Reviewerly/exHarmony huggingface dataset:
GPU Scheduling for Short Tasks in Private Cloud

GPUs are usually very expensive and not easily affordable by individuals. Therefore, GPU sharing is necessary to lower cost and avoid GPU idling among a group of users. Unlike jobs in production environments, which often last for days or weeks, the running time of programs in development and testing environments tend to be much shorter. Assigning a separate GPU to a person for development always leads to idling of the GPU. Therefore, for economic reasons, researchers usually share a small number of GPUs for development, especially in some small teams or labs. Users hope to automatically lease and release GPUs and get job responses as soon as possible. Current GPU sharing approaches either do not have good support for multiple users, or not designed to work effectively for such cases. This paper proposes a GPU-sharing method among multiple users for short GPU tasks. We implement a container-based batch computing system, which accepts and executes users' jobs through container images and specified configurations. A shortest-job-first based scheduling policy is used to ensure the priority of the short tasks and to prevent long tasks from starving. Evaluation demonstrate that our proposed method is effective and the system has a low overhead.
Paradigm of Inheritance in Large-Scale Systems

Based on Standard ISO 14813-1:2007 Intelligent Transport Systems, it can be concluded that: the purpose of this standard is to identify service groups and ITS domains within which these service groups are positioned. Service domains and ITS groups in the form in which they form taxonomic systems provide for the addition of other systems on a global scale , those ITS forms a large-scale network and … reflect the evolution of new technology-oriented developments and practical activities in the field of transport .. which implies heredity. The inheritance paradigm is based on the distributed axioms of the unity of the biosphere: fuzziness - clarity (1D and 2D) of continuous nature and is built on three clusters of inseparability. Two incompatible universal laws of nature are valid -Transformation and Conservation, for the closeness of the basis the third dogma is inevitable - Inheritance 3D.
Spike Coding: Towards Lossy Compression for Dynamic Vision Sensor

Dynamic vision sensor (DVS) as a bio-inspired camera, has shown great advantages in high dynamic range (HDR) and high temporal resolution (us) in vision tasks. However, how to lossy compress asynchronous spikes for meeting the demand of large-scale transmission and storage meanwhile maintaining the analysis performance still remains open. Towards this end, this paper proposes a lossy spike coding framework for DVS.
Evaluation of Prediction of Quality Metrics for IR Images for UAV Applications

This study presents a framework to predict, in a No Reference (NR) manner, Full Reference (FR) objective quality metrics. The methods are applied to infrared (IR) images acquired by Unmanned Aerial Vehicle (UAV) and compressed on-board and then streamed to a ground computer. The proposed method computes two kinds of features, namely Bitstream Based (BB) features which are estimated from the H.264 bitstream and Pixel Based (PB) features which are estimated from the decoded images. Two BB features are computed using the H.264 Quantization Parameter (QP) and estimated PSNR [1]. A total of 53 PB features are calculated based on spatial information and the rest of the features are based on NR quality assessment methods [1, 2, 3]. The most relevant ones are selected and nally mapped to predict FR objective scores using Support Vector Regression. For the performance evaluation, the proposed method is trained to predict scores of 6 FR image quality metrics (SSIM, NQM, MSSIM, FSIM, MAD and PSNR-HMA) using a set of 250 IR aerial images compressed at 4 levels with H.264/AVC as I-frames. For the SVR mapping, 80% of the contents are used for training (200 contents or 800 images) and the remaining 200 images (20%) for testing. We have evaluated our model for three cases; all features, only BB features and finally excluding BB features. The average SROCC values obtained are 0.970, 0.962 and 0.943, respectively. The BB only version achieves very close results to that of using all features. Thus the presented NR BB Image Quality Assessment (IQA) method for the considered IR image material is very ecient. We have compared our method with three NR methods [1, 2, 3]. The proposed method is competitive compared to the state-of-the-art NR algorithms.
Effect of adsorption parameter on the removal of aspirin using tyre waste adsorbent

