Reviewerly/exHarmony huggingface dataset:
The Mobile Teaching Platform Based on WeChat Applet and the Moodle System --- Explore the Impact of Teaching Platforms on Learning in a Mobile Learning Environment

Abstract With the continuous optimization of mobile technology and network environment in recent years, the new learning platform will become an effective support for future mobile learning. We combine the Moodle platform and WeChat applet to build a mobile teaching platform based on the Moodle and WeChat applet. This article takes the Moodle users and the Moodle WeChat applet users as research objects, use a combination of questionnaire survey, statistics and content analysis, compare and analyze the survey subjects’ learning tendency, activity level, and learning period from various aspects. The results of the study indicate that the learner’s activity level is affected by different learning platforms, compared with the Moodle platform, the users’activity of the Moodle WeChat applet is significantly higher than the former, moreover, the duration of a single online connection of users on the applet is generally lower than that of the PC, the reasons include the influence of user learning time, place and learning habits. The research conclusion shows that WeChat applet has an important role in enhancing the learning of the Moodle users, WeChat platform takes full advantage of the convenience of mobile learning.
Case Studies for Achieving a Return on Investment With a Hardware Refresh in Organizations With Small Data Centers

Data centers have been highlighted as a major energy consumer and there has been an increasing trend towards the consolidation of smaller data centers into larger facilities. Yet, small data centers exist for a variety of reasons and account for a significant portion of the total number of servers. Frequent refreshes of IT hardware have emerged as a trend in hyper-scale data centers but little attention has been paid to how these savings can be achieved in smaller facilities. This work provides a comprehensive framework for the energy saving opportunities while determining when a return on investment can be achieved to enable small data center operators to create credible business cases for hardware refreshes. Various data center deployment scenarios are used as case studies (based on real-life datasets) to validate the proposed concepts. Our results show that a return on investment can be achieved for organizations with small data center in less than two years and that these organizations can save more than three times their annual electricity cost over five years.
$\mu$NAS: Constrained Neural Architecture Search for Microcontrollers.

IoT devices are powered by microcontroller units (MCUs) which are extremely resource-scarce: a typical MCU may have an underpowered processor and around 64 KB of memory and persistent storage, which is orders of magnitude fewer computational resources than is typically required for deep learning. Designing neural networks for such a platform requires an intricate balance between keeping high predictive performance (accuracy) while achieving low memory and storage usage and inference latency. This is extremely challenging to achieve manually, so in this work, we build a neural architecture search (NAS) system, called $\mu$NAS, to automate the design of such small-yet-powerful MCU-level networks. $\mu$NAS explicitly targets the three primary aspects of resource scarcity of MCUs: the size of RAM, persistent storage and processor speed. $\mu$NAS represents a significant advance in resource-efficient models, especially for mid-tier MCUs with memory requirements ranging from 0.5 KB to 64 KB. We show that on a variety of image classification datasets $\mu$NAS is able to (a) improve top-1 classification accuracy by up to 4.8%, or (b) reduce memory footprint by 4--13x, or (c) reduce the number of multiply-accumulate operations by at least 2x, compared to existing MCU specialist literature and resource-efficient models.
Semantic Classification and Indexing of Open Educational Resources with Word Embeddings and Ontologies

Abstract The problem of thematic indexing of Open Educational Resources (OERs) is often a time-consuming and costly manual task, relying on expert knowledge. In addition, a lot of online resources may be poorly annotated with arbitrary, ad-hoc keywords instead of standard, controlled vocabularies, a fact that stretches up the search space and hampers interoperability. In this paper, we propose an approach that facilitates curators and instructors to annotate thematically educational content. To achieve this, we combine explicit knowledge graph representations with vector-based learning of formal thesaurus terms. We apply this technique in the domain of biomedical literature and show that it is possible to produce a reasonable set of thematic suggestions which exceed a certain similarity threshold. Our method yields acceptable levels for precision and recall against corpora already indexed by human experts. Ordering of recommendations is significant and this approach can also have satisfactory results for the ranking problem. However, traditional IR metrics may not be adequate due to semantic relations amongst recommended terms being underutilized.
Research on the English Mixed Listening and Speaking Teaching Mode Based on the New Mobile Media on Internet

Abstract With the popularization, application and development of information tech, the construction of English mixed listening &amp; speaking teaching mode based on the network mobile platform has become a hot research and application. However, most of the current research lacks the design of the representative network mobile new media application process. Based on this, this paper first studies the application of mixed listening &amp; speaking teaching theory based on the network mobile new media, then analyses the construction and design steps of the teaching mode, and finally gives the value and significance of the teaching mode.
Virtual Womb

