Reviewerly/exHarmony huggingface dataset:
26.1: <i>Invited Paper:</i> Display Evaluation for Advanced Human‐Robot Interaction

Autonomous robots become increasingly widespread in public spaces. Human‐robot interactions (HRI) with pedestrians is essential by visualizing trajectory information. We evaluated various display technologies for bright indoor conditions such as animated projection and LED lighting as well as displays.
Gated Convolutional Neural Networks with Sentence-Related Selection for Distantly Supervised Relation Extraction

Relation extraction is one of the key basic tasks in natural language processing in which distant supervision is widely used for obtaining large-scale labeled data without expensive labor cost. However, the automatically generated data contains massive noise because of the wrong labeling problem in distant supervision. To address this problem, the existing research work mainly focuses on removing sentence-level noise with various sentence selection strategies, which however could be incompetent for disposing word-level noise. In this paper, we propose a novel neural framework considering both intra-sentence and inter-sentence relevance to deal with word-level and sentence-level noise from distant supervision, which is denoted as Sentence-Related Gated Piecewise Convolutional Neural Networks (SR-GPCNN). Specifically, 1) a gate mechanism with multi-head self-attention is adopted to reduce word-level noise inside sentences; 2) a soft-label strategy is utilized to alleviate wrong-labeling propagation problem; and 3) a sentence-related selection model is designed to filter sentence-level noise further. The extensive experimental results on NYT dataset demonstrate that our approach filters word-level and sentence-level noise effectively, thus significantly outperforms all the baseline models in terms of both AUC and top-n precision metrics.
Research on the Application of Big Data Analysis in English Language Education

With the development of big data technology, it has been widely concerned in the field of English language education and has become the main factor affecting English language education. The reason is that using big data can break the inherent equilibrium of traditional English language education ecosystem. Building an ecological environment for English language education with the help of big data technology can improve the quality of English teaching. This paper studies the relationship among various factors involved in English language education through big data mining technology, so as to build an educational ecosystem oriented to self-adaptive balance adjustment. According to the characteristics of English autonomous learning system in the era of big data, this paper analyzes the problems existing in the current English learning system (such as low level of online and offline collaboration, unsatisfactory combination of instrumental and humanistic effects, and low degree of integration of information technology and curriculum design), constructs English learning environment and language learning situation according to information technology, and puts forward a new English teaching mode combining online and offline, so as to enhance the strategy of guiding students’ English autonomous learning and construct a multi-evaluation system of English autonomous learning, so as to build an ecological English language education environment, realize the ecological balance of English language education, effectively improve the quality of English teaching, and provide new ideas for students’ English language education in the era of big data.
Hybrid Design of Forward and Reverse Converters: A New Approach to Reduce Hardware Complexity of Residue Number System

The Residue Number System (RNS) hardware structure consists of several components, including forward converter, separate arithmetic units for performing modular addition and multiplication, and reverse converter. Forward and reverse converters, essential in any RNS to interface with other digital circuits, represent overhead, resulting in larger chip-area and power-consumption. This work, for the first time, proposes a hybrid converter for RNS, which unifies forward and reverse converters by re-using hardware. To achieve this aim, the mixed-radix conversion (MRC) algorithm has been used for putting up the reverse conversion formulas in a similar format to forward conversion formulas. The VLSI implementation results of the proposed hybrid converter based on TSMC-65nm technology for the moduli set {2n−1, 22n, 2n+1−1} show a reduction up to 19% of the required area in comparison to the total area of the forward and reverse converters. However, the delay of the proposed hybrid converter is just 10% higher than individual reverse converter delay.
A Brief Review of Computer-aided Whole-slide Image Detection Techniques

With the development of artificial intelligence, whole-slide image technology is gradually applied to computer-aided diagnosis. To improve the accuracy and objectivity of histopathological image analysis, whole-slide image technology to detect histopathological images began to be used. In this paper, we present a brief review of detection methods in whole-slide image based on computer-aided diagnosis, and then we summarize, analyze these methods to find a suitable algorithm for detecting.
GPU-Accelerated Vertex Orbit Counting for 5-Vertex Subgraphs

In this paper, we propose a parallel 5-vertex orbit counting method using GPUs. Given a graph and a set of subgraph patterns, the vertex orbit counting problem is to output, for each vertex in the graph, the number of subgraph patterns that involve the vertex. It is useful to characterize the graph and has been used in many graph applications. However, existing methods for 5-vertex orbit counting require long time to complete in particular for large graphs. To this problem, we employ GPUs to accelerate it. Our method is based on one of the state-of-the-art algorithms, and we extend it to run in parallel using GPU's massive threads. To evaluate, we conduct experiments using real-world datasets, and the results show that the proposed method outperforms the existing methods.
The Vulnerability of the Blockchain Network From the Consensus Perspective

