Reviewerly/exHarmony huggingface dataset:
Dually Octagonal Projection for 360 Video with Less-Distortion Introduced

The appearance of virtual and augmented reality has tremendously catalyzed 360 videos that provide immersive visual experience. In order to directly employ the existing compression standards, an efficient projection format is required considering the distortion introduced and compression standards compatibility. The equirectangular projection (ERP) is currently the most prevalent projection scheme, however, it introduces severely high sampling density near the poles. The Craster Parabolic Projection (CPP) is issued for uniform sampling on the sphere, but it does not preserve the shape of objects, especially at two sides of the CPP planes. Therefore, we propose a novel CPP-based projection format, named Dually Octagonal Projection (DOP) that projects the spherical video onto two mirrored octagons, striking a balance between distortion and uniform sampling. Besides, a brief format conversion function helps the improvements of 360 video quality. Our experimental results demonstrate that considerable BD-rate gain can be achieved by the DOP over the ERP particularly on detailed and informative videos. The compact format for the DOP is further verified to be more adaptive to encoders.
Interest Sustainability-Aware Recommender System

The key to successful recommendations is to provide users with items likely to be consumed in the future. From real-world data, we observe that users' consumption patterns for items change over time. For example, users may no longer like some items they liked in the past. However, existing recommender systems model user's preference to items without considering how much users' interests in each item will sustain in the future. Thus, they often recommend less interesting items in the deployment time (i.e., test time). In this work, we propose a novel recommender system, called CRIS, that considers the change of users' interest in each item over time. More precisely, we first predict the interest sustainability of each item, that is, how likely each item will be consumed in the future. Then, our goal is to make users closer to the items with high interest sustainability scores in the representation space than those with low interest sustainability scores. We perform experiments on 11 real-world datasets to show the effectiveness of CRIS. We also show that considering the interest sustainability is indeed crucial for boosting the accuracy of recommendations.
Multilevel Secure Container Deployment Framework in Edge Computing

Large scale distributed IOT applications like smart city, smart building etc. are becoming a reality. The microservice architectural pattern is now becoming common for its ease of development and is also used in edge systems. In order to secure containers, the gVisor container framework is emerging as an alternative to the standard Docker container, but has increased performance overheads in the network stack and file system processing. In this paper, we first characterize the performance of gVisor containers running real programs and demonstrate the loss in performance. Next, we propose a multi-level container deployment framework that chooses the right container framework trading off between performance and security based on the containers use in a microservice application. We demonstrate that using our framework, it is possible to ensure security with a relatively lower impact on performance.
Similarity Graph Learning and Non-linear Deep Representations for Spectral Clusterings

Abstract Spectral clustering is one of the most popular clustering approaches. Despite its good performance and strong theoretical supports, it is limited to high complexity of the graph Laplacian similarity matrix construction and eigen-decomposition problems. Recently, deep learning has been successfully adopted in graph representation. In the paper, we jointly learn the manifold graph construction and non-linear low-dimension mapping of the graph. In addition, we theoretically proved that our model according with spectral clustering theory. Meanwhile, we use the proposed non-linear coders as the building blocks to formulate a deep structure to further refine features of layer wise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both clustering accuracy and normalized mutual information( NMI )
Morphological Transformation Strategy for the Computation of Effective Physical Properties Using Digital Rock

Digital Core Technology using micro-CT images provides a new way for rock physics study with many advantages comparing with traditional rock physics experiment. The effective physical properties of rock sample can be numerically evaluated using 3D digital core model and multi-physics simulator. However, there are various factors that could affect the accuracy of the numerical computation of rock properties, for example, the morphological transformation in the process of digital core modeling using scanned images. In this paper, three digital core models with voxel 600 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> are investigated by using different morphological transformation strategies. By cooperating the software SimpleWare™ and COMSOL Multiphysics®, the porosity and pore structure of the core models are calculated, and the effective electrical properties of core models are simulated. Different morphological transformation strategies applying to the same core sample lead to different porosity calculation results. It is shown that the image processing algorithms affect the pore structure reconstruction as well, which will bring great influence on the computation of sample electrical properties. By examining the influences of different morphological transformation strategies on the physical property computation of rock sample, we propose appropriate morphological transformation strategy based on the core structure. This work can provide some valuable insights for the future digital core experiment.
Study of Usability Parameter for Graphical Based Authentication System

