Reviewerly/exHarmony huggingface dataset:
Score-rating system influence on the formation of general professional competence of students in technical universities

The Ministry of Education and Science of the Russian Federation has established a score-rating system to assess the achievement quality within academic disciplines. The word "rating" is a foreign-language term, literal translation into Russian means "assessment". The second meaning of the word "rating" is a numerical measure; it characterizes performance of a student, pupil, etc., during a certain period of training, usually by the 20-point scale, or 100-point scale. This article highlights the history of formation and implementation of a score-rating system into the curriculum. Due to development of advanced technologies, there is a need for training highly qualified personnel to solve increasingly complex problems in professional activities. In our opinion, pedagogical assessment of student's knowledge in the form of score-rating system is a key role for preparing highly qualified personnel. The authors study processes of training skilled professionals in the course of academic education in technical universities of our country. Key words : score-rating system (SRS), competence, student, progress, credit, rating, factor.
Personalized Video Summarization with Idiom Adaptation

Short videos are becoming key for media consumers exploring the TV and Internet. The production of short videos however remains costly. In this paper, we present a domain specific video summarization application with idiom adaptation that leverages multimedia content analysis and insights from cinematic and persuasive domains. From the back-end, content curators can push raw materials and the pre-processing algorithms will automatically extract the features and encode them as editing idioms. Users can create personalized video summaries based on these idioms. We have validated the effectiveness of the demonstration on a TVC data-set with over 600 videos, enabling production of domain specific video summaries with combinations of editing idioms. This approach has been put into trial in the testbed at Alibaba Wood.
A New Optimal Algorithm on Information Diffusion Maximization Problem

With the rapid development of Internet technology, the social network has become more and more complicated and diversified. Excavating the most influential users from social networks become more difficult and important. The information diffusion maximization algorithm can effectively solve the problem. First, in this paper we proposed a new optimal algorithm (NOA) on the information diffusion maximization problem by synthetically considering the theme feature and degree centrality. Second, we presented a new way of representing the user influence based on the user preference. In addition, in order to compare with other methods more extensively and deeply and verify the effectiveness and superiority of the algorithm, the traditional information diffusion models are extended to the topic-based linear threshold model (TLTM) and the topic-based independent cascade model (TICM) which can describe the information diffusion mechanism more accurately. Experimental results show that NOA algorithm proposed in this paper is 5000 times faster than the climbing greedy algorithm in the worst case and also faster than the CELF(Cost Effective Lazy Forward) algorithm. If the nodes selected through NOA algorithm are used as the initial spreading set, the results have a significant improvement than the climbing greedy algorithm in the diffusion range and the actual influence. And the results also show that the new algorithm is really efficient, accurate and stable.
Quantum Identity Authentication Protocol Based on Three-Photon Quantum Error Avoidance Code

Quantum identity authentication is one of important techniques in quantum secure communications. So far, most of the quantum identity authentication protocols proposed are based on noise-free environments. However, due to the existence of quantum noise in quantum channels is inevitable, the noise immunity is very important to quantum identity authentication. In this paper, a novel quantum identity authentication protocol based on three-photon error avoidance code is proposed. In this protocol, quantum information is encoded on the noiseless subsystem, so the protocol can effectively resist on the interference of noise in the quantum channel to information transmission. And the comprehensive analysis of antinoise performance and the security analysis on various eavesdropping attacks shows that the new protocol proposed in this paper has not only good anti-noise performance, but also good security.
A Survey on Theft Surveillance and Detection Technologies

Nowadays, theft has become a major issue to be dealt with. It is not possible for a human to monitor for several hours continuously. To overcome the complication in thefts, surveillance cameras are used. The main ability of surveillance camera is to just record the act of theft from which only the knowledge of the intruders will be gained. The information about the theft will be received only after the theft had occurred. It fails to detect the intruders and stop the crime earlier. In order to overcome this, a surveillance monitoring system which will be capable of monitoring and detecting these sorts of crime acts are implemented. In this article, the various technologies and the components used in surveillance monitoring system, their pros and cons are discussed. The various features to be considered while evaluating a surveillance system are explored. The overall analysis of the efficiency of currently existing systems is also briefed.
Provable one-XOR matrices for construction of 4 × 4 hardware-oriented MDS diffusion layers

