Reviewerly/exHarmony huggingface dataset:
From TV-L1 to Gated Recurrent Nets

TV-L1 is a classical diffusion-reaction model for low-level vision tasks, which can be solved by a duality based iterative algorithm. Considering the recent success of end-to-end learned representations, we propose a TV-LSTM network to unfold the duality based iterations into long short-term memory (LSTM) cells. To provide a trainable network, we relax the difference operators in the gate and cell update of TV-LSTM to trainable parameters. Then, the proposed end-to-end trainable TV-LSTMs can be naturally connected with various task-specific networks, e.g., optical flow estimation and image decomposition. Extensive experiments on optical flow estimation and structure + texture decomposition have demonstrated the effectiveness and efficiency of the proposed method.
APLIKASI SISTEM INFORMASI PARIWISATA TOURISM PADA DKI JAKARTA BERBASIS ANDROID

DKI Jakarta has many characteristics both in terms of attractions, culture, crafts and culinary. Jakarta is an attractive capital city and is often visited by tourists from various regions and abroad. So from that writing makes an application about tourist information in the City of Jakarta based on Android. Users of this application are expected to easily find tourist information in the city of Jakarta. Information contained in the application in the form of tourist attractions. The application also displays the direction of the location to that place using the Global Possitioning System (GPS) connection. The method used in this application is to contain information and location of each place in the city of Jakarta such as tourist attractions. Each menu selected has information and directions to reach the desired place. This application is only used on Android-based smartphones and the screen size is less than 4.0 inches, besides the size of 4.0 inches this application is not good at displaying tourist information.
Study on Academic Documents –Oriented Automatic Summarization of Short Texts

Traditional automatic text summarization relies heavily on the original text information, and the extensibility is limited. However, generation-style abstractive methods attempt to generate the corresponding summarization by understanding the original semantics. We set out to set up a sequence-to-sequence model for academic document summarization generation. For purpose of reducing the detail loss of input sequence information, we put forward the attention mechanism to assign the weight of each input word. We trained this model on Chinese literature data set. It generated a reliable document summary. Our test shows that the approach has good adaptability to Chinese academic literature and has good performance in text summarization.
학교 내 민주적 협의문화 발전을 위한 개선 방안 탐색

Purpose: The purpose of the study was to present the way to improve Democratic Consultative Culture in School by diagnosing it. Method: Qualitative Research was conducted on surveys of teachers of elementary, middle, and high schools in Busan Metropolitan Office of Education. Focus group interview(F...
Automatic Multi-drone Path Planning using Network Flow Algorithms

The aerial show of 1,218 drones in the Pyeongchang Winter Olympics was realized by animators drawing up the show with 3D design software in advance and by a central computer determining and assigning onsite which drone will play the role of which pixel in the animation. Manual creation of an animation in which a thousand drones move in harmony without collision is an expensive and difficult task. Thus, we propose a drone simulator to automate this process: given initial and final images, the system samples a given number of initial and final locations of drones, computes collision-free drone paths automatically (each starting from an initial location and arriving at a final location), and visualizes the results as a video. The set of drone paths is optimal in terms of the last arrival time, which is a desirable feature for drone paths.BR We propose two methods of computing the paths: a two-phase algorithm with a collision-removal modification and a single-phase algorithm for cost optimization. The former obtains paths by running a maximum flow algorithm and modifies them to be collision-free.BROn the other hand, the latter computes collision-free paths directly by running a minimum cost maximum flow algorithm with a carefully designed cost assignment. Both sets of output paths have a minimum last arrival time. The single-phase method needs more time than the two-phase method but gives much better values of stretch factor (the ratio of path length to the Euclidean distance between the initial and final locations). This trade-off between computation time and stretch factor is revealed empirically in the performance comparison of the two methods.
Universities and Business: Prospects of Development of Diffused Structures and Processes

