Reviewerly/exHarmony huggingface dataset:
Human Action Recognition using 2D Poses

The advances in video capture, storage and sharing technologies have caused a high demand in techniques for automatic recognition of humans actions. Among the main applications, we can highlight surveillance in public places, detection of falls in the elderly, no-checkout-required stores (Amazon Go), self-driving car, inappropriate content posted on the Internet, etc. The automatic recognition of human actions in videos is a challenging task because in order to obtain a good result one has to work with spatial information (e.g., shapes found in a single frame) and temporal information (e.g., movements found across frames). In this work, we present a simple methodology for describing human actions in videos that use extracted data from 2-Dimensional poses. The experimental results show that the proposed technique can encode spatial and temporal information, obtaining competitive accuracy rates compared to state-of-the-art methods.
Resource-Aware Migration Scheme for QoS in Cloud Datacenter

With the rapid growth of data centers, thousands of large data centers with lots of computing nodes are established. In order to user satisfaction, Assurance of QoS is important in CDCs. Also, many of the current research studies have not considered multi-metric for assurance of QoS. In this paper, we categorize QoS through previous work and build the migration scaling scheme for QoS in CDCs with considering multi-metric. And then from evaluation result, we prove that our proposed method is able to efficiently manage the resource and grantee QoS.
MRL Champion Team Paper in Humanoid TeenSize League of RoboCup 2019

RoboCup uses soccer competitions as a research area to promote robotics and artificial intelligence. The ultimate goal of the RoboCup is to develop a team of humanoid robots that can win against the human world champion soccer team in 2050. This paper presents the approach of the MRL TeenSize humanoid team to improve its hardware and software which leads to achieving first place in soccer competition and Drop-In games, second place in the technical challenge and third-best humanoid award.
Implementation of QR Code on E-Certificate for Events at Maranatha Christian University

Every participation in the activity is always proven by the presence of a certificate. Issuance of certificates requires fees for paper, printing and distribution. The emergence of the era of paper less should be able to transform the presence of the certificate. Paper less is stopped by the presence of digital documents that can be accessed independently (portable), easily and across platforms. Portable Document Format or abbreviated as PDF one of the popular documents that can be distributed via the internet. So that PDF can be used as a document base to realize e-Certificate. However, it is necessary to develop a good application to facilitate the presence of the e-Certificate, and present solutions related to the authenticity of the certificates produced.
Group Loss: An Efficient Strategy for Salient Object Detection

Deep convolutional neural networks (CNNs) have recently achieved great improvements in salient object detection. Most existing CNN-based models adopt cross entropy loss to optimize the networks for its capability in probability prediction. The function of cross entropy loss in salient object detection can be seemed as a pixel-wise label classification for images, which automatically predict whether the pixel is salient or non-salient. However, cross entropy loss pays attention to each single pixel of image when classifying the label, which doesn't consider the relationship with other pixels. In this paper, we propose an additional loss function, called group loss, to improve the above limitation of cross entropy loss. In our model, group loss as well as cross entropy loss work together to optimize the network for better saliency detection performance. The purpose of group loss is to make the difference between salient pixels smaller while the distance between salient and non-salient pixels as large as possible. Meanwhile, due to the large computation cost of pixel-wise comparisons, we design a superpixel pooling layer for computing group loss with no additional parameters, which converts the computation of group loss to superpixel level. The experimental results show that the introduction of group loss improves the performance of CNN network in salient object detection, which makes the boundaries of salient objects more distinct.
Hybrid Text Topic Discovery Method for Multi-source Information

With the wide application of information technology and the rapid development of cyberspace, cyberspace security has become a hot topic in society. Topic discovery technology plays a vital role to grasp the direction of public opinion in the governance of cyberspace security. Due to the diversity of information sources, the distribution of hybrid text presents inhomogeneous characteristics. Most of the current topic discovery problems are addressed by clustering algorithms, which are not effective in processing hybrid text. Therefore, the paper proposes a multi-source text clustering method based on the Dirichlet Multinomial Mixture model (DMM). By considering the characteristic difference between multi-source text data, we propose a feature fusion algorithm based on the TextRank algorithm. Furthermore, the problem of text sparse and high-dimensionality in feature fusion is handled by the DMM model. Numerical results reveal that the proposed clustering algorithm significantly improves the multi-source text clustering performance, which relieves the influence of text feature inhomogeneous, text sparse and high-dimensionality after feature fusion. It provides decision support for the governance of cyberspace security and contributes to the stable development of society.
jKarma: A highly-modular framework for pattern-based change detection on evolving data

