Reviewerly/exHarmony huggingface dataset:
Automatic Collection and Management System of Computer Room Assets based on Convolutional Neural Networks

For solving the problems existing in assets information collection and management in offline computer room, such as long time consuming, low reliability, high cost and difficult to manage assets in different places, convolution neural network was introduced into the field of device information automatic collection in this paper. The device classifier model based on convolution neural network was put forward and applied to the automatic collection and storage system. In addition, the DIC application system used for the mobile side and the CRMS system for the server side were implemented by using the architecture that separated the server and the client. The results reveal that this system has great potential to improve the way of information collection and management of device in the room, thus gathering info more effective.
Recaptured image detection based on convolutional neural networks with local binary patterns coding

With the great development of image display technology and the widespread use of various image acquisition device, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes relative convenient. These recaptured images pose serious threats on image forensic technologies and bio-authentication systems. In order to prevent the security loophole of image recapture attack, inspired by the effectiveness of LBP (local binary pattern) on recaptured image detection and the satisfactory performance of deep learning techniques on many image forensics tasks, we propose a recaptured image detection method based on convolutional neural networks with local binary patterns coding. The LBP coded maps are extracted as the input of the proposed convolutional neural networks architecture. Extensive experiments on two public high-quality recaptured image databases under two different scenarios demonstrate the superior of our designed method when compared with the state-of-the-art approaches.
Overview of Knowledge Mapping Construction Technology

With the advent of the era of big data, knowledge engineering has received extensive attention. How to extract useful knowledge from massive data is the key to big data analysis. Knowledge map technology provides a means to extract structured knowledge from massive texts and images, and thus has broad application prospects. This paper first discusses the significance of knowledge mapping research. Secondly, it introduces the key technologies of knowledge map construction, including entity relationship recognition technology, knowledge fusion technology, entity link technology and knowledge reasoning technology. Then give an introduction to the existing open knowledge map dataset. Finally, the application case of knowledge map in intelligence analysis is given.
Sequential Pattern Learning via Kernel Alignment

As a branch of data analysis, pattern alignment has received much attentions in recent years. More specifically, it learns to find intrinsic bridge between different domains and make data handling be transferrable for efficient recognition. In this work, an unsupervised feature learning method is proposed to meet demand on pattern alignment. Compared with existing methods, more efficiency can be reached owing to scalable learning, which is competent to tackle large-scale data for kernel alignment. Experimental results show proposed method can give comparable performance among the state-of-the-art methods.
A Deep Learning Based Multi-color Space Approach for Pedestrian Attribute Recognition

Pedestrian behavior understanding and identification in surveillance scenarios has attraction a tremendous amount of attention over the past many years. An integral part of this problem involves identifying various human visual attributes in the scene. Over the years, researcher have proposed various solutions and have explored various features. However, they have focused on either engineered features or simple RGB images. In this paper, we explore the problem of crowd at- tribute recognition using RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and L*a*b* color models and propose a 3-branch Siamese network to solve the problem. We present a unique approach of using these three color models and fine- tune a pre-trained VGG-19 network for our task. We perform extensive experimentation on the most challenging public PETA dataset, which is by far the largest and the most diverse dataset of its kind. We show an improvement over the state of the art work.
Network Performance of PingER data with respect to the growth of the Telecom industry in India

This paper aims towards analyzing the changes in the Internet Performance in India over a span of 3 years by using the clustering algorithm on PingER data. The paper analyzed the enhanced performance of the internet after major changes in the Telecom industry in India in the year 2016 and some insights into the level of improvement. PingER which is short for Ping End-to-end Reporting is a project started with an aim to monitor end-to-end performance of between Internet hosts by SLAC National Accelerator Laboratory, Stanford, California. And over the last decade or so they have collected a huge amount of data which is stored as space separated flat files along with sophisticated methods in order to enable searching for data fast.
Decision Trees in Data Mining

In this chapter, I explain what happened to make data become so much more available and where Big Data emerged from. I will show what can be searched for in these data and what tools are needed for mining the data. The differences and similarities between a classification and regression are described. Then, the focus is moved to decision trees and classical methods in their induction, but the presentation should not be treated as an extensive overview of this wide area of research. The most important information about decision trees is provided, and this subjective selection is intended to be helpful in understanding the proposed global approach. Finally, the related works on applying evolutionary computation in decision trees are studied.
Towards a Lightweight Approach for the Evaluation of Requirements Engineering Impact on Other IT Project Areas

