Reviewerly/exHarmony huggingface dataset:
Energy-Efficient GPU Graph Processing with On-Demand Page Migration

This paper presents a new approach to extracting improved performance-per-watt on large-scale hybrid graph applications with sparse data access patterns. The proposed technique takes advantage of demand paging, a technology recently introduced on CPU-GPU systems with heterogeneous memory. The strategy combines an analytical cost model, compiler transformations and a runtime system. The cost model, guided by runtime feedback, judiciously selects data structures for host placement which are migrated to the GPU during kernel execution via demand paging. We then introduce, two new code transformations, kernel blocking and compute colocation, to exploit page-level locality in host-resident data.We evaluate our strategy on four important algorithms in graph analytics: BFS, MST, SSSP and PageRank. Demand paging combined with kernel blocking causes significant reduction in PCIe traffic and yields an average speedup of 2.46, and up to a 5× performance improvement on BFS, over state-of-the-art methods. The performance boost does not incur a commensurate increase in GPU power draw, thereby leading to significant gains in energy efficiency. On average, 2.36 improvement in performance-per-watt is achieved across the four algorithms.
Unfoldment of Surface Pattern with Highlighting Decoration Via Rotationally Symmetric Jomon Earthenware

With the development of 3D scanning technology, it is more convenient and simple to obtain the surface patterns of the cultural relics by using the 3D models. In order to observe the surface patterns and decoration parts of cultural relics for archaeologists, a new method is proposed to unfold the surface pattern for rotationally symmetric Jomon earthenware. In the previous study, we have proposed an unfoldment method that could achieve the transformation of the 3D coordinate point cloud from earthenware to a 2D plane. Our 2D unfoldment method solves a distortion problem around the center line. Unfortunately, the unfold elements are only points and it will lead to the insufficient display of the patterns and decoration parts. Therefore, in this paper, the feature points information will be separately extracted from 3D models and it will be added to the unfolded 2D plane.
Dynamic Expression Recognition Based on Hybrid Features and Optimized Extreme Learning Machine Model

Abstract Facial expression recognition is an academic subject, and it can be applied not only in the field of computer vision but also in psychology and sociology. This paper proposed a robust dynamic facial expression recognition method which using hybrid features and the PSOELM (Particle Swarm Optimization-Extreme Learning Machine) model. At first, six PSOELM models were trained on the training set of six basic expressions, respectively. Then by extracting the hybrid features which fusing geometric features and texture features, the dynamic expressions were recognized by PSO-ELM models. These experiments were performed on the Cohn-Kanade+ face database. The results show that this method has a better recognition effect on dynamic facial expression recognition.
A Case Study on a Hybrid Approach to Assessing the Maturity of Requirements Engineering Practices in Agile Projects (REMMA)

Context: Requirements Engineering (RE) is one of the key processes in software development. With the advent of agile software development methods, new challenges have emerged for traditional, prescriptive maturity models aiming to support the improvement of RE process. One of the main problems is that frequently the guidelines prescribed by agile approaches have to be adapted to a project’s context to provide benefits. Therefore, it might be naive to believe that it is possible to propose a prescriptive method of RE process improvement that will suit all agile projects without any alteration. Objective: The aim of the paper is to evaluate a hybrid approach to assessing the maturity of agile RE (REMMA), which combines elements of prescriptive and problem-oriented improvement methods. Method: The usefulness, ease of use, and cost-effectiveness of REMMA were investigated through a case study performed in one of the biggest software houses in Central Europe. Results: The results of the case study suggest that the method seems easy to use, affordable, and is perceived as a useful tool to support the process of improving RE practices in agile projects. Its feature of taking into account the dependencies between practices and the necessity to adapt them to a certain project context was regarded as well suited for the agile context. Conclusions: REMMA, which includes two main components: a maturity model for agile RE (a set of state-of-the-art agile RE practices) and an assessment method that makes it possible to evaluate how well the agile RE practices are implemented, seems to be a useful tool supporting improvement of RE in agile projects.
Knowledge Graph of Maritime Collision Avoidance Rules in Chinese

