Reviewerly/exHarmony huggingface dataset:
V.A. KOTELNIKOV: PROMOTION THE FUTURE OF DIGITALIZATION

A short interaction with a great man is supplying your life with a source that is generating innovative ideas and thoughts. My work at the department of Moscow power engineering institute, headed by academician VA. Kotelnikov, filled my scientific life with the idea of creating an optical computer that can solve in a flesh the problems solved by a modern supercomputer in a month. This idea has different derivatives, for example, points to possible traps of digitalization, especially in the development of collective and artificial intelligence methods.
A Novel Chinese Word Segmentation Method for Rail Transit Codes

The rail transit Codes contain a large number of schemaless data and text, which are difficult to extract and process. Knowledge Graph can handle large-scale schemaless data, thus it is used to effectively store and manipulate relevant information of rail transit Codes. As an essential component, Chinese word segmentation is a difficult problem for building the Knowledge Graph of rail transit Codes. In this paper, we propose a bi-directional maximum matching algorithm and some rules based Chinese word segmentation method, for design Codes of rail transit. In the proposed method, the item information, attribute values and Code names are first segmented by preprocessing. Then, the bidirectional matching algorithm with double dictionary is used to generate the preliminary results. Finally, the preliminary results are further optimized with rules to generate the final results. With testing some design Codes, the experimental results show that the proposed method can improve the accuracy and efficiency of Chinese word segmentation in this field, and it can deal with the update of noun entity library.
Writing Literacy As An Effort To Build Positive Character Of Elementary School Students

The purpose of the study was to analyze the profile of the development of literacy teaching materials in writing prose and produce prototypes of teaching materials for students' prose writing which included the development of positive characters written by elementary school children. Writing habits with the planting of characters familiarize students to be positive following what is usually done. This is based on the character of students currently still concerning. The model of developing prose literacy for elementary students used is the Plomp model with steps: preliminary research, prototyping phase, and assessment phase. The research findings indicate that the characteristic profile of students tends to be difficult in writing prose essays. The profile of needs analysis shows that elementary students are very fond of fantasy fairy tales, fables, parables, myths, realistic tales, and sage. The prototype of literacy teaching materials writes valid prose according to the needs of students.
Further Investigations on Developing an Arabic Sentiment Lexicon

The availability of lexical resources is huge to accelerate and simplify the sentiment analysis in English.In Arabic, there are few resources and these resources are not comprehensive.Most of the current research efforts for constructing Arabic Sentiment Lexicon (ASL) depend on a large number of lexical entities.However, the coverage of all Arabic sentiment expressions can be applied using refined regular expressions rather than a large number of lexical entities.This paper presents an ASL that more comprehensive than the existing lexicons, for covering many expressions with different dialects including Franco-Arabic, and in the same time more compact.Also, this paper shows how to integrate different lexicons and to refine them.To enrich lexical entries with very robust morphological syntactical information, regular expressions, the weight of sentiment polarity and n-gram terms have been augmented to each
AN ERROR ANALYSIS OF ENGLISH VOICELESS CONSONANT [P], [T], [K] AT SMA SWASTA DHARMA PANCASILA MEDAN

The aim of this research is to find the percentage of students’ error in pronounciation of voiceless consonant [p],[t],and [k] and also to find out the dominant error most often made by the first grade of SMA Swasta Dharma Pancasila. This research use qualitative method. The population of this research was the first grade SMA Swasta  Dharma Pancasila and the sample was X IIS-1.We only researching 30 students. Then they were given 30 word tests containing  English voiceless consonant [p], [t], and [k] to be pronounced and recorded. Recording that have been collected as sources of the data. The results of this study indicate that the level of student error in spoken English [p] and [k] consonants is good with consonant percentages [p] 38.4%, consonants [k] 48.3%, but they are fair in spoken consonant sounds [ t] with a percentage of 59.7% Based on the result of this research, We suggest that the students need to learn and practice more about pronounciation of voiceless consonant [p],[t],and [k] to improve their ability in pronounciation English words. For the teachers,Teacher should give more exercise or practice for students in pronounciation of  voiceless consonant [p], [t], [k]. Not only that consonant, but the students must study about all the word of English pronounciation. And the teacher must also learn more about how to pronounce and intonate correctly in teaching words from consonant words.
Heterogenous Information Network Embedding Based Cross-Domain Recommendation System

