Reviewerly/exHarmony huggingface dataset:
Research on Medical Image Storage and Retrieval System Based on Hadoop

Abstract With the Continuous Improvement of Medical Information, the Continuous Development of Image Processing Technology and the Continuous Upgrading of Hospital Image Acquisition Equipment, Medical Image Detection Has Become More and More Reliable and Effective, Becoming an Important Basis for Doctors’ Diagnosis. Now Large Hospitals Use the Combination of Radiology Information Management System and Image Archiving and Communication System to Store and Access Patients’ Images. with the Establishment of Regional Image Sharing and Cooperation Platform, the Online - Near Line - Offline Storage Strategy Adopted by the Traditional Image Archiving and Communication System Can No Longer Meet the Needs of Mass Image Data Storage. in the Retrieval of Mass Image Data, the Relational Database Used by the Image Archiving and Communication System is Inefficient in Reading the Image on the Scale of Mass Data, and the Existing Image the Archive and Communication System Lacks Complete Data Backup and Disaster Recovery Scheme. This Paper Analyses the Feasibility, Advantages and Problems to Be Solved of Using Hdfs to Store Massive Medical Image Files, and Studies How to Realize the Function of Precise Access and Range Access Provided by Pacs Efficiently in Hadoop.
Automating Knowledge Extraction from Unstructured Knowledge Articles

Customer Service Agents face many difficulties in finding suitable knowledge articles for resolution of customers' queries. This paper introduces Information Filtering System that helps customer service agents finding suitable knowledge articles as well as specific sections of text within knowledge articles for resolution of customers' queries through extraction of keywords from customers' interactions with customer service agents. This paper describes two approaches for resolution of queries, Coarse grain Approach that helps in finding Knowledge Article IDs and Fine grain approach helps in finding knowledge article ID and specific section with steps to be followed within that knowledge article using Domain Ontology Builder. This paper also describes two methods for annotation of Knowledge articles, dataset setup, experimental details of different algorithms with results, and list of benefits by using proposed system.
CONNER: A Concurrent ILP Learner in Description Logic

Machine Learning (ML) approaches can achieve impressive results, but many lack transparency or have difficulties handling data of high structural complexity. The class of ML known as Inductive Logic Programming (ILP) draws on the expressivity and rigour of subsets of First Order Logic to represent both data and models. When Description Logics (DL) are used, the approach can be applied directly to knowledge represented as ontologies. ILP output is a prime candidate for explainable artificial intelligence; the expense being computational complexity. We have recently demonstrated how a critical component of ILP learners in DL, namely, cover set testing, can be speeded up through the use of concurrent processing. Here we describe the first prototype of an ILP learner in DL that benefits from this use of concurrency. The result is a fast, scalable tool that can be applied directly to large ontologies.
PECULIARITIES OF PREPARING FUTURE PRIMARY SCHOOL TEACHERS FOR USING MIND MAPS IN MATHEMATICS LESSONS

A great deal of information can be covered up by means of mind maps. This concept, on the basis of which the technology of mind maps was created, was introduced in the 70's by Anthony "Tony" Peter Buzan. The author believed that the use of mind maps was most suitable for higher education and research institutes but their application is so convenient and efficient in covering up information that they are easy to use in mathematics lessons in a primary school in different ways: 1) for creating an interactive lesson plan by a teacher (a mind map that you follow during the lesson) using ICT resources; 2) creating students' own mind maps to cover the theme of the lesson. In the article the technique of preparing future primary school teachers for using mind maps in mathematics lessons to create interactive lesson plans is substantiated. Software resources for creating mind maps are described, in particular FreeMind, Bubbl.us, MindMeister, which are useful in creating mind maps in mathematics lessons in a primary school, their positive and negative sides are identified; the algorithm of creating mind maps for a primary school student is given.
Self-initialized Visual Servoing for Accurate End-effector Positioning

Vision-based control of an end-effector is important for industrial and domestic robotic applications. Direct positioning based on object recognition or pose estimation output is challenging due to inaccuracies of the estimations and system calibration. Visual servoing overcomes these inaccuracies, however, present techniques require initialization of a target region in the input image. This work presents an integrated system to servo towards a target with automatic initialization. In our two-step approach, the first phase positions the end-effector relative to an object using the output of a pose estimation network. The initial coarse alignment is then refined in a second phase using image-based visual servoing (IBVS). The target region in the image for IBVS is initialized by the detection of the pose estimator. The system monitors the tracking quality of the IBVS to automatically switch from the first to the second phase. Experiments with a 7 degree of freedom robotic arm show that our approach results in successful end-effector positioning from arbitrary initial positions.
Distributed Facial Feature Clustering Algorithm Based on Spatiotemporal Locality