The aim of this paper is to establish a general knowledge graph for the field of nautical laws and regulations through COLREGS (International Regulations for Preventing Collisions at Sea) regulation. This paper designs five kinds of ontology: concept, subject, environment, action and catalogue. According to the ontology database, we extract knowledge based on rules and dictionaries, and the index tree of subject is finally established. The question-answering system constructed by the knowledge graph has good feedback and expansibility for military questions in COLREGS.
Rank-consistency-based multi-view learning with Universum

In multi-view learning field, preserving data privacy is an important topic and a good solution is rank-consistency-based multi-view learning (RANC). RANC exploits view relationship and preserves data privacy simultaneously and related experiments also validate that RANC improves the individual view-specific learners with the usage of information from other views and parts of features. While performance of RANC is still limited by the insufficient of prior knowledge. Thus we introduce Universum learning into RANC to create additional unlabeled instances which provide more useful prior knowledge. The developed RANC with Universum learning is abbreviated to RANCU. Related experiments on some multi-view data sets have validated the performance of our RANCU theoretically and empirically.
Multi-contrast MR Image/Volume Alignment via ECC Maximization

The multi-contrast MRI volume registration problem constitutes one of the basic steps in many computer vision applications. Since different types of MRI images have totally different intensity distributions, the use of well known area based image alignment techniques does not consist a good choice for the solution of the problem at hand. In this paper the usage of Self Quotient Volumes as an efficient representation of the candidate volumes, to permit the exploitation of area based techniques, is proposed. In addition, a well known "signal" similarity measure based on the Enhanced Correlation Coefficient, is adapted for solving the volume alignment problem. From the application of the proposed technique in a series of experiments with artificial, as well as real, MRI brain sequences and its comparison against state of the art volume registration techniques it seems that its performance is better for both types (same and different contrast) of alignment problem.
Video-Based Person Re-Identification With Unregulated Sequences

Video-based person re-identification (re-id) has recently attracted widespread attentions because extra space-time information and more appearance cues in videos can be used to improve the performance of image-based person re-id. Most existing approaches equally treat person video images, ignoring their individual discrepancy. However, in real scenarios, captured images are usually contaminated by various noises, especially occlusions, resulting in a series of unregulated sequences. Through investigating the impact of unregulated sequences to feature representation of video-based person re-id, the authors find a remarkable promotion by eliminating noisy sub sequences. Based on this interesting finding, an adaptive unregulated sub sequence detection and refinement method is proposed to purify original video sequence and obtain a more effective and discriminative feature representation for video-based person re-id. Experimental results on two public datasets demonstrate that the proposed method outperforms the state-of-the-art work.
Evaluation of Similarity Measure to Find Coherence

Sentiment Analysis in various applications in diverse domains, plagiarism detection, ambiguity in legal documents, searching an information in Google and translation are few examples of major applications where similarity plays a significant role. Organizing digital documents based on their practical need and usage is need of the hour due to ever-increasing nature of e-resources of documents. We are in the era of transformation from human learning to machine learning. Automatic Organization of Documents into clusters so that intra-cluster possesses high similarity and inter-cluster possess low similarity is the objective of document clustering. Web mining, Information Retrieval, Search Engines, etc., are application of areas of Document Clustering. First, Similarity shall be measured to group documents together. Similarity measurement plays a very important role in organizing documents. In this paper, answers to few questions are pondered and hence few similarity measures are implemented and documents are clustered to realize those answers. Visualization of the clusters reflects satisfactory results.
Diversifying experiences in multi agent reinforcement learning