In recent years, pharmaceutical compound has been detected in small concentration in our surface and ground water. This detection raises a lot of concern as is it reported that pharmaceutical compound can bring adverse effect to the environment even at low concentration. Besides that, there is a growing fear that this compound will eventually end up in human drinking water, thus effecting human health. This prompt a lot of research on the removal method for this particular compound. Adsorption is seen as the most viable option because of its high efficiency, low cost and it is environmentally friendly. In this study, the adsorbent used is carbon black derived from tyre waste via pyrolysis at 800 oC. The carbon black was treated with 6 M of nitric acid (HNO3) at 90 °C for 0.5 h before being subjected to thermal treatment at 600 °C for 1 h. The response of the adsorption study is the removal of aspirin. There were five adsorption parameters that were varied in this study which are the contact time (until equilibrium), initial pH of aspirin solution (pH 3, pH 7, pH 11), temperature (30, 50, 70 °C), initial concentration (10-100 mg/L) and adsorbent dosage (0.1 g, 0.5 g, 1.0 g). The best removal capacity obtained was 40.40 mg/g of aspirin at pH 3, temperature of 30 °C, 100 mg/L initial concentration and 0.02 g adsorbent dosage.
Clustering Time-Series Data Generated by Smart Devices for Human Activity Recognition

Human activity recognition (HAR) from the time-series data generated by smarlt devices like smartphones and smartwathces is absolutely necessary for future intelligent health-care systems. A number of machine learning approaches have been devised for human activity recognition from such devices. However, most of the existing approaches have considered activity recognition as a supervised learning problem, which requires a training dataset for learning the activities. With ever-growing computing power of smart devices, the amount of data generated by these devises is also increasing in manifold. Thus, it becomes a tedious task to annotate the data collected from the smart devices. Being an unsupervised learning approach, cluster analysis provides meaningful insights on hidden patterns in the huge volume of data without training examples. Hence cluster analysis can be used as a preprocessing step in human activity recognition when there is no sufficient information about the number of activities. This paper presents a comparative study of three clustering algorithms such as K-means, hierarchical agglomerative clustering and Fuzzy C-means for human activity recognition problem. A method to automatically determine the number of activities is also demonstrated in this paper. Experimental results on two UCI activity recognition datasets show that FCM algorithm effectively categorizes the activities.
A CU Split Early Termination Algorithm Based KNN for 360-Degree Video

360-degree video has been developed rapidly and widely used in recent years.360Lib is a test platform (software) for JVET panoramic video, hosted by Subversion code. The 360Lib in this article is integrated into the HM.At this stage, coding 360 degrees video needs to convert video according to ERP, EAP, CMP and other formats, then encode with HEVC, and then reverse transform to 360 degree video.Since 360lib just transforms the projection format, it still uses HEVC to code, and does not use the 360-degree video's characteristics. Based on the characteristics of 360lib, this paper proposes a A CU Split Early Termination Algorithm. Experimental results show that the proposed fast algorithm provides an average time reduction rate of 32% compared to the reference HM-16.16+360lib4.0, with only 1.3% BD-rate increase.
An Adaptive Image Registration Technique to Remove Atmospheric Turbulence

Turbulence/Heat Scintillations is the change that arises in the refraction index of air with the temperature. The distortion created by the atmospheric turbulence is proportional to the distance between object and camera. In last few years, several approaches have been proposed to estimate and eliminate geometric distortion and blur. In this paper, a novel technique is proposed that improves the visual quality of video sequences affected by atmospheric turbulence. The proposed method is based on adaptive control grid interpolation (CGI). CGI approximates accurate motion vectors to generate a geometrically correct frame using certain reference frames. For high scintillation sequences, CGI doesn’t mitigate scintillations completely. The new methodology is proposed with updated trajectory estimation. The proposed method can effectively reduce the influence even for high atmospheric turbulence. Experimental results also prove that proposed approach is time efficient compared to traditional CGI.
FEATURES OF COLLABORATION OF THE IT INDUSTRY AND THE EDUCATION SYSTEM IN THE DIGITAL ECONOMY AGE

The article focuses on the current issues involved in training specialists for the digital transformation of the domestic economy via 1C technologies. The work covers the issues of vocational guidance for talented young people in the sphere of IT, including teaching schoolchildren and providing occupational integration. The authors analyse areas of cooperation between the education system, government and business, as well as the development of educational 1C technologies. The focus is on the WorldSkills best practices in the training and assessment of students as well as young professionals working in the field of developing business IT solutions. The issues of automating the management of an educational institution are considered.
Analysis of Regular Sampling of Chaotic Waveform and Chaotic Sampling of Regular Waveform for Random Number Generation