This paper explores the potential of using a virtual reality application to support pregnant individuals strengthen their parental bonds. The application simulates sensory development from a fetal point-of-view inside a virtual womb. More than 20 healthcare professionals comprising midwives, doctors, and nurses assisted the iterative design process through meetings, exposure, and open discussion. The evaluation used the iGroup Presence Questionnaire and Microsoft Product Reaction Cards to assess user presence and emotional impact. 48 participants (19 females, 29 males) comprising pregnant individuals, healthcare professionals, and miscellaneous participants took part in the evaluation. Results indicate feelings of presence, significant emotional impact, as well as positively inclined responses towards the experience. Healthcare professionals supported the evaluation, saw application potential, and expressed interest in further development and implementation.
Sights and Sounds of Big Data: Ryoji Ikeda’s Immersive Installations

Abstract The Japanese multimedia artist Ryoji Ikeda’s work can be interpreted as a contact between mediums, but also as a contact between disciplines. Most of his video installations are based on concepts borrowed from the field of mathematics, physics or information technology. In this paper I will examine Ikeda’s audiovisual installations by presenting these multimedial installations as possible methods of visualizing digital data in the context of contemporary art. Considering their digital and abstract nature, these works can also be analysed as unique audiovisual environments built from different media based on the same data-sets, offering the possibility of immersion. By unfolding the medial relations within Ikeda’s work I will try to demonstrate how the combination of sight and sound creates the inter-sensual experience of getting in touch with digital data.
BP Neural Network-Based Deep Non-negative Matrix Factorization for Image Clustering

Deep non-negative matrix factorization (DNMF) is a promising method for non-negativity multi-layer feature extraction. Most of DNMF algorithms are repeatedly to run single-layer NMF to build the hierarchical structure. They have to eliminate the accumulated error via fine-tuning strategy, which is, however, too time-consuming. To deal with the drawbacks of existing DNMF algorithms, this paper proposes a novel deep auto-encoder using back-propagation neural network (BPNN). It can automatically yield a deep non-negative matrix factorization, called BPNN based DNMF (BP-DNMF). The proposed BP-DNMF algorithm is empirically shown to be convergent. Compared with some state of the art DNMF algorithms, experimental results demonstrate that our approach achieves superior clustering performance and has high computing efficiency as well.
Research on the level of motivation of the students of vocational school towards learning

The subject of this research is educational motivation of the first-year and second-year students of vocational school. The author puts forward an assumption that the second-year students have higher motivation towards learning than the first-year students. This related to the completion of adaptation period and maturing of students, as well as with formation of the responsible professional stance on their future. The conducted research confirmed the advanced hypothesis. The motives for learning highlighted by the second-year students are associated mostly with the current issues of educational activity. The author also underlines the greater importance of motives pertinent to professional competences and professional activity among the second-year students. The main conclusion lies in the thesis that motives for learning indicated by the first-year students are characterized by expectancy, temporary farness of the result, and are merely associated with the current problems. These results reveal the considerable importance of the remote and abstract learning goals for the first-year students. The author detects a trend that acquisition of knowledge is more significant that professional skills in the learning process. Moreover, in course of time, the motive for acquiring knowledge acquires more currency among the students of vocational school.
A Flexible Sliding Mode Controller for Robot Manipulators Using a New Type of Neural-Network Predictor

This paper presents a free-model high-precision controller for robot manipulators using variation sliding mode scheme and a semi-positive neural-network design. The total dynamics of the robot is first estimated by the neural network in which a new type of learning laws is proposed based on twisting excitation signals. A flexible sliding mode control interface is then developed to realize control objectives using the estimation result of the neural network. The control performance of the closed-loop system is verified by the intensively theoretical proof and simulation results.
An Effective Constraint-Based Anomaly Detection Approach on Multivariate Time Series

With the development of IoT, various sensors are deployed in industry applications. Sensors produce multivariate time series, while error data and abnormal values often exist in the data. Correlation in multivariate time series can be used to identify such anomaly. In this paper, we propose an efficient method to utilize the correlation between multivariate time series with constraint-based anomaly detection. We develop a DP algorithm to execute the detection process, and optimize the algorithm efficiency with 2D range tree. Experiments on real IIoT dataset demonstrate the superiority of our proposed method compared to the prediction based models.
Modeling of Reef Distributed Reconnaissance and Early Warning System Based on DoDAF

