Reviewerly/exHarmony huggingface dataset:
A Pedestrian Tracking Method Based on Adaboost and Feature Fusion

In view of the existing time-consuming and identity exchange problems in the pedestrian tracking process. A multi feature fusion tracking method based on adaboost detection is proposed. Firstly, the multi-scale wavelet transform is used to extract the foreground regions of the moving target, and the Haar-like feature is used in the foreground region to detect human heads. Then the particle filter tracking algorithm is introduced by combining color and gradient direction histogram to track the detected heads. Finally, the pedestrians are tracked through the association matrix. The experimental results show that the proposed method has a good tracking result when there are the cross motion and short occlusion between the pedestrians.
GVRf and Blender: A Path for Android Apps and Games Development

Virtual reality on mobile devices has been used in many areas such as entertainment, health care, training and simulations, although it still has limitations in terms of graphics quality and application performance. The ease of portable devices that can be used for virtual experiences in other “worlds” provides its fascinations, however, this technology can not match the virtual reality for personal computers, with high levels of quality in its graphics, with multiple features and high superior processing power while offering simplicity in use and being cheaper than its competitors on other platforms, such as personal computers and video game consoles. Thus, the purpose of this essay is to present a solution that GVRf, together with Blender, the GVRf Exporter, that makes the process of developing applications for VR technologies more optimized.
Dynamic Load Balancing Using Actual Workload Traces Based on Central Processing Unit Temperatures

Abstract With the rapid growth in demand for distributed computing, data centers are a critical physical component of the “cloud.” Recent studies show that the energy consumption of data centers for both cooling and computing keeps increasing, and the growth in server power densities makes it ever more challenging to keep the servers below their maximum operating temperature. This paper presents a new dynamic load-balancing approach based on individual server central processing unit (CPU) temperatures. In this approach, a load balancer assigns a task in real time to a server based on the objective to keep the CPU temperatures below a maximum value. Experimental studies are conducted in a single rack based on production workload traces of Google clusters. This study also compares the performance of this method with two other load balancing approaches, Round Robin, and a CPU utilization-based method in terms of temperature distributions, local fan rotation speeds, system loads, and server processing times. Furthermore, we investigate how the effect of the proposed load balancing changes with different assumed applications run on servers. The results indicate that this new method can more effectively reduce both server CPU temperatures and local fan rotation speed in a rack especially for the most of web applications.
Learning Algorithm for Fractional Dynamical Systems with Autocorrelated Errors-in-Variables

In this paper, the stochastic gradient algorithm for learning fractional-order dynamical systems with noisy input and output is proposed. The proposed algorithm allows estimating the parameters of fractional dynamic systems if the input and output noises are color. The proposed algorithm does not require knowledge of the noise distribution laws. The simulation results demonstrate the high accuracy of the proposed learning algorithm in comparison with the least squares learning algorithm.
A Real-time Intra-pulse Recognition Method of Radar Signals Based on Restricted Boltzmann Machines

Abstract Intra-pulse features extraction of radar is of great research significance in electronic reconnaissance technology. With continuous development and equipment of the new system radar, the characteristics of the modern electromagnetic environment could be summarized as density, complexity and variability, which makes the traditional signal identification methods difficult to achieve the desired effect and determines the recognition must be real-time. Therefore, this paper is devoted to the study a fast method and performance analysis in extracting intra-pulse features of radar emitters in complex electromagnetic environment, especially in low SNR environment, and the experiment process could achieve real-time recognition.
Bridging the gap between software architecture and business model development: A literature study

The software architecture plan describes the high-level structure and logic of a software system. The architectural plan acts as a constitution and dictates the fundamental principles of the system; therefore, the plan also eventually determines which kinds of business models the software system can support. In the modern mercury business, there is need for experimentation in business model and flexibility in architecture. This paper uses a systematic literature review method to collect primary studies from the extant literature addressing business models and software architectures. The aim is to summarize the current knowledge. The selected primary studies (n=10) are qualitatively analyses and synthesized. The results show that the area remain mostly unaddressed and there is need to develop new methods to support flexible architecture design, tools and development methods.
Formulation of Scientific Concepts as a Component of the Research Competence of Future Teachers

