Reviewerly/exHarmony huggingface dataset:
Learning of Hierarchical Temporal Structures for Guided Improvisation

Abstract This article focuses on learning the hierarchical structure of what we call a “temporal scenario” (for instance, a chord progression) to perform automatic improvisation consistently over several different time scales. We first present a way to represent hierarchical structures with a phrase structure grammar. Such a grammar enables us to analyze a scenario at several levels of organization, creating a “multilevel scenario.” We then develop a method to automatically induce this grammar from a corpus, based on sequence selection with mutual information. We applied this method to a corpus of transcribed improvisations based on the chord sequence, also with chord substitutions, from George Gershwin's “I Got Rhythm.” From these we obtained multilevel scenarios similar to the analyses performed by professional musicians. We then present a novel heuristic approach, exploiting the multilevel structure of a scenario to guide the improvisation with anticipatory behavior in an improvisation paradigm driven by a factor oracle. This method ensures consistency of the improvisation with regard to the global form, and it opens up possibilities when playing on chords that do not exist in memory. This system was evaluated by professional improvisers during listening sessions and received excellent feedback.
Feature optimization for pedestrian detection based on faster R-CNN

Due to the rise of convolutional neural network, pedestrian detection has achieved great success. However, the features of many existing methods are not fully utilized, which results in unsatisfactory detection results. A new pedestrian detection model which is named of SE-Faster R-CNN is proposed in this paper. By adding SENet block to Faster RCNN, it can strengthen the expressive force of feature. Then, the GN-Faster R-CNN, which is generated by adding the normalization layer -- Group Normalization layer to Faster R-CNN, is proposed. The proposed architecture is trained and tested on Caltech dataset. In addition, VGG16 model and ZF model are used as the backbone structure of detection network. A comparative experiment is implemented to compare the effectiveness of the two optimization methods. It can be seen from the experimental results that, after adding SENet, the miss rates of ZF model and VGG16 model were reduced by 0.392% and 0.999%, respectively. After adding the GN layer, the miss rate of VGG16 model was reduced by 0.665%, while the miss rate of ZF model was increased by 2.093%.
Research on Improved Pedestrian Dead Reckoning Algorithms

In view of user's requirements of high performance, easy operation and low cost with indoor location, data acquisition software based on Android platform utilize sensors built-in cellphone is used to study the pedestrian dead reckoning (PDR). Step detection and step length estimation are carried out by collecting leg swing angle while walking, and the classical step length estimation algorithm is improved. At the same time, the improved heuristic drift elimination algorithm is used to calculate the direction. Finally, the experiment results show that the PDR technology can better satisfy the requirements of indoor pedestrian positioning, and as for the low-cost multi-sensor. compared with the existing algorithm, the improved algorithm with higher positional accuracy.
EXPERIENCE OF USING INNOVATIVE TECHNOLOGIES IN TEACHING UKRAINIAN AS A FOREIGN LANGUAGE

The article considers innovative methods of teaching Ukrainian language to foreign-language students. These methods assume the use of both – special technical information tools, and developing personal-oriented technologies. Leading among them are computer-training technologies, which allow finding, collecting, processing, storing and transmitting the information using PC. &#x0D; Individualization of training and activation of students’ independent work takes place with the help of computer programs. This leads to the increased in volume tasks, which were completed in class. Multimedia training tools provide greater visibility; allow us to use the inclusion of gaming moments, thus stimulating cognitive activity and motivation. The use of computer tests allows us to quickly obtain an objective picture of the students’ material learning level and correct it in time. &#x0D; Innovations in higher education are associated with the development of modern forms and means, focused on the student personality; on the development of his abilities; on the achievement a positive result due to effective changes in the process of obtaining knowledge. Thanks to this, training gets activated and, as a result, - foreign students have a greater opportunity to acquire the necessary skills and abilities in mastering foreign language in short term; to increase motivation, and also to adapt to the new socio-cultural space successfully. &#x0D; Pupils should be faced with such problematic issues and tasks, solution of which requires thinking, research, and analysis. Students are no longer enough to be able only to read and translate professional texts, to perform various kinds of exercises, they should try to mobilize their knowledge, learn to identify and formulate a problem, find ways to solve it.
Why Structural and Stratigraphic Surface Observations in the Monterey and Sisquoc Formations are Critical Elements to Better Understand the Quality and Style of Miocene Structures in the Subsurface

