Reviewerly/exHarmony huggingface dataset:
Event structures for the reversible early internal Pi-calculus

The pi-calculus is a widely used process calculus, which models communications between processes and allows the passing of communication links. Various operational semantics of the pi-calculus have been proposed, which can be classified according to whether transitions are unlabelled (so-called reductions) or labelled. With labelled transitions, we can distinguish early and late semantics. The early version allows a process to receive names it already knows from the environment, while the late semantics and reduction semantics do not. All existing reversible versions of the pi-calculus use reduction or late semantics, despite the early semantics of the (forward-only) pi-calculus being more widely used than the late. We define piIH, the first reversible early pi-calculus, and give it a denotational semantics in terms of reversible bundle event structures. The new calculus is a reversible form of the internal pi-calculus, which is a subset of the pi-calculus where every link sent by an output is private, yielding greater symmetry between inputs and outputs.
Nearest neighbor representations of Boolean functions

A nearest neighbor representation of a Boolean function is a set of positive and negative prototypes in $R^n$ such that the function has value 1 on an input iff the closest prototype is positive. For $k$-nearest neighbor representation the majority classification of the $k$ closest prototypes is considered. The nearest neighbor complexity of a Boolean function is the minimal number of prototypes needed to represent the function. We give several bounds for this measure. Separations are given between the cases when prototypes can be real or are required to be Boolean. The complexity of parity is determined exactly. An exponential lower bound is given for mod 2 inner product, and a linear lower bound is given for its $k$-nearest neighbor complexity. The results are proven using connections to other models such as polynomial threshold functions over $\{1, 2\}$. We also discuss some of the many open problems arising.
Divergence-Based Adaptive Extreme Video Completion

Extreme image or video completion, where, for instance, we only retain 1% of pixels in random locations, allows for very cheap sampling in terms of the required pre-processing. The consequence is, however, a reconstruction that is challenging for humans and inpainting algorithms alike. We propose an extension of a state-of-the-art extreme image completion algorithm to extreme video completion. We analyze a color-motion estimation approach based on color KL-divergence that is suitable for extremely sparse scenarios. Our algorithm leverages the estimate to adapt between its spatial and temporal filtering when reconstructing the sparse randomly-sampled video. We validate our results on 50 publicly-available videos using reconstruction PSNR and mean opinion scores.
THE IMPLEMENTATION OF PQ4R LEARNING METHOD TO IMPROVE THE STUDENTS’ READING COMPREHENSION OF THE 4th GRADE AT SD MUHAMMADIYAH 6 PEKANBARU

The purpose of this research is to improve students reading comprehension at the fourth grade of SD Muhammadiyah 6 Pekanbaru. The type of this research was classroom action research, which was in two cycles. The result showed that there was an increasing students’ reading comprehension ability in each cycle. The average grade score of students reading comprehension before implementing PQ4R learning method was 68. After the implementation of PQ4R method at cycle I, the average grade score increased to 76 with a large increased of 12%. In the second cycle, the score increased to 84 with a large increased of 23%. In the cycle I, the teacher’s activities in the first meeting were 62.5% in moderate criteria. Then, in the second meeting, the percentage increased to 70.8% in moderate criteria. Then in the cycle II, the teacher’ activities in the first meeting the percentage became 83.3% in high criteria and in the second meeting increased to 87.5% in very high criteria. In the cycle I, the students’ activites in the first meeting were 58.3% in low criteria. In the second meeting, it increased to 66.7% in moderate criteria. In the cycle II, the students’ activities in the first meeting had the percentage becoming 79.2% in high criteria and then increased to 83.3% in high criteria on the second category. The result of the research proves that the application of PQ4R method can improve students’ reading comprehension ability.
Effectiveness of Visual Features on Diverse Reading Orders for Information Extraction

Information extraction from unstructured documents, meant only for human readers, has to be dealt with differently than from the structured documents. Unstructured documents include visual clues that draw human attention and convey the majority of information to readers. There have been several recent advancements in information extraction in such documents using the conventional natural language processing methodologies. However, there has been little to no work towards using the non-sequential relationships that are found only in unstructured documents for the task of information extraction. In this study, we propose novel methodologies to capture the non-sequential relationships present in the unstructured documents for the task of Named Entity Recognition (NER) using Conditional Random Field (CRF). We experiment with two different datasets having different types of logical reading order and we compare three sets of features. The NER model, that uses the proposed novel features, achieves mean F1-Scores of 68.15% on Retail Receipt and 85.54% on Air Ticket documents.
The Library: Weizmann Institute Open Access collection: Search

