Reviewerly/exHarmony huggingface dataset:
The implementation of a probabilistic learner model in LMS-LD course creation application COPROLINE

In the context of e-Learning systems, we can distinguish between two different types of settings. First, we have adaptable systems, which refer to the property of changing the system settings. The learner can change the behavior of the system. Then, the learner is able to customize the system in a specified way to fit the needs of users. The learner model is the key element to generate the adaptation of the system to each specific user. It's a representation of the learner information through which the system is based in to make it adaptable. We will present in this paper the implementation of a probabilistic learner model developed based on multi entities Bayesian networks and artificial intelligence, into a Course Creation Application Compatible with LMS-LD. The results presented in this work favor the implementation of a learner model to endorse the adaptation into some learning situations that the learners have followed during a year of testing.
Metric Method for Long Life Semantic Applications

Ontology is the core of the semantic applications, so the quality of it has a direct proportion with Ontology.It impacts directly on the long life of the semantic applications.There are different negative effects in the design of Ontology' classes as Blob, Lazy Class, Large Class, and Singleton.These negative effects called bad smells.However, detecting smells is not supported in any Ontology editors.This paper proposes a metric method called ONTOPYTHO.It can detect classes' smells automatically from Ontology models even if it is a Big Ontology.We programed ONTOPYTHO via Python and SPARQL languages.We evaluated the proposed method ONTOPYTHO by applying it on twelve publicly OWL Ontology projects.The detected smells appeared 117495 times in the twelve projects.The results showed that both the size and the number of classes of OWL Ontology has no effect in the presence of the smells.The results also showed that 69.24% of the classes are lazy classes.This means that big OWL Ontologies are not big in their nature, but because of theses lazy classes.The proposed method is the first method that detects Lazy class smell in the design of big OWL Ontology.In our random sample of big Ontologies, Lazy class smell appears approximately 99.8% of the smells.
Automated Parasite’s Detection in Microscopic Images of Stools Using Distance Regularized Level Set Evolution Initialized with Hough Transform

Background and purpose: The analysis of biomedical microscopic images is carried out manually in medical laboratories.The manual analysis of clinical images lets to both repetitive tasks and management of huge amounts of data.This is tedious and times consuming for laboratory technicians.Inevitably, it is also prone to human errors.Our objective in this work is to contribute to the automation of the analysis of microscopic images of stools using Distance Regularized Level Set Evolution automatically initialized by Hough transform.Method: We firstly converted the microscopic images to edge maps using canny algorithm.Next, we located the parasite through circular Hough transform and draw circles around them.Those circles stand as initial contours of DRLSE.The contours evolve until they fit the boundaries of the parasites.The final extraction is performed using a complementary method based on the signed distance character of the level set function.Results: The Distance Regularized Level Set Evolution has been automatically initialized.We applied our method to the detection of intestinal parasites in microscopic images.Experimental results show accurate, efficient and less time consuming of our scheme compared to others recently proposed in the literature.Conclusion: This is a notable contribution to the automation of stools examination in the medical laboratories.In forthcoming works, we plan to include this segmentation process in an expert system of parasitic diseases diagnosis.
Virtual Bulletin Board using Man-Machine Interface (MMI) for Authorized Users

Objectives: ‘Notice board’ is most important thing in every institution and public places like bus stops, railway stations and parks. However conveying various notifications day by day is an extremely difficult strategy. This study focuses on advanced notice board called Virtual Bulletin Board (VBB). Methods/Findings: Virtual bulletin Board (VBB) follows the novel procedure to obtain the data for the users. The approved users only can see the content of the VBB. The user can communicate with Board by free hand using MMI. The MMI (Man-Machine Interface) is an integrated interface system that links a network of smart devices together, and allows users to communicate with the physical objects using hand gestures. The user wears a smart glass which shows the user interface in an augmented reality view. Application: The Hand signals are identified by the smart glass and after recognize the correct hand gesture input, VBB will communicate with the associated smart devices to carry out the designated operations. VBB provides common inter-device operations such as file transfer, printing, zooming and touch screen based operations. Keywords: Augmented Reality, Bulletin Board, Hand Gestures, Notice Board, Virtual Board
Model of continuing pedagogical education in the region: implementation experience and development prospects