The data sparsity or the cold start is a general bottleneck of recommendation service systems. Two types of state-of-art schemes address this issue via enriching knowledge. Cross-domain recommendation schemes transfer knowledge from another dense domain, but they only explore a single kind of shared knowledge. Recommendation schemes built on heterogeneous information networks (HIN) utilize knowledge implied in meta-paths connecting various objects, these methods, however, investigate meta-paths in a single domain. To improve recom-mendation performance in a highly sparse scenario, we propose a HIN Embedding based Cross-domain Recommendation (HecRec) framework, which exploits cross-domain information by establishing meta-path based HIN embeddings in both the source and the target domain and conducts personalized recommendation by integrating the obtained HIN embeddings with a rating predictor. To make the best use of cross-domain information and avoid the knowledge confliction between knowledge from different metapaths observed in real-system datasets, we adopt a concept of overpass bridge to integrate the HIN embeddings drawn via different meta-paths. Experiments on two public datasets, i.e., MovieLens and LibraryThing, demonstrate the capacity of HecRec on addressing data sparseness. The recommendation performance of HecRec is improved up to 6.9% in MAE and 5.5% in RMSE compared with the state-of-art schemes in the coldest cases.
Rare Earthquake Swarm Strikes Puerto Rico

Puerto Rico hasn’t seen this many strong quakes in a single sequence since seismic monitoring began 46 years ago. The last earthquake to damage the island this badly occurred in 1918.
Universal Tool for Single-Photon Circuits: Quantum Router Design

We demonstrate that the non-Hermitian Hamiltonian approach can be used as a universal tool to design and describe a performance of single photon quantum electrodynamical circuits (cQED). As an example of the validity of this method, we calculate a novel six port quantum router, constructed from four qubits and three open waveguides. We have obtained analytical expressions, which describe the transmission and reflection coefficients of a single photon in general form taking into account the spread qubit’s parameters. We show that, due to naturally derived interferences, in situ tuning the probability of photon detection in desired ports.
Cluster Based Association Rule Mining for Courses Recommendation System

A course recommender system has a great importance in expecting the selection of courses by students in an university, especially for new students who can't easily select the proper elective courses offered for a specific semester.The computer science department in Ajloun University College at Balqa Applied University (BAU) will be taken as a case study.In this paper, an efficient cluster based rule mining algorithm will be used on a course database to describe a courses recommendation system that assist students to choose elective courses based on students already studied these courses or some of them.
Likelihood Ratio based Loss to finetune CNNs for Very Low Resolution Face Verification

In this paper, we propose a likelihood ratio based loss for very low-resolution face verification. Existing loss functions either improve the softmax loss to learn large-margin facial features or impose Euclidean margin constraints between image pairs. These methods are proved to be better than traditional softmax, but fail to guarantee the best discrimination features. Therefore, we propose a loss function based on likelihood ratio classifier, an optimal classifier in Neyman-Pearson sense, to give the highest verification rate at a given false accept rate, which is suitable for biometrics verification. To verify the efficacy of the proposed loss function, we apply it to address the very low-resolution face recognition problem. We conduct extensive experiments on the challenging SCface dataset with the resolution of the faces to be recognized below 16 × 16. The results show that the proposed approach outperforms state-of-the-art methods.
Improving named entity recognition and disambiguation in news headlines

In this paper, we present a framework for extraction and disambiguation of hyphenated and partially named entities in news headlines. The direct application of state-of-the-art named entity detection and disambiguation approaches on news headlines results in significantly degraded performance due to different headline formatting in comparison with regular text; hyphenated mentions; and partial entity mentions. In this paper, we introduce a novel framework that assists existing named entity recognition and disambiguation systems to deal with introduced challenges. In particular, we deal with hyphenated entity mentions and partial entity mentions present in news headlines. We modify the hyphenated and partial entity in a way that increases the probability of disambiguation to correct entity in knowledge base. Our framework leverages headlines of recent past to improve the entity mentions in headlines. The experimental results showed that presented framework improves the F1-score of mention detection by 12% and 9% in state-of-the-art Stanford and Illinois NER systems, whereas F1-score of disambiguation is improved by 9%, 12%, 7% and 5% in AIDA, Wikifier, TagMe, and YODIE state-of-the-art NED systems respectively.
The Hamster Wheel of Academic Life

One morning, as Gregor Samsa was waking up from anxious dreams, he discovered that in bed he had been changed into a monstrous verminous bug. He lay on his armour-hard back and saw, as he lifted hi...
LW-Sketch-Net on Mobile Phones

With the popularity of smart terminals, people tend to draw simple sketches to express emotions and ideas in communication, which means the era of reading pictures is coming.Therefore, in the field of sketch recognition, the application of deep network models in mobile devices is an irreversible trend.However, using deep learning method, most existing works with good performance has a large number of parameters.In order to further improve recognition speed and ensure the accuracy, we propose a lightweight neural network architecture to recognize sketch object.Specifically, we apply depthwise separable convolution into the network to reduce parameters and adjust the network effectively for the sparsity of sketch.Outperforming the state-of-the-art approaches, we achieves 85.8% and 84.1% on TU-Berlin and QuickDraw benchmarks respectively.Furthermore, the number of parameters is reduced to a large extent, which are 5% and 20% of the amount of Sketch-A-Net and MobileNets.We also develop a sketch recognition application on mobile phones to demonstrate the proposed scheme.
Contextual Predictability of Texts for Texts Processing and Understanding

