Reviewerly/exHarmony huggingface dataset:
Effect of Creative Rhythmical Movement Program on Perceptional Motor Ability, Emotional Experience and Facial Emotion Recognition Ability of Adults with Developmental Disability

Background/Objectives: This study analyzed the effect of creative rhythmical movement program participation on perceptional motor ability, emotional experience and facial emotion recognition ability of adults with developmental disability. Methods/Statistical analysis: This study set 30 male and female who use S social welfare protection center located in Seoul from age 20 to 30 that were diagnosed with intellectual or autistic disability. The study subjects were conducted and randomly arranged them into experiment and control group, each with 15 people. Experiment group and control group each participated in creative rhythmical movement program and yoga program. Findings: First, there was for the change within group, the experiment group showed significant increase from 3.76 to 5.71, and control group showed increase from 3.75 to 4.73, but did not show significant difference. Second, there was for the change within group, the experiment group showed significant decrease from 3.66 to 1.93, and the control group showed decrease from 3.93 to 3.80, but did not show significance. Third, there was for the change within group, the experiment group showed significant increase from 5.46 to 7.06, while the control group showed increase from 5.80 to 5.66,but did not show significant increase. Therefore, experiment group showed significant difference which indicated creative rhythmical movement activity improved perceptional motor ability, emotional experience and facial emotion recognition ability. Improvements/Applications: It is believed that creative rhythmical movement program is helpful to improvement of self-expression, creative ability to create own movement related to the space, sense of directionand furthermore.
FORMATION OF TEACHERS’ COMPETENCES IN FURTHER EDUCATION

The interest of contemporary society in improving the quality of education is mainly reflected in the issue of educational results - competences - and their provision in the learning process. Introducing the 21st Century educational standards, the development of competencies required for teaching become more relevant. International documents indicate that a teacher should have the ability to teach, effectively use technologies in learning, help students gain transversal competencies. The teacher should be able to participate in research, use innovation, reflect on his/her work, collaborate with colleagues and parents. It is the teacher’s duty to direct the student's individual development.Latvian legislation offers wide opportunities for a person employed in education (especially in vocational and education of interests) to become a teacher. It is possible to work as a teacher after attending a short professional development course in education theory. In that case, competences in education (and the right to work at a school) is certified by the B-course certificate after basic training (72 hours total). The goal of this study is to identify and characterize teacher’s competences in education as acquired by teachers during this course. Research methods are a content analysis of 210 questionnaires filled in by vocational education teachers after attending the course and content analysis of their self-assessment. The results of the research show that teacher’s competences are only partially formed and incomplete. The attitude towards the needs of the student's personality is inappropriate, there is a desire for an authoritarian style.
Recovery of missing URLs cited in Annals of Library and Information Studies: a study of Time Travel

This paper intends to know the rate of active and missing URLs cited in the article published in Annals of Library and Information Studies during 2006-2015. A total of 6713 references appended to 342 research articles published during ten years were studied. Of the 6713 references, 1105 have URLs. It is found that 56.56% of URLs are active and remaining 43.44% of articles are missing. ‘HTTP-404’ is the common error code associated with missing URLs. The paper also discusses the usefulness of Time Travel in the recovery of missing URLs. The results show that Time Travel was able to recover more than 50% of missing URLs through different web archives.
The Role of Hybrid Classifiers in Problems of Chest Roentgenogram Classification

The hybrid technology of chest roentgenogram classification, based on three-level hierarchic structure, has been suggested. “Weak classifiers,” based on two ways of data analysis, are formed on the first level. The approach is to make a “weak” classifier using the first way which is based on the analysis of Fourier amplitude spectra in sliding window. An X-ray image is sequentially scanned by windows of various scales. A “weak” classifier is made based on Fourier amplitude spectrum, defined in each window. It refers to an image segment, which got into the sliding window, to a certain class. The second way of making a “weak” classifier is based on the descriptors, which were received because of intensity histogram approximation in the analysis window. The number of received “weak classifiers,” based on the two ways of analysis, depends on the number of analysis window scales chosen. On the second hierarchic level, the solutions of “weak” classifiers within every way of the first hierarchic-level analysis are integrated. A final classifier makes the ultimate solution, which aggregates the solutions of two second hierarchical level classifiers.
Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweet’s Hashtag

Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot spammer. Spam spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot spammers.
A Service Definition for Data Portability

Data portability is one of the new provisions that have been introduced with the General Data Protection Regulation (GDPR) in May 2018. Given certain limitations, the data subject can request a digital copy of her own personal data. Practical guidelines describe how to handle such data portability requests, but do not support in identifying which data has to be handed out. We apply a rigorous method to extract the necessary information properties to fulfill data portability requests. We then use these properties to define an abstract service for data portability. This service is evaluated in seven expert interviews.
Hearing How It Feels to Listen: Perception, embodiment and first-person field recording

This article explores recent theories of listening, perception and embodiment, including those by Mark Grimshaw and Tom Garner, Salomé Voegelin, and Eric Clarke, as well as consequences and possibilities arising from them in relation to field recording and soundscape art practice. These theories of listening propose auditory perception as an embodied process of engaging with and understanding lived environment. Such phenomenological listening is understood as a relational engagement with the world in motion, as movement and change, which grants access to the listener’s emerging presence, agency and place in the world. Such ideas on listening have developed concurrently with new approaches to making and presenting field recordings, with a focus on developing phonographic methods for capturing and presenting the recordist’s embodied auditory perspective. In the present study, ‘first-person’ field recording is defined as both method and culturally significant material whereby a single recordist carries, wears or remains present with a microphone, consciously and reflexively documenting their personal listening encounters. This article examines the practice of first-person field recording and considers its specific applications in a range of sound art and soundscape art examples, including work by Gabi Losoncy, Graham Lambkin, Christopher Delaurenti and Klaysstarr (the author). In the examination of these methods and works, first-person field recording is considered as a means of capturing the proximate auditory space of the recordist as a mediated ‘point of ear’, which may be embodied, inhabited, and listened through by a subsequent listener. The article concludes with a brief summary of the discussion before some closing thoughts on recording, listening and the field, on field recording as practice-research and on potential connections with other fields in which the production of virtual environments is a key focus.
An Experimental Evaluation of Design Space Exploration of Hardware/Software Interfaces

We observe ever increasing variability of hardware/software interfaces (HSIs), e.g., in automotive systems. Hence, there is a need for the reuse of already existing HSIs. In this regard, an important question is whether automated adaptation of an already existing HSI to one that fulfills the requirements on a new HSI is feasible in industrial practice. Ideally, the number of adaptation steps should be minimal, so that new hardware production can be avoided. In this paper, we address the problem of finding such an optimal solution for a given specific HSI and a set of formally specified requirements on a new HSI. We propose using design space exploration employing (heuristic) search with optimality guarantees. Hence, a meta-model of such HSIs has been created together with transformation rules. Based on all that, an experimental evaluation of this approach shows its feasibility for realistic HSIs.
Asynchronous Stochastic Subgradient Methods for General Nonsmooth Nonconvex Optimization

Asynchronous distributed methods are a popular way to reduce the communication and synchronization costs of large-scale optimization. Yet, for all their success, little is known about their convergence guarantees in the challenging case of general non-smooth, non-convex objectives, beyond cases where closed-form proximal operator solutions are available. This is all the more surprising since these objectives are the ones appearing in the training of deep neural networks. In this paper, we introduce the first convergence analysis covering asynchronous methods in the case of general non-smooth, non-convex objectives. Our analysis applies to stochastic sub-gradient descent methods both with and without block variable partitioning, and both with and without momentum. It is phrased in the context of a general probabilistic model of asynchronous scheduling accurately adapted to modern hardware properties. We validate our analysis experimentally in the context of training deep neural network architectures. We show their overall successful asymptotic convergence as well as exploring how momentum, synchronization, and partitioning all affect performance.
ANALISIS KRITIS DAMPAK INTERNET TERHADAP PERILAKU MEMBACA MAHASISWA