Pattern-based change detection (PBCD) describes a class of change detection algorithms for evolving data. Contrary to conventional solutions, PBCD seeks changes exhibited by the patterns over time and therefore works on an abstract form of the data, which prevents the search for changes on the raw data. Moreover, PBCD provides arguments on the validity of the results because patterns mirror changes occurred with any form of evidence. However, the existing solutions differ on data representation, pattern mining algorithm and change identification strategy, which we can deem as main modules of a general architecture, so that any PBCD task could be designed by accommodating custom implementations for those modules. This is what we propose in this paper through jKarma, a highly-modular framework written in Java for defining and performing PBCD.
Design and Development of a Plagiarism Corpus in Thai for Plagiarism Detection

One of the main problems of creating a plagiarism corpus in Thai is that it is quite a difficult task to acquire the plagiarized documents with real cases due to the copyright issue. To solve the problem, we present a design and development of a Thai plagiarism corpus to evaluate and compare plagiarism detection algorithms for Thai. The corpus is developed by using the simulated plagiarism method based on Thai Wikipedia articles and web page articles. For this method, we provide a Thai plagiarism annotation tool and a Thai plagiarism guideline for assisting human annotators to plagiarize text passages. Our corpus contains simulated cases of plagiarized documents based on four classes of Thai plagiarism and linguistic mechanisms including copy-based change, lexicon-based change, structure- based change, and semantic-based change. We show that the suspicious documents in the corpus are manually created by using different obfuscation strategies, which make the suspicious documents more realistic and challenging. We then believe that the corpus developed in this paper will be a valuable contribution in the development, comparison, and evaluation of plagiarism detection algorithms. Moreover, our corpus is free and publicly available for research purposes.
A Secure Hierarchy Tree Algorithm for Efficient and Secure Key Distribution in Cloud Computing

Cloud computing is very flexible to store all types of files; hence an improved level of security means is necessary for protected file transmit. The general way to guard data prior to outsourcing is data encryption. So secure key distribution to the users for decrypting the data is a challenging issue. The main idea of this work is to securely send the symmetric key to the authorized users. In this paper, an efficient hierarchy tree algorithm is proposed to distribute the symmetric key using on-demand security infrastructure for secured file transfer in provisioned cloud. Here the security mechanism relies on two aspects with dynamic security enhancement by providing secured tunnel using SSH, since the virtualized pattern does not have a security model to envision the Sec Cloud Life Cycle model (Sec CLC). The file hosted or distributed in remote host is always subjected to on-demand IaaS model and security parameter in those host have to be analyzed and queries bounding to retrieve the data from model has to be focused clearly. Hence a new model along with the SSH based encryption technique to transfer data from the remote host is proposed. Here the scheme is used to process the secure file transfer along with the remote host. Since the scheme is fully tunneled using SSL and remote attacks is never been possible. The proposed model has been executed in LINUX environment and tested in HADOOP clusters in cloud stack.
Sharing of Functional and Special Means in Pipeline Floating-Point Systems with Strongly Connected Versions

This paper is dedicated to a problem of designing of the multi-version systems with strongly connected versions in area of pipeline data processing in the floating-point formats. Multi-version systems are directed to parrying of common cause failures, including design errors. This problem is traditionally solved on the basis of creation of the most independent versions which number determines considerable complexity of a system. This leads to restriction in development of the multi-version system. Systems with strongly connected versions are aimed at the solution of the same task, but in the conditions of the maximum connectivity of versions at which the task is feasible. The problem is solved in the presence of at least one right version and means of its search. Sections of modern pipeline systems are the matrix structures constructed of uniform elements with regular connections: parallel adders and shifters, iterative array multipliers and dividers. Such structures allow to form strongly connected versions which search can be carried out by cyclic shift of operands. Pipeline floating-point systems execute processing of the data, presented by mantissas and exponents whose interaction is carried out by means of the operations of normalization and a denormalization on the basis of arithmetic shift. We offer sharing of functional means in performance of operations of normalization and a denormalization with special functions of search of the right version on the basis of joint realization of operations of cyclic and arithmetic shift.
Policy-based Revolutionary Ciphertext-policy Attributes-based Encryption

