Reviewerly/exHarmony huggingface dataset:
Unsupervised domain adaptation pedestrian re-identification based on an improved dissimilarity space

Pedestrian re-identification is a key and challenging research topic in intelligent security applications. Because of the need for large-scale manual labeling, pedestrian re-identification methods based on supervised learning cannot be widely used in practical applications. Research on unsupervised pedestrian re-identification has therefore become a hot topic. The DMMD model published in ECCV2020 successfully applied the dissimilarity space to unsupervised pedestrian re-identification. Based on the analysis of the deficiencies of DMMD, a new strong baseline model is composed, an improved dissimilarity space is constructed, a new transfer learning optimization method is proposed, and a time-space-appearance constraint is proposed. The test results show that the R1 accuracy of our model is improved by 21.4% compared with DMMD in the experiment from Market1501 to DukeMTMC.
An Automated Rescue and Service System with Route Deviation using IoT and Blockchain Technologies

The increasing population across the world globally increases the number of vehicles at an enormous rate. Detecting mechanical failures are one of the maj or issues to be resolved in the smart transportation. In addition to this, assisting the travel direction to save fuel and congestion is more important in improving the smart transportation to the next level. As the advanced technologies are shaping the modern world in an efficient and effective way. IoT and Block chain plays a vital role in offering the solution for smart transportation and automobile sector. The goal of this paper is to propose a smart automated automotive rescue and service giving system for highways using IoT and Block chain. This system also comprises of intelligent vehicle deviation module to assist in reducing traffic delays and thereby saving time and fuel consumption. As an extension of this paper, it intends to extend the work and demonstrate suitable results with an appropriate case study.
Mode connectivity in the QCBM loss landscape: ICCAD Special Session Paper

Quantum circuit Born machines (QCBMs) and training via variational quantum algorithms (VQAs) are key applications for near-term quantum hardware. QCBM ansäatze designs are unique in that they do not require prior knowledge of a physical Hamiltonian. Many ansätze are built from fixed designs. In this work, we train and compare the performance of QCBM models built using two commonly employed parameterizations and two commonly employed entangling layer designs. In addition to comparing the overall performance of these models, we look at features and characteristics of the loss landscape-connectivity of minima in particular - to help understand the advantages and disadvantages of each design choice. We show that the rotational gate choices can improve loss landscape connectivity.
Context‐wise attention‐guided network for single image deraining

In this paper, we propose a context-wise attention-guided network for single image deraining. Unlike most existing deraining methods, our network exploits underlying complementary information not only across multiple scales but also between levels. Specifically, our network architecture is designed to transmit the inter-level and inter-scale features. To extract guiding information and improve the discriminating ability of context-wise attention-guided network, we propose a net-context-wise attention module to generate attention maps. Following residual learning, the clean image is created by removing the predicted rain streak layer from the rainy input. Experimental results show our method has better performance on public datasets than some state-of-the-art methods.
Gated Bi-Recurrent Separate Network for Video Super-Resolution

Video super-resolution (VSR) aims to reconstruct high-resolution (HR) video sequences from low-resolution (LR) video sequences by image processing, which has been widely applied to different scenarios, including ultra HD video display, object detection, and medical image reconstruction. VSR exploits both intra-frame information and inter-frame information and makes full use of spatial and temporal information to reconstruct images, thus outperforming single-image super-resolution (SISR). In this paper, a gated bi-recurrent separate network (GBSN) is proposed, which gets spatio-temporal information to ensure a fine and realistic reconstruction of each frame by utilizing the bi-recurrent coupled propagation structure. The gated structure is used to control feature updating, which limits the propagation of error features. For the bidirectional network, the low-frequency part of the image is reconstructed in forward and the corresponding high-frequency part is reconstructed in backward. A fusion module is further developed to integrate these two parts. Experiments have been conducted to evaluate the performance of the proposed method, where Vid4, UDM10, and Vimeo-90K-T datasets are adopted. As compared to the state-of-the-art VSR methods in the literature, the proposed method enhances the peak signal to noise ratio (PSNR) to 28.51dB on Vid4 and 39.84dB on UDM10, respectively.
Meta-Analysis of Learning Strategies in Teaching Using: Augmented Reality Technology