The blockchain is a registry shared among different participants intending to eliminate the need for a central authority to maintain information. The first proposal of this technology was to eliminate financial authorities in transactions of value. However, the application of the same technique for the transaction of information could facilitate trades and offer traceability and diamond tracking around the world. The consensus is at the core of the network because it orchestrates nodes to accept new information, but it operates over a data structure in an open network, consequently leading to many complex behaviours that introduce different vulnerabilities. This work aims to highlight the vulnerability within the blockchain network based on the different participant behaviours that dominate the shared registry. Moreover, different malicious behaviour can appear on the networking layer by taking advantage of the network topology.
Global Software Piracy, Technology and Property Rights Institutions

This study extends the literature on fighting software piracy by investigating how Intellectual Property Rights (IPRs) regimes interact with technology to mitigate software piracy when existing levels of piracy are considered. Two technology metrics (internet penetration rate and number of PC users) and six IPRs mechanisms (constitution, IPR law, main IP laws, WIPO Treaties, bilateral treaties and multilateral treaties) are used in the empirical analysis. The statistical evidence is based on: (i) a panel of 99 countries for the period 1994-2010 and (ii) interactive contemporary and non-contemporary Quantile regressions.The findings show that the relevance of IPR channels in the fight against software piracy is noticeably contingent on the existing levels of technology embodied in the pirated software. There is a twofold policy interest for involving modern estimation techniques such as interactive Quantile regressions. First, it uncovers that the impact of IPR systems on software piracy may differ depending on the nature of technologies used. Second, the success of initiatives to combat software piracy is contingent on existing levels of the piracy problem. Therefore, policies should be designed differently across nations with high-, intermediate- and low-levels of software piracy.
Optimized Adversarial Example Generating Algorithm Based on Probabilistic Graph

Deep learning technology is widely used in various fields. Once the deep model is attacked, it will cause huge economic losses and security problems. Therefore, the security of deep model has become a research hotspot. For attacking the depth model and detecting the robustness of the depth model, the adversarial examples is the core technology. And stAdv is one of the most advanced adversarial examples generation technologies. It has the advantages of a high success rate of attack and small visual distortion of adversarial examples, but it also has the problems of long generation time of adversarial examples and low efficiency. Aiming at the above-mentioned shortcomings, this paper improves the efficiency of adversarial examples generation algorithm, and proposes an optimized adversarial examples generation algorithm P&stAdv based on probabilistic graph. This method combines the steganography algorithm and CAM technique. P&stAdv perform attack evaluation on each pixel of the image, and introduces a method of obtaining the “appropriate point” of the image based on the steganography algorithm to generate the adversarial examples. Then the cost matrix of image modification is obtained and the image probabilistic graph is generated. Finally, use adversarial examples to attack images according to probabilistic graph. The experimental results show that the proposed algorithm has high efficiency. Compared with stAdv, the generation time of adversarial examples is reduced by 35%. This algorithm can modify the minimum number of pixels while ensuring a high success rate of attack. Moreover, it can make more effective attacks on complex texture images.
Evaluation of Models and Measurements to Estimate Solar Radiation for 1-Axis Tracking Modules at NREL’s SRRL

Solar radiation reaching photovoltaic (PV) modules on a 1-axis tracking system can be measured by reference cells or thermopiles. The former is often biased due to the reflection of solar radiation by the glass cover of the PV. The uncertainty can be moderated by applying a correction factor, as a function of solar incident angle and the refractive index of the glass, to the measurement. On the other hand, solar radiation on the inclined PV panels can be computed by transposition models using global horizontal irradiance (GHI) observations from thermopiles. This study examines the models and measurements to estimate solar radiation for 1-axis tracking modules at the National Renewable Energy Laboratory’s (NREL’s) Solar Radiation Research Laboratory (SRRL). The 1-minute plane-of-array (POA) irradiances from 2019 are computed using the observed GHIs and a transposition model developed by Perez et al. The POA irradiances are compared with the observation by an IMT reference cell and a Kipp & Zonen CM Pyranometer 22 (CMP22) thermopile. For the SRRL’s 1-axis tracking system with an annual solar energy of 2323.9 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the POA irradiance is overestimated by ~70 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> using the transposition model. This bias is reduced by more than 50% using the IMT measurements calibrated by a correction factor for a PV surface of antireflection coated glass.
A Survey on LCD Codes

