Reviewerly/exHarmony huggingface dataset:
A Comparative Study on SOM-Based Visualization of Potential Technical Solutions Using Fuzzy Bag-of-Words and Co-occurrence Probability of Technical Words

Self-Organizing Maps (SOM) is a powerful tool in visualizing mutual connection among various objects. In a previous work, SOM-based visualization was applied for revealing potential technical solutions varied in Japanese patent documents, in which meaningful pairs of technical words are implied in SOMs. Before application, text documents were quantified into numerical vectors considering co-occurrence frequency among technical words in sentences, and then, SOMs were constructed summarizing word features of co-occurrence probability vectors or correlation coefficient vectors. Recently, a fuzzy bag-of-words model was proposed for handling sparse characteristics of word feature values and shown to be useful in document classification. In this paper, a comparative study on utilizing fuzzy bag-of-words in conjunction with previous feature values is performed with the goal of revealing potential technical solutions varied in patent documents.
Logical Training in the Information Society: Objectives and structure of the training course

The article contains an analysis of the problems of the logical thinking formation that arise in the information society. The necessity of a practically oriented logic course in the system of modern higher professional education is justified. A program of logic course adapted to the new purposes is proposed.
Authority Claim in Rationale-Containing Online Comments

We examined whether the existence of authority claims signifies one’s rationales in online communication content, potentially contributing to the research on rationale identification and rationale generation. Authority claims are statements that reveal the writer’s intention to bolster the writer’s credibility. In open online communications, the anonymity and the dynamic participation make it challenging to establish the credibility of their viewpoints and reasoning. Therefore, we hypothesize these online participants will tend to use authority claims to bolster their credibility when presenting their justifications. We annotated authority claims in 271 text segments that contain online users’ rationales. These text segments are adapted from the open access corpora provided by Rutgers’ Argument Mining group. Contrary to our hypothesis, we found that in our dataset the users scarcely attempted to bolster their credibility when presenting their reasoning to the others in these activities. We call for more investigations to explore the role of activity context affects participants’ use of authority claims in their reasoning traces. We further state that the effects of communication medium on individuals’ cognitive and meta-cognitive processes are important to consider in argument mining research.
Enhanced measurement-device-independent quantum key distribution in reference-frame-independent scenario: publisher’s note

This publisher’s note corrects a value in Table 1 in J. Opt. Soc. Am. B36, 476 (2019).JOBPDE1520-854010.1364/JOSAB.36.000476
A Clustering-based Approach to Determine a Standardized Statistic for Daily Activities of Elderly Living Alone

The modeling of behavior by monitoring activities of daily living allows caregivers to recognize early stages of dementia. Therefore, many monitoring systems were presented in recent years. In this work, we present a behavior modeling system that is based only on two adjustable parameters and provides a single standardized output statistic. Therefore, this system enhances the comparison of recent and future activity monitoring systems. The approach is comprised of three parts: First, the clustering of power plug data to detect time windows in which appliances are used regularly. Second, the calculation of a comparison Matrix. Third the test of change using the χ2-statistic. We tested this approach successfully in a seven-month field study with two healthy subjects. We showed that the χ2-statistic reflected how regular activities were performed and that one to two months, depending on the regularity of the performed activities, provide the necessary amount of reference data for our approach to work.
Embedding Digital Hidden Data into Hardcopy

This chapter introduces the problematics of securing and authentication of printed documents. We propose two methods for the embedding of imperceptible hidden digital data into a printed text document with the ability to extract this data during scanning. Our approaches are based on the intrusion of clusters with small black dots (patterns) arranged in predefined structures into the document. The patterns of the dots and their positions were selected in such a way that intrusion of the data does not degrade the document itself. The proposed multiple duplications of embedded information and the statistical approach during hidden message extraction provide methods with high immunity against document modification and noise.
Transformasi perpustakaan untuk generasi millenial menuju revolusi industri 4.0

This article discusses library transformation for the millennial generation towards industrial revolution 4.0. The occurrence of the 4th industrial revolution made technology develop extraordinary, while bringing positive and negative effects to humans. The development of these technologies also influenced the use of social media in the community. So is the library. The collection of digital libraries must also be developed not all books, magazines, or journals but other content that can satisfy the thirst of knowledge for users. The existence of Virtual Reality devices allows libraries to have a collection of libraries that are more interesting and visible to the community. Virtual Reality content is not only available on the internet or content provider services, but can also be independently developed through applications that are often available free on the internet. Keywords: millennial generation; library, industrial revolution 4.0
Screenshot to Metafile Conversion for Printing

