Reviewerly/exHarmony huggingface dataset:
Non-Parametric Transformation Networks for Learning General Invariances from Data

ConvNets, through their architecture, only enforce invariance to translation. In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn general invariances and symmetries directly from data. NPTNs are a natural generalization of ConvNets and can be optimized directly using gradient descent. Unlike almost all previous works in deep architectures, they make no assumption regarding the structure of the invariances present in the data and in that aspect are flexible and powerful. We also model ConvNets and NPTNs under a unified framework called Transformation Networks (TN), which yields a better understanding of the connection between the two. We demonstrate the efficacy of NPTNs on data such as MNIST with extreme transformations and CIFAR10 where they outperform baselines, and further outperform several recent algorithms on ETH-80. They do so while having the same number of parameters. We also show that they are more effective than ConvNets in modelling symmetries and invariances from data, without the explicit knowledge of the added arbitrary nuisance transformations. Finally, we replace ConvNets with NPTNs within Capsule Networks and show that this enables Capsule Nets to perform even better.
Agile Methods Selection Model: A Grounded Theory Study

Agile methods adoption has increased in recent years because of its contribution to the success rate of project development.Nevertheless, the success rate of projects implemented using Agile methods has not completely reached its expected mark, and selecting the appropriate Agile methods is one of the reasons for such lag.Selecting the appropriate Agile methods is a challenging task because there are so many methods to select from.In addition, a lot of organizations consider the selection of Agile methods as a mammoth task.Therefore, to assist Agile team members, this study aimed to investigate how the appropriate Agile methods can be determined for different projects.Based on a Grounded Theory study, 23 Agile experts drawn from 19 teams across thirteen countries were interviewed.Hence, this study employed the Ground Theory of selecting Agile methods.Sixteen factors, grouped into five categories, have been found to affect the selection of twenty Agile methods.The nature of project (size, maturity, criticality and decomposability), development team skills (communication skills, domain knowledge, team technical skills and maturity), project constraints (cost/value/ROI, cost of change, time, scope and requirements volatility), customer involvement (collaboration, commitment and domain knowledge) and organizational culture (type of organizational culture) are the key factors that should guide Agile team members in the selection of an appropriate Agile methods based on the value these factors have for different organizations and/or different projects.
A new approach for enhancing LSB steganography using bidirectional coding scheme

This paper proposes a new algorithm for embedding private information within a cover image. Unlike all other already existing algorithms, this one tends to employ the data of the carrier image more efficiently such that the image looks less distorted. As a consequence, the private data is maintained unperceived and the sent information stays unsuspicious. This task is achieved by dividing the least significant bit plane of the cover image into fixed size blocks, and then embedding the required top-secret message within each block using one of two opposite ways depending on the extent of similarity of each block with the private information needed to be hidden. This technique will contribute to lessen the number of bits needed to be changed in the cover image to accommodate the private data, and hence will substantially reduce the amount of distortion in the stego-image when compared to the classic LSB image steganography algorithms.
Novel algorithms for protective digital privacy

Video is the recording, reproducing, or broadcasting of moving visual images. Visual multimedia source that combines a sequence of images to form a moving picture. The video transmits a signal to a screen and processes the order in which the screen captures should be shown. Videos usually have audio components that correspond with the pictures being shown on the screen. Video compression technologies are about reducing and removing redundant video data so that a digital video file can be effectively sent over a network and stored on computer disks. With efficient compression techniques, a significant reduction in file size can be achieved with little or no adverse effect on the visual quality. The video quality, however, can be affected if the file size is further lowered by raising the compression level for a given compression technique. Security is about the protection of assets. Security, in information technology &lt;a href="http://searchdatacenter.techtarget.com/definition/IT"&gt;(IT), &lt;/a&gt;is the defense of digital information and IT assets against internal and external, malicious and accidental threats. This defense includes detection, prevention and response to threats through the use of &lt;a href="http://searchsecurity.techtarget.com/definition/security-policy"&gt;security policies, &lt;/a&gt;software tools and IT services. Security refers to protective digital privacy measures that are applied to prevent unauthorized access to computers, databases and websites. Cryptography is closely related to the disciplines of &lt;a href="http://searchsecurity.techtarget.com/definition/cryptology"&gt;cryptology &lt;/a&gt;and &lt;a href="http://searchsecurity.techtarget.com/definition/cryptanalysis"&gt;cryptanalysis. &lt;/a&gt;Cryptography includes techniques such as microdots, merging words with images, and other ways to hide information in storage or transit. However, in today's computer-centric world, cryptography is most often associated with scrambling &lt;a href="http://searchsecurity.techtarget.com/definition/plaintext"&gt;plaintext &lt;/a&gt;(ordinary text, sometimes referred to as clear text into &lt;a href="http://searchcio-midmarket.techtarget.com/definition/ciphertext"&gt;cipher text &lt;/a&gt;(a process called &lt;a href="http://searchsecurity.techtarget.com/definition/encryption"&gt;encryption), &lt;/a&gt;then back again (known as decryption). Cryptography is evergreen and developments. Cryptography protects users by providing functionality for the encryption of data and authentication of other users. Compression is the process of reducing the number of bits or bytes needed to represent a given set of data. It allows saving more data. The project aims to implement security algorithm for data security. The data will be first encrypted using security techniques and that are done at the same time then it takes less processing time and more speed compression techniques will applied. If encryption and compression are done at the same time then it takes less processing time and more speed.
Geometrical quantum discord and negativity of two separable and mixed qubits*