This paper is the first part of contextual predictability model investigation for Russian, it is focused on linguistic and psychology interpretation of models, features, metrics and sets of features. The aim of this paper is to identify the dependence of the implementation of contextual predictability procedures on the genre characteristics of the text (or text collection): scientific vs. fictional. We construct a model predicting text elements and designate its features for texts of different genres and domains. We analyze various methods for studying contextual predictability, carry out a computational experiment against scientific and fictional texts, and verify its results by the experiment with informants (cloze-tests) and word embeddings (word2vec CBOW model). As a result, text processing model is built. It is evaluated based on the selected contextual predictability features and experiments with informants.
Product Domain Ontology Construction: Based on Online Review Features and Opinion Quintuple

Based on the current literature research of online review and opinion mining demand, this paper focus on ontology construction in ontology-based opinion mining procedure to improve the effect of opinion mining from evaluating online review. This study extracts domain concepts both from professional and user-generated contents. All elements in opinion quintuple are expressed in the ontology. The result shows that this new research method makes the improvement compared with the traditional process, which indicates the efficiency of the ontology created
Design and Evaluation

Following the ADR method, the design of the artifact starts with the problem formulation before the implementation begins. However, the implementation can still affect the initial problem formulation. During the process of problem formulation and implementation, learnings are gathered and used to further improve the artifact.
VISUAL STIMULI FOR IMPROVING PERCEPTUAL-COGNITIVE SKILLS IN SPORT ACTIVITIES: THE TECHNOLOGY SUPPORT

The purpose of this article is to analyze the possible contribution of visual training systems in learning perceptual-cognitive skills. Starting from a serious consideration on executive functions, a deep analysis of scientific literature guided us up to Transfer of Training concept, i.e. to understand that visual stimuli could create new learning fields so that what is learning during the training could be transferred to competition.
Depth Estimation with Multi-Resolution Stereo Matching

Depth estimation has widely demands in autopilot and scene reconstruction. Although depth estimation has been greatly improved by deep learning, there is still some room for improvement. Usually, stereo-matching-based depth estimation matches low-resolution features and then up-samples depth map to full-resolution. Such methods suffer from low accuracy because of information loss in low-resolution features. To solve this problem, a depth estimation method via multi-resolution gradual-refining stereo matching is proposed. As with classic methods, this method first extracts pyramid features by convolution network and estimates low-resolution depth maps. Then, as an innovation, stereo-matching at each pyramid feature is successively executed using the low-resolution maps as initial depth, which limits search range of stereo-matching for accuracy and efficiency. Results of stereo-matching are used as residuals to refine the depth maps. Experimental results demonstrate that accuracy of depth estimation by the proposed method is significantly improved, but computational complexity does not much increase compared with classic methods.
RETRACTED: Word collocation method based on ontological semantic index and spatiotemporal feature clustering analysis

The feature translation and potential semantic-indexing performance, attention and development direction of multilingual text background text clustering in cross-language English translation are an...
What's New in EuReCo? Interoperability, Comparable Corpora, Licensing

This paper reports on the latest developments of the European Reference Corpus EuReCo and the German Reference Corpus in relation to three of the most important CMLC topics: interoperability, collaboration on corpus infrastructure building, and legal issues. Concerning interoperability, we present new ways to access DeReKo via KorAP on the API and on the plugin level. In addition we report about advancements in the EuReCo- and ICC-initiatives with the provision of comparable corpora, and about recent problems with license acquisitions and our solution approaches using an indemnification clause and model licenses that include scientific exploitation.
PENGARUH MEDIA APLIKASI MACROMEDIA DIRECTOR MX 2004 TERHADAP HASIL BELAJAR SISWA KELAS X PADA MATA PELAJARAN KOMPUTER DAN JARINGAN DASAR

Abstract. The problem that occurs is the teaching and learning activities in the classroom centered on the media used by the teacher. The media used by teachers for presentations is to only used powerpoint applications that contain theories only, and teachers also sometimes used the lecture and question and answer method, when delivered material. So students do not understand the material presented by the teacher. The purpose of this study was to determine The Effect of Macromedia Director MX 2004 Application Media Towards Students’ Outcomes Class X in Computer Subjects and Basic Networks at SMKN 1 Lingsar, West Lombok Regency in Academic Year 2019/2020.. Data collection techniques in this study used the test as the main method while documentation and observation as a complement. Data analysis techniques in this study used the ttest formula. The results obtained from the results of the t-test showed the t-test of 43.85, then based on a significant level of 5% and N = 37 it turned out that the amount of the rejection of the null hypothesis stated in the table was 2.042. This statement shows that the t-test value was greater than t-table (43.85> 2.042), because t-test was greater than t-table, this research was said to be significant. This means that the null hypothesis was rejected and the alternative hypothesis was accepted which reads: There was The Effect of Macromedia Director MX 2004 Application Media Towards Students’ Outcomes Class X in Computer Subjects and Basic Networks at SMKN 1 Lingsar, West Lombok Regency in Academic Year 2019/2020., so it can be concluded that the results of this study are “significant”.
Utility Mining Algorithms: A Bird’s Eye View