Deep Reinforcement learning algorithms have traditionally been applied to tasks that train challenging control behavior. Actor Critic based versions of these algorithms have been used to train agents in state of the art settings. While proving to be sample efficient in multi agent learning, these algorithms tend to perform poorly in the exploration phases. In this paper, the experience gained by the replay buffer during the exploration phase is improved by diversifying the input results using a genetic algorithm. We have tested this method on predator prey environment and other team based tasks. The evaluation shows that our method tends to produce a more robust solutions outperforming the traditional methods.
Steganographic Message Transformation with the Partial Antidisturbance Property

In this article we present a new method of steganographic transformation of a binary message, which allows to embed secret data with minimal statistical distortion of the original message. The constructed algorithm should be applied before steganographic embedding message in a container. The transformation renders inefficient a number of stegoanalysis methods based on the statistical deviation analysis in message, extracted from an empty and filled container. When applying this algorithm, the stegoanalyst cannot properly extract from container up to 32% message bits, and accordingly, realize some stegoanalysis methods. According to the new method the message from empty container are transformated and after embedded again at the same container. The size of secret data is 19% of the total container capacity. In this paper various stegoanalysis methods for text data are considered, it was demonstrated their inefficiency after application of the new method.
A Functional Game Application for Korean Words Learning Based on Smartphone Environments

In this paper, the prototyping process for developing syllable-initial consonant-based game 'Korean Guards' is described.Users may effectively learn Korean words using alphabetically sequential approaches, but the easiness of access bestowed on the smart environments and game algorithms could be fully utilized for the functional advantages for educational purposes.This functional game is developed on Android OS and the prototypical outcome is shown.
Harnessing Deep Cross-lingual Word Embeddings to Infer Accurate Phylogenetic Trees

Establishing language relatedness by inferring phylogenetic trees has been a topic of interest in the area of diachronic linguistics. However, existing methods face meaning conflation deficiency due to the usage of lexical similarity-based measures. In this paper, we utilize fourteen linked Indian Wordnets to create inter-language distances using our novel approach to compute 'language distances'. Our pilot study uses deep cross-lingual word embeddings to compute inter-language distances and provide an effective distance matrix to infer phylogenetic trees. We also develop a baseline method using lexical similarity-based metrics for comparison and identify that our approach produces better phylogenetic trees which club related languages closer when compared to the baseline approach.
Improving Hybrid Tracking System for First-Person Interaction in Immersive CAVE Environment

CAVE system gives us access to interact with an immersive virtual environment in the first-person. However, several crucial obstacles have hindered its application in practical engineering, and one of which is the full body motion tracking. This paper addresses the problem of full body motion tracking in a CAVE environment and proposes a novel hybrid tracking system which integrates both the optical and inertial tracking system to overcome the occlusion and interference problem. The proposed approach is evaluated by comparing with the most advanced Xsens full body motion tracking suit in a real CAVE environment, which proves its great improvement of stability and precision. Furthermore, a real case is tested as well to show its applicability in practical engineering applications.
A Hybrid Approach to Develop the First Stemmer in Maithili

This paper presents a hybrid approach to extract the stems from the Maithili words. Development of stemmer for various languages has gained adequate attention of the researchers for decades. Stemmers have also been developed for most of the major Indian languages. However, no work is found on the development of Maithili stemmer. In this first attempt, we use a hybrid model where rule-base acts as the core module. We study the inflections of the major parts-of-speech categories and define rules accordingly. The rule-based module is supported by neural word-embedding and suffix stripping. Suffix stripping is used to increase the coverage and word embedding is used to restrict erroneous stem generation. The system is evaluated using a test data collected from Maithili literature and news text. The final system achieves an accuracy of 84.6%.
Performance Evaluations of Viewport Movement Prediction and Rate Adaptation for Tile-Based 360-Degree Video Delivery

Recently, the demand for high quality 360-degree video delivery is increasing, however, 360-degree videos require extremely high bitrate and large network capacity. Therefore, an efficient (i.e., higher quality and lower traffic) 360-degree video delivery is mandatory. To address this fact, this paper introduces and evaluates a tile-based 360-degree video delivery system that equips viewport movement prediction and rate adaptation.
The Design of English Reading Resource Library Based on WeChat Public Platform