This chapter considers a method for the conversion of a screenshot to a metafile for improving the print quality. The proposed method is based on detection and vectorization of text areas on raster screenshot images. The main study is dedicated to anti-aliased text segmentation, text colour estimation, recovering the contour of text symbols and approximating them with lines and Bezier curves. The result of processing is stored in metafile documents for subsequent printing. The proposed method is resistant to different colours, text sizes, and languages and makes it possible to obtain a sharp and correct text display for printing.
A Sketch Recognition Algorithm Based on Bayesian Network and Convolution Neural Network

Most of the existing sketch recognition algorithms are used to restrict the user’s drawing habits to achieve the stroke grouping and recognition. In order to solve the problem, a new sketch recognition algorithm based on Bayesian network and convolution neural network (CNN) is proposed. First, the input sketch is processed by Gaussian low-pass filter and a smoother stroke can be obtained. The stroke of continuous input is divided, then the Bayesian network and CNN are performed on stroke recognition respectively. The recognition result of Bayesian network is adopted when the reliability of stroke is larger than the threshold, otherwise recognition result of CNN will be adopted. The experiment result shows that the proposed algorithm is effective in circuit symbol recognition. The recognition rate was achieved 80.34% in the drawing process, and the final recognition rate was achieved 93.48%.
The Application of A-CNN in Crowd Counting of Scenic Spots

In places where people are concentrated, such as scenic spots, the statistical accuracy of existing crowd statistics algorithms is not enough. In order to solve this problem, a crowd counting algorithm based on adaptive convolution neural network (A-CNN) is proposed, which is based on video monitoring technology. The process of its pooling is dynamically adjusted according to different feature graphs. Then the pooled weights are adjusted adaptively according to the contents of each pooled domain. Therefore, CNN can extract more accurate features when processing different pooled domains under different iteration times, so as to achieve adaptive effect finally. The experimental results show that the proposed A-CNN algorithm has improved the recognition accuracy.
Analysis of Feature Extraction Methods of Multimedia Information Resources Based on Unstructured Database

This paper analyzes the problems of excessive information resources and insufficient accuracy of data information resource representation and extraction. This paper proposes an analysis of multimedia information resource characterization extraction method based on unstructured database. First, the underlying structure of the database is optimized, and the data resource attributes are identified by combining with the data feature mining algorithm, and the low-level information resource features of the unstructured database are clustered, and the data representation extraction path is reasonably planned with reference to the information feature mining results of the underlying database. The covariance parameters in the extraction process of information resource representation are calculated with Gaussian function algorithm, so as to effectively check the extraction error of resource representation and realize the extraction method of multimedia information resource representation in unstructured database. Finally, the comparison experiment proves that the multimedia information resource representation extraction method based on unstructured database can effectively improve the efficiency and accuracy of information resource representation extraction compared with the traditional method.
A Study on the Effects of Workplace and Learning Dual System on Sustainability, adherency degree and satisfaction of Industry-University Cooperation

This study we tried to find ways to improve satisfaction of industry-university cooperation and to achieve virtuous and industrial cooperation by analyzing the effects of workplace and learning dual system on the sustainability and adherency degree between companies and universities.BR Hypothesis verification shows that two factors, including business performance and joint training center support of the workplace and learning dual system, significantly affect the sustainability of industry-university cooperation and satisfaction of industry-university cooperation.BR First of all, we have prepared a theoretical framework for the study of the relationship between the workplace and learning dual system and the industry-university cooperation. Second, we need to emphasize the business performance factor and the support of the joint training center factor. Third, it is necessary to focus more on corporate performance, joint training center support and sustainability of industry-university cooperation in order to improve satisfaction of industry-university cooperation and expand industry-university cooperation activities.
Neural sensor translation

Neural Machine Translation (NMT), a neural network based approach for language translation, has been shown to be more effective than traditional approaches like statistical machine translation. Inspired by NMT, we propose Neural Sensor Translation (NST), a process of translating data sequences from one or a set of sensors to another using neural networks. NST is a data-driven approach for creating virtual sensors that have useful applications in sensor networks and internet of things. We present a neural network based approach for sensor translation and demonstrate the potential of the approach using sensor data from a home.
Multi-robot motion planning via optimal transport theory