We substantiate the necessity of developing and implementing an innovative model of continuous pedagogical education in the region. As such model, we consider a multilevel educa-tional complex, created on the basis of the Pedagogical Institute of Derzhavin Tambov State Uni-versity, whose activities are aimed at implementing a number of federal projects in the framework of the national project “Education”. Our position is that the developed complex has a number of undoubted advantages in comparison with isolated educational institutions that train personnel and is able to effectively resolve existing problems in the continuous pedagogical education of the region. We substantiate the structure of a multilevel educational complex, which is a hierarchy of stages, in which the previous stage sequentially ensures the functioning of the subsequent ones. Each of the five levels (preschool education, pre-vocational pedagogical training, secondary voca-tional education, higher and postgraduate education) is described in detail: the basic mechanisms of their functioning are examined, the existing experience of implementation is analyzed, and pro-spects for further development are presented. We determine the results of educational complex of continuing pedagogical education influence on the development of the Tambov Region.
Communication Satisfaction, Employee Engagement, Job Satisfaction, and Job Performance in Higher Education Institutions

This quantitative study investigates the relationships and impacts of communication satisfaction, employee engagement, job satisfaction, and job performance in higher education institutions in Thailand. Survey data were collected from 400 faculty members and staff who work in public and private universities in Thailand. The results from simple and multiple regression analyses show that communication satisfaction has a positive impact on job satisfaction and employee engagement; job satisfaction has a positive impact on employee engagement and job performance; and employee engagement has a positive impact on job performance. However, there is no evidence supporting a significant relationship between communication satisfaction and job performance.
Assessing the Readability of Freely Available ICU Notes.

Unstructured data stored in an electronic health record (EHR) system can be very informative but require techniques such as natural language processing to extract the information. Developing such techniques requires shared data, but clinical data are often not easy to access. A freely available intensive care unit database, MIMIC-III, was released in 2016 to address this issue and benefit the informatics research community. While the database has been utilized by a few studies, the text characteristics of the notes have not been summarized. In this study, we present the summary of the basic text characteristics and the readability of the MIMIC-III ICU notes. We further compare the results with our previous study where proprietary EHR notes were used. The results show that the text characteristics of MIMIC-III notes were comparable with proprietary EHR notes, although the note readability index was slightly lower. The clinical notes in MIMIC-III can be a viable option for researchers who are interested in clinicians' language use but have no access to proprietary EHR systems.
A Scalable Attribute Based Encryption for Secure Data Storage and Access in Cloud

Today a large volume of data is stored in cloud that requires fine grained accessibility for heterogeneous users. Cipher-text policy attribute based encryption (CP-ABE) has evolved into a promising solution for secure data storage with fine grained access control. In CP-ABE, the ciphertext is associated with an access policy (set of rules) and users can access the data if their attributes satisfies the access policy. However, existing CP-ABE schemes fail to perform in presence of large number of users and hierarchical relationships among them. Moreover, a majority of the CP-ABE schemes require large computational overhead for light weight applications. In this paper, we present a hierarchical attribute based cryptosystem by introducing hierarchical dependency between the users and thereby achieving multi-layer verification for fine grained data access. Moreover, our proposed cryptosystem is seamless to user revocation. The efficiency and security of our proposed cryptosystem have been analyzed and reported. Further we implement the proposed cryptosystem in Charm to demonstrate its practicality.
Immersive Storage

Hands free and immersive experiences are driving the development of augmented reality (AR) and virtual reality (VR) technologies. 360° rendered and captured content combined with various other gears, such as haptic suits and gloves, treadmills as well as advanced immersive sound are transforming the video game industry. The AR glasses are transforming the way we work, allowing hands free access to instructions, manuals, and remote video help. Figure 1 from the 2019 Augmented World Expo shows the author wearing the latest edition of Google Glass, now focused on industrial use.
Adaptive CSLBP compressed image hashing

Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images.
The Effects of a College English Tutoring Program on Students Academic Achievement and Satisfaction