The Open Access collection contains Weizmann research which is Open Access, including Weizmann PhD theses, datasets, and accepted and submitted versions of some articles
한국초기 선교역사적 관점에서 본 장요나 선교사의 베트남 선교

This paper investigates a Korean missionary, Jonah Jang, who has been a missionary in Vietnam since 1990, with the historical viewpoint of the early Protestant missions in Korea. For this, the writer compares between Jonah Jang and Horace G. Underwood. The two missionaries are very similar in that they evangelized the Gospel in spite of the prohibition of the countries they were appointed. They were supported by various denominational churches in their own countries. Also, they had a negative attitude toward the Roman Catholic Church. On the other hand, unlike Underwood, Jonah Jang did not cooperate with different missionaries in Vietnam, pretended to be a medical doctor, and did not stop his mission work due to his family members and health problems.
High precision range and velocity estimation through fusion processing from P and D band Radar

Detection of high-speed stealth targets is a difficult problem due to its small Radar Cross Section (RCS) and high velocity. A method of high precision range and velocity estimation through fusion processing from P and D band radar for stealth target is proposed in this paper. First, the Doppler ambiguity multiple of the stealth target in D band radar is determined by the P band radar velocity measurement result. Then, Doppler ambiguity multiple and D band radar fuzzy velocity measurement result are combined to estimate high precision and unambiguous target information. Simulation results are demonstrated to verify the efficiency of the proposed method.
Detection of the Face using Triplet Method in Messy or Crowdy Video

Today India is developing country. All the sectors are influenced by technology, Detection of the Face using Triplet method in messy or crowdy video or an input live video stream for Low enforcement or investigative agency, In Our proposed project we are using a new technique of Face detection with Human object detection the technique is called as deep metric learning.
Assessment IT Governance of Human Resources Information System Using COBIT 5

The textile industry in Yogyakarta is one of the industries that manage the human resources in the field of information technology. As one way to achieve organizational goals that have human resources that are able to develop and provide benefits for the organizations. This study focuses on attendance information system in the textile industry located in Yogyakarta, which aims to get an overview how far the industry provides a timely and effective response to user requests of all types of incidents. Problem found by the researchers is the occurrence of inaccuracies data on the attendance, with these inaccurate results it will automatically affect the salary results are not accurate. In this research, the authors use the framework COBIT 5 and focus on the Deliver Service and Support (DSS) domain. Researchers chose a sub-domain DSS that is DSS02. The results of this research are found that sub-domains DSS02 get an average capability level is 2.4. The conclusion is the capability level obtained by the attendance information system is still below the expected level
Locality-Sensitive Hashing Scheme based on Longest Circular Co-Substring

Locality-Sensitive Hashing (LSH) is one of the most popular methods for $c$-Approximate Nearest Neighbor Search ($c$-ANNS) in high-dimensional spaces. In this paper, we propose a novel LSH scheme based on the Longest Circular Co-Substring (LCCS) search framework (LCCS-LSH) with a theoretical guarantee. We introduce a novel concept of LCCS and a new data structure named Circular Shift Array (CSA) for $k$-LCCS search. The insight of LCCS search framework is that close data objects will have a longer LCCS than the far-apart ones with high probability. LCCS-LSH is \emph{LSH-family-independent}, and it supports $c$-ANNS with different kinds of distance metrics. We also introduce a multi-probe version of LCCS-LSH and conduct extensive experiments over five real-life datasets. The experimental results demonstrate that LCCS-LSH outperforms state-of-the-art LSH schemes.
우리나라 주요 산업부문 기술무역의 R&D투자 및 FDI효과 분석

Purpose: Recently, Korea''s international trade has become more uncertain due to international trade issues such as the US-China trade war and the Korea-Japan trade dispute. In addition, in the Fourth Industrial Revolution, the traditional manufacturing trade has limited growth. Therefore, in order to cope with the change of the new technology-oriented era, it is necessary to change into the trade of high value-added goods and services that combine core technologies. Therefore, securing the original technology and expanding the technology trade using it will be a key factor for future economic growth.Research design, data, and methodology: This study examined the characteristics of Korea''s technology trade by major industries by using the national statistics on technology trade provided by the OECD to emphasize the importance of technology trade and to analyze the effects of R&D investment and FDI on technology trade.Results: As a result of empirical analysis of the technology trade model, the increase in total R&D expenditure is positive for all three industries(Pharmaceutical Industry, Computer·electronic and optical Industry, Aerospace Industry) in the effect of R&D related variables on technology trade of three industries, which are the core industries of technology trade. It has been shown to affect. This study analyzes the spatial spillover effect considering spatial dependence, and as a result, the flow of technology trade according to the change of explanatory variables by country has a mutual effect with neighboring countries. 32.7% of the electronics industry and 21.8% of the aerospace and aerospace industries showed the largest indirect effect of the pharmaceutical industry compared to other industries.Conclusions: Also, if the FDI decreases due to a contraction in economic exchange between Korea and Japan due to the trade dispute between Korea and Japan, Japan, which has a relatively large volume of technology trade, shows that the decrease in technology trade due to the decrease in foreign direct investment is greater, and it was found to be influenced by the flow of technological trade in neighboring countries that are spatially dependent. Therefore, it is necessary to make efforts to resolve the relationship between the two countries smoothly in terms of economic and international trade.
Training End-to-end Single Image Generators without GANs

