Reviewerly/exHarmony huggingface dataset:
Development And Evaluation Of Immersive Card Games As Booster Memory In Engineering Science

Abstract Engineering science is one of the core courses that must be taken by students in engineering. There is no doubt that too many laws and concepts to be learned and understood will cause students to feel difficult and less interested in pursuing engineering science. Many studies have shown that learning motivation and understanding can be achieved through educational games. Therefore, the main purpose of this study was to help engineering students learn and understand concepts by using educational card games through immersive technology. Card development and evaluation methods have been done while pre and post test have been conducted to see the effectiveness of the card against respondents. As a result, it shows that card designs play a role in enhancing student understanding while providing positive attitudes towards the use of the educational card games with immersive technology in engineering science learning. Notwithstanding, the outcome of the survey of the respondents involved in the test shows the enthusiasm and determination to use the card games. As a result, it can be highlighted and applied to all students.
A Proposed Trajectory Planning Algorithm for Mobile Robot Navigation Based on A* Algorithm

This chapter proposes a new trajectory planning approach by improving A* algorithm, which is a widely-used, path-planning algorithm. This algorithm is a heuristic method used in maps such as the occupancy grid map. As the resolution increases in these maps, obstacles can be defined more precisely. However, the cell/grid size must be larger than the size of the mobile robot to prevent the robot from crashing into the borders of the working environment or obstacles. The second constraint of the algorithm is that it does not provide continuous headings. In this study, an avoidance area is calculated on the map for the mobile robot to avoid collisions. Then curve-fitting methods, general polynomial and b-spline, are applied to the path calculated by traditional A* algorithm to obtain smooth rotations and continuous headings by staying faithful to the original path calculated. Performance of the proposed trajectory planning method is compared to others for different target points on the grid map by using a software developed in Labview Environment.
Parallel accelerated matting method based on local learning

Learning based (LB) matting is an effective matting algorithm but its usability is greatly limited by the heavy computations. In this paper, we cast some new insights into this algorithm and provide following contributions. First, we present an explanation for LB algorithm from manifold learning perspective and unify LB into standard two-stage matting theory. Second, based on the features of two stages, we propose an acceleration scheme utilizing both CPU and GPU parallelism to speed up LB matting up to 15X. Third, we propose an image partition method which provides optimized loading balance and precision for CPU-based block-level parallelism. Finally, we analyze the performance of sparse linear solver used by general matting problems and provide default optimized choice for solver selection. The experiments on the latest parallel framework and mathematical library prove that our scheme performs well evaluated by both acceleration effects and precision.
Localizing Teleoperator Gaze in 360° Hosted Telepresence

We evaluate the ability of locally present participants to localize an avatar head's gaze direction in 360° hosted telepresence. We performed a controlled user study to test two potential solutions to indicate a remote user's gaze. We analyze the influence of the user's distance to the avatar and display technique on localization accuracy. Our experimental results suggest that all these factors have a significant effect on the localization accuracy with varying effect sizes.
Frequent Item Mining When Obtaining Support Is Costly

Suppose there are n users and m items, and the preference of each user for the items is revealed only upon probing, which takes time and is therefore costly. How can we quickly discover all the frequent items that are favored individually by at least a given number of users? This new problem not only has strong connections with several well-known problems, such as the frequent item mining problem, it also finds applications in fields such as sponsored search and marketing surveys. Unlike traditional frequent item mining, however, our problem assumes no prior knowledge of users' preferences, and thus obtaining the support of an item becomes costly. Although our problem can be settled naively by probing the preferences of all n users, the number of users is typically enormous, and each probing itself can also incur a prohibitive cost. We present a sampling algorithm that drastically reduces the number of users needed to probe to $$O(\log m)$$ —regardless of the number of users—as long as slight inaccuracy in the output is permitted. For reasonably sized input, our algorithm needs to probe only $$0.5\%$$ of the users, whereas the naive approach needs to probe all of them.
Abrupt motion tracking of Ochotona curzoniae via improved motion model based on particle filter

Abstract The movement of Ochotona curzoniae is random, unpredictable, and often accompany by abrupt motion, Ochotona curzoniae has protective coloration, and possesses the characteristic of having low contrast with its typical background. Particle filter algorithm has been widely used to solve tracking problems, they can solve nonlinear and non-Gaussian problems. The motion model of the particle filter tracking algorithm ignores the acceleration factor, the observation values of position could not represent the future position. Due to this problem, this paper introduces acceleration variance of the “current” statistical model to motion model. In addition, this study fuse color feature with gradient feature as an observation model to overcome interference factors like a complex background, and low contrast. The experimental results show that the improved motion model and observation model has a better performance for abrupt motion, and could accurately track Ochotona curzoniae.
Insider Threat and Mitigation for Cloud Architecture Infrastructure in Banking and Financial Services Industry