The distribution and intensity of deformational patterns in fault related folds of siliceous sedimentary rocks are generally controlled by time-transgressive interplays of sedimentation, kinematic history, burial diagenesis, and thickness of competence contrasting stratigraphic layers at different scales.Structural surface analysis of the primarily diatomaceous Sisquoc Formation and the chiefly cherty/porcelanitic Monterey Formation in the southern Santa Maria basin, CA documented significant strain variation along fold strike due to competence contrasting layers at formational scale.At outcrop-scale a variety of structural responses within alternating mechanical packages showed that even in a single anticlinal structure fold kinematics may be dramatically different in various positions of a stratigraphic section.In other words, qualitative characterization of reservoir rocks in structural traps may differ dramatically over short stratigraphic distances that are difficult or impossible to capture if not integrating surface observations.Structural surface studies, outcrop photographs, and cross-sections serve as examples that observations made at the surface can be used to reinterpret deformation in the subsurface that is not always easy to capture by just using geophysical data.
Penentuan Peminatan Mahasiswa Prodi Informatika di Universitas Amikom Yogyakarta Menggunakan SVM

Universitas Amikom Yogyakarta has 16 courses, one of its courses in the Bachelor of Informatics Study Program, which is currently the number of students most commonly compared to other courses. By the time the student is in the final semester, the student is crucified completing the thesis. Thesis interest is still a difficult thing to be mapped by individual students or courses. The interest of the thesis will be in 3 classes, namely programming, networking, and Multimedia. SVM (Support Vector Machine) is one of the algorithms used in classifications. In this research, SVM was used to classify the interest of student thesis based on Holland's interest test assessment of 6 features (realistic, investigative, artistic, social, Enterprising and Conventional) which would then be used in Training data using the Support Vector Machine algorithm. The results of this study involved 150 datasets that resulted in a 66% accuracy rate using the 5-Fold Cross-Validation. Keywords : Classification, interest, SVM
PERANCANGAN DAN IMPLEMENTASI ALGORITMA ELIAS GAMMA CODE UNTUK MENGKOMPRESI RECORD DATABASE PADA APLIKASI RANGKUMAN PENGETAHUAN UMUM LENGKAP

Storage space required by an application is one of the problems on smartphones. This problem can result in a waste of storage space because not all smartphones have a very large storage capacity. One application that has a large file size is the RPUL application and this application is widely accessed by students and the general public. Large file size is what often causes this application can not run effectively on smartphones. One solution that can be used to solve this problem is to compress the application file, so that the size of the storage space needed in the smartphone is much smaller. This study describes how the application of the elias gamma code algorithm as one of the compression technique algorithms to compress the RPUL application database file. This is done so that the RPUL application can run effectively on a smartphone after it is installed. Based on trials conducted on 64 bit of text as samples in this research it was found that compression based on the elias gamma code algorithm is able to compress text from a database file with a ratio of compression is 2 bits, compression ratio is 50% with a redundancy is 50%. Keywords: Compression, RPUL, Smartphone, Elias Gamma Code
MEMPREDIKSI JUMLAH PENERIMAAN DAN PERMINTAAN DARAH DI PALANG MERAH INDONESIA (PMI) KITA MEDAN DENGAN MENGGUNAKAN METODE ID3 (STUDI KASUS: PMI KOTA MEDAN)

The Indonesian Red Cross (PMI) is a national association organization in Indonesia engaged in the field of social humanity. In certain months the blood demand has increased dramatically, this causes the availability of blood in the Indonesian Red Cross Medan City increasingly thinning. In addition to lack of blood, excess blood supply is also common in the Indonesian Red Cross. To overcome the above problems, the Indonesian Red Cross (PMI) of Medan City is supplying blood stock so that when the needs are high they can balance the stock and if their blood needs are low they can stabilize the blood so that the blood is not wasted. To overcome these problems, it is necessary to build an application for the Implementation of the Amount of Blood Receipts and Demand in the Medan Red Cross by using the ID3 (Iterative Dichoimoiser 3) method so that it helps in the process of equalizing blood to make it more accurate and the decisions determined to be valid and satisfactory.Keywords: Data Mining, Blood Acceptance and Demand, Id3 (Iterative Dichoiomiser)
Anarchy, State, and Blockchain Utopia: Rule of Law versus Lex Cryptographia

