Reviewerly/exHarmony huggingface dataset:
Exploration of Time-Oriented Data in Immersive Virtual Reality Using a 3D Radar Chart Approach

In this paper, we present an approach to interact with time-oriented data in Virtual Reality within the context of Immersive Analytics. We implemented a Virtual Reality application that enables its user to explore data in an immersive environment (head-mounted display, 3D gestural input), utilizing potential advantages of immersive technologies, for instance, depth cues for better spatial understanding, natural interaction, and user engagement. The visualization design is inspired by the overall concept of a radar chart, and using the third dimension to represent time-series related data. We conducted a user study with 15 participants, encouraging them to examine a representative dataset within an explorative analysis scenario with no time constraints. Based on the results of usability and user engagement scores, task completion analysis, observations, and interviews, we were able to empirically validate the approach in general, and gain insights in the users’ interaction and data analysis strategies.
Using the Variational-Quantum-Eigensolver (VQE) to Create an Intelligent Social Workers Schedule Problem Solver

The scheduling problem of social workers is a class of combinatorial optimization problems that can be solved in exponential time at best. Because is belongs to class of problems known as NP-Hard, which have huge impact huge impact on our society. Nowadays, the focus on the quantum computer should no longer be just for its enormous computing capacity but also for the use of its imperfection, (Noisy Intermediate-Scale Quantum (NISQ) era) to create a powerful machine learning device that uses the variational principle to solve the optimization problem by reducing their complexity’s class. We propose a formulation of the Vehicle Rooting Problem (VRP) with time windows to solve efficiently the social workers schedule problem using Variational Quantum Eigensolver (VQE). The quantum feasibility of the algorithm will be modelled with docplex and tested on IBMQ computers.
Introduction to Quantum Computing

Quantum computing is a modern way of computing that is based on the science of quantum mechanics and its unbelievable phenomena. It is a beautiful combination of physics, mathematics, computer science and information theory. It provides high computational power, less energy consumption and exponential speed over classical computers by controlling the behavior of small physical objects i.e. microscopic particles like atoms, electrons, photons, etc. Here, we present an introduction to the fundamental concepts and some ideas of quantum computing. This paper starts with the origin of traditional computing and discusses all the improvements and transformations that have been done due to their limitations until now. Then it moves on to the basic working of quantum computing and the quantum properties it follows like superposition, entanglement and interference. To understand the full potentials and challenges of a practical quantum computer that can be launched commercially, the paper covers the architecture, hardware, software, design, types and algorithms that are specifically required by the quantum computers. It uncovers the capability of quantum computers that can impact our lives in various viewpoints like cyber security, traffic optimization, medicines, artificial intelligence and many more. At last, we concluded all the importance, advantages and disadvantages of quantum computers. Small-scale quantum computers are being developed recently. This development is heading towards a great future due to their high potential capabilities and advancements in ongoing research. Before focusing on the significances of a general-purpose quantum computer and exploring the power of the new arising technology, it is better to review the origin, potentials, and limitations of the existing traditional computing. This information helps us in understanding the possible challenges in developing exotic and competitive technology. It will also give us an insight into the ongoing progress in this field.
Disability design and innovation in computing research in low resource settings

80% of people with disabilities worldwide live in low resourced settings, rural areas, informal settlements and in multidimensional poverty. ICT4D leverages technological innovations to deliver programs for international development. But very few do so with a focus on and involving people with disabilities in low resource settings. Also, most studies largely focus on publishing the results of the research with a focus on the positive stories and not the learnings and recommendations regarding research processes. In short, researchers rarely examine what was challenging in the process of collaboration. We present reflections from the field across four studies. Our contributions are: (1) an overview of past work in computing with a focus on disability in low resource settings and (2) learnings and recommendations from four collaborative projects in Uganda, Jordan and Kenya over the last two years, that are relevant for future HCI studies in low resource settings with communities with disabilities. We do this through a lens of Disability Interaction and ICT4D.
Random Node-Asynchronous Graph Computations: Novel Opportunities for Discrete-Time State-Space Recursions

For the analysis of data defined over irregular domains, the recent area of graph signal processing extends classical signal processing techniques to the case of graphs. A prominent idea in this context is the notion of graph shift, which provides a foundation for distributed signal processing over networks. Despite its distributed nature, successive implementations of graph shift require a synchronization over the network, which can be a restriction in practical applications. The main goal of this article is to review the recent advancements in random and asynchronous computations over graphs with a specific emphasis on asynchronous graph-filtering applications. This article describes the behavior of the randomized asynchronous graph shift from a linear system theory viewpoint while making connections with asynchronous fixed-point iterations and Markov jumpswitching systems. In the context of graph signal processing, two specific applications are considered. The first one combines polynomial filtering and asynchronous updates to compute the Fiedler vector for spectral clustering. The second one considers the asynchronous implementation of a given rational graph filter. Asynchronous graph filtering is also extended to implement polynomial graph filter banks and graph neural networks in an asynchronous manner. The considered applications have proven mean-squared convergence guarantees.
Smart Student Attendance System Using QR Code

