Reviewerly/exHarmony huggingface dataset:
Skeleton Tree based Non-rigid 3D Shape Retrieval

We propose a skeleton tree based method for classifying and retrieving non-rigid shapes. Firstly, based on the extracted skeletons of a non-rigid shape and geodesic distance computation, the center point in skeleton is defined and detected. Then, a skeleton tree is constructed based on the connection between the center point and other discrete points in skeleton. After that, a correspondence between the skeleton tree and the area distribution of the non-rigid shape is established. The skeleton tree features are achieved. The advantages of our method can be summarized as follows: (1) Scale-Invariant; (2) Low computational complexity; (3) Automatic topology repair. The experimental results show that our method is more accurate than existing methods.
Secret sharing scheme Circular Visual Cryptography for Color Images

Data security is the most significant part of the innovative work territory, government, industry, association, and so forth. As of late, cybercrime or related hacking issue increments to an ever-increasing extent. So high security is required to verify the data as well as the picture. Visual Cryptography Scheme (VCS) is one of the procedures to verify the data utilizing straightforward calculation while in some other security method complex calculation was utilized. VCS has a straightforward encryption calculation which is secure data changing over into the various offers and for the unscrambling procedure don't require any kind of gadgets or any mind-boggling decoding calculation. The unscrambling procedure is finished utilizing the Human Visual Scheme (HVS). Round Random Grid offers are superior to the rectangular offers since usefulness is expanded by secure various data in the roundabout irregular network to give the secrecy. In the proposed plan, mystery sharing plan is that mystery data is separated into different importance full and negligible offers and is additionally recouped by superimposing handling and it is seen by HVS. In this Research, we depict each technique for VCS and introduced its relative investigation utilizing points of interest and hindrances.
Robust Filter—Dealing with Impulse Noise

ItNoise is well-known that the classical mean filter is optimal for estimating the mean value of a sequence of randomRandom numbers taken from white GaussianGaussian distributed sourceSource.
Public Sentiment Monitoring and Early-Warning for Enterprise

With the increasing global competitiveness and influence, a lot of famous central government-owned companies has taken a massive amount of attention worldwide. Meanwhile, the increasing complex internal and external environment has also bring serious management risk, particularly in terms of public opinion. The public opinion crisis may cause incalculable impact and damage to those companies brand management. Thus, it is significantly to accurately collect public opinion on company major policy, business and product. Then, in order to timely maintenance positive management environment, the company should pay attention to positioning source of public opinion, carrying out public opinion treatment in time. With the rapid development of AI (artificial intelligence) technology, it has become the focus of the new generation information technology. Based on the artificial intelligence technology, this paper realized “topic model”, “position determination” and “public opinion tracing” modules in public opinion monitoring and risk early warning. All those achievements of this paper can be applied to company management, brand maintenance, network public opinion analysis.
KeySlide

KeySlide is a novel interaction technique to position the text cursor in text editors by refashioning standard physical keyboard used in desktop and laptop devices. The technique enables users to position the cursor at the desired location on text documents using natural and intuitive finger-sliding gestures over the keys of the keyboard. The natural mapping of the gesture to the action eliminates the learnability of the action, and minimises the disruption caused due to switching between the keyboard and the mouse. The design, implementation and evaluation of KeySlide is presented. While the theoretical simulation study found that KeySlide is 52% faster than standard keyboard shortcuts for cursor positioning, a comparison user study consisting of 18 participants, found that text editing performance using KeySlide is 19% slower than using keyboard shortcuts. The discrepancy is discussed.
Design of Hardware Accelerator for Artificial Neural Networks Using Multi-operand Adder

Computational requirements of Artificial Neural Networks (ANNs) are so vastly different from the conventional architectures that exploring new computing paradigms, hardware architectures, and their optimization has gained momentum. ANNs use large number of parallel operations because of which their implementation on conventional computer hardware becomes inefficient. This paper presents a new design methodology for Multi-operand adders. These adders require multi-bit carries which makes their design unique. Theoretical upper bound on the size of sum and carry in a multi-operand addition for any base and any number of operands is presented in this paper. This result is used to design modular 4-operand, 4-bit adder. This module computes the partial sums using a look-up-table. These modules can be connected in a hierarchical structure to implement larger adders. Method to build a 16 bit 16 operand adder using this basic 4-bit 4-operand adder block is presented. Verilog simulation results are presented for both 4 × 4 and 16 × 16 adders. Design strategy used for the 16 × 16 adder may further be extended to more number of bits or operands with ease, using the guidelines discussed in the paper.
Waveform selection for multistatic tracking of a maneuvering target