Data clustering, as one of the major algorithms in facial feature data mining, its efficiency and quality plays a key role. DBSCAN is a classic density-based clustering algorithm. It is applicable to any shape of subset or cluster and is comparatively anti-noise, but because of its slow running speed, it has long convergence time when the data set is large. For this reason, the paper presents an improved DBSCAN algorithm based on distributed computing system, by making full use of the characteristic of high time partial similarity of facial feature under the monitoring scene, first to merge the features which belong to the same target, then to segment the merged result using DBSCAN algorithm. The test results show that although this new algorithm has no modification in the aspect of time complexity, but greatly improved clustering speed, which enables the system to process million-level datasets.
A MANAGEMENT TOOL TO AID IN THE TROPICAL OUTBREAK OF THE 21st CENTURY: SENIOR SCIENTISTS AND THEIR KNOWLEDGE OF THE TRIPLE THREAT DENGUE, ZIKA AND CHIKUNGUNYA

The 21st century presents itself as the informational and knowledge age. The aging population and the technological innovation to medicine have been a constant concern due to the search for the complete state of well-being of the individual. Nations are making increasing efforts to continually improve their populations. There is an urgent need for better management of knowledge and integrality of health care, such as the integration, convergence and pragmatism of actions in multiple areas. According to the World Bank and the World Health Organization, half the world's population does not have access to essential health services and more than 1 billion are threatened by neglected diseases. Therefore, information science aid in the complexity of identifying, extracting, and treating the quintillions of data added daily to the Web. After processing by ScripLattes tool thousands of resumes on the Lattes platform, 39,046 triple threat experts have been identified, but only 0.3% are senior competencies. The scientific papers published in Dengue fever, Zika virus and Chikungunya fever are 11,743. The ScripLattes tool proved to be efficient to support in the management of scientific knowledge in public health. Thus, this research provides an overview from the core competencies in triple threat in Brazilian territory and their mainly scientific and technological publications. Keywords: knowledge management, information science, public health, triple threat.
Enhanced Detection of Internet Water Army Based on Supernetwork Theory

The hierarchy and the correct nature of the Internet has been strongly contaminated by the advent of the Internet water armies in recent years. The minute and intense identification and detection of such Internet water armies is an important task. On the basis of the Supernetwork theory, a new model is proposed in this paper that will be employed in the detection of water armies. In this model, a Supernetwork consisting of five layers including User subnetwork, Content subnetwork, Psychological subnetwork, Negative keyword subnetwork and Repeated keywords subnetwork. Literature survey is carried out on the evolution of such spammers over years. The performance of the model is tested with the dataset obtained from the online communication platform, Reddit. A comparative analysis is presented from five existing models of Internet water army detection that were introduced in previous studies.
Technologies for project-based approach to the system of teachers training

The article describes technologies applicable for project-based approach to the system of teachers training, the mechanism of implementation of project-based approach in pedagogical Universities. The paper presents the procedure and outcomes of experimental activities aimed at examination of the real-life effects in organization of project-based activities by students immersed in a high-tech educational environment. System and structure-oriented analysis was applied to managing professional activities of teachers-to-be. Pedagogical experiment, observation and recording the peculiarities of project activities, experience generalization and systematization of the project-based approach in a pedagogical University. Content and conditions of the organization of project activities of students in a high-tech educational environment are updated. Identified qualitative and quantitative indicators characterizing the level of initial professional experience sufficient for the implementation of professional activities in school. The outcomes lead to the conclusion about the effectiveness of applying technologies in project-based approach to teacher training system immersed in a high-tech educational environment.
Interpretation of ResNet by Visualization of the Preferred Stimulus in Receptive Fields

One of the methods used in image recognition is the deep convolutional neural network (DCNN). DCNN is a model in which the expressive power of features is greatly improved by deepening the hidden layer of CNN. The architecture of CNNs is determined based on a model of the visual cortex of mammals. There is a model called residual network (ResNet) that has a skip connection. ResNet is an advanced model in terms of the learning method, but it has not been interpreted from a biological viewpoint. In this research, we investigate the receptive fields of a ResNet on the classification task in ImageNet. We find that ResNet has orientation-selective neurons and double-opponent color neurons. In addition, we suggest that some inactive neurons in the first layer of ResNet affect the classification task.
Analytic Properties of Trackable Weak Models