Every authentication process initially requires the acceptance of the secure system that is easy, flexible and adaptable. One of the authentication methods which take the base of remembrance of secure passwords is the graphical knowledge based authentication. Among its two types i.e. recognition based and recall based, the dimension of this study revolves around the recall type authentication system. For this the research study aim to analyze the usability aspect in the existing recall knowledge based authentication systems. Based on the facts and findings of the existing system, the objective is to design the phases of proposed authentication process. In accordance to the proposed authentication process, next step is to identify the usability evaluation parameters. The details of the existing and proposed specification of each usability evaluation parameter are discussed using the flowcharts.
Robust Variational Bayesian Filter for Systems with Skew t Noise

Considering the pulse interference, measurement outliers and artificial modeling errors, the non-Gaussian heavy-tailed (or skew) noise widely exists in the real environment. However, to data, little literature is related to the state estimation of the system where the process and measurement noises (PMNs) are both expressed as the skew t distribution (STD). To this end, given the hierarchical representation of the STD, a new robust Bayesian filter based on the variational Bayesian (VB) inference is presented to approximately estimate the unknown state via the collected measurements. And an example from the target tracking scenario is given to illustrate the validity of the designed Bayesian filter.
Challenges of Education in the 4th Industrial Revolution

Exponential technologies will change the world as we know it, impacting directly on employment and thus, on education. The majority of the jobs in 2030 have not been invented yet (IFTF Institute for the Future Palo Alto. Forecasting emerging technologies’ impact on work in the next era of human-machine partnership. Future of Work. [online] Available at: http://www.iftf.org/fileadmin/user_upload/images/ourwork/Tech_Horizons/realizing_2030_future_of_work_report_dell_technologies.pdf [Accessed 12 Nov. 2019], 2019). These jobs will not only be different in technological terms but also on their functions and perspective, meaning the creation of new roles. The main reason for these new roles is that our society is not prepared for the ethical and technological challenges ahead. In consequence, education should focus on getting adapted to the needs of the job market, so students get properly trained to achieve robot-proof occupations. This training involves STEAM education as well as both hard and soft skills and it must be ongoing. In conclusion, exponential technology will entail a revolution that will impact all dimensions of our society and both companies, educational institutions and individuals need to take consciousness of the importance of adaptation for successful social progress.
Research on Application of Multi-station Continuous Wave Radar Network Velocity Measurement in Projectile Tracking

When traditional pulse radar is used for projectile tracking, the range measurement system error will seriously affect the tracking accuracy. Based on the advantages of continuous wave (CW) measurement radar with high accuracy of velocity measurement and almost no systematic error in velocity measurement, this paper studies the method of using continuous wave radar network velocity measurement to track projectiles. This method first uses multi-station continuous wave radar network to obtain the initial position of the projectile, and performs curve fitting based on the least squares criterion; then turns to velocity measurement, and obtains the range of the projectile at each moment through velocity integration. Then the measured velocity and the range obtained by velocity integration are used as observation data for unscented Kalman filter (UKF). Finally, simulations verify that this method can improve the tracking accuracy compared to the traditional multi-station network ranging method with pulse radar.
Cyber Forensics: Discovering Traces of Malware on Windows Systems

Malware Crimes are ubiquitous nowadays. One of the challenges faced by Cyber Forensics Investigators is to retrieve the details of a suspicious program, which might be malware, from a computer. Another challenge is that anti-forensics techniques are often adopted by criminals after committing cybercrimes to hide the presence of suspicious programs and their associated details. In such cases, footprints of the suspicious programs and the related details may be deleted or overwritten from the Suspect's computer. This paper presents a detailed procedure to reconstruct or retrieve crucial information related to those suspicious programs from various sources on a Windows Computer. This type of analysis reveals the traces of these programs from their remnants available on a computer. This paper describes methodologies for examining the storage media as well as random access memory content of the Suspect's computer to retrieve such footprints. It also includes the steps for analyzing various Windows 10 artefacts that may hold the footprints of malware even after it is deleted or overwritten from the original location. The paper also explains the results of experiments conducted to identify the traces of suspicious executables which are deleted or still existing on the Computer.
A Study on Comparative Analysis of the Effect of Applying DropOut and DropConnect to Deep Neural Network