Data mining is the process of identifying patterns in data sets by applying appropriate methods with cluster of machine learning techniques. In recent decades, high utility itemset (HUI) mining has become the emerging research area, which focuses on frequency and also on utility related with the itemsets. Each itemset has a value like profit or user’s interest, called as the utility of that itemset. HUIs are present in a customer transaction database which yield a high profit. The target of HUI is to discover the itemsets that have utility value higher than the threshold value. The issues faced in HUIs are dealing with negative item values and number of database scans, mining in XML database, candidate sets and distributed computing network. This paper presents a survey of various algorithms and their restrictions in mining HUIs and the performance analysis of the surveyed algorithms.
APLIKASI PENCARIAN TEMPAT WISATA, HOTEL DAN KULINER BENGKALIS MEMANFAATKAN TEKNOLOGI GPS BERBASIS ANDROID

Information about tourist attractions, hotels and culinary Bengkalis not yet complete and detailed. So that the local tourists who visit Bengkalis less get the information. For that purpose, it is designed the search application of tourist, hotel and culinary place in Bengkalis by utilizing Global Positioning System (GPS) technology on android device and generate a website which is used to input all information needed by android application to database server by admin. This application is designed using Java programming language and MySQL as database. System design uses Unified Modeling Language (UML) which consists of use case diagram, activity diagram, sequence diagram and class diagram while database design using entity relationship diagram. The results achieved is a search application of tourist attractions, hotels and culinary in Bengkalis by utilizing Global Positioning System (GPS) technology found on android device to facilitate users to get information and search for tourist attractions, hotels and culinary in Bengkalis.
Le son d’une voix (1964) by François-Bernard Mâche: the introduction of the Sona-Graph in the composer’s workshop

Just before World War II, American researchers started to develop a new device indented to visualize the internal structure of sounds. Because of related war interests it was given official rating as a war project, and has progressed far enough during the war period. This device firstly named Visible speech was brought to public attention in 1945 and was commercialized in 1951 known as Sona-Graph by Electric Kay Company. This automatic system aimed to translate sound into patterns which may be readily interpreted by the eye: these patterns called sound spectrograms are visual representations of the spectrum of frequencies of the signal as they vary with time. Inventors underlined at this time that the possible uses of this device were numerous but they did not suspect that it will make a stroke for history of music. Spectralism is an influent musical movement developed in France at the beginning of the 1970s. It is based, in part, on transcriptions of sound spectrograms. But before the emergence of this movement, Francois-Bernard Mâche was the first composer to resort to a Sona-Graph for composing music. In 1964, thanks to this device, he analysed the sound of his voice and transcribed the data into musical notes: Le son d'une voix for 15 instruments prefigures the spectral movement. Although its pioneer dimension, this piece was never released on recording and little is known about the creative process. How could Mâche have access to a Sona-Graph at a time where only few scientific laboratories could afford to buy such an expensive device? How did he recite an excerpt of Poesie ininterrompue II by Paul Eluard and how did he proceed for releasing the spectrograms knowing that the Sona-Graph could theoretically only analyse 2,45 seconds of sound. Which elements of the spectrograms were transcribed? Did he only translate the frequencies or did he also consider rhythmic articulations and dynamics fluctuations? To address these questions, we consulted Mâche's writings and realised that the composer never really detailed the compositional processes. He only gave in the last edition of the book Musique-Mythe-Nature a tab indicating how vowels and consonant were attributed to different instrumental sounds. Therefore we conducted a long interview with the composer at his domicile in Paris during which he explained how he conducted his spectral analysis in the Groupe de Recherche Musicale founded by Pierre Schaeffer but also in a zoology laboratory. He also detailed the creative process of this piece building on the score and an original audio recording. During our presentation, we will first remind Mâche's first artisanal attempts to transcribe voice into music in Safous Mele (1958-59) and La Peau du silence (1962). Then, we will bring together our original data to decipher the creative process of Le Son d'une voix, underlining how the composer resorted for the first time in the history of music to a Sona-graph in order to refine his spectral analysis and open the door to the world of mimetic instrumental resynthesis.