Reviewerly/exHarmony huggingface dataset:
An Improved Variational Bayesian Adaptive Robust Kalman Filter

Inaccurate or vague system noise model (Q and R) of the Kalman filter (KF) will seriously affect the accuracy of filter estimation. In this paper, the noise model is adapted by the variational Bayesian method, and the outliers in the measurement are eliminated by the Mahalanobis distance criterion method. The proposed IVBARKF inherits the excellent adaptability of the variational Bayesian method and the strong robustness of the Mahalanobis distance criterion. The effectiveness is verified by the target tracking experiment. The proposed method has better performance than conventional method when the noise model is inaccurate and contaminated by outliers.
Basic Issues of Evidence and Electronic Evidence in Civil and Commercial Dispute Resolution

According to the continuous development of information technology, the electronic evidence and the electronic sources of electronic evidence have been significantly changing, which raises a concern about the basic understanding on electronic evidence. Although nowadays electronic evidence has been used widely in litigations, it is still lack of the common definition of electronic evidence. This chapter, therefore, aims to set the main foundation for electronic evidence in civil and commercial dispute resolution. In order to understand clearly the notion of electronic evidence, the chapter first identify the concept of evidence, the relevance and admissibility of evidence and the main types of evidence in litigations. It will then focus on the definition of electronic evidence, the basic characteristics of electronic evidence and the sources of electronic evidence.
Caption-Aided Product Detection via Collaborative Pseudo-Label Harmonization

Product detection, which aims to localize products of interest in the advertising images, helps advance many potential E-commerce applications like product retrieval and recommendation. However, labeling a massive number of fine-grained product categories and accurate product boxes is costly and especially not practical since products are ever-changing on E-commerce websites. In this work, we step forward to train a fine-grained product detector solely supervised by the advertising captions, which are naturally available but often severely flawed and noisy. To reformulate the weakly supervised detection research into a real-world setting, we introduce a large-scale benchmark, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CapProduct</i> , where more than 80,000 product image-caption pairs are collected from E-commerce websites. The fine-grained nature of products and noisy captions in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CapProduct</i> make it intractable to excavate valid category labels to train a weakly supervised object detector. To tackle this challenge, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co</b> llaborative <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> seudo- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> abel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> armonization (CoPLH) framework that harmonizes self-mined pseudo labels via modeling the global co-occurrence relationships of products. We construct a collaborative co-occurrence graph based on all training samples to improve the reliability of caption-predicted pseudo-labels as well as benefit the self-training procedure in a weakly supervised setting. Extensive experiments on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CapProduct</i> dataset demonstrate the effectiveness and the superiority of the proposed CoPLH over the state-of-the-art baselines.
Sequential Fast Covariance Intersection Fusion Kalman Filter for Networked System with Fading Measurements and Correlated Noises

For the networked systems with fading measurements, multiplicative noises and correlated noises, the model transformation technique with the fictitious noises and Sequential Fast Covariance Intersection (SFCI) fusion Kalman filter are presented. These algorithms can compensate the mixed random uncertainties with the fictitious noises and decrease the large computational burden. Furthermore, the SFCI fusion algorithm overcomes the drawback of the Sequential Covariance Intersection fusion, whose fusion result is sensitive to the fusion orders, and improves the accuracy and efficiency. The accuracy of the presented SFCI fusion Kalman filter is higher than that of each local estimator, less but close to that of the information fusion Kalman filter weighted by matrices. Finally, the effectiveness of the SFCI fusion Kalman algorithm is verified by the simulation example.
Components Regulated Generation of Handwritten Chinese Text-lines in Arbitrary Length

Generating readable images of handwritten Chinese text-lines is very challenging due to complicated topological structures in Chinese. To address this problem, we propose a components regulated model named HCT-GAN to generate the entire lines of Chinese handwriting from text-line labels. Specifically, HCT-GAN is designed as a CGAN-based architecture that additionally integrates a Chinese text encoder (CTE), a sequence recognition module(SRM), and a spatial perception module (SPM). Compared with the one-hot embedding, CTE learns the latent content representation by reusing the structure and component embedding shared among the Chinese characters. SRM provides sequence-level constraints to the generated images. SPM can adaptively constrain the spatial correlation between the generated components, which facilitates the modeling of characters with complicated topological structures. Benefiting from such artful modeling, our model suffices to generate images of handwritten Chinese text-lines in arbitrary length. Extensive experimental results demonstrate that our model achieves state-of-the-art performance in handwritten Chinese lines generation.
A Comprehensive Analysis On Secure Techniques For Keyword-based Search And Data Sharing