With the arrival of the Fourth Industrial Revolution, the learning environment is rapidly changing. Augmented Reality is a popular educational technology. It has benefitted Education a lot, be it inside the classroom or outside, interactive experiences have been promoted. Users can communicate with unreal things which are merged in the actual world and be visible in similar place that too in actual environment utilizing Augmented Reality technology. This meta-analysis aims to find out the several forms of learning methodologies that have been used, based on Augmented Reality. The technique of research is dependent onto doing a thorough and detailed literature hunt on these databases available online such as IEEE, Scopus, Google Scholar, Directory of Open Access Journals (DOAJ) and Springer, as well as in the print journals. Learning based on Games, Learning based on Interaction, Learning based on Cooperation and solving the problems are the most common Augmented Reality tactics in education, according to the findings of this meta-analysis. Such findings will give educators information on Augmented Reality-based learning techniques and their educational potential, leading to more research on how Augmented Reality-based learning methods can be effectively utilized in learning and teaching.
An Approach for Utilizing Correlation among Sensors for Unsupervised Anomaly Detection of Wind Turbine System

An anomaly detection model needs to be built by considering the characteristics of a target system. In this paper, we study unsupervised anomaly detection for a wind turbine system. A wind turbine system has many components and sensors of the same components are highly correlated. Considering these characteristics of a wind turbine system, our approach for unsupervised anomaly detection is to utilize correlation among sensors. We first generate a standard correlation matrix (i.e., the most representative normal correlation matrix) from the given normal data. Then, we measure a distance between the standard matrix and a correlation matrix of a target test data using mean squared error as an anomaly score. Through experiments using real wind turbine system data, we show that our unsupervised approach achieves a maximum of 0.928 ROC AUC (0.894 on average),
3D surface topography measurement of diffractive optical element based on white light interferometry stitching method

Diffractive optical element (DOE) has been widely used in camera lens imaging systems such as mobile phone camera, Monitoring Camera, etc. The measurement of the DOE surface morphology is very important to evaluate the processing quality of the elements, optimize the manufacturing technology and improve the image quality of the imaging system. However, traditional DOE has a small diameter (about 10mm) and the base surface is a wavelength scale 3D relief microstructure. It is a great challenge to realize the high-precision, high-efficiency and multi-scale surface profile measurement of its full aperture. In this paper, new white light interferometry scanning stitching measurement device and method for the 3D surface topography measurement is proposed. Firstly, the white light interferometry scanning stitching measurement device was established. Then, a total of 67 subapertures of the full aperture of the element were measured based on the experimental device. Finally, the profile error of the full aperture was obtained by a microstructural subaperture stitching algorithm. The RMS normal error of the surface profile is 0.3717&mu;m. The measurement results verify the effectiveness of the scanning stitching measurement system and method based on white light interferometer.
An Efficient Extreme-Exposure Image Fusion Method

Abstract Since the existing commercial imaging equipment cannot meet the requirements of high dynamic range, multi-exposure image fusion is an economical and fast method to implement HDR. However, the existing multi-exposure image fusion algorithms have the problems of long fusion time and large data storage. We propose an extreme exposure image fusion method based on deep learning. In this method, two extreme exposure image sequences are sent to the network, channel and spatial attention mechanisms are introduced to automatically learn and optimize the weights, and the optimal fusion weights are output. In addition, the model in this paper adopts real-value training and makes the output closer to the real value through a new custom loss function. Experimental results show that this method is superior to existing methods in both objective and subjective aspects.
A Real-Time UAV Crowd Counting System Based on Edge Computing

In this paper, we develop a real-time unmanned aerial vehicle (UAV) crowd counting system based on edge computing. To be specific, we integrate Nvidia Jetson Nano on a quad-rotor UAV and leverage the YOLOv4-Tiny detection algorithm to efficiently process the task of pedestrian flow detection at local, rather than transmit the original image/video data to the remote ground station. Moreover, to improve the low detection accuracy of the YOLOv4-Tiny algorithm, we construct a self-built pedestrian dataset with 124 manually labeled samples, which is suitable for the crowd counting application by UAVs from the sky, and achieve a training loss as low as 0.1579%, after 10000 iterations on the integrated Nano. Experimental results show that the proposed system has a pedestrian flow detection rate as high as 84.21% within 750 ms.
Reversible Data Hiding Based on Motion-Vectors Ordering

Aiming at the problem that existing video data hiding algorithms affect visual quality, the reversible data hiding algorithm based on pixel value ordering is improved, and a reversible data hiding algorithm based on motion-vectors ordering is proposed. The algorithm divides the motion-vectors into nonoverlapping groups and arranges the motion-vectors of each group in ascending order, embeds secret data in the difference between the maximum (minimum) value and the second maximum (minimum) value. The proposed method provides a new idea for video reversible data hiding.
Target Tracking with Asynchronous Multi-rate Active and Passive Sonars