Ciphertext-policy Attributes-based Encryption (CP-ABE) is an encouraging cryptographic mechanism. It behaves an access control mechanism for data security. A ciphertext and secret key of user are dependent upon attributes. As a nature of CP-ABE, the data owner defines access policy before encrypting plaintext by his right. Therefore, CP-ABE is suitable in a real environment. In CP-ABE, the revocation issue is demanding since each attribute is shared by many users. A policy-based revolutionary CP-ABE scheme is proposed in this paper. In the proposed scheme, revocation takes place in policy level because a policy consists of threshold attributes and each policy is identified as a unique identity number. Policy revocation means that the data owner updates his policy identity number for ciphertext whenever any attribute is changed in his policy. To be a flexible updating policy control, four types of updating policy levels are identified for the data owner. Authorized user gets a secret key from a trusted authority (TA). TA updates the secret key according to the policy updating level done by the data owner. This paper tests personal health records (PHRs) and analyzes execution times among conventional CP-ABE, other enhanced CP-ABE and the proposed scheme.
Coarse grid partition to speed up A* robot navigation

Path planning is one of the most focused on problems in the field of autonomous robots. The autonomous robot should pass around obstacles from a given starting position to a goal position without touching them. Research on path planning has pursued many different approaches to the solution of this problem, in which the A* algorithm is one of the outstanding approaches that has been widely disseminated in applications, but the algorithm utilizes a large amount of time. Therefore, an alternative Partitioning-Based Path Planning approach, namely PBPP, is proposed to reduce time consumption using a hierarchical partitioning method to improve the A* algorithm. A combination of the two concepts, the coarse-to-fine grid map representation and the A* path planner, which achieves a near-shortest path in O(n2) computational complexity is the main contribution of the study. Conceptually, the PBPP uses the principle of divide-and-conquer to divide the global map into several sub-maps in which individual collision-free spaces are able to be decomposed and represented. With the subdivided maps, hierarchical planning can provide a more feasible direction to achieve a smooth path in the result of the optimal path. The experimental results demonstrate the PBPP's utility for reducing time-consumption and finding the shortest path.
Web Content Outlier Mining using Machine Learning and Mathematical Approaches

Due to the massive, dynamic and heterogeneous nature of the web, discovering outliers from the web is demanding than from the numeric dataset. On exploring for information in the web, the inappropriate irrelevant and redundant information may be retrieved to the user. So, it is a big challenge to get and access high quality information on the web effectively and efficiently without including irrelevant and redundant information. Mining web content outliers focus on mining inappropriate duplicate and irrelevant web pages from the other web pages under the same categories. Removing outliers from the web improves the accuracy of search results, decreases the complexity of time for indexing and complexity of time and saves the user time and effort. We applied the Latent Dirichlet Allocation method from the machine learning approaches and a mathematical approach named linear correlation method to move web content outliers. This system tends to improve F1-measure, accuracy results and reduce time complexity.
A Fuzzy Logic-based Approach to Detection of Abnormal Crowd Behaviour

The paper presents an approach to detection of abnormal crowd behaviour at microscopic level. The characteristic individual and group motion patterns are specified with fuzzy predicates. The human interpretation of an video sequences of an abnormal crowd behaviour, based on common-sense knowledge, is mapped into fuzzy logic functions. Based on the evaluation of these functions, the abnormal crowd behaviour is detected. The usability of the proposed approach is tested on video sequences with ground truth annotations. The preliminary experimental results showed that the approach supports an efficient representation of expert knowledge and detection of anomaly crowd behaviour.
Mining the treasure of palm leaf manuscripts through information retrieval techniques

Purpose This paper aims to popularize information retrieval from palm leaf manuscripts among computer scientists to make available the guidance of the age-old heritage in shaping the future. Design/methodology/approach With computer technology penetrating every aspect of life, information retrieval algorithms can be exploited to help build a system which can dig into the ocean of knowledge from these manuscripts. Findings The knowledge in them covers all aspects of life. Be it religious beliefs, literature, science, mathematics, or any other. However, due to discontinuation of practice of copying their content on fresh leaves, they now possess a fragile life which needs to be preserved at the earliest. The modern means of digitization can help in their preservation. Research limitations The Government of India and other organizations are doing commendable job of preserving and safeguarding country’s heritage and age-old knowledge system through the movement of digitization. In the years to come, the agonizing problem of manuscripts degradation will be eradicated completely. However, next when it will come to mining the knowledge treasure out of these manuscripts, we would be confronted with another helpless situation. Practical implications The digitization process would capture the manuscripts from present physical palm leaf to digital image form by clicking high-quality pictures. All the text in a palm leaf will be available in the form of images, but on these images, a simple search for any word would not be possible. Originality/value Working towards mining the treasure of knowledge from the palm leaf manuscripts, hordes of challenges have been outlined. Over and above the problem of preventing decay to palm leaf manuscripts is the challenge of deciphering text, image analysis, information retrieval and search. Search is further associated with issues of meaningful and useful extraction through semantic analysis. This paper advocates the dire need for systematic research to be undertaken in this field opening up avenues for past knowledge to guide future prospects in several domains.
A Study on the Effect of MOOCs Learning on Improving College Students’ E-learning Self-efficacy