본 연구의 목적은 대학의 필수 교양영어 교과목 수업 내용과 연계한 영어 동료 튜터링 프로그램에 참여한 튜터와 튜티들의 만족도와 튜티들의 학업성취도를 중심으로, 튜터링 프로그램의 효과성을 입증하는 데에 있다. 이를 위하여, H 대학의 2018학년도 영어 동료 튜터링 프로그램에 참여한 경험이 있는 튜터 68명과 튜티 164명을 대상으로 설문자료를 수집하였고, 최종적으로는 튜터 59명과 튜티 154명의 설문지로 결과를 확인하였다. 만족도를 알아보기 위해 설문 자료를 분석한 결과, 5점 만점 척도에서 튜티의 만족도가 4.27점, 그리고 튜터의 만족도가 3.97점으로 영어 튜터링 프로그램에 대한 전반적인 만족도가 비교적 높게 나왔다. 튜터의 경우에는 교수방법의 습득, 재정적인 도움, 리더십의 향상의 순으로 만족도를 보여주었고, 튜티의 경우에는 학습동기의 향상, 유대감 증진, 영어 학습능력의 향상의 순으로 만족도를 보여주었다. 튜티의 학업성취도 확인을 위해 교양영어 교과목 담당 교수 4인과 영어영문학과 교수 1인에 의해 출제 및 검토된 50문항의 사전, 사후 시험의 결과를 분석하였다. 2018학년도에 본 프로그램에 참여한 164명의 튜티들 중에서 77퍼센트가 넘는 127명의 학생들이 성적 향상을 보였고, 40점 이상의 큰 향상도를 보인 학생들도 34명에 달하는 것으로 확인되었다. 본 연구의 결과는 각 대학 신입생들의 기초학력 부진 및 학업수준의 차이의 문제를 해결하기 위한 방안으로서의 동료 튜터링 프로그램의 효과성을 입증함으로써 영어와 같은 기초학문 교과목과 연계한 튜터링 프로그램의 지원과 운영을 확대하는 것이 필요함을 확인해 주고 있다.The purpose of this study was to investigate the effectiveness of a peer tutoring program for a required English class at a liberal arts college by focusing on the satisfaction of tutors and tutees. To this end, a questionnaire was collected from 68 tutors and 164 tutees who participated in the program during the 2018 academic year at H university. Survey results of 59 tutors and 154 tutees were analyzed. Analysis showed that the overall satisfaction level for the tutor program was relatively high, with the tutees satisfaction being at 4.27 and the tutors satisfaction being at 3.97 on a five-point scale. In the case of tutors, satisfaction was shown in the order of acquisition of teaching methods, financial help, and improvement of leadership. In the case of tutees, satisfaction was shown in the order of improvement of learning motivation, promotion of bonding, and improvement of English learning proficiency. Both pre- and post-examination results were analyzed to check the academic performance of tutees. Of 164 tutees, 127 (more than 77%), were found to have improved their English academic achievement level, with 34 students showing significant improvement (over 40 points). The results of this study confirm that it is necessary to expand the support and operation of tutoring programs linked to basic academic subjects such as English, by demonstrating the effectiveness of a peer tutoring program in addressing the different academic levels among freshmen in most universities.
Skid Steering of Robotic Vehicle for Autonomous Applications

The paper deals with principles of motion control of skid steered robotic vehicle. The proposed control of drives allows the vehicle to move on uneven terrain with different surface or inclination of the vehicle. Control was checked on physical model of robotic vehicle. Inaccuracy of odometric calculation based on wheel speed sensors is eliminated by visual odometry based on stereoscopic camera system in combination with signals from inertial sensors. Solution is based on robotic operation system ROS to ensure reusability and compatibility with another hardware platforms. Further, it has also been demonstrated that the use of electric drive for vehicles with skid steer can be energy efficient.
Political Stance in Danish.

The task of stance detection consists of classifying the opinion within a text towards some target. This paper seeks to generate a dataset of quotes from Danish politicians, label this dataset to allow the task of stance detection to be performed, and present annotation guidelines to allow further expansion of the generated dataset. Furthermore, three models based on an LSTM architecture are designed, implemented and optimized to perform the task of stance detection for the generated dataset. Experiments are performed using conditionality and bi-directionality for these models, and using either singular word embeddings or averaged word embeddings for an entire quote, to determine the optimal model design. The simplest model design, applying neither conditionality or bi-directionality, and averaged word embeddings across quotes, yields the strongest results. Furthermore, it was found that inclusion of the quotes politician, and the party affiliation of the quoted politician, greatly improved performance of the strongest model.
Time-Aware Probabilistic Knowledge Graphs.

