Reviewerly/exHarmony huggingface dataset:
Analyzing domain specific word embeddings for a large corpus of contemporary German. International Corpus Linguistics Conference, Cardiff, Wales, UK, July 22-26, 2019

Distributional models of word use constitute an indispensable tool in corpus based lexicological research for discovering paradigmatic relations and syntagmatic patterns (Belica et al. 2010). Recently, word embeddings (Mikolov et al. 2013) have revived the field by allowing to construct and analyze distributional models on very large corpora. This is accomplished by reducing the very high dimensionality of word cooccurrence contexts, the size of the vocabulary, to few dimensions, such as 100-200. However, word use and meaning can vary widely along dimensions such as domain, register, and time, and word embeddings tend to represent only the most prevalent meaning. In this paper we thus construct domain specific word embeddings to allow for systematically analyzing variations in word use. Moreover, we also demonstrate how to reconstruct domain specific co-occurrence contexts from the dense word embeddings.
Get Up!: Assessing Postural Activity & Transitions using Bi-Directional Gated Recurrent Units (Bi-GRUs) on Smartphone Motion Data

Many health conditions can affect a person’s mobility. Consequently, a person’s ability to perform transitions between activity states (e.g. sit-to-stand) are accurate measures of their mobility and general health. Mobility impairments can manifest either as discomfort while performing certain activity transitions or a complete inability to perform such transitions. The Timed up and Go (TUG) is an important clinical test that assesses patients’ sit-to-stand abilities. Research into passive methods to assess the quality of patients activity transitions and thus conduct the Timed Up and Go autonomously as they live their lives, have recently become popular. Machine and deep learning analysis of smartphone accelerometer and gyroscope data have demonstrated promising activity and transition recognition results. In this paper, we present Get Up!, a novel deep learning-based method to detect whether a person is performing a certain postural activity or transitioning between activities. Get Up! analyzes data from the accelerometer and gyroscope of the patient’s smartphone using Bi-Directional Gated Recurrent Units (Bi-GRU) neural networks with an attention mechanism. Our method outperforms TAHAR, the current state of the art machine learning method, achieving an error rate of 1.47% for activity classification and an accuracy of 97%. We also achieved an error rate of 0.17% with an accuracy of 93.3% when classifying postural transitions. As Get Up! segments activities and transitions, individual TUG sub-components can be timed to identify sub-components that patients find challenging.
Safeguarding Against Active Routing Attack via Online Learning

The Border Gateway Protocol (BGP) is a vital protocol on the Internet. However, the BGP is susceptible against the prefix interception attack, how to seek a secure route against prefix interception attacks is an important problem. For this reason, we propose a novel and effective router selection method on the Internet. First, we evaluate resilience of autonomous systems against prefix interception attacks. Second, we evaluate security risk of next-hop routers and we also obtain the historical performance of next-hop routers via online learning. Finally, we compromise the two performance metrics of routers’ resilience and historical performance to choose a secure route. Our method is verified by network simulations. The results show that the proposed method has more resilience against prefix interception attacks.
Research on the Management of Live Classroom in Colleges and Universities Based on the Theory of Contingency

With the rapid development of technology, live broadcast has been widely used in teaching. However, there are many problems in the management of live classrooms. The contingency theory believes that there is no management method applicable to any environment. This paper divides the live classrooms into online live classrooms, double teacher live classrooms, and live classrooms in smart classrooms, different types of live classrooms were managed in different ways. Compared with the unified management methods, the teaching result of differentiated management is better.
CycleGAN Based on Relative Loss Functions

At the upsurge of deep learning, leap-forward achievements have been made in the field of computer vision, among which the application of CycleGAN to style transfer is eye-catching. The CycleGAN theory argues that concentration on making fake data closer to the real value alone is unfavorable to the stability of the network output. This paper introduces the concept of relativity to CycleGAN so to improve the stability of the network.
Three-way formal concept clustering technique for matrix completion in recommender system

