CVPR2013, OTB100, DTB70, UAV123, and UAVDT-M databases, respectively.
Invariant of Enhanced AES Algorithm Implementations Against Power Analysis Attacks

The security of Internet of Things (IoT) is a challenging task for researchers due to plethora of IoT networks. Side Channel Attacks (SCA) are one of the major concerns. The prime objective of SCA is to acquire the information by observing the power consumption, electromagnetic (EM) field, timing analysis, and acoustics of the device. Later, the attackers perform statistical functions to recover the key. Advanced Encryption Standard (AES) algorithm has proved to be a good security solution for constrained IoT devices. This paper implements a simulation model which is used to modify the AES algorithm using logical masking properties. This invariant of the AES algorithm hides the array of bits during substitution byte transformation of AES. This model is used against SCA and particularly Power Analysis Attacks (PAAs). Simulation model is designed on MATLAB simulator. Results will give better solution by hiding power profiles of the IoT devices against PAAs. In future, the lightweight AES algorithm with false key mechanisms and power reduction techniques such as wave dynamic differential logic (WDDL) will be used to safeguard IoT devices against side channel attacks by using Arduino and field programmable gate array (FPGA).
English Teaching Evaluation Combined with End-User Computing and Neural Network

A neural network model and English teaching evaluation of university with end-user computing are the focus of this paper. The main research contributions are as follows: (1) Propose an ADA-BP neural network. It applies the adaptive learning rate as well as the momentum term to promote the BP network. Experiments show that the model solves the problems of the existing methods and models such as difficulty in determining weights, prone to overfitting, slow convergence, and prone to local minimums, which verifies the effectiveness in evaluating college English teaching. (2) Propose the DA-SVR network. The model has multiple hidden layers and performs multiple feature conversions to optimize the network. The output layer of the model uses SVR as a predictor to achieve evaluation. (3) Deploy the designed evaluation network on the server, and end users can use the API interface to directly access and test the model functions. Massive experiments have verified the effectiveness of the designed method.
The Relationship between Intelligent Image Simulation and Recognition Technology and the Health Literacy and Quality of Life of the Elderly

In order to explore the relationship between intelligent image recognition technology and the mentality and quality of life of the elderly, this paper combines intelligent image simulation technology to identify the behavior of the elderly, protect the safety of the elderly, and provide timely feedback on the adverse conditions of the elderly. Moreover, this paper improves the traditional intelligent image recognition algorithm, verifies the research method of this paper through experimental research, and puts forward corresponding suggestions. Through investigation and research, we can see that the level of health literacy of elderly patients with chronic diseases is low. Therefore, in the future health education, we should strengthen health education for elderly patients with chronic diseases, use different mass media to propagate health knowledge, and promote the formation of healthy lifestyles and behaviors for elderly patients with chronic diseases. At the same time, the experiment also verified that the intelligent image recognition technology proposed in this paper has a positive effect in improving the mentality and quality of life of the elderly.
A novel approach for the detection of anomalous energy consumption patterns in industrial cyber‐physical systems

Abstract Most scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber‐physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI‐based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real‐world industrial solutions has not so far developed at the same pace. Currently, 90% of AI‐based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI‐based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non‐supervised, human‐understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real‐world case study from the automotive sector, comparing it to an extensive benchmark comprising state‐of‐the‐art unsupervised and semi‐supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI‐based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection.
On the Security of OSIDH

The Oriented Supersingular Isogeny Diffie–Hellman is a post-quantum key exchange scheme recently introduced by Colò and Kohel. It is based on the group action of an ideal class group of a quadratic imaginary order on a subset of supersingular elliptic curves, and in this sense it can be viewed as a generalization of the popular isogeny based key exchange CSIDH. From an algorithmic standpoint, however, OSIDH is quite different from CSIDH. In a sense, OSIDH uses class groups which are more structured than in CSIDH, creating a potential weakness that was already recognized by Colò and Kohel. To circumvent the weakness, they proposed an ingenious way to realize a key exchange by exchanging partial information on how the class group acts in the neighborhood of the public curves, and conjectured that this additional information would not impact security. In this work we revisit the security of OSIDH by presenting a new attack, building upon previous work of Onuki. Our attack has exponential complexity, but it practically breaks Colò and Kohel's parameters unlike Onuki's attack. We also discuss countermeasures to our attack, and analyze their impact on OSIDH, both from an efficiency and a functionality point of view.
A New Camera Calibration Technique for Serious Distortion

A new camera calibration technique based on serious distortion is proposed, which only requires the camera to observe the plane pattern in an arbitrary azimuth. It uses the geometrical imaging principle and radial distortion model to acquire radial lens distortion coefficient and the image coordinate (u0, v0), and then solves the linear equation aiming at the other parameters of the camera. This method has the following characteristics: Firstly, the position of the camera and the plane is arbitrary, and the technique needs only a single observation for plane pattern. Secondly, it is suitable for camera calibration with serious distortion. Thirdly, it does not need expensive ancillary equipment, accurate movement, or lots of photos observed from different orientations. Having been authenticated by computer emulation and actual experiment, the results of the proposed technique have proved to be satisfactory. The research has also paved a new way in camera calibration for further studies.
Because I’m Happy—An Overview on Fostering Positive Emotions Through Virtual Reality