QR Code has a wide range of applications in this evolving technology world. QR code used to store massive information in a smaller space. So we decide to use QR code in our system and proposed smart attendance system using QR code. Secure authentication, is achieved using data-hiding algorithms with the embedded QR Code. In our project by using smartphones student scan QR code which will displayed by the teacher. When student scan this QR code, automatically attendance will be marked according to the user id. It also discusses how the system verifies student identity to eliminate false registrations.
Proxy-Based Federated Authentication: A Transparent Third-Party Solution for Cloud-Edge Federation

Cloud and Edge computing paradigms provide storage and computing services to traditional and internet of Things devices. One computing platform is not suitable to fulfill the requirements of all ioT devices because of their heterogeneity. in this regard, a federation of various computing paradigms has been emerging, in which a user (first party) with an account on one computing platform (second party) can access the services provided by another computing platform (third party federated with the first computing platform). The user needs to authenticate itself with the third-party computing platform which does not have user credentials. This work proposes a transparent and standard-compliant proxy-based federated authentication for solving the third-party authentication problem in federated cloud and edge computing paradigms. Transparency allows cloud and edge operators to deploy this proxy without having to change the existing authentication protocols. Experimental results illustrate that, as compared with the concatenation of authentication protocols in cloud and edge, proxy-based federated authentication of edge-to-cloud and cloud-to-edge can reduce the authentication delay time by 27.7 percent and 37.9 percent, respectively, and it is also standard compliant.
Sentiment Analysis about Product and Service Evaluation of PT Telekomunikasi Indonesia Tbk from Tweets Using TextBlob, Naive Bayes &amp; K-NN Method

Online reviews are very important for any business that wants to control its online reputation. This allows businesses to have active and positive participation from consumers. As an information and communication company in Indonesia PT Telekomunikasi Indonesia Tbk commonly called Telkom require a customer’s perspective or review to maintain the relevance of their digital products on the market. One method often used to analyze online reviews is sentiment analysis. Sentiment Analysis is used to gain an understanding of the opinions, attitudes, and emotions expressed in the mention of online by determining the emotional tone behind a series of words.This research tries to compare classifications in sentiment analysis of Telkom’s product from consumer reviews written in the form of tweets on Twitter. Each tweet about Telkom digital products such as Indihome, UseeTV, and Wifi.id will be collected as data. The use of classification types will be compared to help with the accuracy of sentiment analysis based on three types of methods TextBlob, Naïve Bayes & K-NN (K-Nearest Neighbor).The best result of this research is the K-NN algorithm with an accuracy score of 75% followed by Naïve Bayes 69.44% and the last is TextBolb with 54.67%.
Co-scheML: Interference-aware Container Co-scheduling Scheme Using Machine Learning Application Profiles for GPU Clusters

Recently, efficient execution of applications on Graphic Processing Unit(GPU) has emerged as a research topic to increase overall system throughput in cluster environment. As a current cluster orchestration platform using GPUs only supports an exclusive execution of an application on a GPU, the platform may not utilize resource of GPUs fully relying on application characteristics. Nonetheless, co-execution of GPU applications leads to interference coming from resource contention among applications. If diverse resource usage characteristics of GPU applications are not deliberated, unbalanced usage of computing resources and performance degradation could be induced in a GPU cluster. This study introduces Co-scheML for co-execution of various GPU applications such as High Performance Computing (HPC), Deep Learning (DL) Training, and DL Inference. Interference model is constructed by applying Machine Learning (ML) model with GPU metrics since predicting interference has a difficulty. Predicted interference is utilized and deployment of an application is determined by Co-scheML scheduler. Experimental results of the Co-ScheML strategy show that average job completion time is improved by 23%, and the makespan is shortened by 22% in average, as compared to baseline schedulers.
Dynamic Motion Estimation and Evolution Video Prediction Network

Future video prediction provides valuable information that helps a computer machine understand the surrounding environment and make critical decisions in real-time. However, long-term video prediction remains a challenging problem due to the complicated spatiotemporal dynamics in a video. In this paper, we propose a dynamic motion estimation and evolution (DMEE) network model to generate unseen future videos from the observed videos in the past. Our primary contribution is to use trained kernels in convolutional neural network (CNN) and long short-term memory (LSTM) architectures, adapted to each time step and sample position, to efficiently manage spatiotemporal dynamics. DMEE uses the motion estimation (ME) and motion update (MU) kernels to predict the future video using an end-to-end prediction-update process. In the prediction, the ME kernel estimates the temporal changes. In the update step, the MU kernel combines the estimates with the previously generated frames as reference frames using a weighted average. The kernels are not only used for a current frame, but also are evolved to generate successive frames to enable temporally specific filtering. We perform qualitative performance analysis and quantitative performance analysis based on the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and video classification score developed for examining the visual quality of the generated video. It is demonstrated with experiments that our algorithm provides better qualitative and quantitative performance superior to the current state-of-the-art algorithms. Our source codes are available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Nayoung-Kim-ICP/Video-Generation</uri> .
Multiobjective multiple features fusion: A case study in image segmentation