Server dependability and client security doubt expect data to be revealed prior to sending it to Aiming, and we want to distinguish watchwords in the data that are stowed away from interlopers and malignant cloud suppliers that we consider to be a method for impeding the organization. SSE distributed storage is utilized to look for symmetric encryption schemes (SES) that can't be viewed as genuine mists. Furthermore, cloud-based servers are intended to be secure by presenting a cloud-based information stockpiling framework, and will perceive a cloud-based framework that gives all around conveyed data and essential pursuit administrations. The Internet characterizes a framework that utilizes information intrude on innovation. furthermore, fundamental pursuit administrations. TKSE perceives the server close to the action word to forestall the making of a reasonable server from individuals with terrible data in the information stockpiling region. What’s more, blockchain innovation and hash capacities are utilized to pay web indexes without informing any other individual as to whether the client or the cloud is malevolent. Security investigation and execution assessments show that TKSE is protected, +effective, and appropriate for distributed computing.
White Blood Cell Detection and Classification in Blood Smear Images Using a One-Stage Object Detector and Similarity Learning

White blood cells are a fundamental part of the immune system which protect human body against infections and diseases. The complete blood count is a routine analysis that provides doctors information about the patients. Monitoring the immune system allows doctor to select preventive treatments against several diseases. The complete blood count relies in a rigorous observation of a blood sample through a microscope; the accuracy of the result depends on the expertise and time of the analyst. In this paper, a novel vision-based method using convolutional neural networks for white blood cell detection and classification is presented. The results show the proposed method is robust against the huge number of easy negatives in object detection, as well, the high inter-class similarity among images can be reduced for a better white blood cell classification.
Effective Clustering via Structured Graph Learning

Given an affinity graph of data samples, graph-based clustering aims to partition these samples into disjoint groups based on the affinities, and most previous works are based on spectral clustering. However, two problems among spectral-based methods heavily affect the clustering performance. Firstly, the randomness of post-processing procedures, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means, affects the stability of clustering. Secondly, the separated stages of spectral-based methods, including graph construction, spectral embedding learning, and clustering decision, lead to mismatched problems. In this paper, we explore a structured graph learning (SGL) framework that aims to fuse these stages to improve clustering stability. Specifically, SGL adaptively learns a structured affinity graph that contains exact <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> connected components. Each connected component corresponds to a cluster so clustering assignments can be directly obtained according to the connectivity of the learned graph. In this way, SGL avoids the randomness brought by reliance on traditional post-processing procedures. Meanwhile, the graph construction and structured graph learning procedures happen simultaneously, which alleviates the mismatched problem effectively. Moreover, we propose an efficient algorithm to solve the involved optimization problems and discuss the connections between this work and previous works. Numerical experiments on several synthetic and real datasets demonstrate the effectiveness of our methods.
MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views

We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. To deal with the challenging unsupervised generation of non-rigid shapes, we develop a new Deterministic Basis and Stochastic Deformation scheme in MHR-Net. The non-rigid shape is first expressed as the sum of a coarse shape basis and a flexible shape deformation, then multiple hypotheses are generated with uncertainty modeling of the deformation part. MHR-Net is optimized with reprojection loss on the basis and the best hypothesis. Furthermore, we design a new Procrustean Residual Loss, which reduces the rigid rotations between similar shapes and further improves the performance. Experiments show that MHR-Net achieves state-of-the-art reconstruction accuracy on Human3.6M, SURREAL and 300-VW datasets.
Utilizing Story-telling Method in Combining the Linear Flow Model for Game Level Design

This document is to introduce a first-person RPG game. This game is about two brothers, when they were young because of certain reasons they had to separate from each other. But when they grow up, they had to be tit-for-tat because of the different growing environments. Player will be able to select who to play as, for their first play through, whether the player wants to have a more violent playstyle or a subtler play-style. In the story the player will get to play as two characters and it is also our main character, elder brother and younger brother. In this document, I will introduce this game, background studies, game level concept, level design, implementation, testing, conclusion and future enhancements.
Anomaly Detection in Surveillance Videos via Memory-augmented Frame Prediction