MDS diffusion layers are critical in the design of modern symmetric ciphers. Lightweight MDS matrices are studied for designing ciphers targeting hardware-oriented applications. In 2016, Beierle, Kranz, and Leander studied elements for which x → ax could be implemented with only one XOR in hardware. Then, they searched for such a’s satisfying the desired conditions for MDS-ness of some constructions. We adopt an opposite approach: we fix the conditions that A, A 3⊕ I and A 7 ⊕ I are n × n invertible binary matrices and then give provable binary n × n one-XOR matrices for which the mentioned conditions hold. Note that these conditions are sufficient ones for MDS-ness of many well-known constructions of 4 × 4 MDS matrices over n-bit words such as some recursive MDS diffusion layers as well as circulant AES-like MDS matrices.
A High Reliability 3D Object Tracking Method for Robot Teaching Application

Abstract 3D Object tracking is the task of capturing the 3D position and pose of an object from each time-series image frame. As we known, 3D sensing technique can be realized by stereo vision, structured light, and ToF (time-of-flight) camera. All of them can capture the point cloud data for describing the depth information in a workspace. In past research, the reliability in 3D object tracking was a big problem for real industrial application, therefore, we address a different way to enhance the tracking accuracy and stabilize the tracing path for raising the reliability. In order to build a 3D tracking model and the workspace environment, we adopted an RGB-D camera which is the Intel® RealSense™ D400 Series depth modules to collect the cloud point data and RGB values. The built 3D tracking model should contains the information which includes points, normal and texture for producing many 2D object images with different perspectives. Then the produced images were fed to a SSD (single-shot detector) neural network to learn the object’s features for 2D tracking. In dynamic tracking process, the image frames were through the sematic image segmentation by DeepLabV3+ for only extracting the object information without hands and background. Thus, the reserved data only included object’s cloud point data and texture information in workspace. Then we use the iterative closest point (ICP) algorithm and the RGB intensity correlation method to confirm the object’s position and posture in workspace. The result shows that our method has a better performance than SSD method for tracking a self-predefined object.
ΔNN: Power-efficient Neural Network Acceleration using Differential Weights

The enormous and ever-increasing complexity of state-of-the-art neural networks has impeded the deployment of deep learning on resource-limited embedded and mobile devices. To reduce the complexity of neural networks, this article presents $\Delta$ΔNN, a power-efficient architecture that leverages a combination of the approximate value locality of neuron weights and algorithmic structure of neural networks. $\Delta$ΔNN keeps each weight as its difference ($\Delta$Δ) to the nearest smaller weight: each weight reuses the calculations of the smaller weight, followed by a calculation on the $\Delta$Δ value to make up the difference. We also round up/down the $\Delta$Δ to the closest power of two numbers to further reduce complexity. The experimental results show that $\Delta$ΔNN boosts the average performance by 14%–37% and reduces the average power consumption by 17%–49% over some state-of-the-art neural network designs.
Content-Based Video Relevance Prediction with Multi-view Multi-level Deep Interest Network

This paper presents our solution for the Hulu Content-Based Video Relevance Prediction (CBVRP) challenge, which focuses on cold-start videos as candidates. The keys to success of this prediction scenario are to learn effective user and video representations. To this end, we develop a multi-view multi-level deep interest network (MMDIN), which involves a multi-level deep interest network to learn user and video representations in a single-view, and a late fusion technique to integrate their multi-view representations corresponding to different types of video features. Through the above manner, the cold-start video prediction could be handled well with representations through their past interaction behaviors with videos and video representations based on their multiple types of content profiles.
Atlanta Field: Operational Safety and Integrity Management

Abstract The publication of ANP Resolution 41/2015, which established the Operational Safety Regulation for Subsea System – SGSS and ANP Resolution 46/2016, that established the Technical Regulation of the Well Integrity Management System - SGIP, defined that Operator Companies operating in Brazil must have integrity management programs for their assets, specifically related to wells and subsea system equipment. The Regulations are non-prescriptive, giving the Operators freedom to establish their own integrity management philosophy and methodology. On the other hand, a Company can choose how to set up its integrity management program. Furthermore, many technical decisions must be made to comply with the regulations. In a scenario where operational safety and integrity management can both further develop, implementing a system that manage and monitor all datas related to these issues is a big challenge. This paper shows how a Brazilian independent oil and gas Operator implemented these regulations and which technical solutions were considered in order to manage the integrity of its assets and maintain a high level of operational safety in its operations.
Efficient Hybrid Machine Learning Algorithm for Trajectory Planning in Critical Traffic-Scenarios

