Reviewerly/exHarmony huggingface dataset:
A Skeleton Pattern Representation Method for Anomaly Detection in Wireless Sensor Networks

Anomaly detection of time series in wireless sensor networks has gained significant attention. Researchers employ representation techniques to reduce the dimensionality of time series. The Piecewise Aggregate Pattern Representation (PAPR) method vertically divides the time series and represents as patterns. However, the computational cost of the PAPR method for anomaly detection is high. To overcome this issue, we propose a time series representation method for anomaly detection called Skeleton Pattern Representation for anomaly detection (SPR-AD). An improved Perceptually Important Point (iPIP) identification algorithm is devised to transform the subsequence into a skeleton with PIPs. Then the skeleton is divided into several equal-sized subspaces in the amplitude domain. Statistical information of the PIPs located in subspaces is constructed to represent the skeleton as a pattern. Based on the two-phase representation scheme, anomaly is determined by anomaly scores which are based on the of similarity among pattern. The experimental results demonstrate the performance of the SPR-AD method is better in terms of computational cost and detection accuracy than the PAPR method.
Ontology-based Extractive Text Summarization: The Contribution of Instances

In this paper, we present a text summarization approach focusing on multi-document, extractive and query-focused summarization that relies on an ontology-based semantic similarity measure, that specifically explores ontology instances. We employ the DBpedia Ontology and a theoretical definition of similarity to determine query-sentence and sentence-sentence similarity. Furthermore, we define an instance-linking strategy that builds the most accurate sentence representation possible while achieving a better coverage of sentences that can be represented by ontology instances. Using primarily this instances linking strategy, the semantic similarity measure and the Maximal Marginal Relevance Algorithm- MMR - we propose a summarization model that is capable of avoiding redundancy from a more fine-grained representation of sentences, due to the irrepresentation as ontology instances. We demonstrate that our summarizer is capable of achieving compelling results when compared with relevant DUC systems and recently published related studies using ROUGE metrics. Moreover, our experiments lead us to a better understanding of how ontology instances can be used to represent sentences and what is the role of said instances in this process.
Game Belajar Mengaji Berbasis Android

Technological advancements have affected various fields of information technology, especially on mobile smartphones. Aside from being a communication tool, many parents are worried about the habits and addictions of children on mobile smartphones that are considered to have more adverse effects than good impacts because children only use it as a means of playing games. This study aims to develop the attractiveness and interest of children to better utilize mobile smartphones to positive things. This application development method includes several stages, as well as needs analysis, design, testing, and implementation. Testing this game uses the black box method and questionnaire where all tested scenarios get valid results. Then testing questionnaires were distributed to students at MIM Kertonatan and obtained the highest percentage score of 92% for the question that the content of the material is easy to learn. The final result of this study is an Android-based recite learning game application. An educational game application that can help children learn in the introduction of the basics of Arabic letters and reading.
Colored Transparent Object Matting from a Single Image Using Deep Learning.

This paper proposes a deep learning based method for colored transparent object matting from a single image. Existing approaches for transparent object matting often require multiple images and long processing times, which greatly hinder their applications on real-world transparent objects. The recently proposed TOM-Net can produce a matte for a colorless transparent object from a single image in a single fast feed-forward pass. In this paper, we extend TOM-Net to handle colored transparent object by modeling the intrinsic color of a transparent object with a color filter. We formulate the problem of colored transparent object matting as simultaneously estimating an object mask, a color filter, and a refractive flow field from a single image, and present a deep learning framework for learning this task. We create a large-scale synthetic dataset for training our network. We also capture a real dataset for evaluation. Experiments on both synthetic and real datasets show promising results, which demonstrate the effectiveness of our method.
Test Metrics and Problem-Solving

This chapter presents a collection of test and other metrics to provide visibility into key areas of the organization and to show how metrics are used to facilitate improvement. The Goal, Question, Metrics (GQM) approach is discussed, and this allows metrics related to the organization goals to be defined. A selection of sample metrics is presented, and problem-solving tools such as fishbone diagrams, Pareto charts, trend charts are discussed.
Investigating Students’ Ability to Speak English: a Case Study of the First Semester Students’ of Department of English Education in Universitas Muhammadiyah Surakarta in 2018/2019 Academic Year