This study attempts to explore students reading behavior in online and offline media, and then analyze its impact on students content recall ability and comprehension. This research uses sequential mix method approach; involving sequential qualitative and quantitative research methods. First, surveys were conducted on some samples (N = 279) to explore students' reading behavior profile. Second, two groups of respondents were provided (each, N = 30). Group A is treated to read online, group B offline. Next, they were tested with a written test. The results of this study show that students have a dualistic tendency to choose reading media. Most of them pick and prioritize reading offline. However, on the other hand, they also like to read various online material. This choice is based on the reason that reading on online media has many options for reading materials, efficiency, convenience, low cost, and up-to-date. Interestingly, most students often get lost on searching for sources that not credible, such as Blogspot. Because, when looking for reading material, they only rely on big titles as the keyword. In terms of content recall ability and comprehension, reading material online proved to trigger a pattern of fast reading, short and not deep. This condition causes the students content to recall ability and comprehension to be lower than reading offline. Further research will be more interesting when examining how online materials influence or change students reading behavior. This is useful for lecturers as a consideration in presenting lecture materials amid the euphoria of ICT-based learning trends. Kata Kunci: analisis kritis, internet, perilaku membaca
Weather report on metropolitan cities in India using web scraping technique

In metropolitan cities of India, there are several climate measurement points that are owned by several different institutions such as Indian Institute of Tropical Meteorology, NCMRWF, India Meteorological Department (Pune) and many other institutes. However, obtaining the latest meteorological data in detail within a specific period has restricted the bureaucratic process to each institution. While the availability of meteorological data sets is necessary for conducting research in the field of data analysis to predict climate and analyzes for DSS that require weather patterns. On the other hand, some sites provide real-time weather data for some cities. For this, in this research, we use web scraping technology to collect meteorological data in metropolitan cities in India. Web scraping technology is a technique for retrieving the content of a web page individually. The data collected by this web scraping technique will form a database or data warehouse that can be used for further research on data mining for weather forecasting in metropolitan cities, which in the future can be developed a support application to based decision.
Dimension Reduction Approach for Interpretability of Sequence to Sequence Recurrent Neural Networks.

Encoder-decoder recurrent neural network models (Seq2Seq) have achieved greatsuccess in ubiquitous areas of computation and applications. It was shown to besuccessful in modeling data with both temporal and spatial dependencies fortranslation or prediction tasks. In this study, we propose a dimensionreduction approach to visualize and interpret the representation of the data bythese models. We propose to view the hidden states of the encoder and thedecoder as spatio-temporal snapshots of network dynamics and to apply properorthogonal decomposition to their concatenation to compute a low-dimensionalembedding for hidden state dynamics. Projection of the decoder states onto suchinterpretable embedding space shows that Seq2Seq training to predict sequencesusing gradient-descent back propagation effectively performs dimensionreduction consisting of only a small percentage of dimensions of the network'shidden units. Furthermore, sequences are being clustered into well separableclusters in the low dimensional space each of which corresponds to a differenttype of dynamics. The projection methodology also clarifies the roles of theencoder and the decoder components of the network. We show that the projectionof encoder hidden states onto the low dimensional space provides aninitializing trajectory directing the sequence to the cluster which correspondsto that particular type of distinct dynamics and the projection of the decoderhidden states constitutes the embedded cluster attractor. Inspection of the lowdimensional space and the projections onto it during training shows that theestimation of clusters separability in the embedding can be utilized toestimate the optimality of model training. We test and demonstrate our proposedinterpretability methodology on synthetic examples (dynamics on a circle and anellipse) and on 3D human body movement data.
Artificial Intelligence: A Creative Player in the Game of Copyright

