Reviewerly/exHarmony huggingface dataset:
Locally Structured Multi-Task Multi-Kernel Tracker

Many impressive correlation filter trackers only construct a single kernel space for combined feature vector, or introduce independently multiple kernels with limit performance improvement, and neglect the spatial structure relationship among and inside target samples. In this paper, we propose a structured multi-task multi-kernel correlation particle filter tracker, which introduces multiple non-linear kernels for multiple channel features to fully take the strength of their powerful discriminability. Meanwhile, the developed tracker exploits the interdependencies and cooperations among different features to learn multi-task multi-kernel correlation filter jointly, and makes the learned multi-kernel filters complement and boost each other. Besides, by introducing cross patch similarity with the spatial constraints, the proposed tracker not only exploits the intrinsic structure information among target samples, but also preserves the spatial layout structure and correspondence of local image patches inside target samples. This is achieved by sparsity-induced mixed norm regulator. Furthermore, a shepherded particle filter tracking framework employs the affine motion modal to cover the states of target among adjacent frames as many as possible, including large scale variances and small move. Experiments on three popular datasets demonstrate that our tracker performs significantly better than other state-of-theart methods.
Cybersecurity Threat Vector Assessment for Aviation Assets

Aviation cybersecurity threat vector assessment is critical to estimate the impact of adverse incidences and necessary corrective actions. Prevention, detection, and corrective controls are assessed with quantitative risk assessment models. Aviation cyber threat vector audit matrix is created with the help of graphical probabilistic models. As real time threat assessment indicators change automated preemptive security information and event management actions are taken.
Factors Influencing Consumer Intention and Decision to Purchase Plastic Waste Recycle Machine

About 1.42 million tonnes of plastic waste in Indonesian waters are unable to manage every year. Plastic waste recycling machine may be the solution to this environmental problem. Marketing strategy is required to answer the challenge in the sale of plastic waste recycling machine. This research aims to identify and analyse the factors that influence the interest of purchasing intention and purchasing decision of plastic waste recycling machine manufactured by PT Majestic Buana Group. The research was conducted for eight months from February to September 2019. Primary data were obtained through online questionnaires, while secondary data were gathered from literature, journals, and research reports. This research used purposive sampling, with a sample number of 100 respondents who have purchased the plastic recycling machine from PT. MBG from the last 5 years. Data was then processed using descriptive analysis and Structural Equation Modelling-Partial Least Square (SEM-PLS). The results show that product variables, processes, service quality have a significant influence on the purchase interest while product variables, service quality and company reputation significantly affects purchasing decisions. Keywords: marketing mix, SEM PLS, purchasing intention, plastic waste, recycling machine
Accelerating Parallel ALS for Collaborative Filtering on Hadoop

Collaborative Filtering (CF) is an important building block of recommendation systems. Alternating Least Squares (ALS) is the most popular algorithm used in CF models to calculate the latent factor matrix factorization. Parallel ALS on Hadoop is widely used in the era of big data. However, existing work on the computational efficiency of parallel ALS on Hadoop have two defects. One is the imbalance of data distribution, the other is lacking the fine-grained parallel processing on the rating data. Aiming on these issues, we propose an integrated optimized solution. The solution first optimizes the rating data partition with the consideration of both the number of involved data records and the partitioned data size. Then, the multithread-based fine-grained parallelism is introduced to process rating data records within a map task concurrently. Experimental results demonstrate that our solution can reduce the overall runtime of Hadoop ALS by 82.17% by maximum.
Demystifying and Anticipating Graduate School Admissions using Machine Learning Algorithms

One of the many aspirations of undergraduate students in India is going for further graduate studies. Unfortunately, many students spend months and years of preparation focusing on things that unfortunately won't improve their chances of getting into a good graduate school. This paper evaluates the chances of applicants to get into a particular graduate program using various classification and regression approaches of Machine Learning. Various algorithms have been pitted against each other and also the most important features have been extracted which are useful to get into a graduate school program. Using unsupervised approach, this paper finds various categories of students and pool them together to find if they are perfect fit for admission or not. A novel approach of predicting the chances for admission in graduate school is introduced in this paper.
‘우리 안의 분단’을 넘어 평화와 공존을 지향하는 역사교육 시론