Cloud architecture infrastructure for Banking and Financial Services Corporations operates on access control mechanism and trust over various cloud user. Having established the on demand service delivery model to operate, maintain, control and govern the cloud architecture, Banking and Financial Services Corporations deliver round the clock services to customers. Despite stringent security controls and minimum access level, the cloud users identify and exploit the cloud vulnerabilities. This leads to data leakages and loss of critical information. Depending upon the area of business and sophistication with which vulnerabilities have been exploited, frauds are committed and system are damaged by employees and third party contractors. The reason for such exploitation ranges from disgruntlement to personal issues of employees and third party contractors. In this paper, we have identified the various types of insiders, their motives, risk associated with information systems and methods of mitigation in Banking and Financial Services Corporations.
Review on Smart Video Streaming Schemes for Cloud Based E-Learning

This study is an initial effort to determine the best way to deliver rich educational data such as video materials over cloud computing environment. Cloud computing has been widely applied in various fields of business and private sector to simplify complexity of data management and networking. It also drew attention in educational institutions to improve the existing services of e-learning by adopting cloud technology. However, delivering rich educational content within a mesh network commonly influenced by bandwidth, jitter and loss of data which sometimes significantly reduces its quality at end user side. In dealing with the issues, several smart video schemes have been proposed to improve video streaming quality. The approaches look the issue from different perspectives and use variety of methods to produce the so called smart video streaming scheme as mentioned in literature. This study aims to explore state of the art on smart video streaming schemes and organize them into particular groups. It is expected that the effort will not only beneficial for next steps in the research but will also help other readers better understand advancement in the field as well as indentifying particular gaps where other researchers could contribute in the future.
Color Image Segmentation Using Semi-bounded Finite Mixture Models by Incorporating Mean Templates

Finite mixture models (FMM) are very popular for image segmentation. But, FMM assumes that each pixel is independent from each other. Thus, it does not consider the spatial information of the pixels which makes FMM more sensitive to noise. Generally, the traditional FMM consists of prior probability (PP) and component conditional probability (CP). In this chapter, we have incorporated mean templates, namely weighted geometric mean template (WGMT) and weighted arithmetic mean template (WAMT) to compute the CP. For estimating PP, the weighted geometric mean prior probability (WGMPP) and weighted arithmetic mean prior probability (WAMPP) templates are used. Lastly, the Expectation-Maximization (EM) algorithm is used to estimate the hyper-parameters of the FMM. Our models are proposed based on inverted Dirichlet (ID), generalized inverted Dirichlet (GID), and inverted Beta-Liouville (IBL) mixture models using the mean templates. For experimentation, the Berkeley 500 (BSD500) and MIT’s Computational Visual Cognition Laboratory (CVCL) datasets are used. We have also employed eight image segmentation performance evaluation metrics such as adjusted Rand index and homogeneity score to validate the image segmentation results for the BSD500. Additionally, we have also compared the segmentation outputs for the CVCL dataset which are computed using the traditional RGB and l 1 l 2 l 3 color spaces. The results obtained from IBL mixture models (IBLMM) are more promising than ID mixture models (IDMM) and GID mixture models (GIDMM).
Approximating agreements in formal argumentation dialogues1

In many real applications, to reach an agreement between the participants of a dialogue, which can be for instance a negotiation, is not easy. Indeed, there are application domains such as the medical domain where to reach a consensus among medical professionals is not feasible and might be even re garded as counterproductive. In this paper, we introduce an approach for expressing qualitative preferences between the goals of a dialogue considering ordered disjunction rules. By applying argumentation semantics and degrees of satisfaction of goals, we introduce the so-called dialogue agreement degree. Moreover, by considering sets of dialogue agreement degrees, we define a lattice of agreement degrees. We argue that a lattice of agreement degrees suggests different approximations between the current state of a dialogue and its aimed goals; hence, a lattice of agreement degrees can help to define different heuristics in the settings of strategic argumentation.
An Adaptive Thresholding Method for Background Subtraction Based on Model Variation

Background subtraction is an important task in computer vision. Pixel-based methods have a high processing speed and low complexity. But when the video frame with camouflage problem is processed, this kind of methods usually output incomplete foreground. In addition, the parameters of many algorithms are invariable. These methods cannot tackle non-static background. In this paper, we present an adaptive background subtraction algorithm derived from ViBe. Gaussian Kernel template is used to model initialization and update. Standard deviation is used to measure background dynamics. We test our algorithm on a public dataset, named changedetection.net. The results show that we can handle most of scenarios. Compared to ViBe, we achieve better result generally, especially in dynamic background and camera jitter categories.
Multi-Level Feature Learning for Pedestrian Attribute Recognition

