Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Graph Embedding via Multi-instance Kernel Learning

Graph-structured data is widespread in real tasks. Unlike traditional data, graph-structured data often lacks sufficient labels and can not directly be fed into machine learning models. To cope with this, semi-supervised graph embedding is the representative technique for graph-structured data, which transforms graph-structured data into a traditional vector representation in machine learning and leverages a large amount of unlabeled data. Previous studies on semi-supervised graph embedding focus on pursuing good performance on specific data. In many applications, however, it is important to determine the graph embedding adaptively while there is little uniform perspective on understanding connections among previous graph embedding studies. To this end, we propose a multi-instance kernel learning perspective, which transforms the similarities of examples into the calculation of multi-instance kernel function by considering the neighbors of the sample as a bag of instances, and establishes the association for semi-supervised graph embedding and multi-instance kernel learning. Experiments demonstrate the superior performance of the proposal over many related methods.
Video Security System for Intrusion Detection

Due to the large-scale development of technology in the modern world, institutions, businesses and residential areas need to secure their facilities leading to the need for a security system. There are several ways to help increase the security; one of these methods is to install a surveillance security system. Conventional surveillance systems have disadvantages where they are often human-based, allocated a numerous number of video cameras to monitor the entire environment, and Collecting a large amount of video data requires considerable calculation and manpower to analyze. Broadly, the recorded video files consume much storage space and someone must always monitor to check whether events occurred or not in the surveillance zone. In order to eliminate these problems a need to use effective surveillance security system which only records when motion is detected, which is the purpose of this paper. The system analyzed of an “area of environment being monitored” through used Sum of Absolute Difference (SAD) algorithm to detect motion in real time. Where web camera captures the images which are analyzed to detect the motion (intervention) and generating alarm. The obtained results were satisfactory, where all intrusion scenes were detected without errors. Hence, storage requirement reduced significantly.
Energy Efficient High Speed Multiplier for Image Compression

The aim of this study is to increase the speed of the multiplier for image compression. The image compression is used to reduce the redundancy of the image and store the data in efficient form. This paper show the use of vedic multiplier with pipelining to increase the speed. multiplier is the keyblock of Digital Signal Processing and High performance processors. Vedic mathematics is used in this multiplier to increase the speed. The vedic multiplier is formed by the gates and adder circuits. In this vedic multiplier each and every gate and adder circuit is converted to multiplexer for high speed. CADENCE software is used fordesigning and simulating the system and the system is coded in verilog HDL.
LSC-North Harris Library Research Guides: Music: Websites

This is a guide designed to help students find quality resources when researching classical composers, music theory, performers, western art music, fok music and popular music.
LibGuides: 12 Month Children's Programming: Planning

Created by Jessica Jean Walker for INFO 260A: Children's Programming and Services (Fall 2019), Instructor Wrenn-Estes
Live Tenders: An Incomplete Theory of Social Digestion

April 01 2020 Live Tenders: An Incomplete Theory of Social Digestion Sabrina Chou Sabrina Chou Sabrina Chou is an artist from Los Angeles, CA. Search for other works by this author on: This Site Google Scholar Author and Article Information Sabrina Chou Sabrina Chou is an artist from Los Angeles, CA. Online Issn: 2572-7338 Print Issn: 1091-711X © 2020 Sabrina Chou2020Sabrina Chou Thresholds (2020) (48): 176–185. https://doi.org/10.1162/thld_a_00721 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Search Site Citation Sabrina Chou; Live Tenders: An Incomplete Theory of Social Digestion. Thresholds 2020; (48): 176–185. doi: https://doi.org/10.1162/thld_a_00721 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentAll JournalsThresholds Search Advanced Search This content is only available as a PDF. © 2020 Sabrina Chou2020Sabrina Chou Article PDF first page preview Close Modal You do not currently have access to this content.
APLIKASI RADAR CUACA UNTUK IDENTIFIKASI FLUKTUASI KONDISI CUACA EKSTRIM (STUDI KASUS: BANJIR DI KOTA MEDAN TANGGAL 5 OKTOBER 2018)