Abstract In view of the new requirements of the reconnaissance and early warning system in the complex battlefield environment, a distributed architecture design method for the early warning and reconnaissance of reefs is proposed. This paper presents the design process of DoDAF based distributed reconnaissance and early warning architecture framework for reefs, constructs the architecture model of specific operational activities by using TD-CAP software, describes the relationship between various combat platforms from different perspectives through different views, and evaluates the consistency and operational effectiveness of the distributed detection and early warning system. The evaluation results show that the model has good integrity and consistency, and the combat effectiveness is significantly improved compared with the non distributed reconnaissance and early warning system.
Development of Libraries of the Future: Bank of Ideas of the IFLA “Global Vision” Project

The article presents an overview of the Bank of ideas of the International Federation of Library Associations and Institutions (IFLA), created in the course of implementation of the “Global Vision” project (2017—2018). The Bank of ideas is designed to host innovative proposals in order to strengthen the influence of libraries around the world. With its help, librarians share their experience and best practices. Everyone can contribute to the formation of collective concept for the development of libraries of the future. Over two years of its existence, more than 30 thousand specialists from 190 countries of the world took part in its formation. The paper emphasizes the importance of this project for the implementation of the IFLA Strategy 2019—2024. The author highlights the priorities of innovative activity of librarians in the following areas: ensuring equal and free access to information and knowledge; supporting of reading and information literacy; meeting the information needs of society; applying digital innovations in libraries; protecting the interests of libraries at the national and regional levels; attracting to decision-making the young professionals who are dedicated to their work and striving for leadership. The author focuses primarily on ideas that may be of practical interest to domestic library specialists. The article marks the role of the Russian library community in the formation of ideas for the development of libraries and implementation of the IFLA “Global Vision” project. The ideas collected through the initiative of librarians around the world should take the form of actions to make the Global Vision a reality. The Bank of ideas is a source of inspiration for strategic and day-to-day librarians’ activities aimed at creating strong and unified library community.
VNIR-SWIR Superspectral Mineral Mapping: An Example from Cuprite, Nevada

Cuprite, Nevada, is a location well known for numerous studies of its hydrothermal mineralogy. This region has been used to validate geological interpretations of airborne hyperspectral imagery (AVIRIS HSI ), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) imagery, and most recently eight-band WorldView-3 shortwave infrared (SWIR ) imagery. WorldView-3 is a high-spatial-resolution commercial multispectral satellite sensor with eight visible-to-near-infrared (VNIR ) bands (0.42–1.04 μm) and eight SWIR bands (1.2–2.33 μm). We have applied mineral mapping techniques to all 16 bands to perform a geological analysis of the Cuprite, Nevada, location. Ground truth for the training and validation was derived from AVIRIS hyperspectral data and United States Geological Survey mineral spectral data for this location. We present the results of a supervised mineral-mapping classification applying a random-forest classifier. Our results show that with good ground truth, WorldView-3 SWIR + VNIR imagery produces an accurate geological assessment.
Character Grounding and Re-identification in Story of Videos and Text Descriptions

We address character grounding and re-identification in multiple story-based videos like movies and associated text descriptions. In order to solve these related tasks in a mutually rewarding way, we propose a model named Character in Story Identification Network (CiSIN). Our method builds two semantically informative representations via joint training of multiple objectives for character grounding, video/text re-identification and gender prediction: Visual Track Embedding from videos and Textual Character Embedding from text context. These two representations are learned to retain rich semantic multimodal information that enables even simple MLPs to achieve the state-of-the-art performance on the target tasks. More specifically, our CiSIN model achieves the best performance in the Fill-in the Characters task of LSMDC 2019 challenges [35]. Moreover, it outperforms previous state-of-the-art models in M-VAD Names dataset [30] as a benchmark of multimodal character grounding and re-identification.
Research on the Application of Computer Virtual Reality Technology in College Physical Education Teaching