We present new results on inferring the hidden states in trackable weak models. A weak model is a directed graph where each node has a set of colors which may be emitted when that node is visited. A hypothesis is a node sequence consistent with a given color sequence. A weak model is trackable if the worst case number of hypotheses grows polynomially in the sequence length. We show that the number of hypotheses in strongly-connected trackable models is bounded by a constant. We also consider the problem of reconstructing which branch was taken at a node with same-colored out-neighbors, and show that it is always eventually possible to identify which branch was taken if the model is strongly connected and trackable. We illustrate these properties by employing standard tools for analyzing Markov chains. In addition, we present new results for the entropy rates of weak models. These theorems indicate that the combination of trackability and strong connectivity simplifies the task of reconstructing which nodes were visited. This work has implications for problems which can be described in terms of an agent traversing a colored graph, such as the reconstruction of hidden states in a hidden Markov model (HMM).
Adoption of Mobile Technology in Teaching Moral Values to Children: A Study in Malaysia

In today's modern society, many parents are busy building a successful career and it is very common that there are many young families with both parents working. Hence, the young children of such families will be placed in day-care centres or they are looked after by their grandparents and maids. Working parents would not be spending much quality time with their children and providing them with moral guidance. This group of children, if not given adequate and proper guidance, would not know much about good moral values. There are also children who grow up around people who do not practise good moral values and commit all types of wrongdoings such as cheating, stealing, bullying, being selfish, greedy, uncaring and ill-treating animals. It is easy for children to be influenced by such actions and behaviours. Therefore, this research plans to assist in imparting moral values to children by using the concept of “edutainment” with the use of a mobile application. Based on the results collected in this research, the application is fully functional without any bugs because all the respondents who took part in the testing of the application were able to launch the application on their mobile phones successfully. The results also show that the mobile application is well received by both parents and the children and it has been effective in helping children aged 4 to 9 to have a better understanding of moral values.
Multi-Band Infrared and Visual Video Registration and Fusion Parallel Acceleration Method

Multi-band video fusion can synthesize the advantages of different band-images, which is widely used in military, anti-terrorism, search and rescue fields. The accuracy of image registration affects the quality of image-video fusion. The high precision image registration method runs slowly, which greatly affects the real-time performance of image-video fusion. Parallel registration of multi band images can effectively solve the above problems. Firstly, a parallel processing structure is proposed for multi band image video registration. The multi band image video is divided into several sub parts and processed at the same time, which greatly improves the speed of accompaniment. Secondly, the advantages of this method are analyzed by using parallelism and algorithm complexity. Thirdly, the quality of the fusion image is evaluated subjectively and objectively, and the reliability of the method is verified. At last, through experiments in different scenes, it is proved that this method proposed in this paper can improve the quality of fused images-video while ensuring the real-time performance of image-video fusion.
Intelligent Human Presence Detection and Identification

In the current Internet of Things (IoT) era, user presence or absence detection inside home has become a predominant requirement for handling various scenarios, such as user preferences, Edge computing, IF This Then That (IFTTT) for a smart home environment in home assistance devices such as Amazon Alexa, Google home, Samsung Galaxy home etc. Currently such home assistance devices are lacking the intelligence to detect the human presence or absence detection along with exact user identification inside the home. In this work, we propose a novel unsupervised sophisticated classification idea to deal for the situations, where multiple users can query simultaneously on a single platform for presence detection using various connectivity technologies. The multiple technologies such as Bluetooth, Bluetooth LE, Wi-Fi, Speaker ID, Sonar, Voice, ZigBee, Z-Wave etc., will be queueing inside the system and will be aided sequentially or in parallel fashion to find the presence detection of a particular user. In this paper, we encompass three technologies presence detection, 1) using Bluetooth, 2) using Wi-Fi and 3) using Speaker ID (voice). Along with the presence detection using various methods, here we are focusing equally on user identification and association rules-based inferences generation. Also discussed various use cases and the performance of the system, which is optimized for the parallelizing the methodology for different presence detection methods. The experimentation results of our proposed algorithm's presence detection shows overall accuracy of 99.2% using Bluetooth, 98.6% using Wi-Fi and 92.7% using Speaker ID.
Robust Fuzzy Control using the Type2 Distending Function