The deep development of "Internet +" has constantly changed the current learning mode and pushed the education informatization into the new era. As an important factor affecting the quality of E-learning, self-efficacy intervenes in the individual's E-learning situation from three aspects: individual, behavior, and environment to regulate the individual's E-learning performance. This study carried out according to the process of theoretical analysis, scale compilation, experimental intervention, and effect test by adopting the methods of literature research, investigation, and quasi-experimental research. It clarified the definition and structure of College Students' E-learning self-efficacy and compiled an operable evaluation scale at the theoretical level. It also verified that the college students' E-learning self-efficacy could be improved by MOOCs learning and explored some effective improvement strategies in the practical level.
PFDLIS: Privacy-Preserving and Fair Deep Learning Inference Service under Publicly Verifiable Covert Security Setting

The recent popularity and widespread use of deep learning heralds an era of artificial intelligence. Thanks to the emergence of a deep learning inference service, non-professional clients can enjoy the improvements and profits brought by artificial intelligence as well. However, the input data of the client may be sensitive so that the client does not want to send its input data to the server. Similarly, the pre-trained model of the server is valuable and the server is unwilling to make the model parameters public. Therefore, we propose a privacy-preserving and fair scheme for a deep learning inference service based on secure three-party computation and making commitments under the publicly verifiable covert security setting. We demonstrate that our scheme has the following desirable security properties—input data privacy, model privacy and defamation freeness. Finally, we conduct extensive experiments to evaluate the performance of our scheme on MNIST dataset. The experimental results verify that our scheme can achieve the same prediction accuracy as the pre-trained model with acceptable extra computational cost.
A Proposed Approach to Build an Automated Software Security Assessment Framework using Mined Patterns and Metrics

Software security is a major concern of the developers who intend to deliver a reliable software. Although there is research that focuses on vulnerability prediction and discovery, there is still a need for building security-specific metrics to measure software security and vulnerability-proneness quantitatively. The existing methods are either based on software metrics (defined on the physical characteristics of code; e.g. complexity or lines of code) which are not security-specific or some generic patterns known as nano-patterns (Java method-level traceable patterns that characterize a Java method or function). Other methods predict vulnerabilities using text mining approaches or graph algorithms which perform poorly in cross-project validation and fail to be a generalized prediction model for any system. In this paper, we envision to construct an automated framework that will assist developers to assess the security level of their code and guide them towards developing secure code. To accomplish this goal, we aim to refine and redefine the existing nano-patterns and software metrics to make them more security-centric so that they can be used for measuring the software security level of a source code (either file or function) with higher accuracy. In this paper, we present our visionary approach through a series of three consecutive studies where we (1) will study the challenges of the current software metrics and nano-patterns in vulnerability prediction, (2) will redefine and characterize the nano-patterns and software metrics so that they can capture security-specific properties of code and measure the security level quantitatively, and finally (3) will implement an automated framework for the developers to automatically extract the values of all the patterns and metrics for the given code segment and then flag the estimated security level as a feedback based on our research results. We accomplished some preliminary experiments and presented the results which indicate that our vision can be practically implemented and will have valuable implications in the community of software security.
The First Experience Prototype of The Storytellers Project

This paper reports the first experience prototype of The Storytellers project, which is a remote reading aloud service for children. The interactive interface for children is described, called the Storybell, as well as a low fidelity prototype of it which was developed in order for children and readers to run a first remote reading session.
Convolutional neural network in the images colorization problem

Object of the research are modern structures and architectures of neural networks for image processing. Goal of the work is improving the existing image processing algorithms based on the extraction and compression of features using neural networks using the colorization of black and white images as an example. The subject of the work is the algorithms of neural network image processing using heterogeneous convolutional networks in the colorization problem. The analysis of image processing algorithms with the help of neural networks is carried out, the structure of the neural network processing system for image colorization is developed, colorization algorithms are developed and implemented. To analyze the proposed algorithms, a computational experiment was conducted and conclusions were drawn about the advantages and disadvantages of each of the algorithms.