This chapter considers the problem of adaptive waveform selection for multistatic tracking of a maneuvering target. An interacting multiple model algorithm incorporating extended Kalman filters is employed to deal with the nonlinearity between the radar observation vector and the target state vector and to handle multiple models of target dynamics. Three waveform selection schemes are presented based on minimizing the traces of the most-likely, minimax and combined covariance matrices of the interacting multiple model algorithm. Monte Carlo simulation examples are presented to demonstrate the performance advantages of the waveform selection schemes over conventional fixed waveforms.
Transformation of polygonal description of objects into functional specification based on three-dimensional patches of free forms

A method of transformation of polygonal data into functions based on patches of free forms is proposed. advantages of this approach over the known algorithms of transformation of three-dimensional models for fast transmission of information and its compact storage are demonstrated.
InvisibleEye

Despite their potential for a range of exciting new applications, mobile eye trackers suffer from several fundamental usability problems. InvisibleEye is an innovative approach for mobile eye tracking that uses millimeter-size RGB cameras, which can be fully embedded into normal glasses frames, as well as appearancebased gaze estimation to directly estimate gaze from the eye images. Through evaluation on three large-scale, increasingly realistic datasets, we show that InvisibleEye can achieve a person-specific gaze estimation accuracy of up to 2.04° using three camera pairs with a resolution of only 3x3 pixels.
Understanding visitor interaction with a projection augmented relief model display: insights from an in-the-wild study in the English Lake District

Abstract Projection augmented relief models (PARMs) are a promising tangible display technology for assisting users in orientating themselves within the represented landscape. The production of physical models is now easier than ever thanks to more freely available digital terrain data and 3D fabrication technology. When placed in a public setting, such as a visitor centre, the physical nature of such displays coupled with digital surface projection is compelling, enticing passers-by to notice and interact playfully with the display. In this article, we describe findings from our in-the-wild study of a PARM display designed to engage and orient visitors to the rural landscape of a remote valley in the heart of the English Lake District. The deployment has involved close collaboration with the Lake District National Trust, and the results of our 3-day observation study ( n = 221) contribute to the growing research community seeking to explore and uncover technology designs that are both playful and unobtrusive to the nature experience. Our research also contributes to the literature on public and situated digital displays and, in particular, understandings of visitor behaviour as considered through the so-called audience funnel framework. Our observations revealed that a significant portion (79%) of visitors noticed the PARM display and that, of these, 68% transitioned to giving the display their focal attention. We also observed an apparent expectation for the PARM display to support direct tangible interaction (such as pointing gestures) which contrasts to the phenomenon of interaction blindness discussed in the literature.
Virtual reality streaming at the edge

This poster focuses on addressing the power consumption issues in 360-degree immersive video streaming on smart phones, an emerging virtual reality (VR) application in the consumer video market. We first conducted a power measurement study that indicates VR view generation as the major power consumption source. Then, we developed an edge-based immersive streaming system called EdgeVR that offloads the power-consuming view generation operation from the smart phone to the edge. Through our preliminary evaluations using EdgeVR, we identified the challenge of Motion-to-Photon latency associated with offloading. To reduce such latency, we propose a viewport prediction-based pre-rendering mechanism at the edge to ensure the quality of experience in the VR application.
A Development of Knowledge Management System on Mobile Devices for Higher Education, Thailand

The purpose of this research were: 1) to design and development of knowledge management system on mobile devices for higher education, 2) to evaluate knowledge management system on mobile devices for higher education, 3) to evaluate the user opinions who use knowledge management system on mobile devices. The samples were 30 user academic staffs from 3 universities in Thailand by simple random sampling. The research instruments were 1) structured interviews 2) knowledge management system on mobile devices 3) system evaluation form for experts 4) the questionnaire for user opinions 5) the quality evaluation form for approve model. As the research statistics, mean, standard deviation to analyze the data.
Implementation of Indonesian Language The learning Based on Information and Communication Technology in Improving Senior High School Students’ Achievement in Surakarta

Abstract The study focuses on the Indonesian language the learning based ICT to completely improve senior high school students’ achievement. ICT has an important role in the Indonesian language the learning method development about to its purpose in improving students’ achievement, especially at senior high school level. This development expectedly can produce an appropriate model for the Indonesian language the learning in senior high school. Besides, Indonesian language the learning based ICT expectedly will change students’ behavior and mindset positively, like competitive, tolerant, critical and creative. Consequently, a proper information technology usage is a skill required in Indonesian language skill development in this time.
Security Landscape for Private Cloud