We studied quantum correlation and quantum entanglement of a quantum system in which a coherent state light field interacts with two qubits that are initially prepared in a separable and mixed state. The influence of mean photon number of the coherent field and distribution probability of the atom on the geometrical quantum discord and the negativity are discussed. Our results show that the mean photon number of light field and distribution function of the atom can regulate and control the quantum correlation and quantum entanglement.
Hybrid Moth-Flame and Salp Swarm Optimization Algorithm

In this paper, the hybrid moth-flame, and salp-swarm algorithm is proposed to enhance the performance of the original moth-flame algorithm.As a moth moves spirally around a flame, the chance of investigating the distance between the moth and the flame is reduced.In this study, a salp chain was attached to each moth to investigate the distance.The proposed algorithm was evaluated on benchmark functions and compared with the original moth-flame optimization algorithm and particle swarm optimization algorithm.Results show that the proposed algorithm can generate a better minimum fitness value up to almost 100% for most functions compared with other algorithms.Moreover, the convergence rate of the proposed algorithms converged to a global optimum faster for most functions compared with other algorithms.
A Maximum Likelihood Registration Algorithm for Moving Dissimilar Sensors

Registration is the prerequisite for data fusion in sensor networks. A maximum likelihood(ML) registration algorithm is presented in this paper. Both complete and incomplete measurements provided by dissimilar sensors are taken into account. Sequential filtering technique is adopted to solve the problem of target state estimating with incomplete measurements. Then errors of target state estimate leaded by residual biases of sensors are analyzed. The likelihood function with respect to residual sensor bias in measuring coordinate system is derived. Finally, an iterated batch algorithm is presented to derive sensor biases. Simulations are performed to demonstrate the effectiveness of the presented algorithms.
Formation of Quinary Gordon-Mills-Welch Sequences for Discrete Information Transmission Systems

An algorithm for the formation of the quinary Gordon-Mills-Welch sequences (GMWS) with a period of N=54-1=624 over a finite field with a double extension GF[(52)2] is proposed. The algorithm is based on a matrix representation of a basic M-sequence (MS) with a primitive verification polynomial hмs(x) and a similar period. The transition to non-binary sequences is determined by the increased requirements for the information content of the information transfer processes, the speed of transmission through communication channels and the structural secrecy of the transmitted messages. It is demonstrated that the verification polynomial hG(x) of the GMWS can be represented as a product of fourth-degree polynomials-factors that are indivisible over a simple field GF(5). The relations between roots of the polynomial hмs(x) of the basic MS and roots of the polynomials hсi(x) are obtained. The entire list of GMWS with a period N=624 can be formed on the basis of the obtained ratios. It is demonstrated that for each of the 48 primitive fourth-degree polynomials that are test polynomials for basis MS, three GMWS with equivalent linear complexity (ELC) of ls=12, 24, 40 can be formed. The total number of quinary GMWS with period of N=624 is equal to 144. A device for the formation of a GMWS as a set of shift registers with linear feedbacks is presented. The mod5 multipliers and summators in registers are arranged in accordance with the coefficients of indivisible polynomials hсi(x). The symbols from the registers come to the adder mod5, on the output of which the GMWS is formed. Depending on the required ELC, the GMWS forming device consists of three, six or ten registers. The initial state of cells of the shift registers is determined by the decimation of the symbols of the basic MS at the indexes of decimation, equal to the minimum of the exponents of the roots of polynomials hсi(x). A feature of determining the initial States of the devices for the formation of quinary GMWS with respect to binary sequences is the presence of cyclic shifts of the summed sequences by a multiple of N/(p–1). The obtained results allow to synthesize the devices for the formation of a complete list of 144 quinary GMWS with a period of N=624 and different ELC. The results can also be used to construct other classes of pseudo-random sequences that allow analytical representation in finite fields.
EMLR for multiple radars on a mobile platform

