Reviewerly/exHarmony huggingface dataset:
Fostering Interdisciplinary Data Cultures through Early Career Development: The RDA/US Data Share Fellowship

Openness and interdisciplinarity in research and data are among the challenges that are frequently discussed in the context of changing scientific and scholarly practices. Gradually, the visions of open and widely shared data are being reconciled with complex realities that stem from the disciplinary differences in data cultures. In this paper we discuss interdisciplinarity through data as a way to create research environments that are more flexible and, as a result, more amenable to change. We report our findings from facilitating and evaluating a data-oriented early-career fellowship program that was administered as part of the Research Data Alliance (RDA), a global organization that aims to enable open sharing and re-use of data. We identify ways to foster interdisciplinary data cultures among the future researchers and professionals and propose recommendations for future programs. While the short-term early career programs cannot address the systemic factors that impact openness and interdisciplinarity, such as the systems of reward and recognition or the funding structures, they can introduce mechanisms that support diversity, learning, and leadership and, ultimately, contribute to a culture change.
Knowledge graph augmented advanced learning models for commonsense reasoning

Abstract Machine learning is the key solution to many AI issues, but learning models rely heavily on specific training data. While a Bayesian setup can be used to incorporate some learning patterns with previous knowledge, those patterns can not access any organized world knowledge on requirements. The primary objective is to enable human-capable machines in ordinary everyday circumstances to estimate and make presumptions. In this paper we propose to respond to such common sense issues through a textual inference system with external, organized common sense graphs for explanatory inferences. The framework is based on a schematic map as a pair of questions and answers, a linked subgraph from the semantine to the symbolic space of knowledge-based external information. It displays a schematic map with a new network graphic module for information knowledge and performance with graph representations. LSTMs and graphical networks with a hierarchical attention-based direction are the basis of our model. It is flexible and understandable from the intermediate attention scores, leading to confident results. We also achieved state-of-the-art reliability on CommonsenseQA, a broad database of common sense reasoning utilizing ConceptNet as the only external tool for BERT-based models.
A Multi-target Edge Service Approach to Real-time Image Object Detection

The networked devices over the IoT environments have accumulated a large volume of data, and therefore it becomes challenging for many typical surveillance applications to handle and analyze these massive real-time data. In this paper, we propose a multi-target approach to real-time object detection for surveillance applications that can be used as an edge service for handling and analyzing the real-time massive data acquired by a huge number of terminal devices. A lightweight multi-layered Convolutional Neural Network based on Darnet-53 structure is adopted to learn the multi-target features in training dataset for classifying and detecting image objects in unseen image instances, which is. A pre-processing approach including image screening, image enhancement and manual category annotation is presented for building the training dataset of surveillance applications. At last, extensive experiments were made to validate the effectiveness and efficiency of handling and analyzing the real-time massive data based on the proposed approach.
Professional training future geography teachers

Abstract The article presents the experience of geography teachers’ training at the Pedagogical Institute of Irkutsk State University. We considered the fields of concern and activities that contribute to the effective formation of methodological and professional skills. It was revealed that the diverse extracurricular activities in geography, carried out in the interaction of the pedagogical university with public organizations, municipal information and methodological center and schools, is effective for the development of creative abilities of future teachers. The engagement of students in the created information and education space directly affects the result of the educational process, develops the ability to solve professional issues. Practical knowledge in the development and implementation of joint cultural and educational activities to popularize geography allows students to apply their experience and skills. Hands-on experience with advanced teachers of geography of the city and region, participation in scientific and practical conferences and methodological seminars makes it possible to form a stable motivation of graduates to pursue a master‘s degree and improve their professional qualifications. This practice allows mastering and using basic scientific and theoretical knowledge and practical skills in professional activities, is one of the mechanisms for managing education quality in training competitive specialists.
Rainy Image Enhancement Using Modified Multistage Gaussian Filter and Weighted Median Guided Filter

Rain streaks and droplets removal from an image is quite complex. In rainy days, the performance of outdoor vision system will be degraded due to poor visibility, deformation and blurring caused by rain. In this paper, modified multi-stage Gaussian filtering technique for rain streaks removal and a method for removing rain droplets on windscreen are proposed. Rain removed image is processed using dark channel prior algorithm for haze removal. Dark channel prior is combined with triple clipped dynamic histogram equalization technique for sharpness enhancement. The proposed algorithm is compared with that of Shi et al. [6] in terms of peak signal -to- noise ratio (PSNR) and structural similarity index (SSIM). Simulation results on variety of rain streaks and rain droplets images yielded an average PSNR and SSIM values of 52dB and 0.99 respectively using the proposed algorithm as compared with 32dB and 0.96 obtained using algorithm [6].
Study on analog-based ex-core neutron flux monitoring systems of Korean nuclear power plants for digitization