This paper presents an analysis of random number generators based on continuous-time chaotic oscillators. Two different methods for random number generation have been studied: 1) Regular sampling of a chaotic waveform, and 2) Chaotic sampling of a regular waveform. Kernel density estimation is used to analytically describe the distribution of chaotic state variables and the probability density function corresponding to the output bit stream. Random bit sequences are generated using analytical equations and results from numerical simulations. Applying the concepts of autocorrelation and approximate entropy, randomness quality of the generated bit sequences are assessed to analyze relationships between the frequencies of the regular and chaotic waveforms used in both random number generation methods. It is demonstrated that in both methods, there exists certain ratios between the frequencies of regular and chaotic signal at which the randomness of the output bit stream changes abruptly. Furthermore, both random number generation methods have been compared against their immunity to interference from external signals. Analysis shows that chaotic sampling of regular waveform method provides more robustness against interference compared to regular sampling of chaotic waveform method.
Requirements Engineering for Global Systems: Cultural, Regulatory and Technical Aspects

In this paper we present a formal framework for analysis and optimisation of the requirements specifications of systems developed to apply in several countries. As different countries typically have different regulations/laws as well as different cultural restrictions, the corresponding specific requirements might differ in each particular case. Our framework provides a basis for (1) systematic and formal analysis of the diversity and interdependencies within the sets of the requirements, to avoid non-compliance, contradictions and redundancies; (2) corresponding systematic process for change management in the case of global system development.
Factoring on a quantum annealing computer

This paper is about quantum factoring all integers in an interval. Our goal is to be able to factor all positive integers N &lt; 1,000. We reached this goal on a D-Wave 2000 qubit processor by minimizing (N - (pxq))^2 by using one routine that calls the interface software dw. We sized p&lt;32 and q&lt;256 as Boolean variables. We factored seven semiprimes that could not be factored by the method in a 2016 D-Wave report. We did not use information about the factors for a specific N. The inputs to our routine are N and values for two parameters.
Closed-Eye Gaze Gestures: Detection and Recognition of Closed-Eye Movements with Cameras in Smart Glasses

Gaze gestures bear potential for user input with mobile devices, especially smart glasses, due to being always available and hands-free. So far, gaze gesture recognition approaches have utilized open-eye movements only and disregarded closed-eye movements. This paper is a first investigation of the feasibility of detecting and recognizing closed-eye gaze gestures from close-up optical sources, e.g. eye-facing cameras embedded in smart glasses. We propose four different closed-eye gaze gesture protocols, which extend the alphabet of existing open-eye gaze gesture approaches. We further propose a methodology for detecting and extracting the corresponding closed-eye movements with full optical flow, time series processing, and machine learning. In the evaluation of the four protocols we find closed-eye gaze gestures to be detected 82.8%–91.6% of the time, and extracted gestures to be recognized correctly with an accuracy of 92.9%–99.2%.
Variational Spectral Graph Convolutional Networks.

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly. To address the problem of propagating gradients through latent variables drawn from discrete distributions, we use their continuous relaxations known as Concrete distributions. We show that, on real datasets, our approach can outperform state-of-the-art Bayesian and non-Bayesian graph neural network algorithms on the task of semi-supervised classification in the absence of graph data and when the network structure is subjected to adversarial perturbations.
Correction of Spaces in Persian Sentences for Tokenization

The exponential growth of the Internet and its users and the emergence of Web 2.0 have caused a large volume of textual data to be created. Automatic analysis of such data can be used in making decisions. As online text is created by different producers with different styles of writing, pre-processing is a necessity prior to any processes related to natural language tasks. An essential part of textual preprocessing prior to the recognition of the word vocabulary is normalization, which includes the correction of spaces that particularly in the Persian language this includes both full-spaces between words and half-spaces. Through the review of user comments within social media services, it can be seen that in many cases users do not adhere to grammatical rules of inserting both forms of spaces, which increases the complexity of the identification of words and henceforth, reducing the accuracy of further processing on the text. In this study, current issues in the normalization and tokenization of preprocessing tools within the Persian language and essentially identifying and correcting the separation of words are and the correction of spaces are proposed. The results obtained and compared to leading preprocessing tools highlight the significance of the proposed methodology.
Reducing the network bandwidth requirements for 360<sup>∘</sup> immersive video streaming

Virtual Reality/Augmented Reality applications are getting increasingly popular and new services are emerging in a diverse set of fields such as entertainment, art, e‐health, and smart factories. 360° videos are an advancement of classical two‐dimensional videos giving the user the impression of being immersed at the center of a scene that can be explored freely and dynamically by simply turning the head to a given region of interest (RoI). Providing a high Quality of Experience to users when streaming 360° videos over the Internet is particularly challenging due to the very high bandwidth requirements. In this letter, a scaling technique to reduce bandwidth requirements to stream omni‐directional videos is presented. An experimental investigation of the proposed approach has shown that it is possible to obtain a reduction of the required bitrate up to around 50% while gracefully degrading visual quality far from the RoI.
WNet: Joint Multiple Head Detection and Head Pose Estimation from a Spectator Crowd Image