Neural networks are increasingly applied to real-life problems by training models for solving prediction problems with known training data. Overfitting is one of the major problems that should be considered in designing neural network models. Among various approaches to treat the overfitting problem, DropOut and DropConnect are approaches to adjust the process of training by temporarily changing the structures of neural network models. In this study, we compare the effect of applying DropOut and DropConnect approaches to a deep neural network. The result of this study will help to understand the effect of applying DropOut and DropConnect to neural networks in terms of loss and the accuracy of the trained model. It is also expected to help to design the structure of deep neural networks by effectively applying DropOut and DropConnect to control overfitting in the training of machine learning models.
Augmented Reality-assisted Cyber-Physical Systems of Smart University Campus

This paper discusses necessary stages of design and implementation of the smart university campus system. The modular structure of the system encompasses smart mobility module, smart labs, smart library and smart university museum. The mechanisms of distributed and multilevel data analysis is proposed. This approach can improve safety, reduce consumption of energy and interact effectively with other clusters in a smart-city environment. Realization of "smart fast food" project requires the creation of an information technology platform, which comprises information technology for Big Data processing, a logistics development for complex structured environment as well as efficient algorithms of the optimal route search for delivery of containers taking into account the courier loads.
Elucidation of the term “energy” in the “Philosophy of the name” of A. F. Losev

The subject of this research is the interpretation of the term &amp;ldquo;energy&amp;rdquo; in the &amp;ldquo;Philosophy of the Name&amp;rdquo; of A. F. Losev. An attempt of philosophical comprehension of the nature of the name and word is one of the primary topics in the works of the prominent Russian philosopher. Particular interest to this topic is substantiated by Loves&amp;rsquo;s commitment to philosophically explain such perspective on the nature of the Name of God, which was introduced by the adherers of imyaslavie (onomatodoxy) during the imyaslavic disputes of the first quarter of the XX century. Using the philosophical methods, A. F. Losev attempted to reveal the exceptional and precise sense the Orthodox doctrine on the Name of God, one of paramount components of which is the Palamite theology about the energies of God. The task is set to determine the extent to which Losev's philosophical concept of onomatodoxy, and namey the understanding of the term &amp;ldquo;energy&amp;rdquo;, corresponds to the teaching of the Holy Fathers on the energy of God. The scientific novelty of this research lies in contribution to the comprehensive analysis of Losev's interpretation of the term &amp;ldquo;energy&amp;rdquo; in the context of patristic theology. This is especially relevant due to the fact that the appeal to Palamism (teaching on the energies of God) is one of the key arguments in the apologia of imyaslavie. The conclusion is drawn that proposed by Losev scheme of the Name of God, and his understanding of the term &amp;ldquo;energy&amp;rdquo; in particular, not just formally, but essentially is based on the philosophical provisions of Neo-Platonism. This is why the such system is not quite accurate interpretation of the patristic teaching on the energies of God and the Name of God.
Implementation of Language-Action Reward Network in Reinforcement Learning by Using Natural Language

Key issue in many RL approaches is deferred (long-term and postponed) rewards, this leads to difficulties in learning for an agent. Inspite of fact certain “reward shaping” deals with goal of making process of learning quick and easy (by starting process with an agent assigned with extra input), it leads to come complexity. Ongoing RL approaches have indicated complexity of implementation (example Atari games). So, to avoid such issues Potential-based reward shaping (PBRS) is used to increase performance of RL gents. It is an adaptable strategy to provide fundamental foundation data combining with temporal (time-based) distinction learning in a principled manner. In this PBRS approach, we propose a system LEARN (LanguagEAction Reward Network), certain maps common language (human understandable) to middle (intermediate) rewards based on activities of agent. These intermediate language based rewards canister be integrated (combined) into any standard RL algorithm. Experiments abide run on a grid world and a more complex LanguagE-Action Reward Network (LEARN) a framework certain show certain we canister learn tasks significantly faster when we specify intuitive priors on reward distribution.
Digital Image Aesthetic Composition Optimization Based on Perspective Tilt Correction