Abstract Virtual reality technology is an emerging discipline in computer simulation, and it has been successfully applied in all fields. There is no doubt that the use of this advanced computer virtual technology in university sports will undoubtedly bring huge changes to classroom teaching. This article mainly uses 3D image modeling technology, model drive technology, visual tracking and viewpoint sensing technology and the construction of stereo synthesis technology used on the basis of the new model of physical education teaching of virtual reality technology, so as to supply a secure basis for the improvement of the quality of physical education. Virtual reality technology as a new field of science and technology is the intersection and penetration of many disciplines, set computer graphics, mechanical mechanics, materials, sensing and many other disciplines as a whole, while virtual reality technology has a powerful ability to influence the education model of colleges and universities, it can be said that it has become the most applicable and future hot technology. The model of physical education is very important in college physical education. The traditional teaching model has seriously hindered the progress of college physical education. It is very necessary to find a new model of physical education to solve the disadvantages of the early teaching model, and focus on solving the key and hard question in training.
An Empirical Study of Downstream Analysis Effects of Model Pre-Processing Choices

This study uses an empirical analysis to quantify the downstream analysis effects of data pre-processing choices.Bootstrap data simulation is used to measure the bias-variance decomposition of an empirical risk function, mean square error (MSE).Results of the risk function decomposition are used to measure the effects of model development choices on model bias, variance, and irreducible error.Measurements of bias and variance are then applied as diagnostic procedures for model pre-processing and development.Best performing model-normalization-data structure combinations were found to illustrate the downstream analysis effects of these model development choices.In additions, results found from simulations were verified and expanded to include additional data characteristics (imbalanced, sparse) by testing on benchmark datasets available from the UCI Machine Learning Library.Normalization results on benchmark data were consistent with those found using simulations, while also illustrating that more complex and/or non-linear models provide better performance on datasets with additional complexities.Finally, applying the findings from simulation experiments to previously tested applications led to equivalent or improved results with less model development overhead and processing time.
Multiagent Task Coordination as Task Allocation Plus Task Responsibility

In this work, we present a dynamic Task Coordination framework ( ) for multiagent systems. Here task coordination refers to a twofold problem where an exogenously imposed state of affairs should be satisfied by a multiagent system. To address this problem the involved agents or agent groups need to be assigned tasks to fulfill (task allocation) and the behavior of these agents needs to be monitored to evaluate whether their tasks are fulfilled so that responsibility for dismissing tasks can be determined (task responsibility). We believe the allocation of tasks should regard both the strategic abilities of agents and their epistemic limitations. To date, however, existing work on the application of logical strategic reasoning for task allocation assumes perfect information for agents (dismissing imperfect information settings) and allocates tasks to individual agents (dismissing task allocation to agent groups). In , we address this gap by modeling task allocation using imperfect information semantics for strategic reasoning and integrate it with a notion of task responsibility. We formally verify properties of : on validity as well as stability of task allocations and fairness as well as non-monotonicity of task responsibilities.
Extending SPARQL with Similarity Joins

We propose techniques that support the efficient computation of multidimensional similarity joins in an RDF/SPARQL setting, where similarity in an RDF graph is measured with respect to a set of attributes selected in the SPARQL query. While similarity joins have been studied in other contexts, RDF graphs present unique challenges. We discuss how a similarity join operator can be included in the SPARQL language, and investigate ways in which it can be implemented and optimised. We devise experiments to compare three similarity join algorithms over two datasets. Our results reveal that our techniques outperform DBSimJoin: a PostgreSQL extension that supports similarity joins.
An Bayesian Learning and Nonlinear Regression Model for Photovoltaic Power Output Forecasting

Abstract Photovoltaic power system is taking a significant percentage of power system and the demands for accurate forecasting of the power outputs is surging. In prior works, the forecasting problem was formulated as a regression problem, however, which most cannot guarantee that the forecasted outputs is nonnegative. To solve this problem, we proposed a novel probabilistic model by using nonlinear regression and Bayesian learning method. In the paper, we present the detailed theoretical derivations and interpretations. The simulation results show the validity and feasibility of the proposed algorithm by comparing with the traditional SVM algorithm.
Towards a Framework for Empirical Measurement of Conceptualization Qualities

Conceptualization development is central in modeling language design. As one of their first design steps, language designers need to decide on a set of concepts on which the language will be based and which can be understood and used by a population of modelers for characterizing and representing relevant domain information. Thus, exposing candidate concept sets to future users may offer insights on how well the concepts of choice are understood and distinguished from each other by those who will be called to actually use the language. We propose an empirical measurement framework to allow just that. The framework consists of an instrumentation approach whereby participants sampled from the user population classify domain expressions to the corresponding concepts, and a set of measurement constructs for translating participant observed data into design insights. A small case study is conducted to explore the feasibility and limitations of the proposed approach.