Reviewerly/exHarmony huggingface dataset:
Research on Human Interaction Recognition Algorithm Based on Interest Point of Depth Information Fusion

The traditional feature description methods of human interaction action based on RGB video are greatly affected by illumination change, object occlusion and environmental change. In this paper, depth and color information of the image are fused in the processing of feature extraction, and a novel human interaction recognition algorithm based on interest point of depth information fusion is proposed. Firstly, the spatio-temporal interest points (STIP) are extracted from the video, and the spatio-temporal interest points are processed hierarchically by the corresponding depth information. Secondly, three-dimensional scale invariant feature transform (3D SIFT) are used to obtain the feature description of hierarchical interest points, the model of the bag of words is constructed by using K-means clustering and Gaussian mixture clustering, and the representation of the training video is obtained based on dictionary projection. Finally, the support vector machine is used to classify different layers of video features respectively, then human interaction recognition is achieved by decision-level fusion of recognition probability for different layers of a video. The experimental results show that the accuracy of the human interaction recognition is improved by combing the depth information, and the proposed algorithm achieves 88.75% recognition accuracy in the SBU Kinect interaction dataset. The validity of the proposed method is proved.
Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks

In this paper, we introduce a fully convolutional network for the document layout analysis task. While state-of-the-art methods are using models pre-trained on natural scene images, our method Doc-UFCN relies on a U-shaped model trained from scratch for detecting objects from historical documents. We consider the line segmentation task and more generally the layout analysis problem as a pixel-wise classification task then our model outputs a pixel-labeling of the input images. We show that Doc-UFCN outperforms state-of-the-art methods on various datasets and also demonstrate that the pre-trained parts on natural scene images are not required to reach good results. In addition, we show that pre-training on multiple document datasets can improve the performances. We evaluate the models using various metrics to have a fair and complete comparison between the methods.
Automatic Generation of Multidestination Routes for Autonomous Wheelchairs

To solve the problem of autonomously navigating multiple destinations, which is one of the tasks in the Tsukuba Challenge 2019, this paper proposes a method for automatically generating the optimal travel route based on costs associated with routes. In the proposed method, the route information is generated by playing back the acquired driving data to perform self-localization, and the self-localization log is stored. In addition, the image group of road surfaces is acquired from the driving data. The costs of routes are generated based on texture analysis of the road surface image group and analysis of the self-localization log. The cost-added route information is generated by combining the costs calculated by the two methods, and by assigning the combined costs to the route. The minimum-cost multidestination route is generated by conducting a route search using cost-added route information. Then, we evaluated the proposed method by comparing it with the method of generating the route using only the distance cost. The results confirmed that the proposed method generates travel routes that account for safety when the autonomous wheelchair is being driven.
From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks

Adversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. Natural Language Processing (NLP) has mostly focused on high-level attack scenarios such as paraphrasing input texts. We argue that these are less realistic in typical application scenarios such as in social media, and instead focus on low-level attacks on the character-level. Guided by human cognitive abilities and human robustness, we propose the first large-scale catalogue and benchmark of low-level adversarial attacks, which we dub Zeroe, encompassing nine different attack modes including visual and phonetic adversaries. We show that RoBERTa, NLP’s current workhorse, fails on our attacks. Our dataset provides a benchmark for testing robustness of future more human-like NLP models.
A Fuzzy Based Approach for Empirical Analysis of Unstructured Data

Computational intelligence and soft computing has many promising technologies such as Text Mining. Document Classification using soft computing techniques like fuzzy logic helps to find a more practical solution due to ambiguity and uncertainty present in the text data. Uncertainty and information may be reflected as the part and parcel of any industrial or engineering problem to be solved. Information refers to the facts required to solve it and uncertainty refers to the non-random lack of certainty (‘non-random uncertainty’), ambiguity, haziness in the system. It is very important to ponder on the nature of uncertainty involved in a problem. Father of fuzzy logic, Lofti Zadeh (1965) suggested that decision-making using set membership is the key when it is required to deal with uncertainty. Fuzzy clustering helps to identify patterns which are difficult to be discovered using crisp clustering. Natural languages contain non-random uncertainty. To deal with non-random uncertainty or different degrees of truth or partial truth Fuzzy logic may be used. This work focuses on fuzzy logic based approaches being utilized for identification of coherent patterns. Empirical Analysis are conducted to realize and evaluate the effect of the methodology proposed.
Robust Fast Subclass Discriminant Analysis

