Reviewerly/exHarmony huggingface dataset:
Use Of Interactive Learning Media Based On Macromedia Flash On Student Learning Outcomes

The purpose of this study was to influence differences in learning outcomes between students who use macromedia flash based interactive learning media with students using conventional learning media (model/ display).This research method uses Research and Development.As the control class is class XI IPS 2 using conventional learning media (model / display), while the experimental class is class XI IPS 1 using macromedia flash-based interactive learning media, this form of learning media uses Adobe Flash CS6.There is a significant difference in the learning outcomes of the experimental class students with control class students in geography subjects at SMAN 2 Mesuji.The results of the trial exercise using macromedia flash learning media have the results of the experimental Post-test value is higher than the control class which is 83.5 the average result of the Pre-test experimental class is 73.4 and the average value of the control class Pre-test is 51.7.While the average results of the experimental Post-test value are higher than the control class, namely 83.5 for the experimental class and 68.8 for the control class.
Finding Justifications by Approximating Core for Large-scale Ontologies

Finding justifications for an entailment is one of the major missions in the field of ontology research. Recent advances on finding justifications w.r.t. the light-weight description logics focused on encoding this problem into a propositional formula, and using SAT-based techniques to enumerate all MUSes (minimally unsatisfiable subformulas). It's necessary to import more optimized techniques into finding justifications as emergence of large-scale real-world ontologies. In this paper, we propose a new strategy which introduce local search(in short, LS) technique to compute the approximating core before extracting an exact MUS. Although it is based on a heuristic and LS, such technique is complete in the sense that it always delivers a MUS for any unsatisfiable SAT instance. Our method will find the justifications for large-scale ontologies more effectively.
View Invariant Human Action Recognition Using 3D Geometric Features

Action recognition based on 2D information has encountered intrinsic difficulties such as occlusion and view etc. Especially suffering with complicated changes of perspective. In this paper, we present a straightforward and efficient approach for 3D human action recognition based on skeleton sequences. A rough geometric feature, termed planes of 3D joint motions vector (PoJM3D) is extracted from the raw skeleton data to capture the omnidirectional short-term motion cues. A customized 3D convolutional neural network is employed to learn the global long-term representation of spatial appearance and temporal motion information with a scheme called dynamic temporal sparse sampling (DTSS). Extensive experiments on three public benchmark datasets, including UTD-MVAD, UTD-MHAD, and CAS-YNU-MHAD demonstrate the effectiveness of our method compared to the current state-of-the-art in cross-view evaluation, and significant improvement in cross-subjects evaluation. The code of our proposed approach is available at released on GitHub.
Building A National Research Data Management Course for Health Information Professionals

<strong>Background</strong>: In August 2017 the National Network of Libraries of Medicine Training Office (NTO) was awarded an administrative supplement from the National Library of Medicine (NLM) to create training for librarians in biomedical and health research data management (RDM). The primary goal of the training was to enable information professionals to initiate or enhance RDM at their institutions. <strong>Case Presentation</strong>: An eight-week online course was developed to address key concepts in RDM. Each module was organized around measurable learning objectives using existing subject resources, such as readings, tutorials, and videos. Within each module, an expert in the field co-facilitated relevant discussions, created and graded a practical assignment, and answered questions. Thirty-eight participants were selected for this initial cohort. Mentors were assigned to each participant for guidance in completing a required project action plan to further their RDM goals at their institution. The course was evaluated through pre- and post-tests and an online questionnaire. <strong>Results</strong>: Thirty participants successfully completed the online course work and project, and gathered at the National Institutes of Health for a Capstone Summit. Students demonstrated improved knowledge of RDM concepts between the pre- and post-tests. Most students also self-reported increased skill and confidence. Practical assignments with individual feedback from experienced data librarians were the most valued aspect of the course. Time to complete each module was underestimated. <strong>Conclusions</strong>: The initial offering of this training program improved the RDM skills and knowledge of participants and enabled students to add or enhance services at their institutions. Further investigations are necessary to determine the longer-term impact on the individuals and their libraries. While many of the participants will need additional training to become part of the data-ready workforce of health information professionals, completing this training is an important step in their professional development.
Conditions for Avoiding Node Re-expansions in Bounded Suboptimal Search