If history education as a citizen''s education today is to contribute to the prospect of a peaceful future, we must strive to go beyond the present task and the historical formation of ‘Division within us’. The ‘Division within us’ prevented the historical and objective recognition of the division of the Korean Peninsula, which lasted for 70 years. It also encouraged ideological conflicts within our society, fostering aversion to the underprivileged and minorities, and bystanding violence. Students should be given the opportunity to reflect on their own participation in the reproduction of division and violence, thereby opening up the possibility of changing thoughts and behavior. From the point of view of history education, the key to overcoming the divisional trauma is to get out of ignorance and indifference to the state violence and events of the Korean War, and to expand our awareness of North Korea, which is the subject of publicity and aversion. If a learner''s conversation with the past as a topic of current life is a driving force for change in values, it can influence the direction of personal life and social behavior unfolding in each life. To this end, it is necessary to set up a history learning theme with multiple perspectives and a teaching method that promotes the learners'' historical dialogue.
Improving performance of collaborative recommender system using combination of learning techniques

As the World Wide Web continues to grow at an exponential rate, the size and complexity of many web sites grow along with it.For the users of these web sites it becomes increasingly difficult and time consuming to find the information they are looking for.User interfaces could help users find the information that is in accordance with their interests by personalizing a web site.Recommender systems provide personalized information by learning the user's interests from traces of interaction with that user.We claim that our method performs better than its existing counterparts.We have performed our experiment on book recommendation dataset available online.Our method makes use of k-means clustering technique of machine learning with some variation.
Collection of requirements for teaching in the area of Smart Textiles

Abstract The textile and clothing industries are facing new challenges. Smart Textiles are a huge topic with a high market potential, however there are only few approaches in teaching this interdisciplinary field. Moreover, individuals are expected to continuously adapt to the new requirements by following the concept of lifelong learning. We propose a new concept that will integrate a smart textiles kit. This application-oriented construction kit should help to extend the frontiers of knowledge, stimulate creativity, and give students the ability to solve real-world problems. As added benefit it will accelerate the understanding of innovation and interdisciplinary challenges. The blended learning concept aims to prepare students to think interdisciplinary, broadly, deeply, and last but not least critically. Students will be ready to contribute to the development of new products and face the challenges of structural change along their working life.
Deep Learning application to learn models in Cognitive Robotics

When an artificial neural network (ANN) has to learn in real time, it is not good to train it with ordinary methods (Batch Learning [1]). The problem is that it is difficult to achieve convergence because the data is almost always different and in our experiments, we did not have enough storage available to create a big dataset; also the ANN never stops its learning process. Nowadays, there is an Online Learning [2] available. Real-time learning of a cognitive model can be achieved using Deep Learning [3] with Online training. In addition, there are different techniques that help to make this learning more efficient. The type of training used for an ANN will depend on factors such as data availability, training time, available hardware resources, among others. The training can be offline or online. In the present article, online training has experimented on a robot whose main characteristic is that it uses a Darwinian cognitive mechanism for its survival. The robot learning occurs in real time. It has deep artificial neural networks to predict actions to be performed, training with the least amount of storage space and in the shortest possible time without sacrificing confidence of the deep artificial neural network. The experienced training is Online Deep Learning, Online Deep Learning with memory and Online Mini-Batch Deep Learning with memory.
Tight Convergence Rate of Gradient Descent for Eigenvalue Computation

Riemannian gradient descent (RGD) is a simple, popular and efficient algorithm for leading eigenvector computation [AMS08]. However, the existing analysis of RGD for eigenproblem is still not tight, which is O(log(n/epsilon)/Delta^2) due to [Xu et al., 2018]. In this paper, we show that RGD in fact converges at rate O(log(n/epsilon)/Delta), and give instances to shows the tightness of our result. This improves the best prior analysis by a quadratic factor. Besides, we also give tight convergence analysis of a deterministic variant of Oja's rule due to [Oja, 1982]. We show that it also enjoys fast convergence rate of O(log(n/epsilon)/Delta). Previous papers only gave asymptotic characterizations [Oja, 1982; Oja, 1989; Yi et al., 2005]. Our tools for proving convergence results include an innovative reduction and chaining technique, and a noisy fixed point iteration argument. Besides, we also give empirical justifications of our convergence rates over synthetic and real data.
A Hybrid Model Based on Neural networks to Predict the Power Output of Photovoltaic systems

In recent years, solar power has been a major contributor to the global network. However, due to the nature of the periodicity of this energy, its integration with the network has challenges and problems that simultaneously predict the exact output power of photovoltaic systems (pv) is very important. In this article, a hybrid model consisting of kohonen Self-organizing Map (SOM), Feedforward Neural Network (FFNN), and K-Nearest Neighbors (KNN) algorithms are proposed for prediction of pv power output. In fact, in this model, using the SOM and KNN algorithms, the data of weather input is classified and then by FFNN, a prediction model for each class is presented. The results confirm the high accuracy of the proposed model in the prediction.
Remarks on Mixture and Yoyo Distinguishers for Reduced-Round AES

