Reviewerly/exHarmony huggingface dataset:
Universal Compression with Side Information from a Correlated Source

Packets originated from an information source in the network can be highly correlated. These packets are often routed through different paths, and compressing them requires to process them individually. Traditional universal compression solutions would not perform well over a single packet because of the limited data available for learning the unknown source parameters. In this paper, we define a notion of correlation between information sources and characterize the average redundancy in universal compression with side information from a correlated source. We define the side information gain as the ratio between the average maximin redundancy of universal compression without side information to that with side information. We derive a lower bound on the side information gain, where we show that the presence of side information provides at least 50% traffic reduction over traditional universal compression when applied to network packet data confirming previous empirical studies.
The Selection of the Language in Indonesian Learning at SMK Multi Karya Medan

This study aims to describe the function the code switching used by speakers at SMK Multi Karya Medan. This research is a descriptive qualitative research. The subjects in this study were the teacher and the students. The objects were the utterances of code-switching in Indonesian language learning in SMK Multi Karya Medan. Validity data technique is used triangulation of data source and informant review. Data collection is using listen and speak methods. Data analysis using the intralingual method. The result of the research showed that the function of code-switching at the time of the process of Indonesian language learning are emphasized, reprimanded, reminded, explained, asked and commanded.
Consideration of Fundamental Methods and Principles for Human-Computer Co-Creation of Narratives

The authors propose the conceptual design of a co-creative narrative generation system that co-creates a collection of diverse narratives from a narrative generation program and people. The long-term goal of this study is to vitalize humans' narrative creation by developing generative narrative technology. The key idea is to connect and unify individual narrative productions by many agents, including one or more computer programs and humans, via a collection of narratives produced and accumulated by these agents. Simultaneously, a co-creative narrative generation system is the practice of a computational approach to narratology as a model for the social system of narrative production. This chapter describes the basic concepts of the co-creative narrative generation system.
Toward a Full Integration of the Arabic Language into ‘Intel ACAT’ Assitive Platform

Background:Assisting people with severe physical limitations with information technology has been an active area of research in recent years. Many researchers’ efforts are built on assistive devices which are often used to offset the impact of the resulted physical impairments.The Assistive Context-Aware Toolkit (ACAT) is the widely known project in this area. After being released as open source, the developers’ community helped to integrate many languages such as French and Spanich. However, many languages are still messing and to the best of our knowledge, the Arabic-speaking users still can not use the platform as no significant effort to integrate the Arabic language have been previously undertaken.Methods:This paper firstly, provides an overview on ACAT; the specifically-developed platform by Intel Labs for Dr. Stephen Hawking. Besides, it describes the ways in which ACAT may be used to enhance the capacity to take part in fundamental and instrumental activities of every day living and upgrade one's autonomy in general.Secondly, we outline our contributions in integrating the Arabic language into the keyboard, the intelligent predictive text engine and all interfaces of this unique and highly configurable system. Results:Our integration evolved after resolving many issues and we succeeded in integrating the Arabic language in interfaces, keyboard and word prediction engine. Most of other ACAT features (Facial gesture recognition, Mouse Navigation., etc) are functional.&#x0D; Conclusion:This work is a step forward to make the intel ACAT platform completely available in Arabic language. Therefore, Arabic-speaking patient can now get the benefits from this platform and are able to perform common tasks such as documents editing and management, Web surfing, writing emails and above all, communicating with others easily.The Arabic Text-to-speech engine integration is planned for future works .
Syntax-Semantics Interfaces of Modifiers

The paper introduces a computational approach to syntax-semantics interface of human language. The goal is to develop an efficient, biologically inspired technique to computational processing of human language. The technique integrates two formal languages of syntax and semantics into generalised, type-theoretic grammar that efficiently incorporates lexicon, syntax, and semantics. Modifier expressions are among the most difficult classes of expressions in human language, that introduce great complexity both in their syntax and semantics. The focus of the paper is on providing efficient technique for syntax-semantics of modifier expressions.
Thinking Outside the Pool: Active Training Image Creation for Relative Attributes

Current wisdom suggests more labeled image data is always better, and obtaining labels is the bottleneck. Yet curating a pool of sufficiently diverse and informative images is itself a challenge. In particular, training image curation is problematic for fine-grained attributes, where the subtle visual differences of interest may be rare within traditional image sources. We propose an active image generation approach to address this issue. The main idea is to jointly learn the attribute ranking task while also learning to generate novel realistic image samples that will benefit that task. We introduce an end-to-end framework that dynamically "imagines" image pairs that would confuse the current model, presents them to human annotators for labeling, then improves the predictive model with the new examples. With results on two datasets, we show that by thinking outside the pool of real images, our approach gains generalization accuracy for challenging fine-grained attribute comparisons.
Sparse One-Time Grab Sampling of Inliers