The article presents the authors’ method of teaching undergraduate and Master’s degree students majoring in education to formulate scientific concepts including the key concepts that constitute the theoretical basis of any pedagogical research. We regard this skill as one of the components of the research competence of a modern teacher. In accordance with his labor functions, he (she) has to lead the research and project activities of students in the system of primary, basic and secondary general education. The article reveals the content of research competence which includes two groups of research skills: work with literary sources and actual work with text of a research. The authors consider three types of tasks for the formulation of scientific concepts of different levels of complexity, describe an algorithm for performing by students tasks of each type. In addition, the article reveals the pedagogical significance of teacher’s ability to formulate scientific concepts and describes the positive results of mastering this skill by undergraduates and Master’s degree students in education.
Performance Evaluation of Hourly Solar Radiation Models on Inclined Surfaces in Longdong Area, China

Hourly global irradiations on tilted planes are required in various engineering calculations for solar systems which must be determined by converting the solar radiation intensities measured on a horizontal surface to the incident on the tilted surface of interest. There exist a large number of models designed to perform such a conversion. This paper presents the performance of five arithmetic models used to estimate solar irradiance on inclined surfaces. Utilizing solar radiation data from June to October measured in Longdong area of Gansu province, Model performance is assessed by an inter-comparison between the calculated and measured solar global radiation on a south-oriented surface tilted at 25°. Three statistical parameters (NMBE, NRMSE and R2) have been used to confirm the calculated values. The results show that the accuracy of the models is related to the sky conditions. Liu & Jordan model shows the best calculated performance under clear sky conditions, and Klucher model perform best under overcast and cloudy conditions. The other models included Perez, Hay and Skartveit & Olseth display an intermediary performance under all sky conditions. In order to estimate the tilt angle simply, two simple approximate equations (φ-15° model and Soulayman model) based on geographical latitude and solar declination are suggested for predicting daily optimum tilt angle in summertime.
Dynamic Machine Translation of Croatian Academic Web Sites

Web sites internationalization of Croatian web sites is a problem remained from the beginnings of web. In most cases, only some general information that are rarely changed are available in foreign languages, and the reason for that is the lack of human resources to make translations, as well as the need of software adaptations to support multilingualism. This paper analyses some lexical characteristics of the selected Croatian academic web sites, compared with some other, like daily newspapers and culinary web sites in order to find out their suitability for machine translation. The findings are compared to the actual state of our dynamic translation system for web contents, named as Domain Translator.
Motion Cue Based Instance-level Moving Object Detection

This paper studies the moving object detection, i.e., analyzing the amount, position and size of the moving objects in instance-level, which is meaningful for many computer vision problems. However, the existing methods are still not satisfying in accuracy, portability and speed. In this paper, we propose a novel framework which detects moving objects by analysis the consistency of the moving foreground. Instead of directly performing cluster algorithms on the moving foregound, we take two stages: analyzing the composition according to the local density of the moving foreground points and locating the targets by regressing some anchors. In this way, the proposed method doesn't need any training processes and can be efficiently performed to detect moving objects with arbitrary classes. Besides, we create our own publicly available dataset PDMOD with sufficient data, general challenges and convictive evaluation protocols to fill the scarcity of the evaluational datasets.
Personalized Recommendation via Suppressing by Users and Items

Abstract An efficient recommendation system is fundamental to solve the problem of information overload in modern society. In physical dynamics, material diffusion based on binary networks has a wide range of applications in recommendation systems. However, material diffusion has the disadvantage of excessive diffusion and excessive attention to high-prevalence items. Most of the previous studies focused on reducing the popularity of the item. This paper suppresses the excessive diffusion between people and objects by simultaneously adjusting popular users and items. It evaluates the algorithm through two real datasets (Movielens and Netflix), which proves the method is superior to other algorithms in accuracy, diversity and novelty.
Invariance of Comparisons—Separation of Person and Item Parameters

In the Rasch model, the probability that a person answers one of two dichotomous items correctly, on the condition that only one is answered correctly and the other incorrectly, depends only on the relative difficulties $$ \delta_{1} $$ and $$ \delta_{2} $$ of the items and is independent of the proficiency $$ \beta $$ of the person. Because the Rasch model implies statistical independenceStatistical independence of responses, the probability of answering both items correctly equals the product of the probabilities of answering the separate items correctly. The measurementMeasurement requirement of invariance of comparisonsInvariance of comparisons is met in the Rasch model; the comparison of the difficulties between two items can be made independently of the proficiency of any person, and the comparison between persons can be made independently of the difficulties of the items. This invariance is a property of the Rasch model and instruments must be constructed to achieve this invariance in assessments—they are not present just because assessments are analysed with the Rasch model.
Quality of Experience Comparison Between Binocular and Monocular Augmented Reality Display Under Various Occlusion Conditions for Manipulation Tasks with Virtual Instructions