Many practical problems are too difficult to solve optimally, motivating the need to found suboptimal solutions, particularly those with bounds on the final solution quality. Algorithms like Weighted A*, A*-epsilon, Optimistic Search, EES, and DPS have been developed to find suboptimal solutions with solution quality that is within a constant bound of the optimal solution. However, with the exception of weighted A*, all of these algorithms require performing node re-expansions during search. This paper explores the properties of priority functions that can find bounded suboptimal solution without requiring node re-expansions. After general bounds are developed, two new convex priority functions are developed that can outperform weighted A*.
PENERAPAN ALGORITMA CERTAINTY FACTOR UNTUK SISTEM PAKAR DIAGNOSIS URTIKARIA PADA WANITA DEWASA

Application of the Certainty Factor Algoritm for urticarial diagnostic system experts in adult women.Urticaria is a reaction to the skin due to various causes, synonyms of this disease are biduran, chilblains, hives, nettle rash.Marked by local edema (swelling) that arises suddenly and disappears slowly reddish and pale, rising on the surface of the skin, the surroundings can be surrounded by halo (spherical).The incidence of urticaria is quite high as much as 15% -20% of the population has experienced urticaria in their lives and 25% of them experience chronic urticaria, this disease is more common in women.In this application an expert system is created using Certainty Factor.With the application of an expert system of diagnosis of urticaria in adult women is expected to help popularize the knowledge and experience of experts who are experts in their fields, know the level of disease based on the symptoms experienced from urticaria, no need for cost, save time in decision making and integration of expert system applications more effective covering wider applications.
Breathing Space: Biofeedback Sonification for Meditation in Autonomous Vehicles

The collective, calmspaces, sets out to create spaces for relaxation and contemplation through traditional architectural approach combined with modern digital technology. The ongoing project of the collective, breathing space (ademruimte in Dutch), uses unobtrusive sensing technology to monitor one’s breathing, and through designed light and sonic guides, the project tries to enhance the breathing exercise beneficial to regulating one’s emotion. The paper illustrates the project and its relevance to and potential for in-vehicle development. We then discuss the details of our implementations, along with video documentations of the early prototype, and a recently completed installation work.
Transfer Learning Based Image Visualization Using CNN

Image classification is a popular machine learning based applications of deep learning.Deep learning techniques are very popular because they can be effectively used in performing operations on image data in large-scale.In this paper CNN model was designed to better classify images.We make use of feature extraction part of inception v3 model for feature vector calculation and retrained the classification layer with these feature vector.By using the transfer learning mechanism the classification layer of the CNN model was trained with 20 classes of Caltech101 image dataset and 17 classes of Oxford 17 flower image dataset.After training, network was evaluated with testing dataset images from Oxford 17 flower dataset and Caltech101 image dataset.The mean testing precision of the neural network architecture with Caltech101 dataset was 98 % and with Oxford 17 Flower image dataset was 92.27 %.
The Experience of Freezing Eggs for Social Reasons

The Experience of Freezing Eggs for Social ReasonsAlmost since their very inception social scientists have critically examined the social, legal and political ramifications of assisted reproductive technologies, exploring the ways in which they have given rise to new family forms and generated new questions and ethical debates in relation to the shifting boundaries of human reproduction (Almeling, 2015; Franklin, 2013).Research has also explored the practice and delivery of assisted reproductive technologies within and beyond the walls of the fertility clinic (Nordqvist, 2011; Thompson, 2005; Wahlberg, 2018) and has examined the experiences of a variety of actors in the realm of assisted reproduction, including patients undergoing IVF (Barnes, 2014; Franklin,
Accurate wisdom of the crowd from unsupervised dimension reduction

Wisdom of the crowd, the collective intelligence from responses of multiple human or machine individuals to the same questions, can be more accurate than each individual and improve social decision-making and prediction accuracy. Crowd wisdom estimates each individual's error level and minimizes the overall error in the crowd consensus. However, with problem-specific models mostly concerning binary (yes/no) predictions, crowd wisdom remains overlooked in biomedical disciplines. Here we show, in real-world examples of transcription factor target prediction and skin cancer diagnosis, and with simulated data, that the crowd wisdom problem is analogous to one-dimensional unsupervised dimension reduction in machine learning. This provides a natural class of generalized, accurate and mature crowd wisdom solutions, such as PCA and Isomap, that can handle binary and also continuous responses, like confidence levels. They even outperform supervised-learning-based collective intelligence that is calibrated on historical performance of individuals, e.g. random forest. This study unifies crowd wisdom and unsupervised dimension reduction, and extends its applications to continuous data. As the scales of data acquisition and processing rapidly increase, especially in high-throughput sequencing and imaging, crowd wisdom can provide accurate predictions by combining multiple datasets and/or analytical methods.
Visualization of the Image Geometric Transformation Group Based on Riemannian Manifold