Reduced-round AES has been the popular building block to design new cryptographic schemes. The key-independent distinguishers were known up to 4 rounds of AES until Grassi et al. proposed the multiple-of-8 distinguisher on 5-round AES in Eurocrypt 2017. Later Grassi translated it into a simpler one and proposed the mixture differential distinguisher on 4-round AES. In Asiacrypt 2017, Rønjom et al. proposed the yoyo distinguishers on reduced-round AES, and gave the first key-independent distinguisher on 6-round AES in the adaptively chosen plaintexts/ciphertexts setting. In this paper, we study the relationship between yoyo distinguisher and mixture differential distinguisher for 4-round AES. It is shown that yoyo distinguisher on complete 4-round AES can be understood as a generalized mixture differential distinguisher. The second work of this paper is to improve the yoyo distinguisher on 6-round AES. Instead of finding good pair randomly, we find good pairs by fixing plantexts in some coset of some subspace. Making use of the properties of mixture and good pairs, the number of yoyo needed to do for distinguishing random permutation from 6-round AES can be reduced. We slightly improve the yoyo distinguisher on 6-round AES, reducing the required plaintext/ciphertext pairs from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">122.83</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">121.39</sup> .
MyMuseo

The integration of museum exhibits as a mobile application would give local users even better education since the information about the exhibit is at the palm of their hands. Combining them into one mobile application lessens the hassle of having to access each museum specifically. This would benefit museums that are part of one commission but are functioning individually depending on their location. To improve upon current mobile applications, the system would relegate all notification functions into one place: a dedicated mobile application with push notifications if the visitors wish to be notified firsthand. Moreover, it gives the user information on museum artifacts in an exhibit after scanning the QR code of said artifact in the form of text or audio.
Method of Reducing VM-exit Rate Generated by Handling Access to Virtualized Hardware

Each appeal to virtualized hardware, such as hard drive or net driver leads to VM-exit event. VM-exit is an event of switching between different contexts(or different processor states), and takes up a significant amount of time. There are plenty of methods for reducing the amount of VM-exit events or reducing the time needed for handling such events. But some of these methods require guest OS kernel modifications or do not optimise all of the possible VM-exit events. Designed method allows reduction of VM-exit events without any of the disadvantages mentioned above. This method includes 3 main stages: collecting statistics, analysing potentially reduced time and preventing unnecessary VM-entries. As a result of a conducted research it was found out that this method allows to reduce time consumption by VM-exits even on the scenarios when system doesn't try to access hardware too often.
The Support Vector Method for Solving the Classification Problem with Domain Constraints

SVM based classification is based on supervised learning. The procedure is based on the supposition that the minor error in distinguishing higher will be the depth across hyper planes that are parallel. The key concept of the mechanism put forth interprets the vectors that are actual into a multidimensional area and search for a distinguish the higher gap hyper plane.
System to Support Personalized Learning

The widespread introduction of information and communication technologies in the educational process at all levels has led to the need to create a model of professional training, taking into account new factors that affect the educational process. The aim of the study is to substantiate and develop a model for personalizing vocational training using modern information and communication technologies. Personalization of professional training in the framework of the study allows to organization of the learning process taking into account the most significant individual and unique personality traits of students. Using the Personal E-learning Place software package, students can personalize their educational trajectory by creating a plan for studying topics in one or more disciplines that would take into account their personal characteristics that affect professional education in the conditions of using modern information and communication technologies. The paper presents the results of an experimental study of the system to support personalized learning.
Should the Power of Block Chain Technology be Included in International Reserves Held by the Central Banks? Case Study Ripples as One of the Cryptocurrencies

Investigation on the role of the Monetary authority on the economic growth in Uganda from 1986-2016. Cryptocurrency urbanization has a theoretical measurement between Bitcoin and Ripples on Economic growth throw incorporations. We find that an exchange's transaction volume indicates whether or not it is likely to close. Less popular exchanges are more likely to be shut than popular ones. In the long-run analysis, the results confirm the direction of relationship from economic growth to fiscal consumption which defines causality from the service sector to economic growth.
Soft Decision Signal Detection of MIMO System Based on Deep Neural Network