In this work we establish a simple yet effective strategy, based on optimal transport theory, for enabling a group of robots to accomplish complex tasks, such as shape formation and assembly. We demonstrate the feasibility of this approach and rigorously prove collision avoidance and convergence properties of the proposed algorithms.
Socio-Psychological Factors that Contribute to and Impede the Process of Student Eco-Vocational Consciousness Formation

The relevance of the issue under study is caused by the need to form a new socio-psychological paradigm in the field of environmental education. It was clearly shown by the beginning of the new millennium, that the current environmental education is based on analytical knowledge of nature, which is pragmatically narrow and consumer-oriented, and therefore it could not fundamentally change the worldview of a significant part of the population. As a result, there is an objective need for a transition to a qualitatively new stage in its development. The purpose of the article is to determine the socio-psychological factors that contribute to and hinder the formation of eco-vocational consciousness of students. The study methods: The leading method for studying this issue is modeling, which allows considering the socio-psychological factors that contribute to and hinder the purposeful formation of eco-vocational consciousness structural components. The article identifies the main factors contributing to the process of eco-vocational consciousness formation: the need for a value orientation for the co-evolution of man and the biosphere; the ability of the individual of perceiving, experiencing and comprehension the relationship between the current environmental situation and occupational activities; the willingness of specialists to consciously implement environmentally reasonable occupational activities; public interest in the knowledge, transformation and maintenance of the environment in optimal condition and other significant factors. The factors that prevent the formation of eco-vocational consciousness are identified: the absence of a state customer for an ecologically-oriented vocational education; weak linkage of environmental training with vocational training; poor scientific and methodical provision of environmental orientation in vocational training, theoretical and methodical foundations for organizing the integration of environmental and vocational training; the need to coordinate and manage the system of electronic training, the creation of didactic complexes, appropriate methods. The data obtained in the work can be used in education to build effective educational programs for the development of eco-vocational consciousness; in practical psychology, in ecological psychology, as well as for further theoretical development of this issue.
Blockchain Economics for Investment Professionals

The author reviews recent studies on blockchains, cryptocurrencies, and initial coin offerings (ICOs), and discusses their implications for institutional investors. The research on blockchain technology is organized into two broad categories: those with the economics of decentralized ledger technologies and those studying their impacts on the real economy. The author first clarifies the core functionalities of blockchains and then elaborates on issues concerning information distribution in generating decentralized consensus, industrial organization of mining pools, the role and dynamic pricing of crypto-tokens, and auditing with a federated blockchain. <b>TOPICS:</b>Currency, portfolio construction, wealth management
Participatory design fiction for innovation in everyday wearable IoT systems

This demo will invite attendees to experience a novel technique of Participatory Design Fiction, which uses storytelling to elicit users' ideas for new kinds of wearable IoT devices, starting from a blank slate. The demo will also feature the Gallery Necklace technology probe, an IoT-enabled neckpiece with an ePaper display, which is the first design artifact of the study so far. Overall, this demo will illuminate how Participatory Design Fiction can give insight into what users really want from wearable computing.
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation

Tasnim Mohiuddin, Thanh-Tung Nguyen, Shafiq Joty. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Long-Term Video Generation of Multiple Futures Using Human Poses.

Predicting the near-future from an input video is a useful task for applications such as autonomous driving and robotics. While most previous works predict a single future, multiple futures with different behaviors can possibly occur. Moreover, if the predicted future is too short, it may not be fully usable by a human or other system. In this paper, we propose a novel method for future video prediction capable of generating multiple long-term futures. This makes the predictions more suitable for real applications. First, from an input human video, we generate sequences of future human poses as the image coordinates of their body-joints by adversarial learning. We generate multiple futures by inputting to the generator combinations of a latent code (to reflect various behaviors) and an attraction point (to reflect various trajectories). In addition, we generate long-term future human poses using a novel approach based on unidimensional convolutional neural networks. Last, we generate an output video based on the generated poses for visualization. We evaluate the generated future poses and videos using three criteria (i.e., realism, diversity and accuracy), and show that our proposed method outperforms other state-of-the-art works.
Robust Speaker Recognition Using Improved GFCC and Adaptive Feature Selection