Using optical head-mounted display (HMD) devices, users can see both real world and Augmented Reality (AR) content simultaneously. AR content can be displayed to both eyes (binocular) or in one eye (monocular). For a binocular display, users benefit from (a) using both eyes to focus on the same content, and (b) having depth perception. However, the vergence-accommodation conflict can negatively impact the time and accuracy of fusing the views. For a monocular display, users benefit from (a) easy and quick focal depth switches between the virtual content and the physical world, and (b) having a larger virtual information overlay in one eye while also seeing the real-world in the other eye. In this study, users performed manual tasks by following real-time, step-by-step instructions for 2D tasks (drawing cartoon characters) and 3D tasks (assembling plastic bricks). The instructions were presented on an AR HMD through various occlusion conditions, after which we compared the users’ Quality of Experience (QoE) feedback. Our investigation found that users commonly chose to separate the AR content display from the physical working area, placing them adjacent in the field of view and shifting their attention between them. The overwhelming majority of users preferred the binocular display. For a monocular display, users need to balance the benefits of depth perception (for 3D tasks) and the annoyance of binocular rivalry. While most users can tolerate binocular rivalry, a significant subset have a low tolerance for binocular rivalry and prefer to mask or close the eye without the virtual display.
Exploring Multiple Embedded Features on Event Extraction

Abstract In recent years, the neural network method can automatically learn effectively features. Unlike traditional discrete features, neural network features are mostly continuous features and can be automatically combined to build higher-level features. The efficiency of the features has been proven in numerous tasks in natural language processing and has led to breakthroughs. In this paper, we propose a event extraction system based on combination of multiple embedded features. Our work is mainly based on the three aspects: (1) traditional pipeline systems have serious error propagation problems; (2) there are several different event descriptions in the text; (3) representation learning can provide rich semantic and syntactic representation. As a result, we achieve competitive performance, specifically, F1-measure of 60.25 in event extraction. Meanwhile, evaluation results point out some shortcomings that need to be addressed in future work.
Hardware Root-of-Trust Based Integrity for Shared Library Function Pointers in Embedded Systems

For resource constrained embedded devices security is often an afterthought during design time. As new applications and devices are developed, they are handling more and more sensitive information. It is imperative to come up with effective solutions that can be supported by these constrained systems. In this work we propose a solution to mitigate shared library function attacks by implementing a hardware root of trust from which to store and retrieve function pointers. We offer two system variations which provide designers with flexibility to choose a lighter or a more robust version. Our work renders GOT overwrite attacks ineffective since it isolates and protects shared library function pointers.
Study on Geography Information OLAP and Data Mining System Based On Hadoop

The 21st Century is the century of ocean. Every coastal country makes important development strategies which are connected with national geography rights protection, geography economy development and geography ecological environment protection. As a part of "Digital Ocean", geography information OLAP and data mining can find the geography laws and knowledge hidden in data, which makes great sense to geography environment protection, geography meteorological observation, geography meteorological prediction, geography disaster prevention and geography mitigation.
Structured Output Prediction Using Privileged Information

We study the problem of structured output prediction. Some methods such as structured output support vector machines (SSVM) and conditional random fields (CRFs) are state-of-the-art in dealing with the structured data. However, these classical methods have some limits in scalability because of high memory requirements and slow training speed. Recently, the method joint kernel support estimation (JKSE) has been proposed based on one-class SVM which can be trained efficiently. However, JKSE is not as powerful as those classical methods from the point of prediction performance. To improve the performance of JKSE, we introduce privileged information into it. Learning using privileged information (LUPI) is an advanced machine learning paradigm by taking advantage of some elements of human teaching that are only available at the training stage, not at testing. Motivated by the LUPI, we propose three new models based on JKSE by considering three forms of privileged information. The resulting optimization problems are convex quadratic programming that can be easily solved. Our new models not only persist the advantage of JKSE but also improve its performance. The experimental results show the superiority of new models over the JKSE when solving object detection and multi-class classification problems.
Improved Parameter Uniform Priors in Bayesian Network Structure Learning

Bayesian Dirichlet equivalent uniform score (BDeu) is often used in Bayesian structure learning. But it does not work well when data size is sparse because the equivalence of the prior parameter distribution isn't suit for the specific data set. To break the rules of uniform and equivalent, the paper proposes the Bayesian Dirichlet Sparse score (BDs) which change distribution of prior parameter through the all zero items in the sparse data. The circulation principle of information entropy and simulations are used to explain the reason why BDs is better than BDeu when data size is sparse. In the experiments, we also verify the stability of BDs when hyperparameters change.
LCD MDS Codes From Cyclic Codes

Linear codes with complementary-duals (LCD) have many applications in cryptography, communication systems and data storage. A q-ary linear code C is called LCD if C∩C⊥ = {0} holds. Using method of coset theory, we deduce a characterization of LCD cyclic code by its defining set. Then two families of q-ary MDS cyclic LCD codes with lengthes n| (q+1) and n| (q-1) are determined and many new classes of LCD MDS codes are gained.
An Improved Negatively Correlated Search Inspired by Particle Swarm Optimization

Abstract Negatively correlated search (NCS) is a latest intelligence algorithm which maintains multiple individual search processes in parallel and models the behavior of individual search processes using probability distributions. In this paper, an improved NCS inspired by particle swarm optimization (PSO) is proposed, namely PSO-NCS. This algorithm introduces the global best solution (gbest) and personal best solution (pbest) from PSO to calculate the Bhattacharyya Distance (BD) of individuals in the population, which greatly reduces the computational time. Experimental results on six 100 dimensional functions and 10 CEC2005 benchmark functions show that the proposed PSO-NCS has outstanding capability in solving the optimization problems and outperforms the conventional NCS and other methods on most of the test functions. The computational time has been greatly shortened, which saves about 65% in average compared to NCS.
Measuring the Impact of E-Learning Platforms on Information Security Awareness

Humans play a central role in information security. The behavior of workers at their workplace affects the confidentiality, integrity, and availability of sensitive corporate information. In addition, attackers exploit the “human factor” as a weak point with techniques such as phishing, malware, and social engineering. Exploiting the lack of awareness is often an easy task with minimal risk. To make employees aware of their important role, companies typically carry out security awareness campaigns. Our university created an e-Learning Platform (eLP) to support our awareness campaigns. In order to determine the success, the effectiveness and the impact of such an awareness campaign, suitable measurement methods are needed. A common approach to measure the success of eLPs is to run surveys and questionnaires with the learners. Since the manual evaluation of those surveys and questionnaires is a time-consuming task, we are researching how a possible automation can be achieved. Moreover, the effectiveness is often evaluated through quizzes or knowledge tests. Since knowledge by itself does not improve the behavior of people, the compliant-behavior has to be measured, too. We derived metrics for success and effectiveness but recognized that success can hardly be measured automatically. To reduce the manual effort we decided to only measure the effectiveness automatically. Therefore, we are measuring the behavior and determine if the security-compliance has increased.
A Congestion Control Methodology with Probability Routing Based on MNL for Datacenter Network

The burst traffic is one of the most important reasons that cause congestion in the data centers. One way to reduce network congestion is to reroute elephant flows on new paths. Most of the current researches focus on the scheme of detecting elephant flows and a few such as Offline Increasing First Fit (OIFF) considers the routing algorithm, which chooses the path with the max remaining bandwidth when scheduling elephant flows. OIFF may relieve the network congestion, but it may also put several elephant flows on the same links which results in new congestions. In this paper, we present Max Probability Fit Algorithm (MPF), a new routing methodology which is based on multinomial logit model (MNL). MPF chooses the rerouting path for each flow with probability, and it’s less likely to distribute the traffic to the same links. The experiment shows that MPF can increase performance of throughput by 3.6% and bring down packet loss rate by 26.84% over OIFF.
Identifying Images with Ladders Using Deep CNN Transfer Learning

Deep Convolutional Neural Networks (CNNs) as well as transfer learning using their pre-trained models often find applications in image classification tasks. In this paper, we explore the utilization of pre-trained CNNs for identifying images containing ladders. We target a particular use case, where an insurance firm, in order to decide the price for workers’ compensation insurance for its client companies, would like to assess the risk involved in their workplace environments. For this, the workplace images provided by the client companies can be utilized and the presence of ladders in such images can be considered as a workplace hazard and therefore an indicator of risk. To this end, we explore the utilization of pre-trained CNN models: VGG-16 and VGG-19, to extract features from images in a training set, that in turn are used to train a binary classifier (classifying an image as ladder and no ladder). The trained binary classifier can then be used for future predictions. Moreover, we explore the effect of including standard image augmentation techniques to enrich the training set. We also explore improving classification predictions by combining predictions generated by two individual binary classifiers that utilize features obtained from pre-trained VGG-16 and VGG-19 models. Our experimental results compare accuracies of classifiers that utilize features obtained using pre-trained VGG-16 and VGG-19 models. Furthermore, we analyze improvements in accuracies achieved on using image augmentation techniques as well as on combining predictions from VGG-16 and VGG-19 transfer learning based binary classifiers.