Universities are in a state of active transformation both in the field of developing technologies for creating, storing and transmitting knowledge and in their organizational development. The globalization of education and research markets has formed a steady trend of universities' integration. The last decade sees the general process of institutes merging in many countries. Active reorganization of universities through their integration took place in theRussian Federationas well. Currently, the focus of organizational development of universities is shifting from intra-system restructuring to the formation of project and structural partnerships with key stakeholders, primarily belonging to the sphere of business. The impulse to searching new solutions in the universities' transformations originates from a whole range of state programs. Thus, in many regions of the Russian Federation 2017 saw the start of work on forming university centres of innovation, technological and social development of regions in partnership with enterprises and organizations. According to the results of the 2017 competitive selection, 51 universities received the status of such centres, including 22 key, 3 national research and 10 federal universities from 39 regions. Within the national technology development initiative, the project to create 15 research and educational centres puts forward theoretical and applied issues on designing a new type of universities' structural organization, which would represent diffusion, interpenetration of universities and their key stakeholders. This article aims at discussing the readiness of universities and their partners to create such structures, as well as at analysing existing practices, and institutional constraints. The study is new, as it introduces the term «dif- fuse structures» denoting a new type of weakly formalized universities with key stakeholders, and analyzes the level of maturity of such structures based on surveys. The purpose of the article is to study the attitude of regional stakeholders of universities to the formation and development of diffuse structures and processes that ensure effective interaction for solving the objectives of social and economic development of territories and industries.
Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

Agglomerative hierarchical is a bottom up clustering method, where the distances between documents can be retrieved by extracting feature values using a topic-based latent dirichlet allocation method. To reduce the number of features, term selection can be done using Luhn’s Idea. Those methods can be used to build the better clusters for document. But, there is less research discusses it. Therefore, in this research, the term weighting calculation uses Luhn’s Idea to select the terms by defining upper and lower cut-off, and then extracts the feature of terms using gibbs sampling latent dirichlet allocation combined with term frequency and fuzzy Sugeno method. The feature values used to be the distance between documents, and clustered with single, complete and average link algorithm. The evaluations show the feature extraction with and without lower cut-off have less difference. But, the topic determination of each term based on term frequency and fuzzy Sugeno method is better than Tsukamoto method in finding more relevant documents. The used of lower cut-off and fuzzy Sugeno gibbs latent dirichlet allocation for complete agglomerative hierarchical clustering have consistent metric values. This clustering method suggested as a better method in clustering documents that is more relevant to its gold standard.
Correction to: PyPNS: Multiscale Simulation of a Peripheral Nerve in Python

The original version of this article unfortunately contained a mistake. The following text: "This project has received funding from European Research Council (ERC) Synergy Grant no. 319818." is missing in the Acknowledgments.
Formation of Innovation Educational Environment at High School

The author, based on the achievements of phycological–pedagogical science, researches in the sphere of pedagogy at high school and pedagogical innovations, makes discussions on the theoretical–methodological and practical issues the of formation of innovative educational environment. The emphasis is made on the conditions promoting to formation of effective educational environment, the mechanisms required for its formation, need on planning and implementing the creative projects and a requirement on didactical system with two-level content of training discipline.
CECAN Evaluation and Policy Practice Note (EPPN) for policy analysts and evaluators - A Bayesian Network for Policy Evaluation

A Bayesian network (BN), also known as a probability network or Bayesian belief network is one of the effective theoretical models for knowledge representation and reasoning under the influence of uncertainty.
Criminal Policy in Preventing Cyberharassment as a Negative Impact of Technology

Information and communication technology develop fast nowadays.Advanced technology causes cybercrime.Cybercrime is criminal activity using computer and its network.One of the developing cybercrimes today is cyberharassment.New Jersey 2014NJ Rev Stat § 2C: 33-4.1describesthiscrime while making a communication in an online capacity via any electronic device or through a social networking site and with the purpose to harras another.While the awareness of cyberharassment threat as a crime is still low in Indonesia, thereis no clear legal rule regarding this crime.This study used the theory of criminal policy and theory of criminal law reform.There are two impacts caused by the absence of the law.First, the difficulty of running a criminal policy.Second, cyberharassment has a psychological impactas a frustration, a depression, anda suicidal intention.The result of the researchshowedthat in terms of a penal policy, Indonesia applies the Electronic Information and Transactions LawNo.19 of 2016 in dealing with cyberharassment with article 27 paragraph 3 after comparing with New Jersey 2014NJ Rev Stat § 2C: 33-4.1 In terms of a nonpenal policy, ethics approach, technology approach, and journalistic approach.
The Reliability Of Readability Tools In L2 Reading