The analog-based Ex-core Neutron Flux Monitoring System (ENFMS) in Korean Nuclear Power Plants (NPPs) has been performing its intended functions successfully for a long time. On the other hand, the primary concern with the extended use of analog systems is the aging effect, such as mechanical failures, environmental degradation, and obsolescence. The transition to a digital-based Man-Machine Interface Systems (MMIS) in Korea and other countries has been accelerating, but some systems are still analog-based IC systems, such as the ENFMS in APR1400 NPPs. Digitalized ENFMS can become a reality using computers and microprocessors owing to the progress in digital electronics and information technology. This paper presents the result of the first phase of the research on the digitalization of the ENFMS signal processing electronics for NPPs operated or produced in Korea. It has two main parts: (1) review engineering bases of ex-core neutron flux monitoring system, including nuclear engineering, instrumentation techniques, and analog and digital signal processing techniques, and (2) analysis of analog signal processing electronics of ENFMS for OPR1000 and APR1400 power plants. They are prerequisite to the second phase of the research which is the detailed implementation of the digitalization.
Attention-based Instance Segmentation Network for Cell Segmentation

Segmentation of cell images has been widely explored in medical image analysis and clinical aided diagnoses. Instance segmentation technology, which can detect and distinguish each cell object has been a hot topic in recent years. However, the complex background interference and densely distribution in cell images always make the instance segmentation challenging. This study proposes a new instance segmentation algorithm based on the typical Mask R-CNN, named as attention-based instance segmentation network (AIS-Mask) for more accurate cell segmentation. Specially, an attention module is introduced and imposed on the top-down multi-scale information flow of the feature pyramid network (FPN) for extracting more efficient features and suppressing the background interference simultaneously. Experiments on the cell dataset from the Chinese cargo spacecraft TZ-1 demonstrate the impressing performance of our AIS-Mask under complex background interference. Both the quantitative evaluation and qualitative visual results show that the proposed AIS-Mask outperforms the state-of-the-art Mask R-CNN.
A Novel Framework for Video Retrieval Algorithm Evaluations and Methods for Effective Context-Aware Video Content Retrial Method on Cloud

In recent time, multiple research outcomes have demonstrated diversified approaches for content management and showcased better outcomes. However, the parallel research outcomes are highly criticized for higher time complexity, reduction of the key contents, and finally, the lesser accurate indexing of the contents. Majority of the recent work outcomes have demonstrated the reduction of keywords based on language recommendations rather the context recommendations. This leads to non-context-aware reduction and further leads to incorrect extraction of keywords. Thus, the demand from recent research is to identify the keywords based on the context. Also, based on the previous claim, the proposed works must identify the actual frames if the keyword identification is based on the context. Thus, based on the recommendations by popular research outcomes, a framework is to be proposed to compare the existing video content retrieval methods and propose a novel process to identify the keyframes from the video contents using contextual mining and consider the optimal storage architecture for the proposed process input metadata and results for cloud-based storage service providers. The final outcome of this work is reduced complexity of the framework, compared with the parallel research outcomes, and higher accuracy for video content retrieval with reduction of the size for the searchable contents. The proposed work demonstrated nearly 15% improvements for content retrieval process and 86% improvements for time complexity over the parallel research outcomes for making the video content management and delivery mechanisms better and faster.
AR40ER: A Semantic Platform for Open Educational Augmented Reality Resources

Tablet computers are gaining in presence in modern-day classrooms, enabling the use of a variety of apps for purposes such as note-taking or assessment. Augmented Reality (AR) experiences in the classroom, made possible by current hardware, permit new ways of interaction and visualization, as well as increase student motivation and engagement. They also overcome the need for potentially expensive hardware required for experiments in certain scientific domains. The movement of Open Educational Resources (OER) has enabled the sharing of heterogeneous learning resources. Their retrieval can be improved by enriching their metadata using Semantic Web technologies. In this paper, we present AR4OER, a semantic platform for heterogeneous AR experiences provided as OER. We showcase four AR scenarios from different school subjects. These scenarios can be integrated through a lose coupling in third-party apps. Apart from describing how this integration works, we demonstrate how a note-taking app can benefit from these scenarios.
Integrating knowledge-based sparse representation for image detection