Imagine a space in which the Rule of Law does not have the same prevalence it has in the real space. Imagine a space where fundamental rights are created and enforced by the technology, a space in which your rights are absolute, meaning that you enjoy no limits on them in exchange for not being able to do anything in case of a violation of these rights.Would you enter it? Would you be willing to trade a few of your existing rights to get new ones? Would you also be ready to trade some of them in return for further services? Those are the questions I try to answer in this article. Written as part of a collective book on “General Principles and Digitalisation,” it engages with Robert Nozick’s idea (former Professor of Philosophy at Harvard University) whereby different ecosystems can coexist for the better, and applies it to the realm of fundamental rights.It consists of two main sections.First, this article aims to explain why blockchain raises new legal challenges that have a significant impact in terms of fundamental rights. The State, as it is conceived in our Western societies, has a monopoly of force and justice on its territory to protect the rights of its citizens. Within blockchain, the exercise of such a monopoly is made more complex. We show, however, that this partial ineffectiveness of the Rule of Law is offset by the emergence of another ecosystem—here referred to as the Lex Cryptographia ecosystem—in which it is not the State that ensures the protection of fundamental rights, but the technology.The second chapter discusses how, in the absence of Rule of Law enforceability, users can protect their fundamental rights through/on blockchain thanks to its cryptographic architecture. As such, the Lex Cryptographia ecosystem is not an anarchy to the extent that rules, although cryptographic in nature, apply. In fact, the emergence of blockchain gives rise to an alternative balance for fundamental rights, which is coexisting with the current balance guaranteed by the Rule of Law ecosystem. In it, we show that the duty to protect and enforce fundamental rights is often transferred to citizens. It creates duties and opportunities for them as for the State that may be tempted to hinder the coexistence of the alternative ecosystem.
The development of independent learning activities of students in the process continuing education as a pedagogical problem

process of training, it allows you to improve knowledge, skills and qualifications in their professional activities.
미국 대학 초급 한국어 교실에서 교사의 학습자 모어 사용에 대한 학습자의 인식

This study aims to investigate learners’ perception regarding teacher’s use of learners’ L1 as the instructional language in the American higher education KFL classroom in order to provide insight for using L1(English) and L2(Korean) in American college setting. 36 respondents of undergraduate KFL learners (beginner level) participated in the online survey for this study. The questionnaire is consisted of five categories that are (1) learners’ preference on teacher’s use of instructional language, (2) learners’ feeling on effect of learning, (3) judicious and optimal use of learners L1 by a teacher, (4) learners’ habitual translation toward target language and (5) open question on free opinion. The result showed that most of respondents expected a KFL teacher delivering a course in their L1 although they expressed no clear-cut preference on instructional language between L1 and L2. Moreover, learners felt effective of L1 in all the linguistic functions such as speaking, listening, reading and writing while they showed a strong efficacy on oral practices by L2. The norm for judicious and optimal use of L1 still remained unclear, but the function of L1 as cognitive tools for KFL learners would arouse more judicious insight on teacher’s use of learners’ L1 in the KFL classroom. (Nanyang Technological University, NTU)
Enhancing generic code clone detection model for C based application

Code clone is a term used to describe a code used in a system repeatedly. There are currently four types of code clones, namely type-1, type-2, type-3 and type-4, which can be detected by some code clone detection tools. As far as the quality of a system is concerned, the code clone can cause a system to consume more memory to perform a function, due to the many codes that are repeatedly used. The code clone also affects the system maintenance process. If the copied code fragment contains a bug, all code with similarities to the copied code fragment must be fixed one by one. It takes longer to maintain the system. Applications developed in Java and C usually has the largest occurrence of code clone due to the extreme usage of these languages in application development. Therefore, the main objective of this research is to improve the code clone detection model to detect the code clone in the language of C programming. Various models are available to detect a clone code which is a generic clone model, generic pipeline model, unified clone model and a generic code clone detection model. Generic Code Clone Detection (GCCD) is the state of the art model that detects code clone up to type 4 in Java programs. This model's process is pre-processing, processing, parameterization, categorization and match detection. The aim of this research is to improve the prototype for the detection of code clones in the C programming language. Therefore, the main objective of this research is to improve the prototype of the generic code clone detection model to detect the code clone in the language of C programming. This research focuses on improving two processes, namely pre-processing and transformation. In order to evaluate the improvements made in this research, the GCCD prototype is enhanced and tested using a benchmark data set called Bellon’s benchmark dataset. The expected result of this research is that the GCCD prototype can detect the C programming language code clone.
FSelector: Variable Selection Using Visual Features

Visual representation of large datasets should allow us to focus on essential dimensions when restricted to limited visual space. This paper presents an approach for abstracting multi-dimensional data with a focus on grouping the individual attributes based on visual features (or Scagnostics) such as density, skewness, shape, outliers, and texture. Working directly with these visual characterizations, we propose a prototype, called FSelector, to guide users when interactively exploring high dimensional datasets. In particular, selected (leading) variables are organized in a grid layout, allowing users to rapidly identify interesting pairs of variables and to focus on analyzing the original variables directly.
Extraction of text key words by combining syntactic and statistical approaches in natural language processing