Cloud computing is gaining momentum due to its features like scalability, pay-per-use model, dynamic resource allocation etc. Since many applications and platforms of different organizations or departments are running on the same infrastructure, security is of utmost importance in the cloud. Cloud computing faces common security issues like data loss, malware attacks, phishing attacks, man in the middle attack etc., if proper care is not taken in hosting of applications and services. The technologies that make cloud computing possible like multi-tenancy, virtualization etc. itself can be exploited. Organizations often move to private cloud infrastructure in order to have better control over the data, infrastructure and security policies compared to the public cloud. Moving to a private cloud will not make the cloud more secured in nature. Security measures must be taken to make sure that the private cloud is secured. This paper discusses the security issues that can occur in a private cloud and its possible solutions.
A Framework for Privacy and Security Model Based on Agents in E-Health Care Systems

Usage of various systems for Electronic Healthcare Record (EHR) is increased in recent years. Majority of the hospitals and other laboratories requires health data sets during the treatment process. For enhancing the standard of the services provided by these health centers, the electronic health records should be shared so that it can be used by various users based on their requirements. Various researches have been done in the past for enhancing the security and privacy of these EHR data. Even though, it is not completely achieved. In this paper, a novel framework for maintaining the security and privacy of the EHR data based on agent based system is proposed. The agent based system consists of various intelligent agents such as the user interface agent, authentication agent, connection establishment agent and the connection management agent with distinct and expedient working features. These intelligent agents make ease of use, effective communication between patients/users and the E-service providers. This system monitors the data flow and focuses on various security and privacy concerns that the e-healthcare environment is facing.
iTM-Net: Deep Inverse Tone Mapping Using Novel Loss Function Based on Tone Mapping Operator

A novel inverse tone mapping network, called “iTMNet”, is proposed in this paper. For training iTM-Net, we also propose a novel loss function considering the pixel distribution of HDR images. In inverse tone mapping with CNNs, we first point out that training CNNs with a standard loss function causes a problem, due to the distribution of HDR images. To overcome the problem, the novel loss function non-linearly tone-maps target HDR images into LDR ones, on the basis of a tone mapping operator, and then the distance between the tone-mapped image and a predicted one is calculated. The proposed loss function enables us not only to normalize HDR images but also to distribute pixel values of HDR images, like LDR ones. Experimental results show that HDR images predicted by the proposed iTM-Net have higher-quality than HDR ones predicted by conventional inverse tone mapping methods including state-of the-arts, in terms of both HDR-VDP-2.2 and PU encoding + MSSSIM. In addition, compared with loss functions not considering the HDR pixel distribution, the proposed loss function is shown to improve the performance of CNNs.
Effective 3D object reconstruction from densely sampled circular light fields

Circular Light fields imaging is based on images taken on a regular circle with an equal space. Orientation information in epipolar plane images (EPIs) reveals strong depth clue for 3D reconstruction task. However, EPIs in Circular Light fields show a slightly distorted sinusoidal trajectory in 3D space. Rather than analyzing such spiral line on 2D image processing method, we present an algorithm based on 3D formula. By applying 3D Canny into densely sampled Circular Light fields, we can obtain a 3D point cloud in the image cube. Furthermore, we utilize structure tensor to analyze the disparity information in such 3D data. Finally, we build two Hough spaces to reconstruct depth information and obtain an accurate 3D object. Compared with state-of-the-art image-based 3D reconstruction methods, experiment results show our method can obtain improved reconstruction quality on synthetic data.
Sub-pixel Registration and Stitching Technology Based on Interpolation and Iterative Optimization Algorithm

Image stitching is an important branch of image processing, and image stitching technology is widely used in academia and industry. As the previous step of stitching, the accuracy of image registration plays a key role in the process of stitching. Common algorithms such as SIFT and Harris are often used for feature extracting and feature matching, but the time complexity is high, at the same time, the extracted feature points are mostly pixel level, the accuracy of which is low. This paper proposes an improved Harris detection algorithm, by introducing bilinear interpolation algorithm, the feature points matching is located to the sub-pixel level; In the image stitching stage, iterative optimization is used to refine perspective transformation matrix. The experimental results show that the sub-pixel registration and stitching technology based on interpolation and iterative optimization greatly reduces the time complexity of feature extraction and effectively improves the accuracy of stitching.
Towards Automated Semantic Role Labelling of Hindi-English Code-Mixed Tweets

We present a system for automating Semantic Role Labelling of Hindi-English code-mixed tweets.We explore the issues posed by noisy, user generated code-mixed social media data.We also compare the individual effect of various linguistic features used in our system.Our proposed model is a 2-step system for automated labelling which gives an overall accuracy of 84% for Argument Classification, marking a 10% increase over the existing rulebased baseline model.This is the first attempt at building a statistical Semantic Role Labeller for Hindi-English code-mixed data, to the best of our knowledge.
Hierarchical Learning-Guided human motion quality assessment in big data environment