Crowd image analysis has various application areas such as surveillance, crowd management and augmented reality. Existing techniques can detect multiple faces in a single crowd image, but small head/face size and additional non facial regions in the head bounding box makes the head detection (HD) challenging. Additionally, in existing head pose estimations (HPE) of multiple heads in an image, individual cropped head image is passed through a network one by one, instead of estimating poses of multiple heads at the same time. The proposed WNet, performs both HD and HPE jointly on multiple heads in a single crowd image, in a single pass. Experiments are demonstrated on the spectator crowd S-HOCK dataset and results are compared with the HPE benchmarks. WNet proposes to use lesser number of training images compared to number of cropped images used by benchmarks, and does not utilize transferred weights from other networks. WNet not just performs HPE, but joint HD and HPE efficiently i.e. accuracy for more number of heads while depending on lesser number of testing images, compared to the benchmarks.
Total Directional Variation for Video Denoising

In this paper we propose a variational approach for video denoising, based on a total directional variation (TDV) regulariser proposed in [20, 21] for image denoising and interpolation. In the TDV regulariser, the underlying image structure is encoded by means of weighted derivatives so as to enhance the anisotropic structures in images, e.g. stripes or curves with a dominant local directionality. For the extension of TDV to video denoising, the space-time structure is captured by the volumetric structure tensor guiding the smoothing process. We discuss this and present our whole video denoising workflow. The numerical results are compared with some state-of-the-art video denoising methods.
Lessons Learned from an Organizational Information Security Awareness Campaign

Educating end-users to improve information security awareness plays an important part in securing organizational environments. While best practice standards provide a set of minimum information security awareness controls that should be implemented, little guidance is given on how to implement these controls to ensure the effectiveness of training. This research defined and evaluated a method for implementing an information security awareness campaign (ISAC) within an organization. The method is based on prior research and standards, while assisting the subject in improving their ISAC through the creation of artefacts and measurement techniques. A design science research approach was used with several research cycles to design the method. The method was implemented within an organization and evaluated based on the impact, effectiveness and results of each step, as well as the feedback from participants (two questionnaires were completed by 47 and 36 employees respectively). The research found both positive and negative results. Certain steps within the method proved time consuming and confusing to some participants. Although improvements can be made, the method was found to be adequate as it achieved the required objective within the organization and provided the organization with a risk-based method and visual representation to measure awareness on specific information security awareness topics. The results of the study not only provided value to the organization but provides a validated method for implementing an ISAC which could be applied in other contexts.
Optical inline inspection detecting 3D defects on complex free-form surfaces in harsh production environments

Abstract In today’s industrial manufacturing, quality monitoring is one of the key elements to ensure continuous improvement and to trigger innovations. Especially inline inspection presents considerable challenges to the underlying measurement system. Besides the – in most cases – already not easily satisfiable demands concerning resolution, measurement uncertainty, and cycle time, the robustness of the chosen solution within the overall production environment is an essential requirement for a successful operation in the field. Furthermore, a sufficient ease of operation for the production staff plays a crucial role for the acceptance of the measurement technology. In sheet metal forming, for example, vibrations, ambient light variations and short cycle times as well as oiling and surface variations of the specimen under test challenge and simultaneously limit currently available measurement systems in their application. Especially in automotive manufacturing, visionary design concepts increase the complexity of components with respect to shape and forming capability, leading to critical areas on the object prone to constrictions and cracks. Varying geometry and surface defect characteristics call for an automated 3D inspection to maintain constant product quality for each single part. In this paper, an applied method for the inline inspection of free-form metal surfaces is presented, which allows for quality monitoring even in a harsh environment. So-called inverse fringe projection in combination with single-sideband demodulation facilitates the inspection of free-form metal surfaces in 3D, based on a single camera image (‘single-shot’), which allows for measurement even in a vibrating environment. Only one single camera and a projector are needed, simplifying the integration into series production considerably. Calibration-free, fast setup possibilities enable flexible handling and maintenance in a short-cycled production. First inline tests in a press shop at BMW proof the capability of this method. As an example, constrictions with a depth of about 80 microns could be detected automatically during series production within the press.