Intisari Data Radar cuaca Enterprise Electronics Corporation (EEC) selama 24 jam pada tanggal 5 Oktober 2018 mulai pukul 07.00 WIB hingga 07.00 WIB tanggal 6 Oktober 2018 digunakan pada penelitian ini. Data ini diperoleh dari Balai Besar Meteorologi Klimatologi dan Geofisika Wilayah I dalam format volumetric (.vol) dan memiliki selang waktu per 10 menit. Metode yang digunakan yaitu analisis deskriptif hasil produk turunan Radar yang diolah menggunakan perangkat lunak yang berasal dari produsen radar yaitu Enterprise Doppler Graphic Environment (EDGE) berupa Coloumn Maximum (CMAX), momen intensitas horizontal, momen rata-rata curah hujan, dan Vertical Integrated Reflectivity (VIR), serta grafik curah hujan (RHG). Penelitian ini memperlihatkan bahwa fluktuasi kondisi cuaca yang terjadi pada tanggal 5 Oktober 2018 sangat tinggi. Secara umum, curah hujan intensitas tinggi yang terjadi di Kota Medan pada tanggal 5 Oktober 2018 umumnya terbagi menjadi tiga periode, yaitu hujan pada siang hingga sore (pukul 14.00 – 15.50 WIB), hujan pada petang (pukul 18.20 – 19.40 WIB), dan hujan pada malam hari (21.10 – 23.40 WIB). Fluktuasi tertinggi terjadi pada periode pertama dengan kenaikan curah hujan tertinggi terjadi di Kecamatan Medan Helvetia dengan kenaikan curah hujan sebanyak 32 mm dalam 10 menit yang terjadi pada pukul 14.50 WIB, pada periode kedua terjadi di Kecamatan Medan Kota dengan kenaikan 24 mm pada pukul 18.20 WIB, dan periode ketiga terjadi di Kecamatan Medan Johor dengan kenaikan 17 mm pada pukul 21.20 WIB. Abstract Enterprise Electronics Corporation (EEC) Data Radar for 24 hours on October 5th, 2018, starting at 7:00 LT until 07.00 LT on October 6th, 2018, were used in this research. These data were obtained from the Center for Meteorology, Climatology and Geophysics in Region I in the form of volumetric (.vol) and has an interval of 10 minutes. The method used were descriptive analysis of Radar products processed by software from Radar manufacture, namely Enterprise Doppler Graphic Environment (EDGE) software which in the form of Column Maximum (CMAX) moments of horizontal intensity and moments of average rainfall, Vertical Integrated Reflectivity (VIR), and rainfall graph (RHG). We found that there were high fluctuations in weather conditions that occurred on October 5th, 2018. In general, the high-intensity rainfall occurred in Medan city on October 5th, 2018 was generally divided into three periods, namely rain in the afternoon until evening (at 14:00 - 15:50 LT), rain at dusk (18:20 - 19:40 LT), rain at night (21.10 - 23.40 LT). The highest fluctuation occurred in the first period with the highest increase of rainfall occurred in Medan Helvetia Subdistrict with an increase of 32 mm rainfall in 10 minutes which occurred at 14.50 LT; the second period occurred in Medan Kota District with a 24 mm increase at 18.20 LT, and the third period occurred in Medan Johor District with a 17 mm increase at 21.20 LT.
Ray W. Howard Library: Make an Appointment: Ray Howard Library

Students, faculty, and staff may make appointments to ask about research and borrowing items from the library. Librarians are here to help you succeed.
Crime Deterrent with the Framework of Nasal Entity

Improvement of advances additionally upgrades method of burglary. The majority of the Indian populace let their home alone for quite a long time; they likewise guarantee their security by introducing an entryway lock or surveillance camera and so on. The proposed framework works in two stages. In ongoing patterns robotized is our whole wish, so this task puts on thought to set up a verified home wellbeing framework. The proposed IoT task made for double security framework to open the entryway, with the controlling of electrical apparatuses under hazardous gas spillage. The stock formed the one of a kind Electronic Olfactory Sensory gadget coordinated with profound learn of picture handling, here the calculation seemed well and good the covered criminal with top of the line IR blaster which uses the unmistakable facial highlights to part the individuals. This framework utilizes the looks, ages and face imprints to catches the one of a kind distinguishing pieces of proof of human. This is a learning calculation with auto enlistment of pictures which will in general commit errors of perceiving a face and it recall the issues and improve its degree of precision.
Convergent Tangent Estimator for Discrete Objects Based on Isothetic Covers

In this article, we propose a tangent estimation method for discrete object based on isothetic covers.We introduce a concept of maximal isothetic straight segments as a maximal segment of isothetic covers that are linearly separable.A new tangent estimator is proposed as a function of maximal isothetic straight segments.Upper bound for the tangent estimator are derived and show that it tends toward the directions of the tangents of the underlying real curve as we reduce the grid size.We show how consecutive isothetic tangents are related to the convexity of the isothetic covers.The new tangent estimator is optimal i.e., linear to the number of given points and shows good performance in the presence of noise.
Identifikasi Karakteristik Anak Berkebutuhan Khusus Menggunakan Metode Case Based Reasoning (Studi Kasus di Sekolah Luar Biasa Negeri 1 Linggo Sari Baganti)