LCD codes is a topic of immense importance since their introduction by Massey ([1]) in 1992. After that, in last decade several developments in the field of LCD codes has been carried out by several authors ([2] to [22] etc.). Authors studied these codes over some small finite fields, further study of LCD codes has been carried out on some large fields. Their generalizations over commutative rings is also carried out. The present paper by us, is a honest attempt to throw a light on these gradual developments via the survey. This will certainly become an important document for budding researchers in this field.
Research on Digital Image Copying Watermarking Algorithm Based on Deep Learning

With the rapid development of digital signal processing and computer network technology, the copyright protection of digital products is facing a very severe challenge. The digital watermarking technology mainly hides some digital information in the digital product to achieve the data copyright protection and the content authentication. In this paper, a copy-proof watermarking algorithm for color image based on deep learning is proposed. The YUV space of the color image is selected, and the DCT and binarization of the chrominance component are used to obtain the unique characteristic information of the image. Experimental results show that the proposed algorithm is robust to noise attack, JPEG compression, filtering and cropping attacks, and can resist copying attacks effectively.
Sentiment Analysis of MOOC Reviews Based On Capsule Network

With the development of Internet technology, the mode of "education + technology" is becoming more and more popular. More and more users begin to use MOOC platforms to select the courses they are interested in and leave rich comments with emotional colors in the comment area. The exploding number of comments contains information about learners' attitudes and evaluations of the course.It is very important to analyze and process these comments so as to obtain the key information.In this paper, a comment text sentiment analysis model ALBERT-BiGRU-Capsnet is proposed based on Albert pretraining language model, which integrates BiGRU and Capsule Network.Through many comparative experiments, it is found that the classification effect of this model is better.
Scale adaptive and lightweight super-resolution with a selective hierarchical residual network

Deep convolutional neural networks have made remarkable achievements in single-image super-resolution tasks in recent years. However, current methods do not consider the characteristics of super-resolution that the adjacent areas carry similar information. In this paper, we propose a scale adaptive and lightweight super-resolution with a selective hierarchical residual network (SHRN), which utilizes the repeated texture features. Specifically, SHRN is stacked by several selective hierarchical residual blocks (SHRB). The SHRB mainly contains a hierarchical feature fusion structure (HFFS) and a selective feature fusion structure (SFFS). The HFFS uses multiple branches to obtain multiscale features due to the varying texture size of objects. The SFFS fuses features of adjacent branches to select effective information. Plenty of experiments demonstrate that our lightweight model achieves better performance against other methods by extracting scale adaptive features and utilizing the repeated texture structure.
Vision-based target point tracking and aiming method

Purpose Laser absolute distance measurement has the characteristics of high precision, wide range and non-contact. In laser ranging system, tracking and aiming measurement point is the precondition of automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach For the central point of the hollow angle coupled mirror, this paper proposes a method based on correlation filtering and ellipse fitting. For non-cooperative target points, this paper proposes an extraction method based on correlation filtering and feature matching. Finally, a visual tracking and aiming system was constructed by combining the two-axis turntable, and experiments were carried out. Findings The target tracking algorithm has an accuracy of 91.15% and a speed of 19.5 frames per second. The algorithm can adapt to the change of target scale and short-term occlusion. The mean error and standard deviation of the center point extraction of the hollow Angle coupling mirror are 0.20 and 0.09 mm. The mean error and standard deviation of feature points matching for non-cooperative target were 0.06 mm and 0.16 mm. The visual tracking and aiming system can track a target running at a speed of 0.7 m/s, aiming error mean is 1.74 pixels and standard deviation is 0.67 pixel. Originality/value The results show that this method can achieve fast and high precision target tracking and aiming and has great application value in laser ranging.
Compensation Method for Shadow Effect of Mine Ultrasonic Anemometer

The ultrasonic anemometer is widely used in the field of wind speed measurement in the coal mine. To compensate for the error of wind speed caused by the shadow effect, we propose a method based on kernel extreme learning machine combined with particle swarm optimization (PSO-KELM). Firstly, we established and analyzed the simulation experiment by computational fluid dynamics (CFD). Secondly, we used the PSO to optimize the parameters of the KELM. The evaluation indexes of the PSO-KELM on the test set are better than other regression models. Finally, the accuracy of the PSO-KELM was verified by experimental data obtained by the ultrasonic anemometer in the wind tunnel. The results show that the compensation effect of the PSO-KELM is better than other regression models. The PSO-KELM model proposed in the paper can effectively compensate for the error of wind speed, and make the measurement results of the ultrasonic anemometer more accurate.
CDVT: A Cluster-Based Distributed Video Transcoding Scheme for Mobile Stream Services