In this paper, we propose novel methods to address the challenges of dimensionality reduction related to potential outlier classes and imbalanced classes often present in data. In particular, we propose extensions to Fast Subclass Discriminant Analysis and Subclass Discriminant Analysis that allow to put more attention on uder-represented classes or classes that are likely to be confused with each other. Furthermore, the kernelized variants of the proposed algorithms are presented. The proposed methods lead to faster training time and improved accuracy as shown by experiments on eight datasets of different domains, tasks, and sizes.
THE IMPACTS OF ROBOTS AND ARTIFICIAL INTELLIGENCE ON SERVICE QUALITY IN THE HOTEL INDUSTRY

Service robots and artificial intelligence promise to improve the service quality. Robotics in combination with rapidly improving technologies like artificial intelligence, bring opportunities for a wide range of innovations that have the potential to change service quality in hotel industry. Based on an extensive literature review, this article presents the acceptance of service robots in hotel industry. The paper acknowledges that the adoption of the robots and artificial intelligence on service quality is focused on the challenges of technological characteristics, customers’ readiness and practical effectiveness of the business. The study provides a comprehensive and systematic review of robots and AI concepts in a hotel industry and examines their impacts on service quality. The hotel industry future is going to be affected with high-tech tourism companies offering robot-automated services which rely on guidance to adopt and integrate robotics into their customer service operations.
MemOpLight: Leveraging application feedback to improve container memory consolidation

The container mechanism amortizes costs by consolidating several servers onto the same machine, while keeping them mutually isolated. Specifically, to ensure performance isolation, Linux relies on memory limits. These limits are static, despite the fact that application needs are dynamic; this results in poor performance. To solve this issue, MemOpLight uses dynamic application feedback to rebalance physical memory allocation between containers focusing on under- performing ones. This paper presents the issues, explains the design of MemOpLight, and validates it experimentally. Our approach increases total satisfaction by 13% compared to the default.
A Performance Comparison among Different Amounts of Context on Deep Learning Based Intent Classification Models

Human-computer interaction has attracted much attention from researchers and the public. Detecting user intent from a user utterance is a crucial sub-task for human-computer interaction. For task-oriented dialogue systems, correct detection of user intent makes task completion requested by a user possible. Many traditional methods for intent classification suggest rule-based methods which display inherent limitation caused by handcrafted rules. Recently significant research effort has been spent on deep learning based approaches to overcome the limitation of rule-based methods.In this paper, we conduct experiments to compare the performance among different amounts of context on deep learning based intent classification models. We utilize a Long Short-Term Model (LSTM) network and a Recurrent Convolutional Neural Network (RCNN) for intent classification models. We construct our multi-turn dialogue dataset consisting of intent labeled task-oriented dialogues between a user and a dialogue system in Korean. Both LSTM and RCNN based intent classification models illustrate higher accuracies with more amounts of context.
Will_Go at SemEval-2020 Task 3: An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on BERT

Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years.Our paper mainly discusses a methodology to analyze the effect that context has on human perception of similar words, which is the third task of SemEval 2020.We apply several methods in calculating the distance between two embedding vector generated by Bidirectional Encoder Representation from Transformer (BERT).Our team will go won the 1st place in Finnish language track of subtask1, the second place in English track of subtask1.
Lightning Trip Warning Based on GA-BP Neural Network Technology

Due to the complex operation environment of transmission lines and many factors affecting lightning trip, it is difficult to realize real-time warning of lightning trip of transmission lines. This paper presents a new real-time warning method for lightning trip based on the combination of lightning location system, transmission line data and GA-BP neural network technology. Firstly, the factors affecting line tripping are determined and input data are preprocessed. Then, a lightning trip prediction model based on GA-BP neural network is established. The total number of lightning trips can be predicted by summing up the results of all the intervals of the line to be warned. Finally, according to the classification standard of early warning, the lightning trip warning of the whole line is realized. The example shows that the correct classification rate is 80%, the accuracy rate is 86.67%, the false alarm rate and the leakage alarm rate are 23.53% and 13.33% respectively. The performance index of the model is ideal, so the model has a good prediction effect for the real-time lightning trip warning of transmission lines.
Based on J2EE Research and Realization of National Fitness Sports Convenience Service Platform

Abstract The concept of the national fitness convenient service platform for further study, combined with the current needs, design the convenient universal mobile end the development environment and development tools, at the same time, this paper expounds how to set up the national fitness convenient service platform of mobile terminal development environment, function classification of fitness service platform, each module development mode, the overall framework design.
Deformable Image Registration with a Scale-adaptive Convolutional Neural Network