In recent years, an increased demand for improving mental health and well-being led to developing procedures capable of enhancing positive experiences. One highly attractive candidate for evoking positive experiences is Virtual Reality (VR), as VR enables users to experience various situations in controlled and safe environments. This overview first investigates how positive emotions, well-being and VR are interconnected. Then, an overview about how and why to induce positive emotions in adult users is provided. Methodological and ethical considerations about VR technology, measurements of VR’s efficacy and user characteristics are reviewed. It emerges that VR is efficient in inducing positive emotions across the adult lifespan and in various settings. Levels of immersion, interactivity, Virtual environment contents, sensory modalities involved and users’ characteristics emerged as key determinants for successfully inducing positive emotions with VR. The main applications of positive VR experiences consist in using VR for relaxation, stress and pain management, motivation for physical activities, and gives promising results for apathy treatment in elderly users. Although VR is efficient in eliciting positive emotions and experiences, the underlying operating mechanisms remain unclear and are yet to be further investigated. Finally, the need for a user-centered approach when designing positive VR experiences, clear guidelines for the use of VR, and a better documentation of its potential adverse effects are addressed.
The Sentiment Analysis of Spider-Man: No Way Home Film Based on IMDb Reviews

Sentiment analysis is used to determine the overall sentiment in a movie review. The goal of this paper is to investigate the sentiment analysis using multiple classification methods from Spider-Man: No Way Home movie reviews. The review dataset is procured from the IMDb website. Preprocessing methods are used and compared to determine the difference in accuracy score. The methods proposed for this study include Naïve-Bayes, Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), and Decision Tree to find the best accuracy possible. The sentiment analysis of the movie review resulted in 94 positive reviews and 65 negative reviews. The highest accuracy and f1 score for this study are obtained from the SVM and the SGD classifier with an accuracy of 82% and an F1 score of 81% respectively
Overview of Applied Data Analytic Mechanisms and Approaches Using Permissioned Blockchains

Blockchain technology deployment has surged in diverse domains to secure and maintain valuable data.Wherever valuable data exists, the motivation of applying analytics emerges.However, this case is slightly different since it deals with a distributed system environment with security constraints such as privacy and confidentiality.This study aims to provide an overview of approaches that applied analytics over permissioned blockchains.Moreover, extract key features from these studies to report and discuss common features and best practices.This contributes to determining the requirements to apply analytics and outlines the remaining challenges.The research method was conducted in four phases.The initial phase states the goals and objectives.Subsequently, the analysis phase examines a group of research papers to extract key features from various studies.These features were divided into three categories: general aspects, data management, and an analytics perspective.Afterward, the outcomes are classified according to the findings and observations to point out common aspects and best practices.Finally, the evaluation of the research determines the requirements to apply data analytics over permissioned blockchains.Based on the findings and observations of these research papers.Most of the studies focused on off-chain analytics with the assistance of a third party.Also, most of the analytics types were descriptive and diagnostic, whereas fewer studies proposed predictive analytics.This explains the lack of existing approaches that use artificial intelligence and real-time analysis.The most used blockchain platform for analytics was Hyperledger fabric for multiple reasons mentioned in detail in this research.
Automated User Experience (UX) Testing for Mobile Application: Strengths and Limitations

User Experience (UX) evaluation has a significant importance for any interactive application. Mobile device applications have additional limitations to convey good user experiences (UX) due to the usage and features of mobile devices in ubiquitous computing contexts. There are a lot of automated tools available for evaluating, such applications particularly developed for Android and iOS. Automated UX evaluation provides a number of strengths which are able to overcome the limitations of non-automated (moderated) counterparts. Automated UX evaluation is regarded as unexplored field, especially when it is adopted for the UX evaluation of complex mobile application. Despite these, automated UX evaluation tools are unexplored and should only be used in coupling with traditional UX evaluation techniques. Hence, this study aims to discuss the literature in automated UX evaluation and to highlight the UX measurements that merit further investigation. This paper presents the most frequently used automated UX evaluation tools for mobile applications. Therefore, the study analyzes existing automated UX tools and frameworks and identifies which UX attributes and measurements of automated UX evaluation are likely to be of use in future research. Moreover, we discuss the strengths and limitations of automated UX evaluation methods for mobile applications. Finally, the paper provides an overview of automated UX evaluation techniques and tools. It also presents the strengths and limitations for comparing various evaluation methods.
Uji Validitas Konstruk dengan CFA dan Pelaporannya

Confirmatory Factor Analysis (CFA) is the most reliable method of construct validity analysis in the fields of psychology, education and social sciences. From the author's observations on research articles as well as bachelor and graduate theses, and dissertations using CFA, it was found that there are a lot of misunderstandings and incompleteness in reporting CFA analysis. This paper is intended as an effort to improve this situation and provide recommendations in reporting data analysis using CFA. At the very least, this article is to show the important things that must be considered in understanding and using CFA and test theory in general.
A New Coarse Gating Strategy Driven Multidimensional Assignment for Two-Stage MHT of Bearings-Only Multisensor-Multitarget Tracking