Children with special needs are children who have different characteristics and limitations in ability. This child with special needs is called Tunagrahita. Developmental impairment is classified into three categories namely mild, moderate and severe. This study aims to help the process of identifying the characteristics of mental retardation experienced by children. This study uses the Case Based Reasoning (CBR) method to identify children with special needs using the data of the mentally disabled children in SLBN 1 Linggo Sari Baganti. Similarity results were 51.92% for moderate developmental impairment, 17.5% for mild developmental impairment and 8% for severe developmental impairment. Calculations are performed using Visual Basic Net 2010.
Research Guides: Algorithmic Bias & Search Systems: Algorithmic Bias & Search Systems

This resource explores how bias becomes embedded in algorithms and search systems and offers ways to counteract the negative effects of algorthimic bias.
Semantic Representations for Multilingual Natural Language Processing

The paper focuses on the problems of research and integral modeling of semantic representations of language structures for the implementation of knowledge extraction from texts and machine translation. The method of presenting language structures and resolving their ambiguity is based on logical linguistic rules and vector spaces. An effective method of displaying the vector of natural language structures in an expanded space of features for the classification of new language objects and structures has been developed. A focal sample of parallel texts of business and scientific documents in Russian, English and French has been formed for various fields of science and technology; an expanded system of new categories has been developed to enhance the expressive power of the original version of the unification grammar. The multilingual parsing procedures have been developed, which take into account the translational transformations.
Country report (Korea) - O-COCOSDA 2019

Recent trends in construction of large datasets for AI technology development
To Enhance Election Voting using Blockchain

Developing an electronic voting system that satisfies the legal requirements of legislator. Our current Election system inherit the use of EVMs which in various source have been proven to be hackable & not tampered proof. This makes the candidate & citizen not trust the Election System. The operating assumption for Hyperledger developers is that business blockchain networks will operate in an environment of partial trust. Thus, the voting-based algorithms are advantageous in that they provide low-latency finality. When a majority of nodes validates a transaction or block, consensus exists and finality occurs. Because voting-based algorithms typically require nodes to transfer messages to each of the other nodes on the network, the more nodes that exist on the network, the more time it takes to reach consensus. This results in a trade-off between scalability and speed. This paper aims to evaluate the use of blockchain technology to build distributed electronic voting system. The problem with people living far from the native and not able to migrate for voting is one of the issues. This leads to the smaller number of votes in elections.More generally we aim to build a system that not only improve the voting but also ensure transparency of the process and will also build trust among the people.
Unsupervised Pose Estimation via Inverse Graphics

We introduce a method for pose estimation from sensor array data utilizing an inverse graphics approach. We propose a general unsupervised method based on manipulating a 3D model to discover the joint-space parameters that produce an observed image or sensor reading. Using our novel architecture, we discuss the design of a model for performing human pose estimation on data obtained from a mattress pressure sensor.
Tree based architectures for rule discovery in sequence modeling

A great amount of work has been done using deep learning strategies in sequence modeling, such as the encoder-decoder LSTM. Most solutions for this type of problem involve recurrent neural networks. This approach, while often yielding superior results, has its downsides. Training time can sometimes be prohibitively long and the only remedy for that is higher computing power. Additionally, these models are extremely difficult to interpret, regardless of their performance. The deeper and more complex the network, the harder it is to make sense of it. Neural networks lack a simple representation of the knowledge they learn. Rule-based learners, however, are the opposite in this regard. They represent the knowledge they learn through relational rules, which are easily digested by a human. While most rule-based learners are designed with the intent of discovering dependencies between variables, such as in association rule learning, sometimes rule-based learners can be implemented in a supervised setting. For sequence modeling, though, rule-based solutions are scarce, especially if we consider the case where inputs and targets are variable length sequences. We propose a type of architecture that utilizes binary trees and evolutionary algorithms to discover rules. These models make inferences through sequential actions, similarly to how reinforcement learning agents progress through tasks by choosing actions based on the state of the environment. These new learners can predict on various data types, including multi-output and sequences of variable length. We consider a simple problem where the inputs and targets are variable length sequences. Our strategy perfectly learns the rules from a synthetic dataset. Lastly, we discuss how to apply the strategy more generally.
Methodology for the Formation of Research Competencies in Junior Students of a Pedagogical University

The article deals with the formation of research competence of junior students and the development of a system of continuous innovative training of a future specialist in the framework of research activities.
Self Control dalam Game Online Studi Deskriptif Kualitatif Self Control Gamer PUBG Mobile (Player Unknow's Battle Grounds) Remaja SMA Kota Surakarta