Requirements Engineering (RE) is recognized as one of the most important, but difficult areas of software engineering, with a significant impact on other areas of the IT project and its final outcome. The empirical studies investigating this impact are hard to conduct, mainly due to large effort required. It is thus difficult for researchers and even more for industry practitioners to make evidence-based evaluations, how decisions about RE (e.g. RE process improvements, RE techniques selection) translate into requirements quality and influence other project areas. We propose an idea of a lightweight approach, utilizing the popular tools adopted by numerous software companies, to enable such evaluation without an excessive effort. The proposal is illustrated with a pilot study, where the data from 6 industrial projects from a single organizationJarzębowicz, Aleksander was analyzed and 3 metrics regarding requirements quality, rework effort and testing were used to demonstrate the impactPoniatowska, Katarzyna of different RE techniques applied among considered projects. We also discuss the factors important to enabling adoption of the proposed approach.
Decoupling Semantic Context and Color Correlation with Multi-class Cross Branch Regularization

This paper presents a novel design methodology for architecting a light-weight and faster DNN architecture for vision applications. The effectiveness of the architecture is demonstrated on Color-Constancy (Auto White Balance) use case, an inherent block in camera and imaging pipelines. Specifically, we present a multi-branch architecture that disassembles the contextual featuresand color properties from an image, and later combines them to predict a global property (e.g. Global Illumination). We also propose an implicit regularization technique by designing a cross-branch regularization block that enables the network to retain high generalization accuracy. With a conservative use of best computational operators, the proposed architecture achieves state-of-the-art accuracy with 30X less model parameters and 70X faster inference time for color constancy. It is also demonstrated that the proposed architecture achieves similar efficiency in case of other low-level image restoration problems such as Low-Light enhancement.
Application of vision systems to the navigation of mobile robots using markers

The paper presents a vision system for navigation a mobile robot using markers. This is done by identifying the location of the object in space by means of a marker that has been detected. The first stage of the algorithm is calibration of the camera using a checker board and read the calibration parameters. The next step is to detect the marker and then determine its actual location. Parameters of detected objects can be used to control vehicle movement. The image processing algorithms included in the OpenCV library were used.
Robot Brush-Writing System of Chinese Calligraphy Characters

In this paper, robot calligraphy systems is developed to write Chinese brush characters dynamically. The BBOD algorithm of stroke extraction is optimized by adding a rule for merging stroke. the results show that the accuracy of stroke extraction is 95% for the Simple-style font, 92% for Kai-style font and 90% for Yan-style font. A model of calligraphy features is established, which includes not only the spatial structure features of Chinese characters, but also the dynamic writing features of Chinese characters. In the period of trajectory planning, a rule of Chinese brush characters applies to optimize the trajectory of stroke, and there are two steps of motion planning, cubic B-spline algorithm is used to plan the Cartesian path, then S curve algorithm is used to plan the joint space trajectory, in order to control manipulator smoothly and stably. To verify the proposed method, three types of experiments are conducted and the developed systems achieved good results in Chinese brush calligraphy reproducing.
Neural Network-based Prediction of Visual Quality for Noisy Images

Noise is one of the main factors that degrade image visual quality. Assessment of perceptual quality of noisy images is of critical importance for imaging systems and image processing application. In this paper, we propose a framework that employs multi-layer neural networks to predict visual quality for noisy images. In particular, the proposed method utilizes quite simple features extracted in discrete cosine transform (DCT) domain for predicting values of full-reference visual quality metrics for noisy images. The neural networks are trained and tested on noisy images corrupted by additive white Gaussian noise (AWGN). The experimental results show that prediction is quite accurate and fast. Source codes of the proposed method and datasets will be available at https://github.com/ViA-RiVaL/Blind-Noisy-Image-Visual-Quality-Prediction.
WebXR

WebXR is an experimental JavaScript API that provides interfaces to cross reality hardware, allowing developers to build compelling and comfortable experiences on the web. The implementation of virtual reality (VR) in a web browser, like all other things that adhere to web standards, best practices, and an open technology stack, benefit from having a strong distribution and great accessibility. The web can become the preferred way of distributing VR content; the same way it became the preferred way to distribute video content. It is compatible with many devices, platforms, and its core technologies support interactivity, communication, and boost sharing in unprecedented ways for VR. GuriVR is a free, open source project created to allow anyone to make Virtual Reality experiences with the lowest possible learning curve. It provides an online editor that creates Virtual Reality scenes from the users’ natural language.
Gold Panning from the Mess