Recently proposed algorithms , namely the Hybrid Augmented CL-RRT, the Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. In order to realize these algorithms in a vehicle, an efficient embedded-implementation of the algorithms in an automotive micro-controller is required as the on-board micro-controller resources are limited. This paper proposes methodologies for replacing the computationally intensive modules of trajectory planning algorithms such as checking for collisions with traffic participants predictions using machine learning algorithms and analytical methods for reducing the required static RAM memory. After optimising the algorithms, the results are generated by downloading and running the algorithms on various hardware platforms: automotive micro-controller, a rapid prototyping hardware and raspberry pi. The results show that significant reduction in computational resources and potential of proposed algorithms in real time.
Interactive Modeling Environment Based on the System Entity Structure and Model Base

The modeling and simulation tool must be intuitive so that the users can easily model the problem and simulate the simulation model to develop a solution for engineers who have a different level of knowledge. The person who wants to solve the problem must have prior knowledge to model, simulate, and execute the simulation model. Unfortunately, most modeling and simulation tools require such prior knowledge to the users. This research utilizes the chatbot services to help the simulationists who does not have enough modeling and simulation knowledge. By utilizing the chatbot services, a simulationist may compose a simulator and simulate the system anytime, anywhere.
Interacting with Overlaid Information in Augmented Reality Systems for Maintenance: A Preliminary Review

In this paper, we present a preliminary study over how augmented reality systems for industrial maintenance present and let users interact with virtual information. Ten papers are analyzed through three comparison tables presenting the type of information displayed, how the information is displayed with respect to the real world and the interaction techniques available to users.
An Eye for an Eye

Currently, miners typically join mining pools to solve cryptographic puzzles together, and mining pools are in high competition. This has led to the development of several attack strategies such as block withholding (BWH) and fork after withholding (FAW) attacks that can weaken the health of PoW systems and but maximize mining pools' profits. In this paper, we present strategies called Adaptive Retaliation Strategies (ARS) to mitigate not only BWH attacks but also FAW attacks. In ARS, each pool cooperates with other pools in the normal situation, and adaptively executes either FAW or BWH attacks for the purpose of retaliation only when attacked. In addition, in order for rational pools to adopt ARS, ARS should strike to an adaptive balance between retaliation and selfishness because the pools consider their payoff even when they retaliate. We theoretically and numerically show that ARS would not only lead to the induction of a no-attack state among mining pools, but also achieve the adaptive balance between retaliation and selfishness.
Power System Text Information Matehing Researeh Based on Deep Learning

An information matching technique based on deep learning is proposed: The word sequence of the text is obtained by Chinese text segmentation model using word formation. Then, keyword extraction model is employed to classify large-scale textual information (the texts with the same keyword are treated as in the same group). Finally, the matching degree is given by the two-tier deep learning model, in which the fìrst tier is used to judge whether the texts belong to the same group, and the second tier to calculate the matching degree. The examples indicate that the technique can provide effective results in dispatching/control information matching and operating instruction matching.
Solar Energy and Lighting in the Republic of Srpska

In this chapter, information about geographical position, climate, solar radiationRadiation , and renewable energyRenewable energy Energy policy in the Republic of SrpskaRepublic of Srpska are given. Also, information about Banja Luka, Academy of Sciences and Arts, solar energySolar energy laboratory at the Academy of Sciences and Arts and lightingLighting in the Republic of SrpskaRepublic of Srpska are given.
A Computer Mouse Using Blowing Sensors Intended for People with Disabilities

The computer is an important medium that allows people to connect to the internet. However, people with disabilities are unable to use a computer mouse and thus cannot enjoy internet benefits. Nowadays, there are various types of assistive technologies for controlling a computer mouse, but they all have some operational inconveniences. In this paper, we propose an innovative blowing-controlled mouse assistive tool to replace the conventional hand-controlled mouse. Its main contribution is that it uses microphones to induce small signals through the principle of airflow vibration, and it then converts the received signal into the corresponding pulse width. The co-design of software programming enables various mouse functions to be implemented by the identification of the blowing pulse width of multiple microphones. The proposed tool is evaluated experimentally, and the experimental results show that the average identification rate of the proposed mouse is over 85%. Additionally, compared with the other mouse assistive tools, the proposed mouse has the benefits of low cost and humanized operation. Therefore, the proposed blowing control method can not only improve the life quality of people with disabilities but also overcome the disadvantages of existing assistive tools.
Visually Grounded Language Learning for Robot Navigation