Due to the differences in detection and communication methods, the sampling rates of active and passive sonars are usually different. This paper addresses the problem of target tracking with asynchronous multi-rate active and passive sonars. The key challenges in this problem stem from unobservability of passive sonar, nonlinearity of active and passive sonar systems, the asynchronous measurements due to multi-rate sampling, etc. For the unobservability and strong nonlinearity of passive sonars, it is proposed that measurements across sonars with the same sampling rate are combined and converted to Cartesian coordinate measurements. In order to avoid the increase of computational complexity with the increase of the number of sensors and clutter density, the multiple hypothesis tracking (MHT) algorithm is first performed at each passive sonar. Then, only the measurements with effective data association are combined and augmented to form stacked measurement vectors, which are converted to Cartesian coordinate measurements by iterated least squares (ILS) estimator. For the multi-rate problem of active and passive sonars, an equivalent measurement conversion method is proposed, which transforms the multiple propagated measurements into a single Cartesian coordinate measurement. Thus, the asynchronous multi-rate of active and passive sonars system is aligned to the common rate. Numerical experimental results verify the effectiveness of the proposed algorithm.
Peculiarities of Teenagers' Perception of the Characters of a Film Narrative in a Situation of Moral Choice

The study of the system of moral values of young people using film narrative, which has an effect on the impressions, meanings, significations, attitudes of people and values, human beliefs, treatment of people and values would determine mental health of an individual and psychological well-being of a society as a whole.Teenagers are those most susceptible to the exposure.The novelty of the study was that the evaluation of the impersonations of the characters in a situation of moral choice, transmission of moral values presented in the film narrative, was studied in adolescents without parental oversight, living in residential care facilities for more than 10 years.The methodological foundation of the study was the psychosemiotic approach to content analysis.A toolbox of techniques was used in the study: theoretical analysis, immanent analysis of the film; factorial analysis; a dedicated semantic differential was used to discover the factors revealing the semantics of the impersonations of the characters of the film narrative in a situation of moral collision, and to compare their perception by different groups of teenagers; analysis of letters to the character was carried out using the procedure of content analysis.We proceeded from the assumption that teenagers' perception and semantic evaluation of the characters of the film narrative in the situation of moral choice was determined by their experience of living in the situation of moral choice and conditions of their upbringing; the system of values presented in the film narrative, the situation of moral collision experienced by the characters, determined the perception and semantic evaluation of their impersonations by modern teenagers.The study was carried out on the basis of schools in Irkutsk, facilities for children without parental care (Republic of Buryatia, Irkutsk) (n = 78).'The Boy in the Striped Pajamas' motion picture was used as stimulus material.It was demonstrated that teenagers left without parental care would perceive the characters of the motion picture in positive categories of morality more often than those raised in families.The attitude towards the characters of a movie who were in a situation of moral choice, was determined by the categories of nobility, peacefulness, humanity, tolerance, and piety.The identified differences could be explained by their experience of living in a situation of moral choice, the attitude toward children in situations of living hardships, the individual psychological differences of adolescents.Differences in attitudes could also be seen as the reason for differences in the effectiveness of the impression of the movie.It was substantiated that the situation of moral choice in cinematography served as a tool for shaping moral values.The study sustained the idea of the potential of motion pictures, their effect on shaping moral values of adolescents, complemented the data on the effect of the film narrative on personal development and individual differences in the evaluation of media products.The obtained findings set
A New Credit Data Evaluation Scheme Based on Blockchain and Smart Contract

The traditional model of storing academic information data in colleges and universities is centralization, which will cause data loss, forged resumes, and false academic records. At the same time, rational use of the decentralized characteristics of blockchain can effectively control the robustness of credit data from a global perspective. This paper proposes a partially decentralized credit data management scheme based on the new blockchain technology to promote the practical implementation of blockchain technology in the education field. The stability of blockchain data storage structure, the transparency of proof-of-authority mechanism identity, and the authenticity of event-driven trust data provided by the smart contract can effectively ensure that the entire life cycle of information data can be publicly and securely recorded. The experiment shows that this scheme can effectively solve the problems caused by the traditional storage model.
A Fine-Grained Object Detection Framework Based on Fixed ROI Masking and Feature Optimization in Optical Remote Sensing Images