Multi-resolution hierarchical strategy is typically used in conventional optimization-based image registration to address large deformation and improve the chance of a good local minimum. A rough concept of the scale is captured in deep networks by the reception field of kernels, and it has been realized to be both desirable and challenging to capture convolutions of different scales simultaneously in registration networks. In this study, we propose an image registration network that is conscious of and self-adaptive to deformation of various scales. Dilated inception modules (DIMs) are proposed to incorporate receptive fields of different sizes in a computationally efficient way. Scale adaptive modules (SAMs) are proposed to guide and adjust shallow features using convolutional kernels with spatially adaptive dilation rate learned from deep features. DIMs and SAMs are integrated into the registration network which takes a U-net structure. The network is trained in an unsupervised setting and completes registration with a single evaluation run. Experiment with cardiac MRIs showed that the adaptive dilation rate in SAM corresponded well to the deformation scale. Evaluated with left ventricle segmentation, our method achieved a dice of (0.93±0.02), significantly better than SimpleElastix and networks without DIM or SAM. Assessment with respect to average surface distance was less than 2 millimeters (1.6 pixels), comparable to the best-performing SimpleElastix without statistical significance. Experiment with synthetic data also demonstrated the effectiveness of DIMs and SAMs, which leaded to a significant reduction in target registration error based on dense deformation field. The average registration time was 4 milliseconds for 2D image with size 256×256.
An IR-Based Artificial Bee Colony Approach for Traceability Link Recovery

Multiple software development activities, including change impact analysis, requirements validation, maintenance and evolution of software benefit from adequate requirements traceability link recovery practices. Requirements traceability link recovery increases the overall quality of software products; however, companies often are unsuccessful implementing them due to lack of communication and strict deadlines. Several approaches for semi-automatic link recovery across requirements and source code in which textual analysis and information retrieval (IR) techniques are the baseline have been developed, but there is a need for methods that further enable the automatic generation of links. To advance automatization in the link recovery process, we investigate it as a combinational problem using an optimization approach. In this paper, we studied the requirements traceability recovery problem as a big search space formed by multiple pairs (requirements and source code classes), where requirements are matched to code elements. The artificial bee colony (ABC) algorithm is adapted for searching for the solution that maximizes an objective function which is calculated by a weighted cosine similarity where weighs for each term in the textual content of requirements and source code are defined according to the term frequency (TF-idf). Evaluation over three data sets demonstrates the effectiveness of the proposed approach. It returned high precision and recall values for the recovered links.
Informatization Case against to COVID-19 in Korean Government With Perspective of Government as a Platform: Focusing on ICT and Data Usage

본 연구는 COVID-19에 대한 한국의 대응 정보화 사례를 플랫폼 정부(Government as a Platform, GaaP)의 관점에서 분석하고자 한다. 플랫폼 정부는 애플, 구글, 페이스북 등 민간기업의 플랫폼 전략 성공에 따라 공공부문도 플랫폼 전략을 차용해야 한다고 주장한 O"Reilly로부터 비롯되었다. O"Reilly는 정부가 제공하는 플랫폼에서 공공과 민간이 협업하여 새로운 공공서비스를 창출 할 수 있음을 제시하였다. 본 연구는 플랫폼 정부 관련 문헌을 기반으로 네가지 요인을 도출하고 분석틀로 활용하여, 국내 COVID-19 대응사례를 분석하였다. 첫째 지능정보기술 활용 측면에서는 스마트 검역시스템, 코로나 19 역학조사 지원 시스템과 같이 각 기관별 산재되어 있는 정보시스템의 통합이 두드러졌다. 둘째, 데이터 개방 측면에서는 질병관리청 홈페이지, 코로나 19 마이크로 페이지와 같은 웹사이트는 투명성 증진을 위한 목적이 있었다. 공공데이터 포털과 같은 정보공개 플랫폼은 데이터 개방은 서비스 개발을 통한 부가가치 창출이 목적이었다. 셋째, 이해관계자의 참여 측면에서는 광화문 1번가, 공공데이터 포털, 도전 한국 등의 사례를 확인하였다. 사례분석 결과 참여의 질적 수준을 제고하고, 수요자가 원하는 데이터를 제공하며, 참여를 위한 인센티브의 도입이 필요하였다. 넷째, 혁신적 산출물 측면에서는 확진자 동선앱, 마스크앱 등의 서비스 개발과 광화문 1번가의 공공데이터 개방 관련 정책제안이 확인되었다. 현재까지 확인된 산출물의 소수이고, 앱 개발사례는 특정한 주제에 편중된 것으로 나타났는데, 이는 감염병이라는 정책이슈의 특수성과 수요자 맞춤형 데이터 제공의 부족에 기인한 것으로 보인다. In this study, it aims to analyze informatization case against to COVID-19 in Korean Government with perspective of Government as a Platform(GaaP). GaaP stems from O"Reilly who have argued that governments should adopt platform strategy due to success of platform enterprises like Apple, Google, Facebook and so on. O"Reilly suggested that public-private partnership can create innovative public service on the public platform. It analyzes case against to COVID-19 with framework that including four factors from GaaP literature. First, in the aspect of intelligent information technology, the role of integrated information systems that integrate separated information systems from various organizations are remarkable like smart quarantine system and COVID-19 epidemiological investigation system. Second, in the aspect of open data, public data release through website like homepage of Korea Centers for Disease Control and Prevention and COVID-19 micro page has a purpose on increasing transparency. public data release through open data platform like Data portal has a purpose creating added value with developing services. Third, in the aspect of stakeholder engagement, we find the cases including Gwanghwamun 1bunga, Data portal, and Challenge Korea. As a result of the case analysis, there are needs to increase quality of engagement, provide user demanded data, and introdue incentive. Fourth, in the aspect of innovative output, we find the cases app dvelopment including confirmed person route app and public mask app and policy suggestion through Gwanghwamun 1bunga. There are small number of outputs identified and app development cases are biased towards particluar subject. This result stems from distinctiveness of policy issue like infection disease and lack of user demanded open data.
Prediction of Credit Card Using the Naïve Bayes Method and C4.5 Algorithm