In contrast to the massive volume of data, it is often the rare categories that are of great importance in many high impact domains, ranging from financial fraud detection in online transaction networks to emerging trend detection in social networks, from spam image detection in social media to rare disease diagnosis in the medical decision support system. The unique challenges of rare category analysis include: (1) the highly-skewed class-membership distribution; (2) the non-separability nature of the rare categories from the majority classes; (3) the data and task heterogeneity, e.g., the multi-modal representation of examples, and the analysis of similar rare categories across multiple related tasks. This tutorial aims to provide a concise review of state-of-the-art techniques on complex rare category analysis, where the majority classes have a smooth distribution, while the minority classes exhibit a compactness property in the feature space or subspace. In particular, we start with the context, problem definition and unique challenges of complex rare category analysis; then we present a comprehensive overview of recent advances that are designed for this problem setting, from rare category exploration without any label information to the exposition step that characterizes rare examples with a compact representation, from representing rare patterns in a salient embedding space to interpreting the prediction results and providing relevant clues for the end users' interpretation; at last, we will discuss the potential challenges and shed light on the future directions of complex rare category analysis.
Modeling and Control of a UAV Manipulator

A hierarchical control scheme for control of a quadrotor equipped with 2-link planar manipulator, which takes into consideration the dynamic coupling between the two, is proposed. The control scheme consists of three layers: a planning algorithm to determine the desired position setpoints for the quadrotor and the manipulator, a position control scheme to track these generated set-points and provide attitude references and a model-based attitude control scheme to track the provided attitude references. A simulation case-study is developed to demonstrate the functionality of the proposed control scheme.
Algorithm of Recurring Concept Drift Base on Main Feature Extraction

Recurring concept drift is one of the sub-types of concept drift. In recurring concept drift detection, it is very important to represent concepts and select the most appropriate classifier to classify. We propose an algorithm, conceptual clustering and prediction through main feature extraction (MFCCP), for classifying data stream with recurring concept drifts. MFCCP can recognize recurring concepts by computing the differences of main features and impact factors of different batches of samples. It maintains a classifier for each concept and monitors the classification accuracy to select classifier according to hoeffding inequality in order to enhance the ability of adapting to concept drift. The experimental results over the three datasets illustrate that MFCCP achieves better classification accuracy, adapts faster to concept drift, and detects concept drift more accurately than the other four algorithms on the data streams with recurring concept drifts, and therefore, MFCCP is apt to classify data stream without recurring concept drift.
Themis: Towards Decentralized Escrow of Cryptocurrencies without Trusted Third Parties

We introduce an improvement fair exchange method called Themis, which does not require Trusted Third Parties. Themis system is implemented based on blockchain, and can provide escrow services for secure exchanging between cryptocurrencies and physical goods. We propose many application scenarios for Themis, such as peer-to-peer escrow payments, exchanges among digital currencies. We implement a test-net for Themis and the experimental results shows that Themis protocol is practical in real-world distributed environment.
Picking robot path planning based on improved ant colony algorithm

With the increasing progress of agricultural production systems, the requirements for the degree of automation are also growing. As the most arduous picking link in the whole agricultural production, the development of picking robots has been paid more and more attention by experts and scholars. How to make the picking robot adapt to the complex and diverse agricultural production environment and effectively replace the manual operation, it is necessary to plan the path of the picking robot carefully. In this paper, a path planning method based on ant colony algorithm is proposed. The basic ant colony algorithm has the shortcomings of slow convergence speed and easy to fall into local optimum so that the final algorithm cannot meet the needs of the target. Firstly, the grid method is used to simulate the agricultural production environment to improve the applicability of path planning. Secondly, a new pheromone initialization scheme is proposed to improve the convergence speed because of the absence of pheromone in the initial time of the basic ant colony algorithm. Then, aiming at the problem that the basic ant colony algorithm is easy to fall into an optimal local solution, a new pheromone initialization scheme is proposed. The pheromone is added or subtracted to make the ant colony converge to the optimal path better. Finally, the performance of the algorithm is improved by choosing the appropriate heuristic function. Simulation experiments show that the path planning based on the improved ant colony algorithm has a good effect on the path planning process of the picking robot.
Multi-Modal Human-Aware Image Caption System for Intelligent Service Robotics Applications