Fine-grained object detection in images aims at providing both precise positions and fine-grained categories of objects of interest, which is a challenging but meaningful task. In this paper, fine-grained object detection in optical remote sensing images is explored. To address the inefficiency of previous methods, firstly an end-to-end fine-grained detection framework with a localization branch and a classification branch is proposed. Convolutional Neural Network (CNN) is used for feature extraction and the shallow features are shared by the two branches, while high-level features in the two branches are learned independently. Then a novel fixed ROI Masking method is designed to extract the features of ROIs with fixed size and a grouped spatial attention mechanism is proposed to enhance the ROI feature maps in classification branch by means of teaching the network “where to see”. Experiments with Google Earth data prove the effectiveness of the proposed method.
Improving the Performance of Anomaly Detector based on Geometric Transform-based Deep Neural Networks

The geometric transformation (GT) based anomaly detection is a recent famous method to detect the abnomal sample among many normal data. However, the training process of the GT method is unstable and rather inaccurate for use in the industrial field. The goal of this paper is to suggest a method to improve the performance of GT based anomaly detector. Specifically, this paper proposes a few methods such as loss and activation function redefinition for training efficient GT model, so that it can be used in the real industrial field.
User Security Behavioral Profiling using Historical Browsing Website

Nowadays, the usage of technology is significantly increased. Based on Malaysian Communication and Multimedia Commission (MCMC) annual report 2020, for the first six month of 2020 have received 11,235 reports about hacking, pornography, cyberbullying, forgery of identity, scam, phishing, and others. One of challenges is user has lack of cybersecurity knowledge and awareness that can lead them to be fraud especially when surfing to unauthorized website. Therefore, an awareness among the user in using the internet is crucial to avoid these issues. One ways of coping mechanism is understanding the user behavior. User profiling has been used to recognize user behaviour. This paper aims to develop a user's security behavior profile by analysing important features in browsing history data and the Digital Citizenship elements as guidelines for the classification using Support Vector Machine (SVM). Three different kernels were evaluated to get the most efficient performance. The result shows the best kernel is Linear Kernel. The accuracy when using Linear when the number of users small (1 0 users) is 67%, the number of users medium (20 users) is 83 % and the number of users large (30 users) is 89%.
An enhanced Elo-based student model for polychotomously scored items in adaptive educational system

The adaptive learning environment provides learning support that suits individual characteristics of students, and the student model of the adaptive learning environment is the key element to promote individualized learning. This paper provides a systematic overview of the existing student models, consequently showing that the Elo rating system has greater potential as compared to the other models regarding application in the online learning environment. Based on the Elo model, this study proposes the EELO, an enhanced Elo rating system, in consideration of the application scenarios of polychotomously scored items and multi-dimensional granularity evaluations not covered by the basic Elo rating system. The EELO model estimating students' cognitive abilities and predicting their future performances on unknown questions is evaluated based on one public set (Assigment2) and one proprietary dataset (HSK), and achieved an AUC of 0.92 for Assigment2 and 0.84 for HSK, which shows that the EELO model has the best performance regarding the above-mentioned objectives as compared with the latest extensions of the IRT and BKT models. Subsequently, the EELO model was tested and applied successfully in a real large-scale online learning environment to demonstrate the potential of the EELO model in adaptive learning applications.
Extending Chromium: Memento-Aware Browser

Users rely on their web browser to provide information about the websites they are visiting, such as the security state of the web page their viewing. Current browsers do not differentiate between the live Web and the past Web. If a user loads an archived web page, known as a memento, they have to rely on user interface (UI) elements within the page itself to inform them that the page they are viewing is not the live Web. Memento-awareness extends beyond recognizing a page that has already been archived. The browser should give users the ability to easily archive live web pages as they are browsing. This report presents a proof-of-concept browser that is memento-aware and is created by extending Google&#x0027;s open-source web browser Chromium.
Secure Digital Voting system based on Aadhaar Authentication by using Blockchain Technology

The paper specifies a digital voting system based on Aadhaar Authentication by using Blockchain Technology. Blockchain could be a record of data distributed among the complete network of computer systems and makes it difficult to interchange the data. The voting data is stored in blocks of the digital voting system and these blocks are linked to each other. Aadhaar integration to Digital Voting proves the uniqueness and avoids the replication of votes. This system uses electronic devices to register and calculate the votes. The proposed system provides a safe Digital Voting system by using the Virtual ID of the Aadhaar database, and this data is securely stored in blocks of the Blockchain. To ensure security to the blocks in the blockchain, SHA 256 hashing algorithm is used. SHA 256 algorithm assigns the hash value to each block and links the hash value to the next blocks, so voting data remains safe. Aadhaar Authentication and Blockchain technology by using SHA 256 algorithm provides safety to the proposed digital voting system.