This study aims at investigating ability to speak in English at the Universitas Muhammadiyah Surakarta at the Department English Education in semester 1 of the academic year 2018/2019. This research is a qualitative descriptive research. The data are students’ speaking abilities taken from the students as informants. The methods of collecting data are observation, interviews and documents. The technique of analyzing data are data reduction, data appearance, conclusions and verification. The findings of this research are that, the students level is B2 Level has 4 aspects namely, (1) Grammar and vocabulary, (2) Discourse management, (3) Pronunciation, (4) Interactive communication, and the aspects of students master better are grammar and vocabulary. The conclusion is that they have good speaking and their grammar and vocabulary can be understood.
Game Edukasi Menyusun dan Mempelajari Organ Peredaran Darah Manusia Untuk Sekolah Dasar Kelas V Berbasis Android (Studi Kasus : SD IT Nur Hidayah Surakarta)

Learning in elementary schools with the 2013 curriculum is however it performed integratively, in which every elementary student's books are no longer based on subjects. Based on the combined theme of several subjects relevant to the competence in SD. Most educators still use methods of learning that are conventional. For example, when teachers explain the material about human circulatory organs, the teacher must draw first before explaining the material to the students. Using that learning method many students are difficult to remember and understand the material presented. Therefore, it is necessary for teachers to change learning methods using educational games. Educational Game to develop and study human circulatory organs, created based on curriculum KTSP 2013 SD IT Nur Hidayah Surakarta. The author has conducted interviews with class V teachers whose results will be used for the creation of this game. This application created by author using application, called Construct 2. Result of this research in the form of an educational game application arranging and studying the human circulatory for the class V elementary school IT Nur Hidayah. App testing is done by exposing the app to students in grade 5 and teachers and trying it out directly. Based on the results of the questionnaire which is filled directly by students and teachers can be concluded that the application makes it easier for teachers to convey the material to the students in the learning process.Keywords : education game, construct 2, circulatory organs
Automatic detection of the support points in relational clustering

The task of clustering is at the same time challenging and very important in Artificial Intelligence. One of the most popular family of clustering algorithms is the prototype-based approach. Prototype-based algorithms compute a representation of the clusters in the form of a set of prototypes, usually vectors approximating each cluster's barycenter. However, the objects in a data set are not necessarily vectors, especially in real-world applications. These non-vectorial data sets are often represented by the dissimilarities, distances, or relations between all pairs of objects. They are usually referred as relational data sets. For this kind of data, the algorithms must be adapted to different measures of distance. There are a few state-of-the-art algorithms adapted to relational data sets through the use of barycentric coordinates formalism, in which the objects of a relational data sets are embedded in a space defined by the distances between a subset of the objects, called support points. In this paper, we propose an approach that is able to automatically select the optimal set of support points. We also extend the method to relational data streams, in order to detect variations in the intrinsic dimensionality of the representation space over time. We have compared experimentally the quality of the proposed algorithms on real and artificial data sets. We show that the automatic selection of support points allows an optimal quality in a minimal computation time.
Scheduling Independent Parallel Jobs in Cloud Computing: A Survey

The impressive and rapid development of the internet and wireless networks leads to growing of users in the last decade. Therefore, the limited resources of these systems are now more evident than in the past. Cloud computing is the latest technology to handle the limitation of resources for users. Type of jobs play the main role in the design of scheduling algorithms. A job can be run simultaneously by multi-processor called parallel job, while the job can run by a single processor called serial job. In addition, based on dependency of jobs to each other, the jobs can be divided into dependent and independent jobs. Scheduling the independent parallel jobs is one of important challenges in cloud computing. Hence, in this paper, we classified the existing algorithms of scheduling independent parallel jobs into two main categories including Non-Layer and Two-Layer. This division is performed based on the number of jobs running on a processor simultaneously. Furthermore, the existing scheduling algorithms belong to each categories are divided into two subcategories based on their solving techniques including heuristic and metaheuristic. Then, the algorithms belong to each category are described in detail. After that, these algorithms are compared to each other based on their different attributes. Our analysis show that the existing Two-Layer scheduling algorithms focus on cost parameter to increase the performance of scheduling algorithms by reducing the waste time of CPU through simultaneous assigning more than one job to each physical machine, while Non-Layer scheduling algorithms didn't pay attention to this issue and only employ techniques to manage the scheduling queue in order to improve the different parameters such as cost, energy, load balancing and deadline.
Penanaman Budaya Literasi pada Kelas Rendah di SD Muhammadiyah 1 Surakarta

The purpose of this research are to 1) know the management of efforts to cultivate a literacy culture in the lower class, 2) identify the obstacles faced in the effort to cultivate a literacy culture in the low class, and 3) describe the solutions to overcome existing obstacles in an effort to cultivate culture literacy in the low class. This research is descriptive qualitative. The techniques for collecting data are observation, interviews, and documentation. Qualitative data analysis techniques using data reduction, data display and conclusions. The validity of the data was obtained through source and technical triangulation. The results showed that: 1) the cultivation of literacy culture in the lower classes was realized in the development of facilities and programs that support a culture of literacy. 2) lack of book collections, lack of awareness of students to care for books, students who have not read fluently writing and students who have special characters 3) collaborate with the Regional Archives and Library Service, parents of students and conduct home visits.
Visual Representation Learning with 3D View-Contrastive Inverse Graphics Networks

Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction. One of the most basic prediction tasks is view prediction: how would a given scene look from an alternative viewpoint? Humans excel at this task. Our ability to imagine and fill in missing visual information is tightly coupled with perception: we feel as if we see the world in 3 dimensions, while in fact, information from only the front surface of the world hits our (2D) retinas. This paper explores the connection between view-predictive representation learning and its role in the development of 3D visual recognition. We propose inverse graphics networks, which take as input 2.5D video streams captured by a moving camera, and map to stable 3D feature maps of the scene, by disentangling the scene content from the motion of the camera. The model can also project its 3D feature maps to novel viewpoints, to predict and match against target views. We propose contrastive prediction losses that can handle stochasticity of the visual input and can scale view-predictive learning to more photorealistic scenes than those considered in previous works. We show that the proposed model learns 3D visual representations useful for (1) semi-supervised learning of 3D object detectors, and (2) unsupervised learning of 3D moving object detectors, by estimating motion of the inferred 3D feature maps in videos of dynamic scenes. To the best of our knowledge, this is the first work that empirically shows view prediction to be a useful and scalable self-supervised task beneficial to 3D object detection.
Software Testing Tools

The goal of this chapter is to give a flavour of a selection of the plethora of tools that are available to support the performance of testing throughout the development lifecycle. Testing plays a key role in verifying that the software system satisfies the requirements and is fit for purpose. There are various categories of test tools ranging from tools that manage software testing to tools that perform specific functions such as automated software inspections or automation of regression tests.
On the Fast Algebraic Immunity of Majority Functions.

In different contexts such as filtered LFSR, Goldreich’s PRG, and FLIP stream ciphers, the security of a cryptographic primitive mostly depends on the algebraic properties of one Boolean function. Since the Seventies, more and more efficient attacks have been exhibited in this context, related to more and more general algebraic properties, such as the degree, the algebraic immunity, and finally, the fast algebraic immunity. Once the properties to estimate the attack complexities are identified, it remains to determine the exact parameters of interesting family of functions with these properties. Then, these functions can be combined in secondary constructions to guarantee the good algebraic properties of a main function. In particular, the family of symmetric functions, and more precisely the subclass of majority functions, has been intensively studied in the area of cryptography, because of their practical advantages and good properties.
Arithmetic Garbling from Bilinear Maps.

We consider the problem of garbling arithmetic circuits and present a garbling scheme for inner-product predicates over exponentially large fields. Our construction stems from a generic transformation from predicate encryption which makes only blackbox calls to the underlying primitive. The resulting garbling scheme has practical efficiency and can be used as a garbling gadget to securely compute common arithmetic subroutines. We also show that inner-product predicates are complete by generically bootstrapping our construction to arithmetic garbling for polynomial-size circuits, albeit with a loss of concrete efficiency.
@arush: The #IndieWeb community should be really ...

@arush: The #IndieWeb community should be really proud of itself. You are helping to build a better web everyday and I hope the small community
1364년 김속명의 진해현 전투와 육전론(陸戰論)

It was the first year(1352) of the king Gongmin that the proposition of the rebuilding of the naval forces of the Goryo dynasty was proposed by Yi sack(李穡), the famous politician and scholar and poet. The proposition was to be put into effect in the 22and year of the king Gongmin. There were arguments against the Yi’s proposition for over 20 years.The so-called ‘anti-proposition of the rebuilding of the naval forces’ or ‘theory of a land fight.’Kim sokmyong was the center of the proposition who had a big political status in the Court of the king Gongmin(恭愍王) and Wu(禑王), was greatly influenced by the combats and naval combats against the invader in the 13th year of king Gongmin.Especially, we have to pay attention to the miserable defeat of the naval combat of Izakdo(Izak island) and the great victory of the combat of Jinhaehyon against the Waegu(Japanese pirates).
Efficient Sketching Algorithm for Sparse Binary Data.