We present an end-to-end deep learning model for robot navigation from raw visual pixel input and natural text instructions. The proposed model is an LSTM-based sequence-to-sequence neural network architecture with attention, which is trained on instruction-perception data samples collected in a synthetic environment. We conduct experiments on the SAIL dataset which we reconstruct in 3D so as to generate the 2D images associated with the data. Our experiments show that the performance of our model is on a par with state-of-the-art, despite the fact that it learns navigational language with end-to-end training from raw visual data.
Comparison of Approaches for Querying Chemical Compounds

Chemical compounds form a database with specific features that can be utilized for more efficient query processing. Currently, there exists no comparison of performance and memory usage of the respective and most efficient approaches on the same data set. In this paper, we address this lack of information and we create an unbiased benchmark of the most popular index building methods for subgraph querying of chemical databases. In addition, we compare the results with the performance of an SQL and a graph database for which there exist various unconfirmed hypotheses on their efficiency.
QoS Prediction for Web Services Using User Based Classification

Abstract With the development of technology, the web services increase dynamically with using of the internet. In order to avoid the time consuming and the expensive selection for selecting the best web service, it is becoming more important to design effective approaches for the selection and recommendation of the internet web services. This paper proposed a new collaborative for web service QoS prediction by using user-based classification technique, specifically the voting method. The experimental result shows the effectiveness and efficient QoS prediction of our approach. For comparison purpose, we employ real-world web service QoS datasets (namely DS-DREAM datasets). In sum, the experimental results compete with the recent related works.
ACTwithpain - Building an online platform for helping Chronic Pain Patients

The main objective of this multidisciplinary project is to think up and build a digital application that joins health to engineering and design. iACTWITHPAIN is a web-platform and mobile application which aims to help chronic pain patients, by providing tools to improve their life quality. The platform will make it available a set of different digital tools and exercise plans inspired on pain self-management strategies. As iACTWITHPAIN is targeted to a specific range of people which are commonly anxious and more vulnerable to stress, the application should be dynamic and have particular attention to User Experience details. Consequently will require a whole array of specific functionalities as a chronic patient needing as an end-user. For this reason, the aim of this article is to disclose the research concept as the core focus to provide a personalized user experience design in an intuitive and user-friendly environment. The platform and this research project are being developed in ISR - Institute of Systems and Robotics of Coimbra and CINEICC - Centre for Research in Neuropsychology and Cognitive Behavioral Intervention of Coimbra.
Evaluation of videos of mathematical concepts made by students / Evaluación de vídeos de conceptos matemáticos elaborados por los estudiantes

This study presents a teaching experience in which students use ICT to develop video tutorials of the mathematical concepts from the Mathematics for Business I course in the first year of the Bachelor’s in Business Administration. This is an active methodology which not only covers the class contents but is also sure to motivate the students. To evaluate the suitability and mathematical validity of each video, the students themselves filled out a survey which was designed based on a video evaluation model which enables the videos to be ranked according to six didactic facets. We applied the TOPSIS discreet multi-criterion method to these results in order to get the final mark of the videos.
IFOC: Intensity Fitting on Overlapping Cover for Image Segmentation

Region-based image segmentation methods often require a global statistical description of the image intensities in the entire foreground and background, which however is in general not available for real world images due to the complicated intensity variation within the regions of interest. In this paper, we propose a foreground-background segmentation algorithm for images with intensity inhomogeneity in a level set framework, which exploits simple distribution of local image intensities. We assume that the intensities of the foreground and background within a small enough neighborhood are separable and can be well approximated by two constants. We call such a neighborhood an intensity separable neighborhood (ISN). Given a set of overlapping ISNs that form a cover of the entire image domain or a region of interest, we formulate image segmentation as a problem of seeking for an optimal level set function that represents the foreground and background with its positive and negative sign, and a pair of constants that approximate the local foreground and background intensities within the ISNs. This formulation is a significant extension and improvement of our previous work. The main contributions in this paper include: (1) We eliminate an intrinsic drawback in our previous work that the fitting functions are not well defined for points far away from the zero level set, which causes unstable performance of segmentation; (2) The new algorithm is more efficient than our previous algorithm due to the sparse placement of the ISNs.