The literature on the reliability of readability tools to predict the second language (L2) reading comprehension is limited and lacks consensus, particularly in exploring the role of cohesion in L2 reading. Thus, readability metrics used in the first language (L1) reading, which assess only sentence-level difficulty, are ineffective at predicting L2 reader difficulty despite their frequent usage by L2 educators and materials developers. Readability tools are used frequently in L2 environments despite the limited number of empirical studies exploring their effectiveness in predicting L2 comprehension. Thus, traditional readability formula predictions are likely to be even more problematic for L2 readers than for L1 readers due to the exclusive focus on surface features of texts in such formulas. Background knowledge required to read the texts may also have affected how learners comprehend the text. The readability predictions were compared to the comprehension score analysis to determine if one or both tools were reliable in predicting the difference in reading comprehension scores.
Subset Matching based Selection and Ranking (SMSR) of Web Services

Web service is a software application, which is accessible using platform independent and language neutral web protocols.However, selecting the most relevant services became one of the vital challenges.Quality of services plays very important role in web service selection, as it determines the quality and usability of a service, including its non-functional properties such as scalability, accessibility, integrity, efficiency, etc.When agent application send request with a set of quality attributes, it becomes challenging to find out the best service for satisfying maximum quality requirements.Among the existing approaches, the single value decomposition technique is popular one; however, it suffers for computational complexity.To overcome this limitation, this paper proposed a subset matching based web service selection and ranking by considering the quality of service attributes.This proposed method creates a quality-web matrix to store available web services and associated quality of service attributes.Then, matrix subsets are created using web service repository and requested quality attributes.Finally, web services are efficiently selected and ranked based on calculated weights of corresponding web services to reduce composition time.Experimental results showed that proposed method performs more efficient and scalable than existing several techniques such as single value decomposition.
Recurrent Space-time Graphs for Video Understanding.

Learning in the space-time domain remains a very challenging problem in machine learning and computer vision. Current computational models for understanding spatio-temporal visual data are heavily rooted in the classical single-image based paradigm. It is not yet well understood how to integrate information in space and time into a single, general model. We propose a neural graph model, recurrent in space and time, suitable for capturing both the local appearance and the complex higher-level interactions of different entities and objects within the changing world scene. Nodes and edges in our graph have dedicated neural networks for processing information. Nodes operate over features extracted from local parts in space and time and previous memory states. Edges process messages between connected nodes at different locations and spatial scales or between past and present time. Messages are passed iteratively in order to transmit information globally and establish long range interactions. Our model is general and could learn to recognize a variety of high level spatio-temporal concepts and be applied to different learning tasks. We demonstrate, through extensive experiments and ablation studies, that our model outperforms strong baselines and top published methods on recognizing complex activities in video. Moreover, we obtain state-of-the-art performance on the challenging Something-Something human-object interaction dataset.
Investigation of Sampling Techniques for Maximum Entropy Language Modeling Training

Maximum entropy language models (MaxEnt LMs) are log-linear models which are able to incorporate various hand-crafted features and non-linguistic information. Standard MaxEnt LMs are computationally heavy for tasks with a large vocabulary size due to the expensive normalization computation in the denominator. To address this issue, most recent works on MaxEnt LMs have used class based MaxEnt LMs. However, the performance of class based MaxEnt LMs might be sensitive to word clustering and it is also time-consuming to generate high-quality word classes. Motivated by the recent success of sampling techniques in accelerating the training of neural network language models, in this paper, three widely used sampling techniques, importance sampling, noise contrastive estimation (NCE) and sampled softmax, are investigated for the MaxEnt LM training. Experimental results on the Google One Billion corpus and an internal speech recognition system demonstrate the effectiveness of sampled softmax and NCE on MaxEnt LM training. However, importance sampling is not effective for MaxEnt LM training despite its similarity to sampled softmax. To our knowledge, this is the first work applying sampling techniques on MaxEnt LM training.
First commercial ion-based quantum computer built

The first commercial quantum computer using trapped ions for quantum bits (qubits) has been launched by IonQ, a spin-out company from the University of Maryland in the US.
THE USE OF ANIMATION CLIPS AS MEDIA IN TEACHING WRITING

This study is aimed at describing the implementation of animation clips as a media in teaching writing to the eleventh grade students of Madrasah Aliyah (MA) Sunan Giri Gondang in academic year 2017/2018, how is the students' writing score after being taught using Animation clips, and problems faced by the teacher in teaching writing using animation clips.This research is a descriptive qualitative research.The subjects are the teacher and the eleventh grade students of MA Sunan Giri Gondang in academic year 2017/2018 totally 20 students.The instruments used in this research are test, observation checklist, and interview guide.The result shows that the implementation of animation clips in teaching writing is successful.The students' writing score is getting better after being taught using Animation clips.The mean score in pre-test is 74 while in post-test is 79.Based on the data taken from interview with the English teacher and the students after animation clips was implemented in their writing class, it can be described that applying animation clips in writing class has some advantages such as the class is easy to manage, the students feel free to select the writing topic, etc. Besides, there are some disadvantages of it such as it needs more cost, it needs a lot of bandwidth, etc
Scalable Rule Learning in Probabilistic Knowledge Bases