Recent advancement of the WWW, IOT, social network, e-commerce, etc. have generated a large volume of data. These datasets are mostly represented by high dimensional and sparse datasets. Many fundamental subroutines of common data analytic tasks such as clustering, classification, ranking, nearest neighbour search, etc. scale poorly with the dimension of the dataset. In this work, we address this problem and propose a sketching (alternatively, dimensionality reduction) algorithm -- $\binsketch$ (Binary Data Sketch) -- for sparse binary datasets. $\binsketch$ preserves the binary version of the dataset after sketching and maintains estimates for multiple similarity measures such as Jaccard, Cosine, Inner-Product similarities, and Hamming distance, on the same sketch. We present a theoretical analysis of our algorithm and complement it with extensive experimentation on several real-world datasets. We compare the performance of our algorithm with the state-of-the-art algorithms on the task of mean-square-error and ranking. Our proposed algorithm offers a comparable accuracy while suggesting a significant speedup in the dimensionality reduction time, with respect to the other candidate algorithms. Our proposal is simple, easy to implement, and therefore can be adopted in practice.
Lengths and L-motifs of rhythmical units in formal British speech

The lengths of rhythmical units (as defined by Karl Marbe in 1904) were identified, and their frequencies counted, in twelve complete texts from the Aix-MARSEC database of formal spoken British English. The texts all belonged to the genre of current affairs com¬mentary. L-motifs (i.e. maximal monotone non-decreasing sequences) of the rhythmical unit lengths were also identified, and the frequencies of the different L-motif lengths were count¬ed. The frequencies of both rhythmical unit lengths and L-motif lengths were modelled using a continuous approach with the Zipf-Alekseev function. Good qualities of fit were obtained for both kinds of unit on all texts. The parameters a and b of the Zipf-Alekseev function for the rhythmical unit lengths (though not for the L-motif lengths) were also found to be related in the form of a further Zipf-Alekseev function. Further research should aim to extend the application of the motif approach to rhythmical units.
Citation analysis of doctoral theses of Earth Science accepted by the Manipur University during 1989-2011

The study is to analyse the bibliographic citations which have been cited in the doctoral theses of the research scholars in Earth Science, Manipur University, during the year 1981-2011.The study is to determine the principle forms of literature used in theses; to determine country-wise, language-wise and subject-wise distribution of literature; and the applicability of Bradford's law of scattering to the pattern used in Earth Science. The bibliographical references were used at the end of each chapter and end of the doctoral theses were taken as the source of data for the study. The collected data were analysed and interpreted with table's format. Analyses of several parameters like country-wise, language-wise, subject-wise distribution of literature and the applicability of Bradford's law of scattering. lnteraction with faculties of concern department was carried out. lt was found that researcher emphasized more on journal source, they depend literature from UK, Netherland & USA; English was the predominant language; the total 2273 citations of journals are scattered primarily among s connected in one major subject fields; 9 (Nine) Core journal which were the sourcejournals leads the majority of the citations.
Complexiteit van ziekten

Complexity of patient care is rapidly increasing as a consequence of rising numbers of patients with complex multimorbidity. Not just the patient as a whole, but also the networks of organs, tissues and cells are forming a complex adaptive system (CAS). A CAS is defined as a network of several components ('agents') with lots of mutual feedback loops between which there are circular causalities; the predictability of a CAS is limited by definition. However, current guidelines and evidence-based medicine assume that diseases and the medical interventions to address them are predictable. Physicians' brains are complex neural networks that are much better at dealing with complex situations than guidelines. In the near future, physicians will also get help from advanced computer simulation models that make better diagnostic analyses on the basis of detailed phenotyping and are more accurate when predicting possible courses of disease and treatment outcomes.
AI Training Grounds : A study of adaptive AI in RPGs with low stochastic dependence

Artificial intelligence is used in video games for controlling AI agents but often follow a static pre-defined behaviour. An AI capable of online learning would be able to adapt their behaviour to ...
Collection development and management for 21st Century Library collections: an introduction

"Collection development and management for 21st Century Library collections: an introduction." Technical Services Quarterly, 36(4), pp. 426–427
USING NOTE TAKING STRATEGY TO ENHANCE STUDENTS’ LISTENING COMPREHENSION ABILITY

This article points out the results of the research investigating the effect of note takingstrategy on the third semester students’ listening comprehension of English Education StudyProgram, Faculty of Teaching and Educational Sciences, Universitas Islam As-Syafi’iyahJakarta. The research samples were taken using purposive sampling, with the total numberof samples is 30 students.The research methodology was pre-experiment method, with onegrouppretest posttest design. Data collection procedure was completed by the students byobtaining scoresin the listening section of PET test,30 items.The findings indicated that therewas significant difference of score between the students who received note taking strategyand students who did not receive note taking strategy. It can be seen from the data analysisusing ANOVA that showed significance value of 0.000 was smaller than (&lt;) Alpha 0.05 andFobserved (15.873) is higher than the critical value of Ftable(2.35). It means that note takingstrategy has significant effect on students’ listening comprehension ability.The Implicationof result research can benefit both lecturer and students in listening comprehension strategy.