Pedestrian attribute recognition is important for many subjects such as pedestrian tracking and person re-identification in monitoring scenario. Recently plenty of models address this task with deeply learned feature representations, but there still great potentials to make further progress due to some variations including low resolution, occlusion and so on. In this paper, we propose a new deep network structure for attribute classification, which takes advantage of multi-level features and an attention weighted scheme to combine multiple predictions from different layers. At last, we evaluate our method on PA-100K benchmark and the experimental results show the effectiveness of our proposed approach.
INFORMATION AND COMMUNICATION TECHNOLOGIES AS A SUBJECT OF SOCIAL AND HUMANITARIAN STUDIES

The article considers some socio-humanitarian aspects of info-communication technologies (ICT) related to info-social reality 5.0 and post-non-classical science.The authors summarize the experience of theoretical research in computer science which revealed cognitive problems of computer modeling and design.The article analyzes the body of information-technical and philosophical texts making up "pessimistic" and "optimistic" approaches to technology.It is shown that humanitarization of natural science is carried out by means of technical sciences because of their contact with society.Based on the study of neoliberal-economic influences on technical creativity, it is established that cognitive problems are caused by both paradigmatic scientific and social reasons.The authors updated the problems of the study area at the intersection of Informatics and Social Sciences and proposed the perspectives of ICT development connected with environmental challenges of post-modernity (info-chemistry, nanocomputers).
Collaborative Filtering Recommendation Algorithm Based on Xml Fuzzy Data

Abstract To analyse the limitations of traditional recommendation algorithms, we propose a collaborative filtering recommendation algorithm based on XML fuzzy data. Firstly, the method grasped and modelled the fuzzy attribute features of items and established the membership matrix of fuzzy attribute features, then used XML similarity calculation to extract the similarity between project fuzzy attribute features and combined it with the traditional collaborative similarity to get the comprehensive similarity. Finally, recommendations were made through this comprehensive similar. Experimental results show that, compared with the traditional collaborative filtering recommendation algorithm, the establishment of membership matrix of fuzzy attribute features solves the problem that the description of goods or items is usually fuzzy, which makes the calculation of neighboring items more accurate and improves the accuracy of the recommendation system. At the same time, when the new item has no user scoring information, it can be recommended through the similar of the fuzzy attribute features among items, thus effectively solve the cold-start problem.
Unsupervised Deep Shape from Template

This paper presents Unsupervised Deep Shape from Template (UDSfT), a novel method that leverages deep neural networks (DNNs) for reconstructing the 3D surface of an object using a single image. More specifically, the reconstruction of isometric deformable objects is achieved in the proposed UDSfT method via a DNN-based template-based framework. Unlike previous approaches that leverage supervised learning, the proposed UDSfT method leverages the notion of unsupervised learning to overcome this obstacle and provide real-time 3D reconstruction. More specifically, UDSfT achieves this via an unsupervised structure that leverages a combination of real-data and synthetic data. Experimental results show that the proposed UDSfT method outperforms the state-of-the-art Shape from Template methods in object 3D reconstruction.
PDG-PIO: Predicting Disease-genes Based on Pigeon-inspired Optimization

Combining large-scale biological data, using computational methods to mine potential disease-gene associations is a popular strategy. At the same time, bio-inspired intelligent optimization has always been a hot research field of intelligent computing. In this study, we apply the pigeon-inspired optimization (PIO) algorithm to the identification of human disease-genes. The problem of predicting disease-genes is translated into a single-objective optimization problem. A reasonable objective function is designed to measure the association between genes and inquiring diseases in a heterogeneous network, and the corresponding probability matrix is generated. The experimental results show that the proposed method (PDG-PIO) can accurately identify disease-genes.
Researches on Qualitative Recognition of Flushed Zone in Q Block on Deep Forest

Abstract In this paper we propose a method on qualitative recognition of flushed zone. We extract the morphometric parameters of logging curves to better reflect the change relations. We use deep forest model which learns the representation layer by layer. The model is more robust and have a higher accuracy than other model. This method has an excellent application performance in Q block.
Image Inpainting Under Chessboard-Like Masking