Artificial intelligence (AI) may be understood in countless ways. However, it may be understood to be an entity sufficiently simulating the cognitive aspects of human thinking. As such, AI can make valuable outcomes which are able to meet the individual conceptual features of copyrighted works  and to gain copyright protection. Based on that we could call AI an author of such works. Nevertheless, despite all the development around intellectual property, AI is not reflected enough. Copyright is still based on the ideas of previous centuries and persists on the criterion of a  natural person  as the author. Therefore, it is needed to analyse AI in this context, its creative options, place in the world of intellectual property law, and to shift the paradigm of copyright towards the modern age, the age of AI. The paper focuses on a rudimentary metanalysis of AI and copyright in mutual interactions. In the beginning, the author's research is introduced outlining all the intended proposed phases of regulation of AI within the copyright law and the methodology suitable for such a move. In the second chapter, AI definition and outcomes for the further operating are presented as well as its creative options which are rudimental for the assessment of the conceptual features. The third chapter deals with the copyright aspects of an AI itself focusing on an analysis of the applicable law on the national, supranational and international level. The fourth chapter represents the critical analysis and demarcation of the main problematic friction surfaces of copyright and earlier defined AI. The paper is concluded by an overall assessment of the analysis.
An Adaptable Framework for Deep Adversarial Label Learning from Weak Supervision.

In this paper, we propose a general framework for using adversarial label learning (ALL) [1] for multiclass classification when the data is weakly supervised. We introduce a new variant of ALL that incorporates human knowledge through multiple constraint types. Like adversarial label learning, we learn by adversarially finding labels constrained to be partially consistent with the weak supervision. However, we describe a different approach to solve the optimization that enjoys faster convergence when training large deep models. Our framework allows for human knowledge to be encoded into the algorithm as a set of linear constraints. We then solve a two-player game optimization subject to these constraints. We test our method on three data sets by training convolutional neural network models that learn to classify image objects with limited access to training labels. Our approach is able to learn even in settings where the weak supervision confounds state-of-the-art weakly supervised learning methods. The results of our experiments demonstrate the applicability of this approach to general classification tasks.
From Expert Consulting to Co-creation in Medical Education; Co-creating an Exploratory Educational Space for Orthopedic Medical Education

Medical education is increasingly turning to technology enhanced learning for facilitating the learners’ understanding of difficult concepts and honing the decision making skills of future healthcare professionals. Experiential digital content creation for medicine faces the challenge of keeping up with the rapidly growing volume of medical knowledge. A suggested avenue for addressing this challenge is the use of co-creative methodologies in order to engage the expert medical user base in the content creation process. This work describes the prototyping of such a co-creative process through several orthopedic educational topics. The design process of the content is described, along with the provision for facilitating a non-tech savvy expert towards the co-creative endeavor. Initial insights along with future steps for evaluation of this process are also discussed.
A Novel Prediction Algorithm for the Cross Temperature Estimation of Blast Furnace

Abstract In order to predict the distribution of gas flow, we need to get the temperature of each point in blast furnace throat in advance. In this paper, firstly, two intelligent modeling methods are used to establish a multiple-input multiple-output prediction model, one is extreme learning machine (ELM) algorithm and the other is online sequential extreme learning machine (OS-ELM) algorithm. And the model is a single-step prediction model of temperature in blast furnace, single-step prediction means the prediction of temperature in the next moment. We use autocorrelation analysis to determine input vector and output vector of the model. The result of autocorrelation analysis indicates that the method of temperature sequence prediction has a higher prediction accuracy and better prediction stability than the method of single point prediction. Next, based on real industrial data, we make a comparison between the multiple - input multiple-output model and least squares support vector machines (LS-SVM) model used in common. The experiment results show that OS-ELM model has a better forecast effect than the ELM model and LS-SVM model.
Current State of the Art of the General Rank Decoding Problem

This talk gives an overview of the latest results in the general rank decoding problem. The main question is, how efficiently one can decode a given rank-metric code, without any known algebraic structure, from a random error. Answers to this question are of high interest, in particular in code-based cryptography, where rank-metric codes have extensively been studied to be used in a McEliece-type public key cryptosystem. During the last 5 years several algorithms have been proposed to solve (or at least improve) this problem – some deterministic, some probabilistic. We will introduce these algorithms, show the corresponding computational complexities, and state some open problems in this regard.