Geometric transformations of images are the predominant factor, which influences the effectiveness of visual tracking and detection tasks in computer vision. Naturally, although it makes significant sense to grasp the process of image geometric transformations, the numerical relationship of geometric transformations cannot be revealed directly from images themselves. Even if the geometric transformation matrices form the three-dimensional special linear group, Sl(3, ℝ) group, it is difficult to comprehend the manifold of this invisible visual motion, which resides in the high-dimensional space. Furthermore, the main challenge is the deficiency of analytic expressions of the Riemannian logarithmic map to compute the geodesic distance on the Sl(3, ℝ) manifold. Facing these issues, this paper comes up with a novel approach to visualize the geometric transformation in images by presenting a new metric, and then, computes a set of coordinate-vectors in the three-dimensional state transition space for visualization using the Riemannian stress majorization. The superiority of the presented framework for visualization, in terms of accuracy and efficiency, is demonstrated through abundant experiments on aerial images and moving objects.
Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search: Preliminary Results

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for Multi-Agent Pathfinding. Recent work introduced an admissible heuristic to guide the high-level search of CBS. In this work, we prove the limitation of this heuristic, as it is based on cardinal conflicts only. We then introduce two new admissible heuristics by reasoning about the pairwise dependency between agents. Empirically, CBS with both new heuristics significantly improves the success rate over CBS with the recent heuristic and reduces the number of expanded nodes and runtime by up to a factor of 50, yielding a new state-of-the-art CBS-based algorithm.
THE MONITORING OF MONITORING: WHAT’S WRONG WITH THE MINISTRY’S NEW APPROACH TO SUPERVISION OF EFFECTIVENESS OF HIGHER EDUCATION INSTITUTIONS’ PERFORMANCE?

The article discusses the Monitoring of higher education institutions’ (HEIs’) effectiveness, which has been conducted annually by the Ministry of Education and Science in the last five years. The method of the Monitoring is based on the technology of collecting and systematizing information used by the state accreditation in 1997–2010. The reasons for the introduction of the Monitoring by the Ministry are described; the features of the performance indicators are justified by the state educational policy. A model of presentation of the Monitoring results in the league table format is suggested, which makes it possible to use its results more flexibly for the management of the higher education system. It also allows higher education institutions to analyze their positions and to form their own ways of the development.
D2D Big Data Privacy-Preserving Framework Based on (<i>a</i>, <i>k</i>)-Anonymity Model

As a novel and promising technology for 5G networks, device-to-device (D2D) communication has garnered a significant amount of research interest because of the advantages of rapid sharing and high accuracy on deliveries as well as its variety of applications and services. Big data technology offers unprecedented opportunities and poses a daunting challenge to D2D communication and sharing, where the data often contain private information concerning users or organizations and thus are at risk of being leaked. Privacy preservation is necessary for D2D services but has not been extensively studied. In this paper, we propose an ( a , k )-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. Then, we propose an ( a , k )-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. In our privacy-preserving framework, we adopt ( a , k )-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets. The results of experiments and theoretical analysis show that our privacy-preserving algorithm deployed on MapReduce is effective for D2D big data privacy protection with less information loss and computing time.
Parsing Weighted Order-Preserving Hyperedge Replacement Grammars

We introduce a weighted extension of the recently proposed notion of order-preserving hyperedge-replacement grammars and prove that the weight of a graph according to such a weighted graph grammar can be computed uniformly in quadratic time (under assumptions made precise in the paper).
Flambé: A Customizable Framework for Machine Learning Experiments

Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Flambé achieves both flexibility and simplicity by allowing users to write custom code but instantly include that code as a component in a larger system which is represented by a concise configuration file format. We demonstrate the application of the framework through a cutting-edge multistage use case: fine-tuning and distillation of a state of the art pretrained language model used for text classification.
A Comprehensive Evaluation of Cue-Words based Features and In-text Citations based Features for Citation Classification