This paper proposes a multiple-input multiple-output (MIMO) soft decision signal detection method for a timevarying communication system. In this algorithm, the training samples, including system channel state information and received data, are input to a deep neural network (DNN), and then we employ cross-entropy loss function and root mean square propagation (RMSProp) descent algorithm to offline train and optimize the parameters of the DNN. Besides, the output layer of the DNN uses the sigmoid function as the activation function, and the negative value of the input value of the sigmoid function is the log-likelihood ratio (LLR). In this way, we can obtain the LLR value via removing the sigmoid function during the online testing without the complicated process of calculating the LLR value. Combining the DNN with the soft decision technology improves signal detection performance. Simulation results show that the proposed algorithm is better than the MMSE algorithm and similar to ML algorithm.
R Markdown Workshop

This document outlines basic information about R Markdown and how to start using it in your own work. The document itself is produced using R Markdown within R Studio. If you would like to use this document as your R Markdown template, you should go ahead and check out the code in the following repository: https://osf.io/24dbj/
Oriented distance regularized level set evolution for image segmentation

Abstract The conventional distance regularized level set evolution method has been very popular in image segmentation, but usually it cannot converge to the desired boundary when there are multiple and unwanted boundaries in the image. By observation, the gradient direction between the target boundaries and the unwanted boundaries are usually different in one image. The gradient direction information of the boundaries can guide the orientation of the level set function evolution. In this study, the authors improved the conventional distance regularized level set evolution method, introduced new edge indicator functions and proposed an oriented distance regularized level set evolution method for image segmentation. The experiment results show the proposed method has a better segmentation result in images with multiple boundaries. Moreover, alternately selecting the edge indicator functions we proposed during the level set evolution can lead the zero level set contour to converge to the desired boundaries in complicated images.
Discussion on multiple UAVs cooperative mission planning in precision agriculture

Abstract In this paper, the cooperative task planning of agricultural plant protection multi-UAVs is studied. The related concepts of cooperative task allocation are discussed. Firstly, the content of cooperative task is summarized; secondly, many kinds of constraints, including individual constraints and cooperative constraints, are discussed and studied, the connotation of the problem is analyzed from different levels, different aircraft trajectory planning methods are studied, the mathematical model of practical engineering is established, and the results are analyzed. This research has certain guidance and practical reference significance for solving the application of UAV cooperative task allocation in the agricultural field.
Sim2Win: How simulation can help data centers to benefit from controlling their power profile

Abstract To support the grid and integrate renewables, demand response schemes reward the power flexibility of energy consumers. Data centers can profit from this by using power management techniques on all levels of data center architecture: infrastructure, hardware, workload, applications. Even though lately, demand response with data centers has been well researched, most works focus on just one or two techniques and one or two valorization options. This leaves data centers stranded that are not represented by the specific combinations of assumptions and techniques presented in research, and thus a huge potential remains barely touched. To address this challenge, the goal of the presented work is to provide data centers with a framework that can be flexibly instantiated by each data center to assess its individual demand response potential. To achieve this goal, this work presents Sim2Win, a data center simulation framework that can replay any set of different power management strategies in the face of any set of markets for power flexibility. A part of the framework is then instantiated and applied to the workload of a real high-performance data center. It uses workload shifting and frequency scaling in order to market their flexibility on the EPEX spot market and the secondary reserve market in Germany. The results show that by using the inherent flexibility of their power profile on the EPEX spot market the considered data center in 2014 could have earned savings of 7.3% of their power bill.
An Empirical Analysis of the Influencing Factors of Farmers’ Income Growth in the Middle and Lower Reaches of the Yangtze River Based on the Grey Correlation Model

With the deep implementation of the rural revitalization strategy, increasing farmers’ income has also become a key measure to solve the three rural issues. The transformation of rural production environment and ecological environment has brought opportunities and challenges for farmers to increase their income. Analysis of factors affecting agricultural income is of great significance for improving farmers’ lives and increasing farmers’ income. In this paper, the grey relational theory is used to analyze the correlation between indicators and farmers’ income. The internal relationship between natural disasters and agricultural income is studied, and the effective way to increase farmers’ income is proposed. The assessment results show that, first of all, first of all, the income of farmers’ family business in the study area is affected by the level of agricultural science and technology, land productivity and disaster mechanism. Secondly, the application of agricultural science and technology with machinery and fertilizer as the mains has the greatest impact on farmers’ income. Third, in the middle and lower reaches of the Yangtze River, the income of farmers’ family business is threatened by droughts and floods. Through the analysis of the grey correlation model, the intrinsic link between farmers’ income and indicators is more intuitive, and the difference in the importance of indicators is more obvious. According to the analysis, the government and farmers can better allocate limited resources, be alert to the threat of natural disasters, and increase farmers’ income.