Estimating structures in "big data" and clustering them are among the most fundamental problems in computer vision, pattern recognition, data mining, and many other other research fields. Over the past few decades, many studies have been conducted focusing on different aspects of these problems. One of the main approaches that is explored in the literature to tackle the problems of size and dimensionality is sampling subsets of the data in order to estimate the characteristics of the whole population, e.g. estimating the underlying clusters or structures in the data. In this paper, we propose a `one-time-grab' sampling algorithm\cite{jaberi2015swift,jaberi2018sparse}. This method can be used as the front end to any supervised or unsupervised clustering method. Rather than focusing on the strategy of maximizing the probability of sampling inliers, our goal is to minimize the number of samples needed to instantiate all underlying model instances. More specifically, our goal is to answer the following question: {\em `Given a very large population of points with $C$ embedded structures and gross outliers, what is the minimum number of points $r$ to be selected randomly in one grab in order to make sure with probability $P$ that at least $\varepsilon$ points are selected on each structure, where $\varepsilon$ is the number of degrees of freedom of each structure.'}
Exploration on Training Mode of Local Teachers’ Colleges for Interdisciplinary Talents

With the frequent flow of talents from various ethnic groups and regions and the increasingly integrated and diversified development of science, technology and culture, society has an increasingly strong demand for talents who have broad professional knowledge, extensive cultural upbringing, compound ability and creativity. Colleges and universities at all levels are also keenly aware of this social demand. Local normal universities in China mainly undertake the important responsibility of cultivating basic education teachers, and they should take the opportunity of basic education reform to cultivate talents for basic education. Strengthen professional curriculum integration. Create double degree. Cultivate students' multicultural learning mode. Enhance students' comprehensive practical skills training. Attach importance to teachers' professional skills training. Based on region's economic and cultural development demand, colleges must cultivate interdisciplinary talents with high and comprehensive quality.
FID: Function Modeling-based Data-Independent and Channel-Robust Physical-Layer Identification

Trusted identification is critical to secure IoT devices. However, the limited memory and computation power of low-end IoT devices prevent the direct usage of conventional identification systems. RF fingerprinting is a promising technique to identify low-end IoT devices since it only requires the RF signals that most IoT devices can produce for communication. However, most existing RF fingerprinting systems are data-dependent and/or not robust to impacts from wireless channels. To address the above problems, we propose to exploit the mathematical expression of the physical-layer process, regarded as a function $\mathbf{\mathcal{F}(\cdot)}$, for device identification. $\mathbf{\mathcal{F}(\cdot)}$ is not directly derivable, so we further propose a model to learn it and employ this function model as the device fingerprint in our system, namely $\mathcal{F}$ID. Our proposed function model characterizes the unique physical-layer process of a device that is independent of the transmitted data, and hence, our system $\mathcal{F}$ID is data-independent and thus resilient against signal replay attacks. Modeling and further separating channel effects from the function model makes $\mathcal{F}$ID channel-robust. We evaluate $\mathcal{F}$ID on thousands of random signal packets from $33$ different devices in different environments and scenarios, and the overall identification accuracy is over $99\%$.
Interactive Image Segmentation using Label Propagation through Complex Networks

Interactive image segmentation is a topic of many studies in image processing. In a conventional approach, a user marks some pixels of the object(s) of interest and background, and an algorithm propagates these labels to the rest of the image. This paper presents a new graph-based method for interactive segmentation with two stages. In the first stage, nodes representing pixels are connected to their $k$-nearest neighbors to build a complex network with the small-world property to propagate the labels quickly. In the second stage, a regular network in a grid format is used to refine the segmentation on the object borders. Despite its simplicity, the proposed method can perform the task with high accuracy. Computer simulations are performed using some real-world images to show its effectiveness in both two-classes and multi-classes problems. It is also applied to all the images from the Microsoft GrabCut dataset for comparison, and the segmentation accuracy is comparable to those achieved by some state-of-the-art methods, while it is faster than them. In particular, it outperforms some recent approaches when the user input is composed only by a few scribbles draw over the objects. Its computational complexity is only linear on the image size at the best-case scenario and linearithmic in the worst case.
A Study on Effective Repetition of Bilateral Filter for Medical Images