In the field of the digital image intelligent aesthetic, many efforts have been made in automatic aesthetic assessment and composition optimization. Comparatively, few studies focus on image tilt correction. In this paper, we propose a novel method for automatically correct image tilt and optimize image visual balance. In our method, the perspective transformation based on Cartesian coordinates is innovatively used to improve the visual balance of the flat image. Meanwhile, in feature extraction, learning-based algorithm is employed as the backbone. In order to further boost the robustness of our method, a novel line segment clustering detection algorithm (LSCD) is proposed to detect line features. Then, the line features are used to calculate the tilt compensation angle. The LSCD algorithm can effectively make up for the defects of the traditional Hough transform line detection algorithm. We perform multiple experiments using images to qualitatively and quantitative verify the scientificity and validity of the proposed method. Experimental results demonstrate that the optimization performance of our method is significantly better than the state-of-the-art straight line-based and affine transformation-based correction algorithm.
ARMLAAPPS: Augmented Reality Application in Microeconomics

Augmented Reality (AR) is a technology that combines both virtual and real environments simultaneously. It is designed to let users experience activities in a way close to real-world experience within a safe environment. It can also supplement the difficult information presented in a conventional approach to enhance users' understanding of the complex concept. It has been used widely in various fields due to the effectiveness of this technology. However, the application of AR is scarcely explored, especially for the Microeconomics field. Therefore, this study aims to develop and investigate augmented reality application development to enhance students' visualization skills in Microeconomic courses. Through this study, a mobile augmented reality application called Augmented Reality Mobile Learning Apps (ARMLAAPPS) was developed to give an interactive learning experience that can enhance students' visualization skills and help students learn more efficiently and in a more flexible way. After the development process, the evaluation of ARMLAAPPS is conducted to identify the effectiveness of the ARMLAAPPS in enhancing visualization skills among Microeconomics students in higher institutions. Based on the results, this study has successfully developed the ARMLAAPPS, and it has proven to be effective in improving students' visualization when learning Microeconomics.
Towards a Semantic-enabled Agent-oriented Distributed Clinical Decision Model

In a distributed Clinical Decision Support environment, a decision model is usually defined and coupled with a group of decision rules that together drive the decision support process. These can then be applied to patient-specific data to provide customised clinical advices. Being useful and able to deliver improved clinical service, such systems are often closed and thus lead to the difficulties in keeping aligned with up-to-date best evidences, interoperating with local Electronic Health Records (EHRs) systems, and advising in multi-disciplinary decisions whereas disparate participants work towards the same decision goals. In this paper, a semantic-enabled agent-oriented distributed clinical decision model is proposed. Group decisions are modelled as goal trees, which can be managed altogether and later chosen, decomposed and distributed to appropriate decision participants. Individual decisions are modelled in the form of RDF-based argumentation rules, which can be universally interpreted by a Rule Engine (RE) against the local EHRs. An Interactive Decision Interface Engine (IDIE) is developed to guide local decisions step by step and a Choreographer Agent (CA) synthetizes decisions among a collaborative group in a distributed environment. Ultimately, specialists can easily maintain decision knowledge in open and shared triple stores, and decision makers can better understand and investigate decision recommendation rationale using knowledge graphs that are intimately integrated within their decision-making environment. We demonstrate the idea with a case study of triple assessment of breast cancer.
A Modified Frame Synchronization Algorithm Based on Average Likelihood Ratio Testing

A modified frame synchronization algorithm based on average likelihood ratio testing is proposed in this paper through data-aided recognition of the Unique Word (UW) preamble in the transmission flow. To improve the computational efficiency, several approximations are proposed and discussed in the paper, to provide the designer with practical solutions that are still able to outperform other approaches in specific application scenarios. Simulations have been done to verify the performance and improvements. In practice, the optimum algorithm should be chosen according to the results.
Research On A Fast Location Algorithm For Human Eye Center Used In Automatic Optometry Unit

Fast, accurate, and locating the pupil center on low-cost hardware is the goal followed by developing the automatic optometry unit. In this paper, we propose a double-filtered pupil center location algorithm tackle with the goal followed. First, the collected image is processed into a binary image. Second, we double-filter all connected components in the binarized image by the pixel number and relative position of the target connected component. Third, we use the spatial moment algorithm to locate the center of the target connected components. Finally, the center point of the target connected components are fitted into an ellipse by using a least-squares ellipse-fitting algorithm, and the center of the ellipse is the center of the pupil. This paper uses simulated eyes to experiment with the designed algorithm on Raspberry Pi 4B. The experimental results show that: Compared with the traditional location algorithm in the same experimental environment, the designed algorithm improves the running speed and anti-interference ability while ensuring the accuracy of the center locating. Therefore, the designed algorithm can fully meet to carry out on low-cost hardware real-time identification and location.