Anomaly detection in surveillance videos is a challenging task in computer vision, and can be defined as the detection of actions or events that do not conform to the expected behaviors. Most of the existing methods solve the task by minimizing the reconstruction errors between the ground-truth video frames and their reconstructed frames. However, these methods sometimes reconstruct anomalies well that results in high false detections and a decrease of the performance. Therefore, we propose a frame prediction method which is based on a memory-augmented scheme for anomaly detection. Our method regards anomaly detection as a frame prediction task, and uses a generative network to achieve the frame prediction. For generating high quality video frames, we embed a memory module into the generative network, which effectively improves the feature representation of normal events and reduces the representation of abnormal events. In addition, we adapt an attention mechanism to model the interdependence between feature channels. In order to evaluate our method, we introduce a new anomaly detection dataset that consists of real and multi-scene surveillance videos. Extensive experiments on our dataset and publicly available datasets validate the effectiveness and robustness of our proposed method.
Agenda-Setting of the Public Health Emergencies (COVID-19) Based on Audience's Perspective

The outbreak of sudden public health incidents notes the agenda setting of the media in the process of message diffusion, but the audience's expectation for the media framework is seldom paid attention to. Through an online questionnaire, the media usage, satisfaction, intuitive evaluation of different message framing and placement methods of the audience (N=401) are investigated. The results notes that: audiences attach the most importance to message placement and gain-framing, where the importance of message placement is basically the same as the agenda setting of official media; audiences are satisfied with media coverage overall and are most concerned about the speed of media response, while the ability to deal with false message needs to be improved; some audiences attach equal importance to additional message on sudden public events. This study proposes an audience-led agenda-setting in response to public health emergencies, providing a new perspective to understand the audience's message needs and agenda setting.
Clustered Server Consolidation for Energy Efficient Cloud

Energy efficiency for cloud clusters requires a better allocation of resources. With the goal of energy-wise optimization in clouds, we aim to minimize the number of active servers on clouds by migrating VMs without exceeding any server’s memory capacity. We introduced a hierarchical algorithm and compared its efficiency against the implementations of Best-Fit Greedy (BFG), Worst-Fit Greedy (WFG), and Simulated Annealing (SA). Our algorithm performed better in almost all scenarios for small, medium, and large-size problems, even with time limitations.
Pengunaan Media Audio Visual dalam Meningkatkan Kemampuan Membaca Siswa pada Mata Pelajaran Bahasa Indonesia

The purpose of this research was to improve students' reading ability in Bahasa Indonesia subjects in grade 1 students at SDN 02 Ujan Mas by using Audio visual media. This research uses a type of Class Action (PTK) research conducted in 2 cycles, each cycle consists of 4 stages, namely the stage of planning, implementation of actions, observation and reflection. This research subsection is all 24 students in grade 1B. Data collection techniques are carried out through tests, and observations. Data analysis is done through observation data management. As a result of the study, it is known that students' reading ability is still very low because the average score of new students reaches 59.62 and the completion of new students' learning outcomes reaches 33.33, in the first cycle that the test results mangalami improvement, before the student improvement is complete is 8 students (33.33%), increased to 12 students (50.00%) with an average score of 1,611 or an increase of 4 students (19, 04%). and the second cycle pad increased drastically with an average score of 1,718 and a complete number of 19 students with a completion percentage of 79.16%. In conclusion, the use of audiovisual media effectively improves the reading ability of grade 1 students in Indonesian language subjects at SD Negeri 02 Ujan Mas.&#x0D; Keywords: Indonesian Language, Reading Ability, Audio Visual Media.
A Quantitative Assessment of the Impact of Homogeneity in Personality Traits on Software Quality and Team Productivity

Software industry is increasingly focusing on the improvement of software quality and team performance to survive in a competitive and rapidly growing environment. Previous studies reveal the importance of social and human factors in software engineering. Despite the importance of these personality factors, only a handful of empirical studies have quantitatively evaluated the impact of personality traits on software quality and effectiveness of software engineering teams. This study quantifies the abstract notion of trait-wise team homogeneity based on the Five-Factor Model (FFM). Additionally, this study also aims to measure its impact on team productivity and software quality. Trait-wise team homogeneity measures the Team Homogeneity Index (THI) for each of the five personality traits of FFM. Therefore, five new metrics i.e. Openness THI (O-THI), Conscientiousness THI (C-THI), Extraversion THI (E-THI), Agreeableness THI (A-THI), and Neuroticism THI (N-THI) are proposed. The utility of these five metrics is evaluated by conducting experiments in academic and industrial environments for three different phases of the Software Development Life Cycle (SDLC) i.e. analysis and design, implementation, and testing. During the analysis and design phase, it was observed that teams (composed of either students or professionals) with greater values of C-THI and A-THI produced better quality design models and teams with greater values of C-THI were more productive. In the implementation phase, teams with higher values of O-THI, C-THI, and E-THI were noticeably more productive and produced better quality code. For the testing phase, teams with greater values of C-THI and A-THI produced better quality test cases and were more productive. Results obtained so far indicate that the newly proposed five metrics – O-THI, C-THI, E-THI, A-THI, and N-THI – appear to have the potential to aid managers and academics alike in building productive teams which can produce better quality software.
Graph Convolutional Network with Two Types of Weight Mechanisms for Aspect-Based Sentiment Analysis