Here, we develop a novel robust type2 fuzzy controller (FC). It is based on a new type-2 membership function called the type2 Distending Function (T2DF). It can model the uncertainties associated with the measurement noise. The design process does not include the implication and the type reduction steps, hence it is computationally efficient. Also, aggregation is carried out using fuzzy arithmetic operations and T2DF is closed under linear combination. Because of these unique features, the proposed type2 FC performs better even in the presence of large measurement noise. The efficiency of the proposed FC is demonstrated using the altitude control of a quadcopter with a noisy feedback signal.
A holistic cross-layer optimization approach for mitigating stragglers in in-memory data processing

In-memory data processing frameworks (e.g., Spark) make big data analysis greatly simpler and efficient. However, stragglers that take much longer to finish than other tasks significantly degrade performance. There exist multiple factors that cause stragglers, either from the hardware resource layer or application layer, e.g. hardware heterogeneity, interference, data locality and data skew. While state-of-the-art straggler mitigation techniques have presented partial solutions on data skew and data locality, we experimentally demonstrate that the other factors can also result in serious problems. We present Clio, a cross-layer interference-aware optimization system that can effectively mitigate stragglers for data processing frameworks. Clio supports the scheduling of both map and reduce tasks. It heuristically dispatches intermediate data in proportion to the actual computing ability of each worker node, which is estimated considering various straggler factors, to balance the completion times of tasks in a much finer way. We implement Clio in Apache Spark, and evaluate its performance using both synthetic and real datasets. Experiment results show that, Clio can speed up the execution of applications by up to 67%, compared with the existing algorithms.
Evaluation of Ontologies based on experts knowledge

Ontologies are a structure used to represent and model domains, they have been related to the development of software since the decade of the '90s. Different methodologies for designing ontologies have existed to lead and serve as a guide. Like all software development, it is necessary to evaluate the ontology to determine the representativeness of the knowledge domain. This research proposes an ontology evaluation system based on expert knowledge. The system decomposes an ontology into its main elements to evaluate it based on expert opinion. To test the functioning of the system, the experiment used an ontology to model a simple school system. To evaluate the ontology, it was employed university teachers related to the domain. Results indicated strong and weak aspects of the ontology to be perfected. The evaluation of the structure of the ontology allows subsequent evaluations to show better results.
Solving The Classic Snake Game Using AI

In this paper we did survey of various papers based on the classic snake game and compared their various traits and features. In this paper we introduce an AI bot to enhance the skills of the player and the AI bot uses the algorithms further discussed in this paper. Player can follow the simultaneously running AI bot to play the game effectively. In this we use the classic snake game, for that we present different algorithms or methods for AI bot. It includes three searching algorithms related to artificial intelligence, Best First Search, A* Search and improved A* Search with forward checking, and two baseline methods Random Move and Almighty Move [1].
Implementation of Blended Learning into ESP for Medical Staff

The use of blended design of teaching and learning foreign languages in higher education has increased rapidly in the twenty-first century. For many universities, the tool of blended learning is a part of mainstream education and represents a sort of continuum between traditional and pure online courses. Reflecting the latest results of technical and technological development, smart solutions are applied in numerous fields of human lives, including education. In this article, attention is paid to the education at tertiary schools for medical professionals, particularly to English for specific purposes (ESP) courses. The main objective of the presented research was to discover whether/how the smart blended approach could enhance and support the process of learning ESP in these students. The research was conducted at the Tertiary School for Medical Staff in Olomouc, Czech Republic. The research sample included 86 first-year students divided in the experimental group (N = 46) and control group (N = 40). Traditional pedagogical experiment was conducted defining the form of study materials (electronic or printed) to be the variable. Content of the study materials was the same in both groups. Data were collected via two written credit tests administered in the middle and at the end of semester. It was discovered that there are no significant differences in the test scores between these two groups. However, the students with lower level of English performed slightly better in the experimental group.
CodeTripping

This study is a preliminary case study examining the use of a free Web Development learning platform in order to en-deepen and facilitate further knowledge in App Development beginners. The use of CodeTrip.gr was examined with regard to perceived learning outcomes and effect on the need for cognition from the viewpoint of undergraduate non-major students with basic Web Engineering and programming skills. A didactic intervention including CodeTrip was conducted with a randomized sample of 18 undergraduate students, who were asked during the tool introduction and after doing the course, using qualitative analysis and validated items from technology and media effects research. The results are promising and support the idea that the user interface of the tool adds to the learning of the students since they show higher need for cognition scores and better self-evaluation after its use. Generalization of results is limited due to the small sample size. Future work includes the validation of the questionnaire with a bigger sample.