Reading is an important learning goal for college students, which can get information and improve their quality, it is particularly important in English learning. The construction of WeChat public platform’s English reading resource library can expand the actual time and space dimensions of campus. This paper uses WeChat public platform to build an English reading resource database, and studies how to apply WeChat public platform to extracurricular online education and teaching effectively, so as to promote the development of College English education.
Stop Hiding Behind Windshield

Windshield images captured by surveillance cameras are usually difficult to be seen through due to severe image degradation such as reflection, motion blur, low light, haze, and noise. Such image degradation hinders the capability of identifying and tracking people. In this paper, we aim to address this challenging windshield images enhancement task by presenting a novel deep learning model based on a two-way generative adversarial network, called Two-way Individual Normalization Perceptual Adversarial Network, TWIN-PAN. TWIN-PAN is an unpaired learning network which does not require pairs of degraded and corresponding ground truth images for training. Also, unlike existing image restoration algorithms which only address one specific type of degradation at once, TWIN-PAN can restore the image from various types of degradation. To restore the content inside the extremely degraded windshield and ensure the semantic consistency of the image, we introduce cyclic perceptual loss to the network and combine it with cycle-consistency loss. Moreover, to generate better restoration images, we introduce individual instance normalization layers for the generators, which can help our generators better adapt to their own input distributions. Furthermore, we collect a large high-quality windshield image dataset (WIE-Dataset) to train our network and to validate the robustness of our method in restoring degraded windshield images. Experimental results on human detection, vehicle ReID and user study manifest that the proposed method is effective for windshield image restoration.
A Study on Exploring the Implications of the Seonbi Spirit for the Development of Youth’s Character Competencies - Focused on Toegye’s Seonbi Spirit -

이 연구의 목적은 우리의 역사와 전통에 기초한 선비정신이 미래사회가 요구하는 청소년 역량개발에 주는 시사점을 이론적으로 탐색하는 것이다. 먼저 이론적 기초로서 (1) 선비와 선비정신, (2) 선비의 덕목, (3) 선비정신의 현대적 의의를 중심으로 고찰하였다. 선비정신에 내재된 덕목과 역량이 학교교육에서 청소년들의 역량개발에 주는 시사점에 대한 연구결과를 요약하면 다음과 같다. 첫째, 제4차 산업혁명 시대를 대비하기 위한 ‘인성역량’으로서 ‘공감적 역량’, ‘문제해결 역량’, ‘의사소통 역량’, ‘자기주도 학습역량’, ‘창의성’, ‘협력적 역량’을 도출하였다. 둘째, 전통사회의 선비정신에서 제4차 산업혁명 시대의 인재상으로 요구되는 ‘공감적 역량’, ‘협력적 역량’이 내재되어 있음을 확인하였으며, 이와 관련된 내용을 퇴계의 삶을 통하여 예시 자료로 제시하였다. 결론적으로 선비정신에 내재된 역량교육은 물질만능의 인간성 상실의 상태에 처해 있는 청소년들의 윤리적 문제점을 해결하는 대안이며, 인성교육의 효과적인 자료로서 활용 가능성을 확인하였다. 후속연구로서 학교교육에서 선비정신의 재해석을 통한 미래사회가 요구되는 다양한 역량개발을 위한 실천방안 마련의 필요성을 제안하였다.The purpose of the present study is to theoretically explore the implications of Seonbi spirit for the development of youth’s character competence in the era of the fourth industrial revolution. As the theoretical bases, (1) the Seonbi and the spirit of the Seonbi, (2) the core competence in the era of the fourth industrial revolution, and (3) modern reconsideration of Seonbi spirit were reviewed. The results of the study on the implications of Seonbi spirit for the development of youth’s character competencies in school education are summarized as follows: Firstly, as the ‘character competencies’ to prepare for the era of the fourth industrial revolution, we have integrated ‘empathy capacity’, ‘problem solving capacity’, ‘communication capacity’, ‘self-directed learning capacity’, ‘creativity’, and ‘collaborative capabilities.’ Secondly, it was confirmed that ‘empathic competence’ and ‘collaborative competence’ required for the qualified person in the era of the fourth industrial revolution are inherent in the Seonbi spirit of the traditional society. We have also found and illustrated these examples in the life of the Seonbi, especially Toegye’s academic life. In conclusion, we have confirmed that character education inherent in the spirit of Seonbi is an alternative to solving the ethical problems of youths who are suffering from mental poverty with ‘mental anomie.’ We have also confirmed the possibility of using the material related to the spirit of Seonbi in character education. As a follow-up study, we proposed the necessity of preparing a practical plan for the development of various competencies required in the era of fourth industrial revolution through reinterpretation of the spirit of Seonbi in school education.
Application of Association Rules Algorithm in Teaching Reform Under the Background of Internet Plus Era – Taking the Teaching Reform of International Trade as an Example