Knowledge Bases (KBs) are becoming increasingly large, sparse and probabilistic. These KBs are typically used to perform query inferences and rule mining. But their efficacy is only as high as their completeness. Efficiently utilizing incomplete KBs remains a major challenge as the current KB completion techniques either do not take into account the inherent uncertainty associated with each KB tuple or do not scale to large KBs. Probabilistic rule learning not only considers the probability of every KB tuple but also tackles the problem of KB completion in an explainable way. For any given probabilistic KB, it learns probabilistic first-order rules from its relations to identify interesting patterns. But, the current probabilistic rule learning techniques perform grounding to do probabilistic inference for evaluation of candidate rules. It does not scale well to large KBs as the time complexity of inference using grounding is exponential over the size of the KB. In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding. We compared SafeLearner to the state-of-the-art probabilistic rule learner ProbFOIL+ and to its deterministic contemporary AMIE+ on standard probabilistic KBs of NELL (Never-Ending Language Learner) and Yago. Our results demonstrate that SafeLearner scales as good as AMIE+ when learning simple rules and is also significantly faster than ProbFOIL+.
SPECIAL TOPIC ON MULTISCALE MODELING OF GRANULAR MEDIA: A TRIBUTE TO PROF. DOMINIQUE JEULIN

A few words on the present special topic, devoted to the multiscale modeling of granular media, and published in honor of Prof. Dominique Jeulin’s enduring contribution to the wide field of image analysis, random structures and material science.
Shiny R Dashboards

R Markdown and htmlwidgets will give you the ability to create rich and even interactive experiences with popovers. However, some use cases require more than what these types of static tools can provide. For instance, you may need to ask users to supply parameters that will be used in an analysis, connect to a backend database, or execute new R scripts as the user explores your analysis.
TOOLS FOR INTEROPERABILITY BETWEEN REPOSITORIES OF DIGITAL RESOURCES

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 6292-6300ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1530Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Picture Series as a Means to Improve Reading Ability of Freshmen at English Department STKIP PGRI Bangkalan

Reading is a complex cognitive process of decoding symbols for the purpose of deriving meaning (reading comprehension) and constructing meaning. It means that learning reading is significant because it has an aim to understand the meaning of what is read. The researcher should make students interested in the material given because the first semester students of English Department of STKIP PGRI Bangkalan have problem to understand the contents of what is read. Based on the observation, the use of picture series was able to increase students' skill of reading narrative text. And students could comprehend the narrative text easily. Besides that, it will contribute in making teaching learning of reading more relax, enjoyable, the good response and can avoid from boredom. Third, other researcher, It is suggested to give useful to the next researcher in develop their study especially the using of picture series in teaching reading.
A comprehensive survey on cross-language information retrieval system

Cross language information retrieval (CLIR) is a retrieval process in which the user fires queries in one language to retrieve information from another (different) language. The diversity of information and language barriers are the serious issues for communication and cultural exchange across the world. To solve such barriers, Cross language information retrieval system, are nowadays in strong demand. CLIR is a subset of Information Retrieval (IR) system. Information Retrieval deals with finding useful information from a large collection of unstructured, structured and semi-structured data to a user query where the query is a set of keywords. Information Retrieval can be classified into different classes such as Monolingual information retrieval, Bi-Lingual Information Retrieval, Multilingual information retrieval and Cross language information retrieval. This paper focuses on the various IR variants and techniques used in CLIR system. Further, based on available literature, a number of challenges and issues in CLIR have been identified and discussed. It gives an overview of the advantages, limitations, tools available in CLIR research. It also describes new application areas of CLIR such as medical, multimedia, question answering system etc. The need for exploring and building more specialized information system that enable speakers of an Odia language to discover valuable information beyond linguistic and cultural barriers. This study is aimed at building an experimental CLIR system between one of the under-resourced language (i.e. Odia) and one of the most commonly used online language (i.e. English) in future.