This paper details our image inpainting solutions of the IEEE ICME 2019 Grand Challenge on Learning-Based Image Inpainting. Specifically we utilize two different methods to fill in the holes in natural images. While both of them are based on the generative adversarial networks, one method fills it in one step and another one solves it progressively. Based on the masking patterns we find in testing data, we propose an approach that divides a cropped image with size 256×256 into 16 squares just like a chessboard and masks some squares to create the training data. To fill the religions in the testing images, we use the same strategy to crop the chessboard-like patches with size 256×256 that cover several masks to feed the trained models. We submit the results with 72 test images and PSNR is 32.2932dB and SSIM is 0.9793.
Eye Gaze Orientation and Pupil Size Variation on Time-Series Vigilance Task

Monitoring work performance declined has became need solution of important problem, and eye gaze orientation and pupil size related Monitoring task performance also worth to explore, to prevent the security monitoring personnel’s fatigue and performance decline. This research in simulating monitoring task based on Machworth’s Clock Test use the eye tracker to measure the attention direction and pupil size of the subject, and subjective evaluation of fatigue and attention. The results show monitoring work performance declined with the time-series on. Pupil size relates to fatigue and eye gaze orientation relates attention. It is discussed to evaluate the attention by controlling eye gaze orientation and fatigue to enhance monitoring performances.
ACNET: Attention-based Convolution Network with Additional Discriminative Features for DCM Classification (S)

For dilated cardiomyopathy (DCM) patients, immediate emergency diagnosis and treatment are critical for life saving and later recovery.T1 mapping is a non-invasive and effective diagnostic imaging approach to detect DCM.However, it is a demanding and time-consuming approach.In this paper, we propose an attention-based network structure, which can automatically identify DCM patients in a speedy manner to prioritize their treatment.In the proposed method, we adopt attention modules to generate attention-aware features.Inside each attention module, a bottom-up top-down feed-forward structure is used to unfold the feed-forward and feed-back attention processes into a single feed-forward process.It allows the network to focus more on determining useful information about the current output that is significant in the input data.Moreover, inspired by the residual network idea, we make full use of the characteristics of the original data.Combined residual block, we design down-residual modules for classification tasks.It consists of seven convolution layers and three layers of residual blocks.Our network achieves the most advanced recognition performance on cardiac datasets.We evaluated our approach on CMR(cardiac magnetic resonance) T1 mapping images with lower PSNR(peak signal to noise ratio), and the results demonstrate that our architecture outperforms previous approaches.
A Visual Tool for Supporting Collaborative Code Quality

Organizations have been increasingly relying on software development to manage their business, making it an essential activity. Companies have been using collaborative development environments to speed up their deliveries, resulting in projects tending to be produced by developers from different parts of the world with different cultures. In this context, controlling the software's overall quality by ensuring code quality is a crucial aspect of software development. Software quality can be analyzed based on software parameters like complexity, reusability, testability and maintainability. Such parameters are associated with different group of code metrics which are sets of software measures, providing better insights to developers about their code. In this research we aim to support software developers in better understanding code metrics information. We present a visual tool based on an original method which applies code metrics to an open source collaborative software development environment, grouping them into software product quality parameters. Developers are provided with dashboards and recommendations related to the part of code requiring quality improvement.
Remote sensing image texture enhancement based on HSV-BEMD algorithm

Remote sensing image texture feature is of great importance, especially used in geological mapping and tectonic interpretation. In this paper, HSV algorithm is used to transform a multi-band color image into HSV colorspace, texture feature in the brightness component V and a panchromatic image is extracted by bi-dimensional empirical mode decomposition (BEMD), in which radial basis function(RBF) interpolation is used to fit the envelops. As the texture feature of high frequency usually exists in the first IMF, the summation of their first IMF and the panchromatic image is taken as a substitute for the brightness component V, then the enhanced color image is obtained by HSV inverse transformation. Compared with other texture enhancement algorithms like Canny and wavelet transform, objectively evaluated by variance, Shannon information, average gradient and spatial frequency, the algorithm in this paper is proved effective in multi-band color image texture enhancement with panchromatic band. The whole process is executed on MATLAB2010b platform self-adaptively.
Weighted Nuclear Norm Minimization Image Denoising Method Based on Noise Variance Estimation

Weighted nuclear norm minimization (WNNM) uses image non-local similarity to deal with image denoising; this method not only maintains the detailed texture edge structure but also reduces the impact on distortion of the image after denoising. However, WNNM method assumes that the noise variance of the image is known, where the parameter is set by subjective experience that will result in incompleteness in theory. To handle this issue, it is proposed to pre-estimate noise variance based on discrete wavelet transformation (DWT). The simulation result shows that compared with original WNNM method, pre-estimate noise variance in image denoising has a faster algorithm running speed and a higher image signal-to-noise ratio after denoising.