Abstract Prediction credit card submission is a system that is able to provide an assessment of alternatives in order to help credit card applicants in making decisions. Many methods can be used in building a prediction system for credit card submissions. This research will compare two methods in predicting credit card submission, namely the naïve bayes method and c4.5 algorithm, case study of Credit Card Submission Prediction, the results of the research are, knowing the level of accuracy of the two methods. Criteria that form the basis of decision making include age, sex, recent education, marital status, number of dependents, type of company, monthly income, and salary slip. The final result found that the naïve bayes method and the c4.5 algorithm are relatively the same.
Sensor-Based Temporal Logic Planning in Uncertain Semantic Maps.

This paper addresses a multi-robot mission planning problem in uncertain semantic environments. The environment is modeled by static labeled landmarks with uncertain positions and classes giving rise to an uncertain semantic map generated by semantic SLAM algorithms. Our goal is to design control policies for sensing robots so that they can accomplish complex collaborative high level tasks captured by global temporal logic specifications. To account for environmental and sensing uncertainty, we extend Linear Temporal Logic (LTL) by including sensor-based predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task specification. The sensor-based LTL planning problem gives rise to an optimal control problem, solved by a novel sampling-based algorithm, that generates open-loop control policies that can be updated online to adapt to the map that is continuously learned by existing semantic SLAM methods. We provide extensive experiments that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale planning tasks in the presence of uncertainty.
Variants of Generative Adversarial Networks for Credit Card Fraud Detection

One of the major problems in the field of Banking, Financial Services and Insurance (BFSI) is detecting fraudulent transactions. It is a big challenge to accurately detect fraudulent transactions because of the huge variation in the number of fraudulent and non-fraudulent samples, called the class imbalance problem. Many approaches address this problem, such as over-sampling, under-sampling, cost-sensitive methods, etc. to name a few. In recent years, Generative Adversarial Network (GAN)-based approaches for oversampling have drawn attention from both industry and academia to overcome this problem. In this paper, we have explored and compared various state-of-the-art GAN based approaches such as Vanilla GAN, Conditional GAN (cGAN), Wasserstein GAN (WGAN), and Conditional WGAN (WcGAN) for the problem of credit card fraud detection. It is found that the recent techniques have been very sensitive to the hyperparameters. To solve this problem we introduced two new methods, WGAN with Gradient Penalty (WGAN-GP) and Conditional WGAN with Gradient Penalty (WcGAN-GP) for credit card fraud detection. It is found that these approaches not only generate more realistic data but also provide more stable results.
Analysis and Evaluation of Hash Access for Blockchain Radio Access Networks

The Hash Access (HA) protocol was proposed to prevent selfish access and address the trust issues between client devices for blockchain radio access networks (B-RAN). However, its performance is still unclear. In this work, we establish an analytical model for the HA protocol in B-RAN and evaluate its performance comprehensively. We first present the workflow and principle of the B-RAN-based HA protocol in detail. We then characterize the traffic under the HA protocol via a Markov chain model and further assess key performance indicators, including transmission success probability, access delay, and network throughput. Finally, simulation results illustrate the effectiveness of our established model.
A Proposed Cyber security framework for auditing in financial institutions

The continued rise of cyber threats has led some scholars to question the role of auditors and the effectiveness of audits in ensuring cyber security. This paper aims to examine cyber security from an auditing perspective and develop a framework that can enhance cyber security in financial institutions. Given the limitations of the existing frameworks, this paper develops a more comprehensive framework for auditing cyber security that integrates aspects of the role theory, lending credibility theory, the policemen theory, defense in depth theory, COBIT5, NIST cyber security framework and ISO standards, among others. The framework can enable more effective ways of auditing cyber security with the aim of improving cyber safety in financial institutions.