Distributed video transcoding has been used to huge video data storage overhead and reduce transcoding delay caused by the rapid development of mobile video services. Distributed transcoding can leverage the computing power of clusters for various user requests and diverse video processing demands. However, it imposes a remaining challenge on how to efficiently utilize the computing power of the cluster as well as achieve optimized performance through reasonable system parameters and video processing configurations. In this paper, we design a Cluster-based Distributed Video Transcoding System called CDVT using Hadoop, FFmpeg, and Mkvmerge to achieve on-demand video splitting, on-demand transcoding, and distributed processing, which can be applied to large scale video sharing over mobile devices. In order to further optimize system performance, we conducted extensive experiments on various data sets to find relevant factors that affect transcoding efficiency. We dynamically reconfigure the cluster and evaluate the impacts of different intermediate tasks, splitting strategies, and memory configuration strategies on system performance. Experimental results obtained under various workloads demonstrate that the proposed system can ensure the quality of transcoding tasks while reducing the time cost by up to 50%.
Application of XR to Regional Development

Attempts have been made to apply XR technology that superimposes virtual information over real space to lead to regional development. In addition to the examples that the authors are currently working on, two examples are discussed that apply to the second offline theory attracting attention in Japan, and how these balance with the new theory of the second offline, “the doubling of place and time,” is addressed. In addition to the relationship between the real and the virtual in the second offline theory, adding the overlap of time and place (“the doubling of time and place”) to the point of view makes the theory more sophisticated. By superimposing content that transcends time and place in the most important space (real space), that is, the “here and now,” the examples introduced in this chapter demonstrate the possibilities for new results and effects that have never been achieved before, such as evoking behavior that leads people to a place, creating novel, hitherto unknown tourist experiences, and, from there, bringing about new economic effects.
Strategies for distance support of advanced professional training for teachers of Russian as the second language

The effectiveness of advanced professional training for a teacher of Russian as the second language is possible only based on the complex use of modern distance learning technologies that are aimed at usage in a specific subject area and reflect its specifics as adequately as possible. This article summarizes the results of project activities on the formation of a system of advanced professional education for teachers of Russian as the second language, carried out at the premises of The Kosygin State University of Russia. The project aimed to develop strategies for creating an integrated system of distance support, including 1) a model of an electronic educational environment based on cloud technologies and representing a secure solution in a controlled segment of the Internet, capable of accumulating resources for teaching the Russian language in Russia and abroad; 2) a system of diagnostic measures to identify the competence gaps of RSL (Russian as the Second Language) teachers in the subject, organizational, methodological, psychological spheres; 3) recommendations for the creation of advanced training programs (modules) capable of eliminating the identified competence deficiency in these areas. The following can be considered as objectives of the project: 1) selection of distance technologies (elements of distance technologies), relevant for use in the process of professional training of an RSL teacher; 2) formation of a cluster of diagnostic measures; 3) modeling educational content; 4) determination of cognitive strategies for teaching and self-education of an RSL teacher in the system of advanced professional training. The system of diagnostic measures described in the work assumed the assessment (measurement) of the teacher’s activities in three aspects: 1) from the standpoint of subject knowledge, 2) from the organizational and methodological standpoint, 3) from the psychological standpoint. This approach made it possible to single out the components of professional competencies and form a methodology for expert assessment of the educational environment, including the Russian-speaking educational environment abroad. In this case, the instrument of pedagogical measurement was testing carried out remotely on the constantly operating platform of The Kosygin State University of Russia at https://edu.rguk.ru .
Light-Weight Solution for High Speed Obstacle Avoidance

This paper proposes and evaluates a method of autonomous high-speed maneuvering, while simultaneously implementing an obstacle avoidance technique, in an unknown environment. Maneuvering in this context applies to controlling the Ackermann steering angle and speed of a small Unmanned Ground Vehicle (sUGV). The underlying principle applied in this research is a modified version of the ‘Open Path’ algorithm proposed by Wahlde et al., which processes LiDAR point clouds to make steering decisions. The original ’Open Path’ Algorithm was designed for a differential drive robot while the version proposed in this paper modifes that algorithm to fit an Ackermann steering vehicle.