Image information may be distorted during acquisition, processing, compression, and transmission. It is necessary to propose an intelligent image quality assessment model toward big data environment to quantify the degree of distortion of the image. This paper proposes a quality assessment model for human motion images. In complex scenes, the human body's action posture can be taken as an important feature point. Usually, in different scenes, the parts that affect the quality of the human body's posture are different. In other words, the weights of feature points that affect quality are different in different scenarios. However, due to the categorization of human movements, we can learn the quality assessment methods of different types of movements through sample training. Inspired by feature learning in the field of machine learning, we propose a hierarchical quality learning approach. We cast quality assessment as quality feature learning and layer by layer. The hierarchical quality learning method is based on deep reinforcement learning. The key part is that the method focuses on the region that containing more information on the features of the quality and enlarges the region layer by layer. Finally, we can determine the part of the body that affects the quality assessment. We compare this method with the subjective quality assessment results of the human observers and find that the proposed method achieves effective performance in big data environment to evaluate human motion quality.
Priority Queue Scheduling Approach for Resource Allocation in Containerized Clouds

Finding the appropriate resources and allocating them for executing software applications in cloud environments is very challenging because of its dynamic nature. Cloud service and resource providers do not render any performance assurance. Bare Metals, Virtual Machines and containers are being statically assigned to users’ workloads. This leads to a lot of resource wastage. The dynamic allocation of resources is sorely missing in the current and conventional cloud centers. This paper tackles the problem of static resource allocation inside a cloud center for running heterogeneous applications in a high-performance manner. This specifically focuses on how to accurately identify the various resources such as the memory (RAM), the number of cores, the amount of storage, the input/output requirements, etc. and dynamically allocate the identified resources to execute the different tasks of workloads to ensure the mandated performance requirements. In this work, we have focused a priority queue scheduling (PQS) algorithm in cloud environment and fair share policies are defined at each queue to deal with the dynamic priority of the requests submitted by users. According to the dynamic priority of user requests, they are scheduled at two levels on the basis of their resource accessibility. The proposed scheduling algorithm hosts the containers on cloud nodes to utilize the resources in a well-organized manner and the performance is evaluated and compared with the conventional scheduling methods.
Securing Hadoop Distributed File System using Intelligence based Sensitivity bits

Abstract Hadoop is a widely used analytical tool for analyzing large volume of data which are in terms of Exa &amp; Zetta bytes. Hadoop Distributed File System(HDFS) is a well-known storage system which provides distributed storage using commodity hardware. Since Big Data consists of highly sensitive data like Electronic Health Records, Financial Data etc., harnessing big data access and security plays a vital role. The bottom most layer of security can be provided by encryption. Since the data stored is highly unstructured, wider in variety and the velocity at which data is stored is also unpredictable, traditionally used ciphers like AES, RSA, RC4 etc. will not work efficiently on Big Data. The solution must have to consider at-risk sensitive data, variety of data which needs intelligence to classify the data. When data blocks are stored in data nodes, with each block 4-bits called sensitivity bits has to be added. Two bits indicate the level of sensitivity and two more bits indicate the type of data. These 4-bits are used by the map and reduce functions to effectively encrypt the required data blocks alone and not all, in a time efficient manner by using the available commodity hardware.
Mapping a Quantum Circuit to 2D Nearest Neighbor Architecture by Changing the Gate Order

This paper proposes a new approach to optimize the number of necessary SWAP gates when we perform a quantum circuit on a two-dimensional (2D) NNA. Our new idea is to change the order of quantum gates (if possible) so that each sub-circuit has only gates performing on adjacent qubits. For each sub-circuit, we utilize a SAT solver to find the best qubit placement such that the sub-circuit has only gates on adjacent qubits. Each sub-circuit may have a different qubit placement such that we do not need SWAP gates for the sub-circuit. Thus, we insert SWAP gates between two sub-circuits to change the qubit placement which is desirable for the following sub-circuit. To reduce the number of such SWAP gates between two sub-circuits, we utilize A* algorithm.
High voltage insulator monitoring using infrared camera and FPGA

This research investigates the use of FPGAs to create a fully autonomous system to detect hotspots on high voltage insulators by processing infrared videos. Initial algorithm development was conducted with MATLAB. The algorithm was automated by the addition of automatic temperature tracking and arc detection giving a sixtyfold speed increase (85 Hz). Explored two FPGA implementations of the algorithm: Verilog only; an embedded processor running compiled C++ code. Applications include post processing and live stream processing where alerts can be fed to maintenance teams of transmission system operators.