Aiming at the problem of radar sensor registration on a mobile platform, this study uses multiple radars to estimate the biases of the radar under the earth-centred earth-fixed coordinate system, which provides more accurate information for data fusion. Compared to the maximum likelihood registration (MLR) algorithm, which can only estimate radar offset biases, a novel extended MLR (EMLR) algorithm is proposed. The algorithm could estimate radar-offset biases, attitude biases, and the target real location simultaneously. The simulation results verified that the EMLR algorithm can effectively improve the precision of registration.
Multi-Agent Coordination under Uncertain Communication

Multi-agent coordination is not a simple problem. While significant research has gone into computing plans efficiently and managing competing preferences, the execution of multiagent plans can still fail even when the plan space is small and agent goals are universally aligned. The reason for this difficulty is that in order to guarantee successful execution of a plan, effective multi-agent coordination requires communication to ensure that all actors have accurate beliefs about the state of the world. My thesis will focus on the problem of characterizing, modeling, and providing efficient algorithms for addressing planning and execution when there agents cannot maintain perfect communication.
Sensor data identification based reagent cabinet management system

Recently, a reagent cabinet is used in a laboratory or a laboratory is required to have a system capable of identifying a dangerous situation through sensor data as various sensors are utilized. The existing system identifies the dangerous situation through various sensor data, but there is a problem that the server performs all the operations and the operation of the device is performed manually. In order to solve this problem, this paper proposes a system that can identify the dangerous situation and automatically operate the equipment through the internal environment data of the reagent cabinet. Identification of the hazardous situation is done through the master node used in the reagent cabinet, not the server. The server can continuously update the sensor data through the master node and monitor the real-time status of the reagent cabinet through the application. In this way, it is expected that the risk situation will be promptly addressed by identifying the dangerous situation in the reagent cabinet and operating the device.
Study of Blended-Learning Method Assisted By Edmodo in Teaching English at State Vocational School in Deli Serdang

This study aims to describe blended-learning method assisted by Edmodo in teaching English at State Vocational School in Deli Serdang. Descriptive qualitative method was used in this study in order to examine factors, expected methods, and the pattern in teaching English at State Vocational School in Deli Serdang, by administrating questioner, interview, and observation as source of data taken from the sample. The research was conducted at two State Vocational Schools in Deli Serdang, namely SMK Negeri 1 Percut and SMK Negeri. The results show that there are: 1) 7 factors to be considered in selecting the teaching method for English subject, such as learning objectives, materials, resources, members of the learning process, facilities, time, and the number of study groups), 2) the expected method of teaching English in the vocational school are the student center learning method and technology-based learning method, and 3) there are 2 patterns of blended-learning learning methods assisted by Edmodo, and which are obtained based on lesson time.
COMPARISON OF AGILE AND TRADITIONAL PROJECT MANAGEMENT: SIMULATION OF PROCESS MODELS

There are various process models for the successful implementation of product development projects.In addition to traditional project management -mostly represented by the waterfall or V model -methods of agile project management, e.g.Scrum, are increasingly in use.In the literature, the different approaches have already been described in detail.Here, an attempt is made to compare both process models by means of a simulation.This paper describes the implementation in a simulation model by using the software Matlab/Simulink®.The different approaches are modelled in a way that their effects on the project result can be statistically evaluated.A generic project was created with randomly generated parameters of the project properties.The various approaches of project management are then applied in sub-models to this project.The aim of this paper is to discuss the transfer of the characteristics of the project management approaches into the simulation and to show that the differences can be modelled accordingly.
Visual Analysis of Citation Context-Based Article Influence Ranking

Article influence ranking is an effective way to reduce information redundancy and improve the efficiency of article retrieval. A large number of ranking models for network items have been employed for the ranking of article influence, such as PageRank and Spamming-resistant Expertise Analysis and Ranking. However, the effectiveness of article influence ranking based on the models of PageRank and SPEAR declines with the rapid growth of academic datasets, because of the increasing complexity of citation network. In order to take a rich set of contextual structures of citation context into consideration, we propose a visualization system VAIR for the citation context-based article influence ranking. Firstly, the word2vec model, a renowned technique in the field of natural language processing, is applied to transform articles into vectorized representations according to citation context. Then, a novel citation context-based article influence ranking model is designed according to the complex relationships quantified in a semantic vectorised space. Several visual designs are implemented, allowing users to perceive and compare the ranking results visually and intuitively. A set of user-friendly interactions are provided in the visualization framework, enabling users to explore the desirable article influence and obtain deep insights into the ranking model. Moreover, a series of case studies and comparison experiments are carried out based on real-world datasets, which further demonstrate the effectiveness of our algorithm for article influence ranking.
Single Image Super-Resolution using Adaptive Upsampling Convolutional Network