Self Control is a person's ability to guide his or her own quir't. One's ability to withstand lust, craving, and a momentary urge to please themselves and that is contrary to societal behaviour and norms in society. The purpose of this research is to describe self control in the online game of PUBG gamers that are performed by teenage high school Surakarta. The data source used is the interview of the informant. Sampling techniques are purposive sampling. The data collection techniques used are interviews in-depth. The validity test technique used is the trianggulation of the data source. The research method used is qualitative with qualitative descriptive methods. Based on the results of this study can be concluded that the informant can conduct self control due to the internal and external factors affecting.
Changing Role of Library Services and Librarians in Digital Era

Impact of ICT and digitization of library resources, change the meaning of library from document preservation to knowledge management. Nowadays library professionals are facing new challenges because of information explosion. Information is generated in different forms and formats. Due to globalization every aspect of human life is advanced and emergence of ICT and its application in generation, communication, and access of information become easy. The role of librarian is not of merely custodian but it is of information provider. The modern librarianship needs to change in their philosophy and practice in order to success these problems. Users are not satisfied with the traditional libraries. They are always in search of new knowledge in short span of a time. In this article author tries focus on changing role libraries and librarians in digital era.
Research Guides: Using HathiTrust: Creating and sharing collections

Description of how to find and use Hathi Trust resources at the University of New Hampshire
Isolation of Prostate Gland in T1-Weighted Magnetic Resonance Images using Computer Vision

In this work, segmentation of the region of prostate gland from T1-weighted magnetic resonant (MR) images is considered. There are many practical challenged that are encountered while segmentation such as the regions surrounding the prostate gland may be inconsistently defined, which can pose a problem for the algorithm to differentiate the pixels of the gland from the neighborhood pixels. The other problem may be the region around the gland may not be homogenous. Therefore, in this study, we have proposed a semi-automatic approach that can efficiently segment out the prostate gland in a computationally effective and robust manner as compared to the approaches available in the literature. Here, we have used image processing algorithms like anisotropic diffusion, Gaussian filtering, k-means clustering, and region-growing approach. The proposed method has been evaluated on the T1-weighted MR images of 30 subjects. The proposed method has given a Dice coefficient of 86.95% and a Jaccard index of nearly 80.56%.
Application of Internet of Things to Live Logging of Chinese Chess

The Internet of Things embeds electronics in everyday devices to enable new products and services. Board games such as Chinese chess (Xiàngqí) are a useful test case, as they pose a limited problem with well defined goals. In this paper, we report on two approaches to live logging of Xiàngqí games. One approach uses a camera phone, image processing and a convolutional neural network, while the other embeds magnets in the pieces and Hall effect sensors into the board. Both approaches permit the live transmission of a game using a web server. Game clocks were also constructed and integrated into the systems.
Introduction of Phrase Structures into the Example-Based Machine Translation System

The formal model of the syntax structure of texts based on representations of phrase structures as generalized syntagms and methods of their automatic construction are described. A new solution to a number of current problems of automatic processing of text information in the field of information search and machine translation, as well as an approach to the problem of unification of syntax structures of sentences and phrase structures constituting the sentences has been proposed. Declarative tools which are a complex of dictionaries and grammatical tables in machine form were created on the basis of large-scale studies of extensive volumes of scientific and technical texts using linguistic and statistical methods.
K-Means Bootstrap Analysis in the Birth Weight Classification of the Born Babies

Low birth weight babies are caused by poor nutritional status before and during pregnancy.The impact of low birth weight is the slowing of the growth of infants seen in weight gain that does not reach normal levels when one-year-old.Classification of birth weight babies will be grouped into several groups, namely, low birth weight, normal baby weight, and excess baby weight.Classification of birth weight is done by cluster analysis.Cluster analysis is one of the statistical analyzes which aims to group objects based on the similarity of characteristics between those objects.Clusters of K-Means begin by determining in advance the number of clusters desired, so the measure of inaccuracy must be measured again.The inaccurate size of the K-Means cluster analysis can be done with the application of Clustering Bootstrap.This study aims to classify the birth weight of a baby using a cluster and a bootstrap cluster.The results showed a grouping of birthweight babies grouped into three clusters, cluster I had a baby's birth weight above 3130.171kilograms, cluster II had a baby's weight under 3033.327kilograms and cluster III had a baby's birth weight below 2299.994kilograms.K-Means Bootstrap results produce relatively the same cluster.The best method is K-Means Bootstrap B = 75, with a total accuracy rate (TAR) of 0.7993 and a total error rate (TER) of 0.2007.K-Means Bootstrap can be used as an alternative way to determine the group of birth weight babies.
Analysis of changes of the content and forms of organization of the educational process in the conditions of the introduction of emergent technologies

Planning and Teaching Engineering Staff for the Industrial and Economic Complex of the Region 2019: Proceedings of 2019 XVIII Russian Scientific and Practical Conference on Planning and Teaching Engineering Staff for the Industrial and Economic Complex of the Region (PTES)