In this paper, a method is proposed to extract key words of Persian texts by combining syntactic and statistical methods. In the proposed method, text keywords are extracted by using Persian language grammar and semantic combinations. Human judgment and specialized corpus are used to evaluate the proposed method. The results obtained by human groups showed that the proposed method with an average of 3.77 from 5 could extract keywords of texts, while the average of the quality of the keywords in texts was evaluated as  4.1 by the judging team. In terms of studying it with specialized corpus, the findings showed that the proposed method could extract at least three keywords in 82% of the corpus and at least four keywords in 52% of corpus according to the main keywords of text.
Sistem Monitoring Pelanggaran Siswa Berbasis Web

This study aims to build an application that serves to monitor violations committed by students on the research object, namely SMA Negeri 10 Pekanbaru State High School, the appointment of the title of this study is based on the found cases where parents do not know the violations committed by their children at school, because each violation has points assessment and if the research points have reached the maximum value, parents will be summoned, from information obtained from the school, parents will feel disappointed if they are called to school because their children have committed many violations, therefore an application is built so that parents can monitor the extent of their children violating. the application is built based on the web so that it can be accessed anytime and anywhere, the method of software development uses the waterfall method, the design is done using UML diagrams for modeling the application.
New Approach for Spatiotemporal Data Mining: Combining Spatiotemporal and Visual Data Mining for Traffic Analysis

Most of the large databases, which are currently available, have a strong spatiotemporal component, particularly for, transport databases. Spatiotemporal data mining is a complex process, since it takes into account the specificities of spatial and temporal information. Unlike the spatiotemporal data mining process, the visual data mining one uses visualization for exploratory purposes at all levels of the knowledge discovery process. The automatic and visual extractions of data have their respective advantages. Computers can process large amounts of data much faster than humans, while humans are able to recognize objects and visually explore data much more efficiently than computers. This article proposes a new process for the spatiotemporal data mining which integrates visualization as a confirmatory and exploratory tool in the drilling process of large spatiotemporal databases. This consists in combining the spatiotemporal data mining and the visual data mining process in one and the same global approach. On the other hand, it proposes a methodological study that allows judging the utility of combining the spatiotemporal data mining methods with several visualization techniques. For each applied spatiotemporal data mining method, several visualization techniques have been used. In order to validate the presented approach, this study proposes a case study that applies the proposed spatiotemporal data mining process to traffic analysis in the city of Oran.
Laser Speckle in Multimode Waveguides for Random Projections in Compressive Sensing and Reservoir Computing

Detection of wideband RF signals has applications in sensing and communications. When the signals of interest are sparse, compressive sensing (CS) provides a sub-Nyquist sampling strategy with potential size, weight, and power savings. The critical element in a CS receiver is the device that produces the wideband CS measurement matrix (MM), a MxN matrix with M <; <; N satisfying certain properties [1]. We have shown that passive optical speckle in multimode waveguides provides excellent MMs for CS. The M rows of the MM are obtained from M photodetectors placed at different locations within the output speckle pattern. A range of algorithms can be used to recover the sparse input signal from the resulting measurement vector. We have experimentally demonstrated two speckle-based CS systems: (1) a real-time system with M = 16 implemented using multimode fiber (MMF) that recovers RF frequency, amplitude, and phase, and (2) a simplified spectrometer system implemented using a multimode planar waveguide on a silicon photonic chip that detects only RF frequency and amplitude.
LibGuides: Classroom Management: Books

This guide is for the subject of Classroom Management. It contains resources available in the NCU Library.
Accelerating Deep Convnets via Sparse Subspace Clustering

While the research on convolutional neural networks (CNNs) is progressing quickly, the real-world deployment of these models is often limited by computing resources and memory constraints. In this paper, we address this issue by proposing a novel filter pruning method to compress and accelerate CNNs. Our method reduces the redundancy in one convolutional layer by applying sparse subspace clustering to its output feature maps. In this way, most of the representative information in the network can be retained in each cluster. Therefore, our method provides an effective solution to filter pruning for which most existing methods directly remove filters based on simple heuristics. The proposed method is independent of the network structure, and thus it can be adopted by any off-the-shelf deep learning libraries. Evaluated on VGG-16 and ResNet-50 using ImageNet, our method outperforms existing techniques before fine-tuning, and achieves state-of-the-art results after fine-tuning.