Recently, a camera has the function of gain bright- ness that makes a bright photo from the dark photo. The function is realized by amplifying an attenuated signal from a photosensor in a digital camera. But it amplifies noise with signal. The noise makes salt-pepper-noise on a monochrome photo; Gaussian noise on a color picture. In the previous study, the bilateral filter was used for denoise on a picture. The bilateral filter is a nonlinear filter that is able to denoise and save important corner, edge, and contours. The bilateral filter needs two or three parameters that are different to do best for each input pictures. The previous study searches the optimum parameter, but the method needs to be run the bilateral filter for each suggested parameter and the execution time was long. The bilateral filter has a character is able to remove noise effectively by filtering several times than search optimum parameters. In this article, our purpose is considered of the effective number of repetitions of Bilateral Filter. We estimate how many repeat filtering by using SSIM and PSNR for image quality measuring.
MAGIC ROUND: TEACHING MEDIA FOR COMPILING NEGATIVE SENTENCES

Learning English is much harder for Indonesian students because of various aspects. Yet, the obstacles are able to overcome with any methods, strategies or media to achieve the goals of the teaching and learning process (Manan, 2017). This research focuses on developing teaching media, named Magic Round for teaching negative sentences. It was made to facilitate students in compiling grammatically correct sentences. This research was carried out by applying Research and Development (R&amp;D) which consists of several steps, including; preliminary studies, research planning, product design, product validation, product trial, and producing final product. The trial result in limited scale gained 81.67% from technology expert and 86.67% from material expert. Besides, the assessment of its usefulness, feasibility, and students� responses toward the media reached 86.38%, 88.64%, and 87.18%, respectively. It means that Magic Round is valid and can be used without revision. In conclusion, Magic Round is appropriate to use in teaching English negative sentences.Keywords: magic round; negative sentences; research and development; teaching media; tenses.
Ensemble Feature for Person Re-Identification

In person re-identification (re-ID), the key task is feature representation, which is used to compute distance or similarity in prediction. Person re-ID achieves great improvement when deep learning methods are introduced to tackle this problem. The features extracted by convolutional neural networks (CNN) are more effective and discriminative than the hand-crafted features. However, deep feature extracted by a single CNN network is not robust enough in testing stage. To improve the ability of feature representation, we propose a new ensemble network (EnsembleNet) by dividing a single network into multiple end-to-end branches. The ensemble feature is obtained by concatenating each of the branch features to represent a person. EnsembleNet is designed based on ResNet-50 and its backbone shares most of the parameters for saving computation and memory cost. Experimental results show that our EnsembleNet achieves the state-of-the-art performance on the public Market1501, DukeMTMC-reID and CUHK03 person re-ID benchmarks.
The seventh ACM SIGSPATIAL international workshop on analysis for big spatial data seattle, wa, USA - November 6, 2018

Big data is emerging as an important area of research for data researchers and scientists. This area has also seen significant interest from the industry and federal agencies alike, as evidenced by the recent White House initiative on "Big data research and development". Within the realm of big data, spatial and spatiotemporal data is one of fastest growing types of data. With advances in remote sensors, sensor networks, and the proliferation of location sensing devices in daily life activities and common business practices, the generation of disparate, dynamic, and geographically distributed spatiotemporal data has exploded in recent years. In addition, significant progress in ground, air- and space-borne sensor technologies has led to an unprecedented access to earth science data for scientists from different disciplines, interested in studying the complementary nature of different parameters. Today, analyzing this data poses a massive challenge to researchers.
Rancang Bangun Sistem Informasi untuk Menunjang Penyusunan Laporan Keuangan pada Farm Layer Raja Telur

Post amnesty tax that has been rolled out by the government encourages some business people to compile tax reports in bookkeeping. Farm layer is an industry that is engaged in the production of eggs, is one of the business people with the requirements of reporting tax books. The important thing in the presentation of financial statements to support tax reporting is the availability of adequate human resources and the ability to process data quickly and accurately. In general, business people in the field of farm layer data processing process only on recording data recording and only contains recapitulation of population growth costs. Of course this has not been sufficient for the purposes of preparing financial statements.These problems can be solved by creating a computer application program that functions to accommodate all the activities in the farm layer company. With this computer application program, it is expected that all data management from beginning to end is recorded in detail and is able to produce financial reports quickly and accurately. Keywords: Farm Layer, Recording, Financial Reports, Computer Application Programs
Model and Algorithm for Time-Consistent Risk-Aware Markov Games.