The emergence of open information extraction as a tool for constructing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata maintain the valid time of facts, NELL records the time point at which a fact is retrieved from some Web corpora. Collectively, these knowledge graphs (KG) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KG, such as NELL, the facts in the KG are weighted (a confidence value representing the correctness of a fact). Additionally, NELL can be considered as a transaction time KG because every fact is associated with extraction date. On the other hand, YAGO and Wikidata use the valid time model because they maintain facts together with their validity time (temporal scope). In this paper, we propose a bitemporal model (that combines transaction and valid time models) for maintaining and querying bitemporal probabilistic knowledge graphs. We study coalescing and scalability of marginal and MAP inference. Moreover, we show that complexity of reasoning tasks in atemporal probabilistic KG carry over to the bitemporal setting. Finally, we report our evaluation results of the proposed model.
PWRR Algorithm for Video Streaming Process Using Fog Computing

The most popular medium that being used by people on the internet nowadays is video streaming. Nevertheless, streaming a video consumes much of the internet traffics. The massive quantity of internet usage goes for video streaming that disburses nearly 70% of the internet. Some constraints of interactive media might be detached; such as augmented bandwidth usage and lateness. The need for real-time transmission of video streaming while live leads to employing of Fog computing technologies which is an intermediary layer between the cloud and end user. The latter technology has been introduced to alleviate those problems by providing high real-time response and computational resources near to the client at the network boundary. The present research paper proposes priority weighted round robin (PWRR) algorithm for streaming operations scheduling in the fog architecture. This will give preemptive for streaming live video request to be delivered in a very short response time and real-time communication. The results of experimenting the PWRR in the proposed architecture display a minimize latency and good quality of live video requests which has been achieved with bandwidth changes as well as meeting all other clients requests at the same time
RANCANG BANGUN APLIKASI KRIPTOGRAFI PADA TEKS MENGGUNAKAN METODE REVERSE CHIPER DAN RSA BERBASIS ANDROID

Quick progress also occurs in the field of cellular communication networks with the concept of open systems, making it easier for someone to enter into the network. This can cause the process of sending data to be unsafe because it can be used by other parties who are not responsible for taking data and information in the middle of the road. To achieve the goal of maintaining confidentiality, various kinds of security techniques have been developed. To protect and maintain the confidentiality of data to avoid people who are not entitled to obtain this information, namely using cryptographic methods. In terms of data security techniques, many cryptographic methods can be used. Cryptographic methods have their own techniques and methods. One of the cryptographic methods that can be used is the Reverse Cipher method. But if you only use the Reverse Cipher method, text data security is very weak. So to achieve a higher level of security this method is combined with the RSA method which uses a public key and has high security because the difficulty of factoring large numbers becomes prime factors in the RSA algorithm.
Autonomous Driving Towards Mass Production

Visual recognition technology is very important for autonomous driving especially in direction of mass production. In this talk, we will introduce the algorimic progress for SenseTime in autonoumous driving, as well as our platform foundation for AI technology. Based on this, we illustrate how we make use of these technology into mass production product for autonomous driving.
Automatic Extraction of Weak Labeled Samples From Existing Thematic Products For Training Convolutional Neural Networks

The accuracy in classification of remote sensing (RS) images using deep learning architectures is affected by the lack of large sets of training samples. Although a significant effort is currently devoted to generate databases of annotated satellite images, these datasets may not be large enough to accurately model at global level different types of land-cover surfaces. To solve such a problem, this paper presents an unsupervised approach which aims to exploit the RS image that has to be classified and publicly available thematic products to generate a training database of weak samples representative of the considered study area. First, we harmonize the thematic map and the RS image. Then, samples having the highest probability to be correctly associated to their labels are extracted from the map by exploiting the information provided by the RS image to be classified. Finally, the weak labeled samples are used to train a convolutional neural network (CNN). Experimental results obtained training a CNN on Sentinel 2 images with weak labels extracted from the 2018 corine land cover (CLC) map demonstrate the effectiveness of the proposed method.
Comparison of Problem Based Learning with Project Based Learning Assisted by Mobile Based Learning Media on Students’ Learning Outcomes

This Study presents the utilization of mobile-based learning media in a computer and basic networks. The use of mobile-based interactive media aims to help vocational students majoring in Computer and Network Engineering in learning productive subjects in computers and basic networks. Therefore, this study has a three purposes there are: (1) to discover the influence of Problem Based Learning (PBL) model on students’ learning outcomes, (2) to discover the influence of Project Based Learning (PjBL) model on students’ learning outcomes, and (3) to compare the PBL and PjBL on students’ learning outcomes. The quasi experiment method is used in this research including control and experiment group with different treatment. The subject consists of 108 10th grade students of SMKN 1 Bagor. Data analysis using quantitative technique in this study. Result shows that there is good impact from PBL and PjBL model implementation assisted with mobile-based learning media on the students’ learning outcome. In addition, there is a difference between PBL and PjBL implementation that the PBL give a more significant effect on students’ learning outcomes.
Region Division for Large-scale Image Retrieval

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track.In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature.A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information.Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed.Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval.Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.