Under the background of internet plus era, the teaching reform of international trade major is imperative, but the reform is very difficult. Therefore, an algorithm based on association rules was established to calculate and analyze the related issues of teaching reform. In this way, the difficulty of artificial reform can be simplified, and the goal of reform can be achieved. Through the test and experiment of algorithm, it is proved that our algorithm has great advantages. It not only has short time and high accuracy, but also is an algorithm that has been utilized well, which is suitable for our teaching reform.
New embedded system for retrieving meter index

In this work we propose a new public dataset and an automatic approach to detect and recognize text from meter image. Unlike other techniques which extract text from the whole image, our approach is trained to detect a particular area of interest where performs the search of text. The customer remotely launches an online command to pick up the meter index, then an image is taken from meter index in order to be processed and finally send the result to the customer. In the Automatic Meter Reading literature, a few datasets are available to the research community. In this sense, we introduce a new public dataset, called MR-AMR dataset (Moroccan AMR), with more than 15000 images. This dataset has the advantage to be the largest public dataset found in the literature. Despite the complexity of the problem, our approach shows good results and low computational time.
TECHNOLOGICAL APPROACH TO SOLVING THE PROBLEM OF MINIMIZING THE INFLUENCE OF DESTABILIZING FACTORS ON THE WORK OF DISTRIBUTED STRUCTURES OF THE GROUND-ROCKOTECHNICAL COMPLEX IN AN INTEGRAL PROJECT «OBJECT-SYSTEM»

Mobile structures of the ground-based robotic complex (RTC) are investigated as an active component formation of an integrated project “object-system”, which is operated in a destabilizing environment. The relevant problem of minimizing the influence of external destabilizing factors on the operation of mobile spatially-distributed structures of the ground-based RTC is stated in the descriptions of the conceptual apparatus of complex, poorly formalized multicomponent technical systems. Following the logic of this approach, the basic principles of distributed control are determined and their applications are implemented in the mobile structures of the ground-based RTC with elements of subsidiarity. The quintessence of the latter is represented by the technology of multi-antenna MIMO systems, which made it possible to determine the trade-offs of using classical transmission methods and strategies for receiving and processing MIMO signals in the multi-sensory channels of the information-control system (ICS) and radio communication with the data transmission system and commands. Given the complexity of performing the stated tasks, a set of technological functions of reducing the influence of destabilizing factors and their practical variations in the algorithms for obtaining the target result are proposed. A situational model of reducing (minimizing) information losses at the output of the information-control system of the ground-based robotic complex under destabilization has been built. Options have been proposed to achieve the target result: integration of structural and parametric adaptation methods, MIMO technologies, code division multiplexing techniques with CDMA channels, taking into account the heterogeneity factor of information exchange channels and the artificial redundancy of the system itself with respect to the number of external interference sources.