In this paper, we propose a model for non-cooperative Markov games with time-consistent risk-aware players. In particular, our model characterizes the risk arising from both the stochastic state transitions and the randomized strategies of the other players. We give an appropriate equilibrium concept for our risk-aware Markov game model and we demonstrate the existence of such equilibria in stationary strategies. We then propose and analyze a simulation-based $Q$-learning type algorithm for equilibrium computation, and work through the details for some specific risk measures. Our numerical experiments on a two player queuing game demonstrate the worth and applicability of our model and corresponding $Q$-learning algorithm.
A Method for Automatic Medical Diagnosis

This research paper presents a new method for the automatic diagnosis of diseases using a personal computer. Forming a basis for the characterization of diseases, a wide set of symptoms is introduced, and a particular disease is characterized by a set of statistical weights assigned to those symptoms. Information about the patient’s state is provided by a graphic interface in which the user confirms symptom indicators. Agreement between these symptoms and classified symptoms of a particular disease is then estimated by the sum of corresponding weights, where the disease corresponding to the maximal agreement is proposed as the result of the diagnosis. A disease likelihood estimator is calculated and presented to assess the reliability of the diagnosis. With regard to the automatic assessment of the diagnosis the corresponding algorithm and the properties of the computer program are included. Finally, the effectiveness of this method of medical diagnosis is demonstrated through four typical examples involving differently expressed symptoms. The diagnostic system resembles semantically driven sensory-neural network.
Instance-Level Microtubule Segmentation Using Recurrent Attention.

We propose a new deep learning algorithm for multiple microtubule (MT) segmentation in time-lapse images using the recurrent attention. Segmentation results from each pair of succeeding frames are being fed into a Hungarian algorithm to assign correspondences among MTs to generate a distinct path through the frames. Based on the obtained trajectories, we calculate MT velocities. Results of this work is expected to help biologists to characterize MT behaviors as well as their potential interactions. To validate our technique, we first use the statistics derived from the real time-lapse series of MT gliding assays to produce a large set of simulated data. We employ this dataset to train our network and optimize its hyperparameters. Then, we utilize the trained model to initialize the network while learning about the real data. Our experimental results show that the proposed algorithm improves the precision for MT instance velocity estimation to 71.3% from the baseline result (29.3%). We also demonstrate how the injection of temporal information into our network can reduce the false negative rates from 67.8% (baseline) down to 28.7% (proposed).
Multi-threaded Programs

Outcomes: By the end of this chapter students should be able to:
Amplify Your Impact: My Body is Ready: Best Practices for Using Memes on Library Social Media

In this column, librarian and social media consultant Andy Woodworth gives an unexpected approach toward library social media strategy with the use of memes. Part whimsical and part informative, memes have the unique potential to go viral on the internet, thus introducing your library’s brand to a much wider audience.—Editor
High-speed Video from Asynchronous Camera Array

This paper presents a method for capturing high-speed video using an asynchronous camera array. Our method sequentially fires each sensor in a camera array with a small time offset and assembles captured frames into a high-speed video according to the time stamps. The resulting video, however, suffers from parallax jittering caused by the viewpoint difference among sensors in the camera array. To address this problem, we develop a dedicated novel view synthesis algorithm that transforms the video frames as if they were captured by a single reference sensor. Specifically, for any frame from a non-reference sensor, we find the two temporally neighboring frames captured by the reference sensor. Using these three frames, we render a new frame with the same time stamp as the non-reference frame but from the viewpoint of the reference sensor. Specifically, we segment these frames into super-pixels and then apply local content-preserving warping to warp them to form the new frame. We employ a multi-label Markov Random Field method to blend these warped frames. Our experiments show that our method can produce high-quality and high-speed video of a wide variety of scenes with large parallax, scene dynamics, and camera motion and outperforms several baseline and state-of-the-art approaches.
USE OF WEB 2.0 TOOLS AT NORTHERN BORDER UNIVERSITY

This study aimed to identify the current level of Web 2.0 tools used by the academic staff at Northern Border University (NBU) in the Kingdom of Saudi Arabia, for their academic pursuits. It also aimed to identify the multiple barriers that stand in the way of using Web 2.0 tools, concluding with suggestions to overcome such barriers at university level. The researcher has used the qualitative method (based on interviews), comprising of interviewing 25 male and female faculty members. The findings and revelations of this study are that at present the use of Web 2.0 technology by NBU faculty members is very much limited and therefore low, except for the use of social networking sites. The results of this research study showed certain barriers which include a lack of necessary knowledge about the use of Web 2.0 in education, as well as other potential factors such as a lack of training courses, poor technical support, and lack of access to computer labs at all times. Interestingly, resistance to change is one of the main factors among the academic members. Through this study, it has been suggested that faculty members make some potential improvements through the use of Web 2.0 tools. Spreading the culture to use Web 2.0 tools in education, intensifying training courses for academic staff, and setting a clear strategy plan for the integration of technology in education at university level.