Image detection has been a primary step and widely adopted in diverse domains, such as autopilot, risk inspection, and robot vision. The main body of current models for image detection treat this task as the feature-based recognition of multiple independent objects. The adoption of semantic relationships, which can provide auxiliary information for joint recognition, is still in its early stage. Inspired by deep dictionary learning, this work proposes a novel framework named SEGNN, which aims at finding and using the sparse representation knowledge to improve the result of image detection. According to this design, both the efficiency and rationality of reasoning over semantic relationships are well captured. The framework includes an improved GGNN model. It can efficiently extract the semantic informations from the image, and map it to sparse vector. The framework also applies a novel SimRank method, to justify the rationality of the semantic reasoning. The performance of the framework is validated extensively on two datasets Visual Genome (VG) and COCO. The experimental results and case study have demonstrated the effectiveness of the proposed framework.
Efficient Cryptographic Technique to Secure Data in Cloud Environment

Cloud computing technology offers various services such as infrastructure, platform and application to its client on demand through its data center which consists of hardware and software components. Work with cloud environment needs to address several design challenges such as scalability, reliability, concurrency, transparency and security. In this paper, we address one of the design challenges of cloud computing which is security. The limitation of the existing cloud security technique with respect to data storage and access is that there is a possibility of third-party vendors to decrypt the data; therefore, the third-party vendors can access and misuse the data. We have proposed a new algorithm which is a combination of Paillier key encryption (PKC) with multiparty computation (MPC) to secure data from the third-party vendors. We name the proposed new algorithm as Paillier key with multiparty computation (PMPC). In addition to this, we have compared the proposed algorithm with the existing algorithms such as homomorphic encryption (HoE), multiparty computation (MPC), and the proposed PMPC algorithm results indicated that the proposed PMPC algorithm performs better than HoE and MPC algorithms.
Design and Implementation of Lempel-Ziv Data Compression using FPGA

When transmitting the data in digital communication, it is well desired that the transmitting data bits should be as minimal as possible, so many techniques are used to compress the data.In this paper, a Lempel-Ziv algorithm for data compression was implemented through VHDL coding.The Lempel-Ziv algorithm is one of the most widely used among lossless data compression algorithms for hardware implementation.The work in this paper is devoted to improve the compression rate, space-saving, and utilization of the Lempel-Ziv algorithm using a systolic array approach.The developed design is validated with VHDL simulations using Xilinx ISE 14.5 and synthesized on Virtex-6 FPGA chip.The results show that our design is efficient in providing high compression rates as well as improved utilization.The Throughput is increased by 50% and the design area is decreased by more than 23% with a high compression ratio compared to comparable previous designs.
한국어 교재의 단원 구성에 대한 텍스트기호학적 분석 -상호텍스트성을 중심으로-

This research aims to study the unit configuration method of Korean language textbook into text-semiotics method. In this study, it specifically examined how the texts forming a unit of Korean language textbook has the intertextual relations. To this end, the following analysis procedures were conducted in this study. First, it classified the subject texts forming the unit, and examined that which teaching and learning unit composes the subject texts. Second, it identifies its pattern where and how the teaching and learning contents appears in each subject text. Third, it figured out whether the element in the preceding subject text appears in the following subject text and, if so, what kind of relation it has. It is to examine how the cohesion of formal layer and the coherence of semantic layer form in textbook text. Finally, based on this, it examined that the input and output were structured in the textbook unit from the perspective of second language acquisition. It showed that the unit of analysis subject combined the narration and teaching and learning contents of a specific person, and repeatedly entered the target sentence and communication function with the story of the person. As a result, the texts within the unit of analysis subject had a strong intertextual relation each other, and it was verified that this was an efficient way of providing input to learners and teachers. (Chonnam National University)
Review of <i>The Game Audio Strategy Guide</i>