Purpose In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the online users to spend more time and money. The recommender systems suggest some possible and relevant recommendation to the online users by applying the recommendation filtering techniques to the available source of information. The recommendation filtering techniques take the input data denoted as the matrix representation which is generally very sparse and high dimensional data in nature. Hence, the sparse data matrix is completed by filling the unknown or missing entries by using many matrix completion techniques. One of the most popular techniques used is the matrix factorization (MF) which aims to decompose the sparse data matrix into two new and small dimensional data matrix and whose dot product completes the matrix by filling the logical values. However, the MF technique failed to retain the loss of original information when it tried to decompose the matrix, and the error rate is relatively high which clearly shows the loss of such valuable information. Design/methodology/approach To alleviate the problem of data loss and data sparsity, the new algorithm from formal concept analysis (FCA), a mathematical model, is proposed for matrix completion which aims at filling the unknown or missing entries without loss of valuable information to a greater extent. The proposed matrix completion algorithm uses the clustering technique where the users who have commonly rated the items and have not commonly rated the items are captured into two classes. The matrix completion algorithm fills the mean cluster value of the unknown entries which well completes the matrix without actually decomposing the matrix. Findings The experiment was conducted on the available public data set, MovieLens, whose result shows the prediction error rate is minimal, and the comparison with the existing algorithms is also studied. Thus, the application of FCA in recommender systems proves minimum or no data loss and improvement in the prediction accuracy of rating score. Social implications The proposed matrix completion algorithm using FCA performs good recommendation which will be more useful for today’s online users in making decision with regard to the online purchasing of products. Originality/value This paper presents the new technique of matrix completion adopting the vital properties from FCA which is applied in the recommender systems. Hence, the proposed algorithm performs well when compared to other existing algorithms in terms of prediction accuracy.
GPU BASED TOOLBOX FOR FUZZY LOGIC SYSTEM USING WHALE OPTIMIZATION ALGORITHM 

Fuzzy Logic System (FLS) is an efficient method to solve engineering problems.However, the training of a Fuzzy Logic System is a time-consuming task.Optimization Algorithm can be used to optimize the rule base of any FLS.Out of Type-1 FLS and Type-2 FLS, the type-2 found to be more effective to deal with noisy data.Due to their computational requirements Interval Type-2 (IT2) has been preferred over General Type-2.Whale Optimization Algorithm (WOA)has been introduced recently.The algorithm has been tested on different engineering problems and is found to be more effective.General Purpose Computing using graphics Processing Unit (GPGPU) is a new way to solve compute intensive problems on Graphics Processing Unit (GPU).CUDA-C is a parallel language that can be used to execute parallel code NVIDIA GPU.This paper integrates IT2 FLS, WOA and Processing Power of GPU.A toolbox is proposed that can be used to optimize the rule base in parallel.The toolbox provides both the implementations, i.e. serial and Parallel.FLS WOA Toolbox is design in such a way that user can pass parameter dynamically according to their need without interfacing with the code.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI ADMINISTRASI ARSIP BERBASIS WEB PADA KANTOR BIRO PBMD SETDA PROVINSI JAMBI

Now in making administration archive a letter the office management bureau goods belonging to a regionsetda jambi there are several obstacles such as the preparation of reports on administration archiveespecially when will seen reports in accordance the period desired, Search document have been storedwhen are still difficult to and long because have to open the documents old and look for one by one andoften difficulty when will make copy the last on the period required, Hence writer provide solutions ofdesign administration archive information system to ease employees in process administrative dataarchives and ease in for finding data and the report, In this research researchers used tool uml ( the unifiedmodeling language ) and models the prototype.Where expected outcomes be design a system that canmake searching system faster and preparing reports more easily and fast.
Coordination of Ubiquitous Devices in Pervasive Environments: A Proposal Based on WS-CDL

Resumen-Context: Ubiquitous devices are usual in our daily lives.We are permanently interacting with those devices, and with the services they offer.Problem: Ubiquitous devices do not provide services in isolation; they cooperate with other devices.Cooperation mechanisms are primarily proprietary.The few proposals from academy have hardly achieved an impact in practice.This contrasts with web services in the Internet environment, which have standardized mechanisms for the composition of services.Aim: We explore the application of Service Oriented Architecture (SOA) specifications and standards for the coordination of ubiquitous devices in pervasive environments.Methodology: We apply Design Science.This methodology allows the iterative construction and evaluation of artifacts.Results: A coordination framework based on WS-CDL has been built for ubiquitous devices, along with a proof of concept that demonstrates that the framework works properly in a laboratory environment.The transition to real environments looks straightforward.
Penggunaan metode pembelajaran card sort untuk meningkatkan keterampilan menulis puisi pada peserta didik kelas v sekolah dasar