We present AugurOne, a novel approach for training single image generative models. Our approach trains an upscaling neural network using non-affine augmentations of the (single) input image, particularly including non-rigid thin plate spline image warps. The extensive augmentations significantly increase the in-sample distribution for the upsampling network enabling the upscaling of highly variable inputs. A compact latent space is jointly learned allowing for controlled image synthesis. Differently from Single Image GAN, our approach does not require GAN training and takes place in an end-to-end fashion allowing fast and stable training. We experimentally evaluate our method and show that it obtains compelling novel animations of single-image, as well as, state-of-the-art performance on conditional generation tasks e.g. paint-to-image and edges-to-image.
A Web-Based Platform for Mining and Ranking Association Rules

In this demo, we introduce an interactive system, which effectively applies multiple criteria analysis to rank association rules. We first use association rules techniques to explore the correlations between variables in given data (i.e., database and linked data (LD)), and secondly apply multiple criteria analysis (MCA) to select the most relevant rules according to user preferences. The developed system is flexible and allows intuitive creation and execution of different algorithms for an extensive range of advanced data analysis topics. Furthermore, we demonstrate a case study of association rule mining and ranking on road accident data.
Blockchain Technology, Challenges and Opportunities

Blockchain technology [1] poses a daunting challenge to democracy.It allows both participatory and decentralized governance, but is also the expression of unbridled liberalism, the enemy of democracy.In this paper, we are attempting to provide an overview of Blockchain, its characteristics and the challenges and opportunities of Blockchain technology.
Depthwise Discrete Representation Learning

Recent advancements in learning Discrete Representations as opposed to continuous ones have led to state of art results in tasks that involve Language, Audio and Vision. Some latent factors such as words, phonemes and shapes are better represented by discrete latent variables as opposed to continuous. Vector Quantized Variational Autoencoders (VQVAE) have produced remarkable results in multiple domains. VQVAE learns a prior distribution $z_e$ along with its mapping to a discrete number of $K$ vectors (Vector Quantization). We propose applying VQ along the feature axis. We hypothesize that by doing so, we are learning a mapping between the codebook vectors and the marginal distribution of the prior feature space. Our approach leads to 33\% improvement as compared to prevous discrete models and has similar performance to state of the art auto-regressive models (e.g. PixelSNAIL). We evaluate our approach on a static prior using an artificial toy dataset (blobs). We further evaluate our approach on benchmarks for CIFAR-10 and ImageNet.
Veterans and Their Inherent Cybersecurity Preparedness: Myth or Reality?

Increasingly, the cybersecurity job market is lacking in qualified talent. Although lack of available workers with the proper skills is part of it, it is primarily due to the increasing breadth of the cyber domain. To combat this, many companies and government agencies are looking to train combat veterans in the necessary skills to be effective and capable in the domain of cybersecurity. Combat veterans, who have experience with risk and adapting to difficult situations and unknown threats, are believed to be better suited than the civilian population to deal with the threat landscape of cybersecurity. The purpose of the research is to examine this preconceived notion through four overarching research questions: 1. Do combat veterans make better cybersecurity professionals? 2. How much does their experience with risk and threat assessment come into play? 3. Do veterans make better cybersecurity professionals for other reasons? 4. Or is the notion that vets make better cybersecurity professions flawed because the required skills are so technical in nature? As a precursor to a comprehensive study, a large-scale survey was conducted to see what differences, if any, there are between individuals with combat experience and those that do not have such experience. These results are discussed. Future research will employ a mixed methods design consisting of a general survey (phase I) followed by interviews with Chief Information Security Officers (CISOs) (phase II), and finally interviews with Veterans and non-veterans (phase III). The phased approach will allow us to make the most efficient use of our time by using the information learned in one phase to help inform subsequent phases. This will result in a richer set of data and more meaningful results.
Teacher-Class Network: A Neural Network Compression Mechanism.