Book Review| January 01 2021 Review of The Game Audio Strategy Guide: A Practical Course by Gina Zdanowicz and Spencer Bambrick (Routledge, 2020, 416 pp, $75.95) Marios Aristopoulos Marios Aristopoulos Search for other works by this author on: This Site PubMed Google Scholar Journal of Sound and Music in Games (2021) 2 (1): 65–69. https://doi.org/10.1525/jsmg.2021.2.1.65 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Marios Aristopoulos; Review of The Game Audio Strategy Guide: A Practical Course by Gina Zdanowicz and Spencer Bambrick (Routledge, 2020, 416 pp, $75.95). Journal of Sound and Music in Games 1 January 2021; 2 (1): 65–69. doi: https://doi.org/10.1525/jsmg.2021.2.1.65 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentJournal of Sound and Music in Games Search Keywords: Implementation, composition, dynamic music, careers, sound design, business The Game Audio Strategy Guide is a dense educational textbook that packs as much information as possible into a single source and aims to fulfill multiple goals. The authors present a theoretical and practical framework that covers all major aspects of game audio (sound design, composition, implementation, business) from the fundamentals all the way to advanced topics. Further, it aims to bridge the gap between academia and industry practice. While these are certainly challenging goals, the authors are knowledgeable and experienced industry professionals and educators who hold, in my opinion, the correct attitude for game audio education: “Game audio is not separate from game development, it is a specialization within game development” (4). This approach is evident throughout the book and is reinforced by the excellent and relevant use of contributions in the form of vignettes and quotations from well-known (and at times legendary) practitioners, such as Tom Salta,... You do not currently have access to this content.
Electronic voting protocol based on ring signature and secure multi-party computing

In the context of the rapid development of the Internet, traditional voting activities are generally replaced by electronic voting. In the process of electronic voting, the counting of votes mostly relies on third-party organizations, and the third party's non-dependency and complete control over the voting information can easily lead to the insecurity of information and a large number of privacy leaks. To solve this problem, this paper uses secure multi-party computing and blockchain ring signature technology to propose a fair and safe electronic voting protocol. Our uses secure multi-party calculations to enable the counting of votes to be completed without a trusted third party, and uses the unconditional anonymity and unforgeability of ring signature technology to further ensure the security of the protocol. In the end, the security of the protocol is analyzed one by one. The analysis results show that the voting protocol satisfies several security attributes of electronic voting, and is more secure and reliable than the traditional electronic voting protocol.
Vanishing Point Detection based on Line Set Optimization

Abstract Vanishing point detection plays an important role in camera calibration and 3D scene reconstruction. There are usually a lot of parallel lines in the real scene. Vanishing point is the intersection point of these spatial parallel lines projected onto the image. Commonly used Hough algorithm to detect vanishing points, which has high complexity and low efficiency. This paper proposes a vanishing point detection algorithm based on optimization of line set. Firstly, the LSD algorithm is used to detect the line. Secondly, the extracted line set is optimized to remove the invalid interference line in the image, which improves the accuracy of vanishing point detection. Thirdly, K-means algorithm is used to cluster and group the optimized line set, which improves the overall efficiency of the algorithm. Finally, random sampling fitting algorithm is used to fit the grouped line set to calculate the precise vanishing point. Compared with Hough algorithm, the running speed of this algorithm is improved by 19% in the actual scene. The experimental results show that the algorithm has low complexity and short running time.
Research on Equipment Damage Grade Evaluation Model Based on ISODATA Fuzzy Clustering

Aiming at the problem of equipment battlefield damage assessment, an index system and quantitative method for equipment battlefield damage assessment are established. The basic principles of ISODATA fuzzy clustering are used to analyze and discuss the equipment battlefield damage assessment, which overcomes the identify difficult issues using previous fuzzy comprehensive evaluation weights and provides a basic foundation for effectively making equipment battlefield repair decisions. Finally, the rationality and reliability of the model were verified by an example analysis.
An initial clustering center optimization method based on neighbourhood density for K-means

Abstract The selection of initial clustering centers of traditional K-means algorithm is random and sensitive to outliers, which leads to unstable clustering results and low accuracy. To solve the above problems, the NDK-means algorithm based on neighbourhood density is proposed. Firstly, the grid distribution characteristics of samples are obtained by multi-dimensional grid division. Then, by defining the grid density and the grid neighbourhood density, several local high-density grids are determined. At the same time, the iteration factor is introduced to merge adjacent high-density grids to obtain a candidate set of initial clustering centers. Finally, combined with grid density and distance, K initial clustering centers are obtained by using Max-Min-distance algorithm. Experiments on UCI dataset show that compared with k-means algorithm and literature algorithm, the accuracy of NDK-means algorithm is improved by nearly 11% and 4%, and the iteration speed is improved by 70% and 60% respectively. The algorithm improves the accuracy of clustering and the results have good stability.