&lt;p&gt;&lt;span style="font-size: small;"&gt;&lt;span style="font-family: Times;"&gt;&lt;strong&gt;&lt;em&gt;Abstract&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;. The purpose of this research is to improve the student poetry writing skills through the use of Card Sort method. This is an action classroom research that carried out for three cycles. Every cycle consist of two meeting and four steps there are planning, implementation, observation, and reflection. Technique that used for collect data are observation, interview, test, and documentation. The data validity test technique uses content validity and triangulation. The data analysis technique uses interactive model learning. The result of this research showed that the initial pre-action test score were 62,88 with classical completeness of 23%, increased in the first cycle by 71,8 with classical completeness of 38,46%, then in second cycle it increased to 74,7 with classical completeness of 53,9%, then in third cycle it increased to 76,8 with classical completeness of 80,75%. Referring to the results of this research it can be conclude that the use of Card Sort method can improve the student writing skills in fifth grades students of SDN 2 Ngadirojo Lor Wonogiri academic year 2018/2019. &lt;/em&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;strong&gt;&lt;em&gt;Keywords: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;Card Sort&lt;/em&gt;, &lt;em&gt;poetry writing skills, Elementary School&lt;/em&gt;
Difference of Visual Information Metric Based on Entropy of Primitive

Image sparse representation is a method of efficient compression and coding of image signal in the process of digital image processing. Image after sparse representation, to enhance the transmission efficiency of the image signal. Entropy of Primitive (EoP) is a statistical representation of the sparse representation of the image, which indicates the probability of each base element. Based on the EoP, this paper presents an image quality evaluation method-Difference of Visual Information Metric (DVIM). The principle of this method is to evaluate the image quality with the difference between the original image and the distorted image. The comparative experiments between DVIM & PSNR & SSIM are carried out. It was found that there was a great improvement in the image quality evaluation of geometric changes. This method is an effective image quality evaluation method, which overcomes the weakness of other quality evaluation methods for geometrically changing images to a certain extent, and is more consistent with the subjective observation of the human eye.
Pufferfish Privacy Mechanism Based on Multi-dimensional Markov Chain Model for Correlated Categorical Data Sequences

Differential privacy is a rigorous standard for protecting data privacy and has been extensively used in data publishing and data mining. However, because of its vulnerable assumption that tuples in the database are in-dependent, it cannot guarantee privacy if the data are correlated. Kifer et al. proposed the Pufferfish Privacy framework to protect correlated data privacy, while till now under this framework there is only some practical mechanism for protecting correlations among attributes of one individual sequence. In this paper, we extend this framework to the cases of multiple correlated sequences, in which we protect correlations among individual records, as well as correlations of attributes. Application scenarios can be different people’s time-series data and the objective is to protect each individual’s privacy while publishing useful information. We firstly define privacy based on Pufferfish privacy framework in our application, and when the data are correlated, the privacy level can be assessed through the framework. Then we present a multi-dimensional Markov Chain model, which can be used to accurately describe the structure of multi-dimensional data correlations. We also propose a mechanism to implement the privacy framework, and finally conduct experiments to demonstrate that our mechanism achieves both high utility and privacy.
Middleman covert channel establishment based on MORE routing protocol using network coding in ad hoc networks

Summary Covert channels have been recently the subject of the study in both creation and countermeasure aspects. There are many different ways to embed the covert data in network standards and protocols, especially in wireless networks. MORE (MAC‐independent opportunistic routing) is an opportunistic routing protocol which uses networks coding to enhance routing performance by reducing the repetitions. This protocol can be a suitable medium for covert channel establishment. A middleman covert channel establishment method is proposed in this paper over MORE routing protocol and with the use of network coding. Hidden data are transferred through packet's payload bytes. Covert sender manipulates coding mechanism by calculating packets' coefficients instead of random selection. The proposed covert channel provides the average throughput of 218 and 231 bps, using two different data length approaches which is relatively a good comparing to the previous network layer covert channels. The proposed covert channel is also a covert storage channel and cannot be removed or restricted. Effect of different network characteristics on the proposed method's capacity and security is investigated by a simulation study, and the results are discussed.
Quantum annealing distributed integer decomposition study of local field coefficient &amp;lt;italic&amp;gt;h&amp;lt;/italic&amp;gt; and coupling coefficient &amp;lt;italic&amp;gt;J&amp;lt;/italic&amp;gt; with stability Ising model

The difficulty in factoring large integers is a security basis of Rivest-Shamir-Adleman public key cryptography. Quantum annealing, a completely different method as compared with that of Shor’s algorithm, can transform an integer factorization problem into a combinatorial optimization problem using the quantum tunneling effects of D-wave quantum annealing to leap from local minima. In this paper, a new distributed integer factorization algorithm based on quantum annealing, which transforms an arbitrary integer into a stable Ising model executable using a D-wave quantum computer, is proposed. The stability and range of the local field coefficient h and the coupling term coefficient J of the Ising model are important factors that affect the success rate of integer factorization. Compared with the algorithm of Purdue University’s Jiang et al., the proposed method reduces the number of logical bits and the parameters of h and J by more than 60% and 40%, respectively, and the ranges of the coefficients of the Ising model are stable. Compared with the algorithm of Lockheed Martin Warren, the parameters of the proposed method are reduced in the orders from 106 to 102 of magnitude when the Ising model is guaranteed to be stable. In addition, to prove the correctness of the algorithm, Warren traversed factorization within 1000 integers. The proposed method traverses all integers within 10000 and all of them are successfully factored. The experimental results of this paper exceed the results of Shor’s algorithm, Jiang’s algorithm and the maximum published factorization scale of Lockheed Martin Warren’s algorithm.
Robust template matching algorithm with multi-feature using best-buddies similarity

In order to solve the problem of matching failure of BBS (Best-Buddies Similarity) algorithm when the target image has a partial occlusion, cluttered background, imbalance illumination, and nonrigid deformation. A multi-feature template matching algorithm based on the BBS algorithm is proposed in this paper. On the basis of the location features and appearance features, we add HOG (Histogram of Oriented Gradients) features to make full use of the color, position and structural contour of the target image to match. In addition, we also perform mean filtering on the confidence map. The experimental results show that the AUC (Area Under Curve) score of the proposed algorithm is 0.6119, which is 6.38% higher than the BBS algorithm. Moreover, our algorithm has stronger robustness and higher matching accuracy.
The Relative Smoothed Throughput Approach for Adaptive HTTP Streaming

HTTP streaming has become a practical technique to distribute multimedia content. This work proposes an adaptation method that improves the user’s quality of experience when they consume video streaming over HTTP, compared to other methods considered state of the art. Based on the study of existing adaptation methods, we propose a new approach called Relative Smoothed Throughput (RST) method. Experiments with varying bandwidth conditions and multiple flows show that RST is able to outperform state of the art methods in efficiency.
Towards Learning from User Feedback for Ontology-based Information Extraction

Many engineering projects involve the integration of various hardware parts from different suppliers.In preparation, parts that are best suited for the project requirements have to be selected.Information on these parts' characteristics is published in so called data sheets usually only available in textual form, e.g. as PDF files.To realize the automated processing, these characteristics have to be extracted into a machine-interpretable format. Such a process requires a lot of manual intervention and is prone to errors.Domain ontologies, among other approaches, can be used to implement the automated information extraction from the data sheets.However, ontologies rely solely on the experiences and perspectives of their creators at the time of creation.To automate the evolution of ontologies, we developed ConTrOn - Continuously Trained Ontology - that automatically extracts information from data sheets to augment an ontology created by domain experts.The evaluation results of ConTrOn show that the enriched ontology can help improve the information extraction from technical documents. Nonetheless, the extracted information should be reviewed by experts before using it in the integration process.We want to provide an intuitive way of reviewing, in which the extracted information will be highlighted on the data sheets.The experts will be able to accept, reject, or correct the extracted data via a graphical interface.This process of revision and correction can be leveraged by the system to improve itself: learning from its own mistakes and identifying common patterns to adapt in the next extraction iteration.This paper presents ideas how to use machine learning based on user feedback to improve the information extraction process.
Analysis of Unsupervised Training Approaches for LSTM-Based OCR

In the context of historical documents, where labeled training data is especially expensive to acquire, the prospect of using unlabeled training data to improve and speed up the overall training is desirable. The most common way to use unlabeled data is unsupervised pretraining, which has been successfully applied to various CNN and RNN architectures in different domains. There is however not sufficient work for its application in the field of OCR. In this paper we investigate multiple architectures and how unlabeled data could be applied to them. We show that in combination with Connectionist Temporal Classification (CTC), a reconstruction objective has no apparent synergistic effect, with both objectives learning different representations. We therefore investigate the use of an LSTM-based Seq2Seq OCR architecture which shows promise regarding unsupervised pretraining.
Design of an adaptive estimator for tracking football in flight

Estimation of variables in presence of nonlinearity in the trajectory equations as well as observations is a challenge that demands the attention of the researchers in every domain. In recent years there has been great progress in the generic filter design in which the filter undergoes a huge amount of stochastic data processing in real time, a cost that is incurred for getting refined estimates of the system variables. Different variants of Kalman filter and particle filter can be designed. The success of the filter depends heavily upon the ability to formulate the system and measurement equations together with the noise models. In this work, extended and unscented Kalman filter design is conducted for tracking a football during a free kick under some restrictions and assumptions. The design is made adaptive through the incorporation of knowledge regarding the players involved as well as ambient noise statistics. Performance of the filter is tested in a simulation environment through Monte Carlo runs for the proof of concept and it is found to be satisfactory and also leads to important answers regarding real-time integration.