To solve the problem of the overwhelming size of Deep Neural Networks (DNN) several compression schemes have been proposed, one of them is teacher-student. Teacher-student tries to transfer knowledge from a complex teacher network to a simple student network. In this paper, we propose a novel method called a teacher-class network consisting of a single teacher and multiple student networks (i.e. class of students). Instead of transferring knowledge to one student only, the proposed method transfers a chunk of knowledge about the entire solution to each student. Our students are not trained for problem-specific logits, they are trained to mimic knowledge (dense representation) learned by the teacher network. Thus unlike the logits-based single student approach, the combined knowledge learned by the class of students can be used to solve other problems as well. These students can be designed to satisfy a given budget, e.g. for comparative purposes we kept the collective parameters of all the students less than or equivalent to that of a single student in the teacher-student approach . These small student networks are trained independently, making it possible to train and deploy models on memory deficient devices as well as on parallel processing systems such as data centers. The proposed teacher-class architecture is evaluated on several benchmark datasets including MNIST, FashionMNIST, IMDB Movie Reviews and CAMVid on multiple tasks including classification, sentiment classification and segmentation. Our approach outperforms the state-of-the-art single student approach in terms of accuracy as well as computational cost and in many cases it achieves an accuracy equivalent to the teacher network while having 10-30 times fewer parameters.
Deblurring using Analysis-Synthesis Networks Pair

Blind image deblurring remains a challenging problem for modern artificial neural networks. Unlike other image restoration problems, deblurring networks fail behind the performance of existing deblurring algorithms in case of uniform and 3D blur models. This follows from the diverse and profound effect that the unknown blur-kernel has on the deblurring operator. We propose a new architecture which breaks the deblurring network into an analysis network which estimates the blur, and a synthesis network that uses this kernel to deblur the image. Unlike existing deblurring networks, this design allows us to explicitly incorporate the blur-kernel in the network's training. In addition, we introduce new cross-correlation layers that allow better blur estimations, as well as unique components that allow the estimate blur to control the action of the synthesis deblurring action. Evaluating the new approach over established benchmark datasets shows its ability to achieve state-of-the-art deblurring accuracy on various tests, as well as offer a major speedup in runtime.
Byte-Pair and N-Gram Convolutional Methods of Analysing Automatically Disseminated Content on Social Platforms

In this experiment, an efficient and accurate network of detecting automatically disseminated (bot) content on social platforms is devised. Through the utilisation of parallel convolutional neural network (CNN) which processes variable n-grams of text 15, 20, and 25 tokens in length encoded by Byte Pair Encoding (BPE), the complexities of linguistic content on social platforms are effectively captured and analysed. With validation on two sets of previously unexposed data, the model was able to achieve an accuracy of around 96.6% and 97.4% respectively &amp;mdash; meeting or exceeding the performance of other comparable supervised ML solutions to this problem. Through testing, it is concluded that this method of text processing and analysis proves to be an effective way of classifying potentially artificially synthesized user data &amp;mdash; aiding the security and integrity of social platforms.
Review on Stereo Vision Based Depth Estimation

Stereo vision is a challenging problem and it is a wide research topic in computer vision. It has got a lot of attraction because it is a cost efficient way in place of using costly sensors. Stereo vision has found a great importance in many fields and applications in today’s world. Some of the applications include robotics, 3-D scanning, 3-D reconstruction, driver assistance systems, forensics, 3-D tracking etc. The fundamental test of sound system vision is to create exact difference map. Sound system vision calculations for the most part perform four stages: first, coordinating cost calculation; second, cost collection; third, dissimilarity calculation or enhancement; and fourth, divergence refinement. Sound system coordinating issues are likewise examined. An enormous number of calculations have been produced for sound system vision. But characterization of their performance has achieved less attraction. This paper gives a brief overview of the existing stereo vision algorithms. After evaluating the papers we can say that focus has been on cost aggregation and multi-step refinement process. Segment-based methods have also attracted attention due to their good performance. Also, using improved filter for cost aggregation in stereo matching achieves better results.
Using Coarse Grain Geological Study to Predict the Origin of Sediments SW-Iraq

This research deals with coarse grain sediments obtained from sieving of quaternary depression and valley fill sediments from desert and desert boarder's regions southwest Najaf city-Iraq.Physical, chemical and mineralogical analyses of these sediments were done to reveal the sedimentary origin of these sediments.The results indicated that parent rocks of these sediments were not from the Arabian shield where they were transported for short distances from sedimentary rocks cover that is located at the outskirts of the Arabian shield.
NVU Libraries Research Guides: Business and Economics: Searching NVU Libraries

This guide provides access to library materials on topics regarding business and economics.
Rules, Regulations and By-Laws - Subject - Index to the AMA archive

Rules, Regulations and By-Laws - Subject - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED