Reviewerly/exHarmony huggingface dataset:
Matching Algorithm of Statistical Optimization Feature Based on Grid Method

The matching algorithm based on image feature points is widely used in image retrieval, target detection, identification and other image processing fields. Aiming at the problem that the feature matching algorithm has low recall rate, a statistical optimization feature based on grid of the normalized cross correlation function is proposed. The matching main direction difference and scale ratio are introduced to feature matching process, for comprehensively utilizing SIFT feature points' information, such as the main direction, scale and position constrains, to accelerate the solution of matching position constraint under the grid framework, which optimizes the feature matching results and improves the recall rate and comprehensive match performance. Firstly, the nearest neighbor matching feature points of each feature point in the original image are found in the target image, and the initial matching results are obtained. Secondly, the matching main direction difference is used to eliminate most mismatches of the initial matching. Thirdly, the matching images are meshed based on the matching scale ratio information, and the position information of the matching feature points distributed among the grids is gathered statistics. Finally, the normalized cross correlation function of each small grid in the original image is calculated to determine whether the matching in the small grid is correct, and the optimized feature matching results are obtained. The experimental results show that the matching accuracy of the new algorithm is similar to that of classical feature matching algorithms, meanwhile the matching recall rate is increased by more than 10%, and a better comprehensive matching performance is obtained.
Evaluating Performance of Web Applications in (Cloud) Virtualized Environments

Web applications usually involve a number of different software libraries and tools (usually referred to as frameworks) each carrying out specific task/s and generating the corresponding overhead. In this paper, we show how to evaluate and even find out several configuration performance characteristics by using virtualized environments which are now used in data centers and cloud environments. We use specific and simple web software architectures as proof of concept, and explain several experiments that show performance issues not always expected from a conceptual point of view. We also explain that adding software libraries and tools also generate performance analysis complexities. We also shown that as an application is shown to scale, the problems to identify performance details and bottlenecks also scale, and the performance analysis also requires deeper levels of details.
A 225-950mV 1.5Tbps/W Whirlpool Hashing Accelerator for Secure Automotive Platforms in 14nm CMOS

A Whirlpool cryptographic hashing hardware accelerator targeted for automotive security is fabricated in 14nm tri-gate CMOS with 640MHz operation and throughput of 33Gbps, measured at 750mV, 25°C. Pre-addition of key and message with delayed round computation, Galois Field arithmetic optimized Sbox and multiply-less Galois Field scaling with fused reduction results in 23% area reduction compared to a conventional LUT-based design. Fully-combinational Sbox implementation enables robust sub-threshold operation down to 225mV, with a peak energy efficiency of 1.5Tbps/W measured at 250mV.
Research on Airport Perimeter Monitoring Alarm Method of Based on Video Signal and Capacitance Disturbance Signals

There are some problems of the single perimeter alarm system, such as false positives, etc. As a result, a decision rule is proposed to fuse the alarm with the capacitor disturbance perimeter alarm and the video monitor. Firstly, the cross-correlation factor is introduced to improve the D-S evidence theory of fuzzy weighted support. Then, the characteristics of the capacitive disturbance perimeter alarm signal and the video signal and the corresponding Basic Probability Assignment (BPA) are extracted, and the cross-correlation between the evidences of the target recognition target are calculated. Finally, the cross-correlation is used to improve the D-S evidence theory of fuzzy weighted support.
Research on Event Recognition Based on Multi-feature Fusion

Event recognition as an important part of the event study, get more and more attention. To handle the problems of single feature selection and the weakling to a recognition model because of irrelevant and redundant features in a event recognition, an event recognition method uniting multi-features was proposed. This method first relies on LTP for processing the lexical features, syntactic features, semantic role features and semantic dependency and other characteristics of the raw data, and then use Relief algorithm to select features to construct the feature vector, and finally use the SVM classification to classify the feature vector to event recognition. The experimental results demonstrate that the recognition methods have achieved good results.
Software and Hardware in the Formation of the Personal Account of the Academic Staff

This paper presents the main aspects of the basic principles of developing an electronic personal account of a scientific and pedagogical person of a higher educational institution. Since each of these aspects can be a separate topic for further research, we should point out some problems and outline some possible solutions. The relevance of the paper is due to the necessity of effective realization of the existing potential of the academic staff of the University in conditions of increasing requirements to the quality and effectiveness of core activities due to the intensive use of the information technology.
A Semiautomated Human Resource Management System

This paper is an extension of our previous work where we have introduced a skill-based mathematical model of resource allocation. This paper expands our skill based approach by introducing adaptive skill sets for employees and a history-based initial evaluation strategy. For this purpose, the mathematical model is adjusted in order to modify skill vectors after a task allocation. In turn this enables estimations of the time to task completion based on employee history. We experimentally evaluate the impact of the skill adjustment on the project duration and cost in an agent-based simulation environment. The conclusion of the experiment is that taking into account the implicit skill gain of employees during their daily activity decreases projected costs and execution time significantly, which is this papers contribution to the state of the art. This approach is a good way to keep the teams skill sets automatically updated. The experiment is designed as an agent society simulation and through their interactions raw data is collected in order to calculate the performance measures. A scalability experiment is also presented showing slight (1%) decrease in project duration when tasks double while costs decrease between 7-32 %.
Enhanced Residual Dense Intrinsic Network for Intrinsic Image Decomposition

Intrinsic image decomposition is a challenging task, which aims at recovering intrinsic components from the observation. Hand-crafted priors have been widely used in traditional methods, yet with unsatisfactory performance of quality and runtime. Recently, network-based approaches have been greatly developed, but the physical imaging principle is ignored causing the multiplication of estimated components is hard to reconstruct the observation. To overcome these limitations, we develop an enhanced residual dense intrinsic network (ERDIN) for intrinsic decomposition. Specifically, we construct the basic module (i.e., enhanced residual dense block (ERDB)) to fully exploit the hierarchical features. The physical imaging principle is designed as the reconstruction loss to ensure the consistency between the observation and the multiplication of estimated components, which is of equal importance with the data loss. Extensive experimental results illustrate our excellent performance compared with other state-of-the-art methods.
QRD RLS Algorithm for Hand Gesture Recognition Applications

The paper is focused on an algorithmic technique for detection of hand presence and distance from a hand to device transmitting ultrasound signals. The described method is based on a QRD Recursive Least Squares (RLS) algorithm with double precision arithmetic and exponential forgetting (EF). Modelling of a hand detection problem is based on linear Finite Impulse Response (FIR) based regression models and performed using MATLAB tools. The modelled system comprises an environment model, a hand model and an identification block. A series of experiments testing both time-invariant and time-variant environment models and time-variant hand models show the importance of a correct choice of the EF factor. The experiments have proven the accuracy of the algorithm and the possibility to calculate a distance from the hand to the device. The final version of the algorithm is supposed to be implemented on the embedded Xilinx Zynq device equipped with a microphone and ultrasound transducers.
A Non-Adjacent Form (NAF) Based ECC for Scalar Multiplication that Assure Computation Reduction on Outsourcing

Sustaining privacy and securing the various data sources using lightweight cryptosystem is becoming essential in this digital world. Any sensitive information or resource container in the web that are exposed in the form of url must be preserved using suitable cryptographic techniques. One of the technique that belong to public key cryptography and truly identified for providing security towards digitial resource is Elliptical Curve Cryptography (ECC). ECC is one of the small key sized and fast computation cryptographic model that consumes less power, memory and bandwidth to ensure equivalent security than already existing one. Digital world engaged in on-line data security is the most pressing issues to transaction, have to trust the data disclosure in storage, ensure modification immunity in transit and assures identity among the communicating parties. Hence, the CIA packed computing paradigm that impact towards confidentiality, integrity and authentication is an urgent need. The first one and most important security traits is the confidentiality. This can be achieved by encryption operation. In ECC, encryption and decryption operation is carried using Elliptical curve arithmetic mechanism. This mechanism is based on point multiplication that is performed using repeated point addition and point doubling. The proposed alternative efficient method for point multiplication is binary form or NAF (Non-Adjacent Form). The computation performance with NAF preprocessed and on-the-fly approach ensures optimisation than the binary form of scalar multiplication.
Intelligent Design for Image Search Engine

With the explosive growth of the Internet, Web search technology marked by keywords has acquired a great success in the tremendous information retrieval. A search engine can gather new pages for its universal ways. Content-based image search is an urgent problem as the next generation of search engines. This paper firstly analyzes and discusses its main features and key techniques, and then presents a designs method of image search engine based on image content. Next, we give a detailed elaboration about the main function modules and introduce the testing process. By using data filtering technology and ELF Hash algorithms, the search time-consume is less than 1 s. Using data-filtering technology and the ELF Hash algorithm, our search engine reaches a good effect. The testing results finally prove the overall performance of our image search engine is excellent and achieves the desired design requirements. After being modified, our search engine could be applied to the intelligent wearable devices.
Data Distribution Based Weighted Extreme Learning Machine

As an effective learning approach, extreme learning machine (ELM) has been applied to multiple fields with its faster learning speed and better generalization performance. To solve the classification problem especially the data with imbalanced class distribution, some solutions are proposed based on cost sensitive ELM. However, the existing methods only consider the effect of the misclassified sample on the class to which it belongs but ignoring the overall loss. In this paper, we propose a new weighting scheme used in ELM, data distribution based weighted extreme learning machine (D-WELM) for binary and multiclass classification problems with imbalanced data distributions. It is noteworthy that the proposed method maintains the advantages from original ELM. D-WELM considers not only the effect of sample sizes in each class, but also class distribution. Meanwhile, this work takes overall loss into account. Experimental results show that D-WELM can achieve better performance for classification problems with imbalanced data distributions than original ELM, weighted ELM(WELM) and class-specific cost regulation ELM (CCR-ELM). In addition, D-WELM with kernel can also get good performance.
Collaboration of Virtual Reality in Our Professional Life

Virtual Reality has gone forward to grow into one of the substantial measure of almost every field related to engineering, entertainment, industry, medicine and many more. Virtual Reality has been an overnight sensation and proving to be very useful in ways which had never been imagined. Endless applications have been developed for VR.Implementation of virtual reality bring about to get the combination of hardware, software and sensory synchronicity just right it attains something known as a sense of presence. The objective of this paper is to motivate the use of emerging technology Virtual Meeting Room where user will be given an official environment of a meeting room. The subject really feels like they are present in that environment, where they can see other persons sitting in front of them, carry out meetings through voice and can also share images from gallery on a virtual projection screen.
Generating Rhythmic Graphic Patterns and Paul Klee’s Concept of the Active Line

Abstract Most studies that investigate auditory influence on visual design scenarios disregard the inherent temporal nature of sonic entities. This article addresses problems posed in the interaction of multiple data types—e.g. the visual representation of musical data—and the ways in which one type of information may shape another in simultaneous generations. The author’s proposed method for analogy takes advantage of the structural organization behind several rhythmic sequences to generate visual geometric patterns in which the temporal characteristics of the former affect the spatial features of the latter. The structural analysis of auditory rhythms shows that similar cyclical organizations also exist in their geometric correspondents.
Fast Calculation Method of Video Saliency Based on Temporal and Spatial Edge-Preserving Filter

How to detect video saliency accurately is an very challenging problem in computer vision, and has gained great research and application values. A fast video saliency detection method, which based on spatio-temporal edge-preserving filter, was proposed in this paper. Firstly, object appearance features and motion features were extracted in video frames. Secondly, a new spatio-temporal edge-preserving filter was constructed by combining object temporal information in video. Then, the video saliency detection is formulated as an energy minimization problem, which fuses the appearance and motion features, and stand out salient object in video uniformly by using the spatio-temporal edge-preserving filter. Finally, extensive experiments, including comparisons analysis with three video saliency detection approaches and components analysis of the proposed approach, on three datasets suggest that the proposed approach nearly outperforms other approaches in both accuracy and efficiency.
Deep Random Splines for Point Process Intensity Estimation.

Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity). Here we propose Deep Random Splines, a flexible class of random functions obtained by transforming Gaussian noise through a deep neural network whose output are the parameters of a spline. Unlike Gaussian processes, Deep Random Splines allow us to readily enforce shape constraints while inheriting the richness and tractability of deep generative models. We also present an observational model for point process data which uses Deep Random Splines to model the intensity function of each point process and apply it to neural population data to obtain a low-dimensional representation of spiking activity. Inference is performed via a variational autoencoder that uses a novel recurrent encoder architecture that can handle multiple point processes as input. We use a newly collected dataset where a primate completes a pedaling task, and observe better dimensionality reduction with our model than with competing alternatives.
Review of Convolutional Neural Network in Video Classification

Convolutional Neural Network (CNN), which is a class of deep, Artificial Neural Networks, is most commonly used for image recognition. CNNs have been established as a powerful class for image recognition. But, there hasn't been much research done on the usage of CNNs for video classification. There is very little knowledge on how different CNNs perform for video classification. So, the challenge here is to recognise which CNN architecture is the best for video recognition. The contribution here is to list down all the different CNN architecture, which were used to classify videos, and compare them with each other based on their performance. The architectures are trained on either the UFC-101 or the sports 1M dataset and their performance is evaluated based on the same. The different groups of CNN architectures which were used for the purpose of video classification are:
EarlGrey: iOS UI Automation Testing Tool

In this paper, we outline the challenges faced in scalable UI testing of applications on the iOS platform. We analyze how different existing UI testing tools have provided solutions for them and highlight EarlGrey, Google's in-house multi-process UI Automation testing tool. We delve into the technical aspects of EarlGrey in regards to these challenges.
Sun finders based on a pinhole camera

Sun tracking is the most diffused strategy to enhance the performance of solar systems based on the concentration of sunlight. The proposed solar finders were developed for Concentrating Photo Voltaic systems, which require pointing precision is of the order of 0.01 angular degrees. These sun trackers are simple, effective, adaptable and low-cost. The sun finders were experimented in laboratory, under controlled and reproducible conditions, assessing their main optical features. The solar finder could be mainly characterized by angular accuracy and field of view of the detection. The field of view can be customized by varying the geometry of the sun finder allowing to use it in many applications.
Risk and Security of Information Systems in the Portuguese Financial Sector

This work follows the need of safety standards, update ISO27002:2013, in the major central banks of several European countries. This need has been studied by establishing a focus group that integrated European experts from major central banks. The analysis carried out was supported in the current methodology of information risk management, used by central banks in the safety management of information systems. This methodology is used to analyze and evaluate the adequacy of practices to risk management in the financial activity. The main objective was to present a proposal, sufficiently comprehensive and consistent, to a new risk management process of Information Systems within the European System of Central Banks. And a definition of a practical guide to risk management throughout the different stages of the Information Systems Life Cycle. The proposed model provides a higher degree of protection systems, technologies and information, especially in Central Banks, taking as reference the Portuguese Central Bank.
Minimization of the electricity bill of Brazilian consumers with PV system through the optimization of contracting demand and the orientation of photovoltaic panels

The objective of this paper is to carry out a study to reduce costs with the electric energy bill of the Federal Institute of Goiás - Campus Goiânia, with grid-connected PV system installed, by optimizing the contracting of active power demand and the orientation of the photovoltaic panels. The study is carried out through a case study and takes into account the effects of the Brazilian tariff structure, the local climatic conditions and the load curve profile of the consumer unit in question. The results have shown that, with the adoption of the blue tariff modality and using the optimized parameters, it is possible to reduce electricity costs in the analysed period by 12.68%. It is concluded that in order to obtain a greater economic benefit from the installation of a grid-connected PV system, one can not only focus on the maximization of energy production, it is necessary to take into account other factors such as the tariff structure, local climatic conditions and load curve.
Image Denoising via Multiple Images Nonlocally Means and Residual Tensor Decomposition

The nonlocal means and iterate filtering techniques have attracted much research effort due to their superior performances. In this paper, the two approaches are combined into a framework to perform denoising based on residual tensor decomposition in loop of iteration. The search of accurate similar patches is essential for image denoising, to reconstruct the damaged part, we utilize multiple images nonlocal means method to exploit the image nonlocal self-similarity and obtain accurate weight, thus eliminate the interference of unsimilar patches. Although the degraded or lost slight structure of the image due to imperfect denoising methods, we propose the use of iterating residual image to compensate the sharpness of image texture via patch-based tensor decomposition, which can describe the intrinsic geometrical structure of there similar data. We use standard test images and multi-frame video test sequences to illustrate that the proposed denoising algorithm outperforms the leading algorithms such as weighted nuclear norm minimization (WNNM), BM3D and NCSR in terms of the quantitative and perceptual evaluation.
Generation of Action Recognition Training Data Through Rotoscoping and Augmentation of Synthetic Animations

In this paper, we present a method to synthetically generate the training material needed by machine learning algorithms to perform human action recognition from 2D videos. As a baseline pipeline, we consider a 2D video stream passing through a skeleton extractor (OpenPose), whose 2D joint coordinates are analyzed by a random forest. Such a pipeline is trained and tested using real live videos. As an alternative approach, we propose to train the random forest using automatically generated 3D synthetic videos. For each action, given a single reference live video, we edit a 3D animation (in Blender) using the rotoscoping technique. This prior animation is then used to produce a full training set of synthetic videos via perturbation of the original animation curves. Our tests, performed on live videos, show that our alternative pipeline leads to comparable accuracy, with the advantage of drastically reducing both the human effort and the computing power needed to produce the live training material.