Image captioning is a high-level task that generates the context scenario descriptions from an image. There are many ways to implement such kinds of ability that can generate a smooth sentence about the context scenario. Because of the open and extensive domain of the dataset, seldom of them can really help people to get the meaningful information of a specified scenario. In this paper, we propose a novel framework called Human-Aware Context Generator (HACG). We adopt the concept of dividing and conquer, which combines the ability of face recognition and facial expression recognition while retaining the capability of image caption model. This model aims to offer a significant context scenario sentence to the people, especially for those who are urgent to master the overall situation.
Design and Development of Mobile Augmented Reality for Mathematical Experiments

In the information age, augmented reality has entered a period of rapid development, widely used in various fields. This paper is meant to firstly state the application status of AR in the field of education briefly and then summarize three types of teaching tools based on augmented reality: head-mounted AR, desktop AR and mobile AR. After comparing the three types of AR applications, taking the three views in the mathematics textbook of middle school as an example, the study designs and develops a mobile augmented reality software "Exploring the Three Views" to supplement class teaching, encouraging hands-on practice of students, with the aim to help students acquire the knowledge of three views, cultivate practical ability and improve spatial imagination ability.
Distributed monitoring of election winners

We consider distributed elections, where there is a center and k sites. In such distributed elections, each voter has preferences over some set of candidates, and each voter is assigned to exactly one site such that each site is aware only of the voters assigned to it. The center is able to directly communicate with all sites. We are interested in designing communication-efficient protocols, allowing the center to maintain a candidate which, with arbitrarily high probability, is guaranteed to be a winner, or at least close to being a winner. We consider various single-winner voting rules, such as variants of Approval voting and scoring rules, tournament-based voting rules, and several round-based voting rules. For the voting rules we consider, we show that, using communication which is logarithmic in the number of voters, it is possible for the center to maintain such approximate winners; that is, upon a query at any time the center can immediately return a candidate which is guaranteed to be an approximate winner with high probability. We complement our protocols with lower bounds. Our results are theoretical in nature and relate to various scenarios, such as aggregating customer preferences in online shopping websites or supermarket chains and collecting votes from different polling stations of political elections.
Objects of Alternative Set Theory in Set@l Programming Language

Software porting between high-performance computer systems with different architectures requires a major code revision due to the architectural limitation of available programming languages. To solve the problem, we have proposed an architecture-independent Set@l programming language based on the principles of set-theoretic codeview and aspect-oriented programming. In Set@l, a program consists of a source code, which describes an information graph of a computational problem, and aspects, which adapt an algorithm to the architecture and configuration of a computer system. If an algorithm remains unchanged during its architectural adaptation, calculations and their parallelizing are described within the Cantor-Bolzano set theory. In the case of algorithm modification, some collections are indefinite, and we can not treat them as traditional sets with sharply defined elements. To describe indefinite objects, Set@l applies the alternative set theory developed by P. Vopenka. If collection has indefinite type and structure at some level of abstraction, it belongs to a “class” type. In contrast to a class, the indefiniteness of a semiset is an essential and inalienable attribute. The application of classes, sets and semisets allows to describe various methods of the algorithm implementation and parallelizing as an entire Set@l program. In this paper the Jacobi algorithm for the solution of linear equation systems is considered as an example of the utilization of classes and semisets.
Designing a Generator of Random Electronic Message Based on Chaotic Algorithm

The use of information technology based on dynamic chaos for the generation of random electronic messages is considered. On the basis of nonlinear systems with dynamic chaos are developed and investigated chaotic algorithms with high reliability of data mining. The complexity and correlation characteristics of random electronic messages are investigated. For the functioning of generators of random electronic messages, algorithms are developed, the work of which is modeled on chaotic cards, which are determined on a limited interval in multidimensional dynamic space.
Human-AGV Interaction: Real-Time Gesture Detection Using Deep Learning

In this paper, we present a real-time human body gesture recognition for controlling Automated Guided Vehicle (AGV) in facility. Exploiting the breakthrough of deep convolutional networks in computers, we have developed a system that can detect the human gestures and give corresponding commands to the AGV according to different gestures. For avoiding interference of multiple operational targets in an image, we proposed a method to filter out the non-operator. In addition, we propose a human gesture interpreter with clear semantic information and build a new human gesture dataset with 8 gestures to train or fine-tune the deep neural networks for human gesture detection. In order to balance accuracy and response speed, we choose MobileNet-SSD as the detection network.