Citation plays a vital role in the scientific community of evaluating the contributions of scientific authors.Citing sources delivers a measurable way of evaluating the impact factor of journals and authors and allows for the recognition of new research issues.Different techniques for classifying citations have been proposed.Citations that provide background knowledge in the citing document have been classified as non-important or incidental by previous researchers.Citations that extend previous work in the citing document are classified as important.The accuracy achieved by existing citation models is not much higher.Better features need to be included for accurate predictions.A hybrid approach would present all possible combinations of cue-words and in-text citation-based features for citation classifications.
What’s Most Broken? A Tool to Assist Data-Driven Iterative Improvement of an Intelligent Tutoring System

Intelligent Tutoring Systems (ITS) have great potential to change the educational landscape by bringing scientifically tested one-to-one tutoring to remote and under-served areas. However, effective ITSs are too complex to perfect. Instead, a practical guiding principle for ITS development and improvement is to fix what’s most broken. In this paper we present SPOT (Statistical Probe of Tutoring): a tool that mines data logged by an Intelligent Tutoring System to identify the ‘hot spots’ most detrimental to its efficiency and effectiveness in terms of its software reliability, usability, task difficulty, student engagement, and other criteria. SPOT uses heuristics and machine learning to discover, characterize, and prioritize such hot spots in order to focus ITS refinement on what matters most. We applied SPOT to data logged by RoboTutor, an ITS that teaches children basic reading, writing and arithmetic.
Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning

Zeroth-order (ZO) optimization is increasingly embraced for solving big data and machine learning problems when explicit expressions of the gradients are difficult or infeasible to obtain. It achieves gradient-free optimization by approximating the full gradient via efficient gradient estimators. Some recent important applications include: a) generation of prediction-evasive, black-box adversarial attacks on deep neural networks, b) online network management with limited computation capacity, c) parameter inference of black-box/complex systems, and d) bandit optimization in which a player receives partial feedback in terms of loss function values revealed by her adversary.
An Improved Geometrical Path Planning Algorithm for UAV in Irregular-obstacle Environment

Recently unmanned aerial vehicle (UAV) has been widely used in both military and civil fields and becomes an important equipment widely used in many fields. With the diversification of UAV environment, it is very important to find a collision-free path in complex environment. Traditional path planning algorithms can find feasible paths, but they do not have a good balance between time efficiency and path length. Traditional geometric algorithms can only avoid obstacles with special shapes. In this paper, an improved geometric path planning algorithm is proposed, which can make UAV avoid obstacles of arbitrary shape in complex environment and find shorter paths. First, for irregular obstacles, a convex polygon model is established. Then, the heuristic method is used to select the path segment, which solves the disadvantage of traditional geometric algorithm falling into local optimal solution. And the idea of quadratic programming is proposed to generate collision-free paths from adjacent paths, and a new security threshold strategy is proposed for this method. Finally, in order to verify the performance of the algorithm, this paper simulates in different complex environments and compares the proposed algorithm with A* algorithm from several aspects.
Building AR-based Optical Experiment Applications in a VR Course

The demand for VR courses in universities is growing since VR technology is widely used in industry, entertainment, and education today. Traditional lectures and exercises have problems in motivating and engaging students, especially non-CS-majors. We design a VR course with a teamwork project assignment for Opto-Electronic Engineering undergraduates, providing them with project-based learning (PBL) experience. The task requires three students to group a team to build an AR-based optical experiment application over the course, aiming to develop students' practical engineering ability. The process of the project consists of three main stages: preparation, designing and implementing. We also evaluate students' work from different aspects and survey to analyze the students' attitude toward the project.
Random Walk Mutation-based DE with EDA for Nonlinear Equations Systems

Finding multiple roots of nonlinear equations systems (NESs) in a single run is an important yet difficult task. It requires to keep a balance between explorative and exploitative traits. In this paper, we present a random walk mutation-based differential evolution (DE) with estimation of distribution algorithm (EDA) to address this problem. The major characteristics are: i) the random walk mutation is capable of preserving the population diversity , which guides individuals to move toward different promising regions; ii) probability selection is employed to provide suitable parent individuals for evolution; iii) EDA is used to accelerate the convergence and obtains the roots. To evaluate the performance of our approach, 30 NESs with diverse features are selected as test suite. Experimental results indicate that the proposed approach is able to yield better performance compared with other state-of-the-art methods.