These methods based on deep convolutional neural networks have achieved dramatic improvements in image super-resolution reconstruction. In this paper, we propose an adaptive upsampling convolutional network for single image super-resolution. The proposed network is mainly based on the adaptive upsampling convolutional unit (AUCU) which is composed of convolutional layers, parametric rectified linear units, sub-pixel convolution layer and the adaptive shortcuts. In the AUCU model, different weights will be assigned to different inputs of the adaptive shortcuts. And these weights are obtained adaptively from training. Owing to the special structure of AUCU, the proposed algorithm can recovery the fine texture details for a large upscaling factor. Besides the AUCU, bicubic interpolation algorithm is also used for the super-resolution restoration during the reconstruction process. In order to enhance the quality of the reconstructed image, a perceptual loss function is proposed for training the feed-forward networks. The proposed loss function consists of two parts of loss: feature loss and MSE loss. The experimental results on the pubic benchmark datasets demonstrate that the proposed algorithm outperforms many other state-of-the-art super-resolution methods.
Scalable Bayesian Non-linear Matrix Completion

Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed. A standard solution is matrix factorization, which predicts unobserved entries as linear combinations of latent variables. We generalize to non-linear combinations in massive-scale matrices. Bayesian approaches have been proven beneficial in linear matrix completion, but not applied in the more general non-linear case, due to limited scalability. We introduce a Bayesian non-linear matrix completion algorithm, which is based on a recent Bayesian formulation of Gaussian process latent variable models. To solve the challenges regarding scalability and computation, we propose a data-parallel distributed computational approach with a restricted communication scheme. We evaluate our method on challenging out-of-matrix prediction tasks using both simulated and real-world data.
EDUKASI GIZI DENGAN PERMAINAN KOMUNIKATA TERHADAP PENGETAHUAN DAN SIKAP KONSUMSI SAYUR BUAH, JAJANAN DAN SARAPAN

Consumption problems that often occur in school children are children less to consume fruits and vegetables, often skip breakfast and consume unhealthy snacks. The purpose of this study was to determine the effect of education using Komunkata games on the knowledge and attitudes of consumption of fruits, snacks and breakfast in 4th grade students. The research design used was quasi experiment with pre and post test control group design models with purposive sampling samples. The sample amounted to 17 people for each group. Data analysis in this study used the t-dependent test and independent t-test with alternative Wilcoxon and Mann Whitney tests. The statistical test results of knowledge obtained p = 0.031 (p 5 0.05) which means that there are differences of nutritional education on knowledge using media communication games for elementary school students, for attitudes obtained p value of 0.322 (p 5 0.05) which means there is no difference of nutrition education on attitudes using media communication games to elementary school students. For schools there is a need for repeated education using communication media from the teacher's side to students interrupted by teaching and learning activities about the importance of consuming vegetables and fruit, snacks and breakfast. &#x0D; Key words: Nutrition education, komunikata , knowledge, attitude, consumption of fruit vegetables, snacks, breakfast
Improved converted measurement Kalman filter for satellite‐borne multi‐target tracking

This study introduces a special converted measurement Kalman filter (CMKF) algorithm which apply to satellite for tracking target. Typical CMKF applies to radar which measures targets in spherical coordinate system. The new algorithm can be used by satellite which measures target's latitude and longitude. Improved CMKF algorithm tracks target in simple situation but is not able to deal with multi-target and information missing situation. Multiple hypothesis tracking (MHT) can track multi-target accurately and has the ability to initial track and end track at all times. For satellite, MHT use improved CMKF to calculate cost matrix and update the state estimate and estimate covariance. In the simulation, tracking process is simulated by MATLAB. Single target measured by satellite can be tracked accurately by CMKF algorithm. MHT can track multi-target accurately and distinguish the information missing rightly.
The Seeding Algorithm for Spherical k-Means Clustering with Penalties

Spherical k-means clustering is a generalization of k-means problem which is NP-hard and has widely applications in data mining. It aims to partition a collection of given data with unit length into k sets so as to minimize the within-cluster sum of cosine dissimilarity. In this paper, we introduce the spherical k-means clustering with penalties and give a $$2\max \{2,M\}(1+M)(\ln k+2)$$ -approximate algorithm, where M is the ratio of the maximal and the minimal penalty values of the given data set.