The problem of two-dimensional bearings-only multisensor-multitarget tracking is addressed in this work. For this type of target tracking problem, the multidimensional assignment (MDA) is crucial for identifying measurements originating from the same targets. However, the computation of the assignment cost of all possible associations is extremely high. To reduce the computational complexity of MDA, a new coarse gating strategy is proposed. This is realized by comparing the Mahalanobis distance between the current estimate and initial estimate in an iterative process for the maximum likelihood estimation of the target position with a certain threshold to eliminate potential infeasible associations. When the Mahalanobis distance is less than the threshold, the iteration will exit in advance so as to avoid the expensive computational costs caused by invalid iteration. Furthermore, the proposed strategy is combined with the two-stage multiple hypothesis tracking framework for bearings-only multisensor-multitarget tracking. Numerical experimental results verify its effectiveness.
An Analytical Investigation of Anomaly Detection Methods Based on Sequence to Sequence Model in Satellite Power Subsystem

The satellite power subsystem is responsible for all power supply in a satellite, and is an important component of it. The system's performance has a direct impact on the operations of other systems as well as the satellite's lifespan. Sequence to sequence (seq2seq) learning has recently advanced, gaining even more power in evaluating complicated and large-scale data. The potential of the seq2seq model in detecting anomalies in the satellite power subsystem is investigated in this work. A seq2seq-based scheme is given, with a thorough comparison of different neural-network cell types and levels of data smoothness. Three specific approaches were created to evaluate the seq2seq model performance, taking into account the unsupervised learning mechanism. The findings reveal that a CNN-based seq2seq with attention model under suitable data-smoothing conditions has a better ability to detect anomalies in the satellite power subsystem.
Multimode Gesture Recognition Algorithm Based on Convolutional Long Short-Term Memory Network

Gesture recognition utilizes deep learning network model to automatically extract deep features of data; however, traditional machine learning algorithms rely on manual feature extraction and poor model generalization ability. In this paper, a multimodal gesture recognition algorithm based on convolutional long-term memory network is proposed. First, a convolutional neural network (CNN) is employed to automatically extract the deeply hidden features of multimodal gesture data. Then, a time series model is constructed using a long short-term memory (LSTM) network to learn the long-term dependence of multimodal gesture features on the time series. On this basis, the classification of multimodal gestures is realized by the SoftMax classifier. Finally, the method is experimented and evaluated on two dynamic gesture datasets, VIVA and NVGesture. Experimental results indicate that the accuracy rates of the proposed method on the VIVA and NVGesture datasets are 92.55% and 87.38%, respectively, and its recognition accuracy and convergence performance are better than those of other comparison algorithms.
The Research Competence, Critical Thinking Skills and Digital Literacy of Indonesian EFL Students

This project aims to investigate the relationship between English as Foreign Language (EFL) students’ research skills, critical thinking skills, and engagement with technology or their digital literacy. EFL students' research competence is expected to support their academic performance on writing papers and assignments. It also demands the development of their critical thinking skills. Their engagement in technology involves the use of social media, online networks, and the use of several applications. This study takes the sample of EFL students from two institutions, namely UIN Malang and IAIN Kediri, Indonesia, to see whether there is a similar performance in both Islamic higher education institutions in terms of students’ online research competence, critical thinking skills, and similar trend on the engagement with technology. The data collection method covers questionnaire and quiz. This result demonstrates a significant correlation between research competence and digital literacy. Concerning the insignificant relation between digital literacy and critical thinking, it proves that the digital literacy does not always automatically support the enhancement of critical thinking skills. Understanding the relationship between online research competence, critical thinking, and digital literacy is a crucial step in developing Indonesian EFL teaching syllabi and material supporting students' academic success in higher education context.
Wide-Area and Real-Time Object Search System of UAV

The method of collecting aerial images or videos by unmanned aerial vehicles (UAVs) for object search has the advantages of high flexibility and low cost, and has been widely used in various fields, such as pipeline inspection, disaster rescue, and forest fire prevention. However, in the case of object search in a wide area, the scanning efficiency and real-time performance of UAV are often difficult to satisfy at the same time, which may lead to missing the best time to perform the task. In this paper, we design a wide-area and real-time object search system of UAV based on deep learning for this problem. The system first solves the problem of area scanning efficiency by controlling the high-resolution camera in order to collect aerial images with a large field of view. For real-time requirements, we adopted three strategies to accelerate the system, as follows: design a parallel system, simplify the object detection algorithm, and use TensorRT on the edge device to optimize the object detection model. We selected the NVIDIA Jetson AGX Xavier edge device as the central processor and verified the feasibility and practicability of the system through the actual application of suspicious vehicle search in the grazing area of the prairie. Experiments have proved that the parallel design of the system can effectively meet the real-time requirements. For the most time-consuming image object detection link, with a slight loss of precision, most algorithms can reach the 400% inference speed of the benchmark in total, after algorithm simplification, and corresponding model’s deployment by TensorRT.