Speaker recognition systems have shown good performance in noise-free environments, but the performance will severely deteriorate in the presence of noises. At the front end of the systems, Mel-Frequency Cepstral Coefficient (MFCC), or a relatively noise-robust feature Gammatone Frequency Cepstral Coefficients (GFCC), is commonly used as time-frequency feature. To further improve the noise-robustness of GFCC, signal processing techniques, such as DC removal, pre-emphasis and Cepstral Mean Variance Normalization (CMVN), are investigated in the extraction of GFCC. Being aware the advantages and disadvantages of MFCC and GFCC, an adaptive strategy was proposed to make feature selection based on the quality of speech. Experiments were conducted on TIMIT dataset to evaluate our approach. Compared with ordinary GFCC and MFCC features, our method significantly reduced the EER in speech data with miscellaneous SNRs.
Essential R Packages: Tidyverse

Now that you have the essential RStudio workflow, we are ready to start highlighting some of the tools that will really supercharge your data analysis. Not all of these tools are exclusive to RStudio and you may see them in other R environments, but they do fit in very nicely with the RStudio IDE.
CHALLENGES OF STRATEGIC PLANNING IN HIGHER EDUCATION INSTITUTIONS

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 1515-1518ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0459Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Infusive Discourse: Principles of Organization and Logics of Functioning

One of the most serious problems in modern information society is the socalled infusive discourse, when a certain kind of subject indoctrination involved in a relevant discourse is carried out by manipulative means. This article is devoted to the analysis of infusive discourse, its principles of organization and the logics of functioning. Human mentality psychodynamic and cognitive features, that provide conditions for the discourse indoctrination implementation are considered. The general methodology of discourse indoctrination is revealed paying special attention to the manipulative discourse. Most common methods and techniques used to implement such kind of discourse indoctrination are described and conceptually analyzed.
Estimating the Number of Correlated Components Based on Random Projections

Estimating the number of correlated components between two data sets is a challenging task in the case of small sample support. Typically, a rank-reduction preprocessing step based on principal component analysis (PCA) is carried out on each data set individually to reduce the dimensionality before analyzing correlation between the data sets. However, PCA retains the components with the largest variance within a data set, and therefore fails when these components are not the ones that account for the correlation between the data sets. To overcome this, we propose an alternative technique that, instead of projecting the data into a single subspace, uses a large number of random projections.
Privacy-Preserving Hierarchical Clustering: Formal Security and Efficient Approximation.

Machine Learning (ML) is widely used for predictive tasks in a number of critical applications. Recently, collaborative or federated learning is a new paradigm that enables multiple parties to jointly learn ML models on their combined datasets. Yet, in most application domains, such as healthcare and security analytics, privacy risks limit entities to individually learning local models over the sensitive datasets they own. In this work, we present the first formal study for privacy-preserving collaborative hierarchical clustering, overall featuring scalable cryptographic protocols that allow two parties to privately compute joint clusters on their combined sensitive datasets. First, we provide a formal definition that balances accuracy and privacy, and we present a provably secure protocol along with an optimized version for single linkage clustering. Second, we explore the integration of our protocol with existing approximation algorithms for hierarchical clustering, resulting in a protocol that can efficiently scale to very large datasets. Finally, we provide a prototype implementation and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results. For example, for a dataset of one million records and 10 dimensions, our optimized privacy-preserving approximation protocol requires 35 seconds for end-to-end execution, just 896KB of communication, and achieves 97.09% accuracy.
Analysis of Time Series Prediction using Recurrent Neural Networks

Time series prediction is the heart of forecasting data that is based on past information of any particular dataset, recurrent neural network combines with the time series algorithm and provide much reliable outcomes having high matching efficiency with actual real-time results as the combination of RNN and time series can predict almost anything which has time as its managing factor as RNN has ability to iterate efficiency with time feeds, it tells about the trend of future and it is particularly important in prediction of crucial data such as weather forecast or financial data, because proper forecast can provide the vital help and safety or the advancement for the change, though the future is uncertain but people must know their future as near it could be to the future.Based on the research this paper contains analytical data of recurrent neural network and its use with time series alongside the experimental data analysis of weather forecast and financial forecast data.