In this paper, we propose an approach to leverage inter aspects relation and Rely Graph Convolutional Networks (RelyGCN) for aspect sentiment analysis. More specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we extract aspects by L-Layer GCNs and construct their relation. Finally, we will predict the sentiment polarity (negative, neutral and positive) of the sentence S towards the aspect A by the feature fusing layer and the output layer.
Bitcoin Price Prediction and Automated Trading via LSTM Networks and Reinforcement Learning

The Bitcoin (BTC) is one of the most popular cryptocurrencies and now is one type of investment on the stock market. The price prediction is a real challenge as it depends by too many factors, an investment to BTC characterized too risky because the price has too many upside-downs. However BTC was the occasion for many people to start explore the stock market. In the last few years, the prediction of BTC has occupied the scientific community and many approaches have been made. In this research we try to predict the price of BTC per minute with long short-term memory networks, and then pass these predictions to a recurrent reinforcement learning (RRL) model to trade the BTC with United States dollars (USD). In the end of the paper we present the profits that the models made.
A Proposed Approach for Identifying the Connotative Relationship of English Sentences and paragraphs using the NLP package of Python

Connotative Relationship of sentences and paragraphs provide a much-needed method that can extract the sentiment, emotion and knowledge of a writer. Identifying this inherent relationship from the hidden layers of English paragraphs will help in understanding the overall sentiments and emotions of the paragraphs written by various authors. In this context the Natural Language Processing features and packages available in Python can provide a simple and unique solution for the extraction of inferred sentiments and emotions expressed by authors. In this research, an initial attempt has been made to extract the hidden sentiments of individual sentences and paragraphs from English text written by authors. Based on the work done it was found that even though the sentences of a paragraph were providing negative connotations the overall sentiment of the complete paragraph showed a positive sentiment and vice versa. Hence, it is inferred that analyzing and finding the overall sentiment of the paragraphs makes better understanding on the connotative meaning expressed by the authors in their textual writings.
Detecting and Defending CSRF at API-Level

Cross-Site Request Forgery (CSRF) vulnerabilities are severe web vulnerabilities since their characteristics of extreme concealment and heavy harmfulness. However, they have received marginal attention from both the academic and the industry and the detection and protection of CSRF vulnerabilities are still performed predominantly manually. This paper proposes CSRFSolver for API-level CSRF detection and protection with two components: CSRF detector and CSRF defender. The former helps to identify and locate CSRF points where they need CSRF protection, and the latter provides CSRF protection by generating and verifying CSRFToken. We evaluate the effectiveness and efficiency of CSRFSolver on Cisco Webex public URL APIs with the state-of-the-art method. The results indicate that CSRFSolver can effectively and efficiently protect the system from CSRF attacks and have no side effects on systems' functionality. Meanwhile, the practical usefulness of CSRFSolver has also been verified through four years of deployment in Cisco Webex.
Effects of Virtual Reality on School Students’ Learning in Safety Education: A Meta-analysis

Virtual reality (VR) has been used to create immersive and realistic learning environments. Safety education increases students’ awareness of potential hazards. Effective safety education should include a variety of scenarios in which students can practice recognising and responding appropriately to hazardous situations. VR has been shown to benefit students’ safety education by increasing their awareness of hazards. This meta-analysis was performed to examine the overall effect of VR use on students’ safety education, including safety knowledge, safety behaviour, motivation and presence, compared with control interventions. Locomotion, the input device and the display hardware were examined as moderating variables. Fifteen publications with 39 effect sizes (n = 2,411 participants) were included. No publication bias was detected. VR had a significantly positive effect on the overall effect size for safety education compared with control interventions (e.g., educational documents, lectures, animations). Effect sizes were large for motivation, medium for presence and safety knowledge, and small for safety behaviour. Only locomotion (large effect size) had a moderating effect, improving safety education. These findings support the effectiveness of VR use in safety education. Additional research is needed to examine more independent and moderating variables for this educational approach.