Most of existing image segmentation algorithms are only based on the color feature. However, the spatial distribution of an image can not be well described by using the color feature alone. Thus, it is necessary to add additional features to design efficient segmentation algorithms. Although many researchers also try to use multiple features for image segmentation, it is extremely difficult to combine multiple features automatically. This paper proposes a multiojective multiple features fusion strategy for image segmentation. The basic idea is to convert the segmentation problem into a multiobjective optimization problem, in which each objective considers one feature. It contains three steps. First, the original image is split into a set of over-segmented regions by using Meanshift to preserve the spatial details and to simplify the segmentation problem. Second, both the color and texture features are extracted to describe the regions. And two similarity matrices are designed by computing the similarity between each pair of regions in two features respectively. Third, a multiobjective evolutionary clustering algorithm is applied to merge these over-segmented regions. In this stage, two objective functions are designed based on the color and texture features respectively. A region index encoding scheme is introduced to design the individual, which contains some cluster representative regions. Some evolutionary operators are proposed to generate the new population. In the final generation, the best solution is selected from nondominated solutions for subsequent segmentation. Experiment results show that the proposed method provides promising segmentation results in combining the color and texture features.
AWARE: A Situational Awareness Framework for Facilitating Adaptive Behavior of Autonomous Vehicles in Manufacturing

In this paper, we introduce Aware, a knowledge-enabled framework for robots’ situational awareness. It is designed to support autonomous logistics vehicles operating in automobile manufacturing plants. Aware comprises an ontology grounding robots’ observations, a knowledge reasoner, and a set of behavioral rules: The Aware ontology models data streams of proprioceptive and exteroceptive sensors into high-level semantic representations. The knowledge reasoner infers adequate policy by reasoning over a sliding window of observations, presumably depicting the robot’s perceptions and actual state of knowledge. The behavioral rules, in analogy to road traffic rules and common sense, regulate the operation of autonomous robots in a manufacturing environment despite their obvious peculiarity. Our rules are the first ones facilitating the orderly and timely flow of vehicles. We show the applicability of Aware in an industrial set up. Overall, we posit that situational awareness is a fundamental element towards functional autonomy and argue that it can provide a reliable basis for organizing and controlling robots in a smart factory in the near future.
A Comprehensive Survey on Computer Vision Based Approaches for Moving Object Detection

Moving object detection has achieved a noticeable attention in many computer vision applications. The research community have contributed lot of works for dealing with major challenges of moving object detection in real-world scenarios. The paper presents a comprehensive review on different moving object detection techniques classified into four categories: Background Modeling Based techniques; Frame Difference Based techniques; Optical Flow Based techniques and Deep Learning Based techniques. Moreover, detailed descriptions of various methods in each of this category are also provided.
Automatic optimal projected light intensity control for digital fringe projection technique

Automatically adapting the projected light intensity is crucial for high dynamic range 3D shape measurement, where minimum human intervention is usually desirable. This paper presents a method to automatically determine the optimal number of light intensities and corresponding intensity values for fringe pattern projection according to the surface reflectivity of the measured object. The raw fringe images captured at different light intensities are synthesized pixel-by-pixel into the composite fringe images, which can be further used for phase retrieval and conversion to 3D coordinates. Our experiments demonstrate that the proposed method can achieve high measurement accuracy and effectiveness when measuring an object with a large range of reflectivity variations.
Blind quality assessment of omnidirectional videos using spatio-temporal convolutional neural networks

Panoramic videos are different from 2D videos in the spherical viewing mode. Normally, 2D videos are viewed by consumers on flat screen televisions, while panoramic videos need specific head-mounted displays to create virtual reality experience. Therefore, it is meaningful to study a quality evaluation model suitable for panoramic videos, which will bring a better quality of experience in the field of visual consumption. Unfortunately, to the best of our knowledge, there are few omnidirectional video quality assessments especially in no-reference quality evaluation modes. Besides, the existing 2D quality evaluation methods cannot well evaluate panoramic content quality. Based on above motivation and the vacancy of related research, we propose a blind deep learning-driven quality evaluation framework for panoramic videos to address the correlative issues. From the perspective of the viewport format, we eliminate the sampling distortion of panoramic videos in equirectangular projection (ERP) format via cubemap projection (CMP) format projection. In addition, we use a two-stream convolutional network to fully extract the intra-frame and inter-frame (defined as spatio-temporal) information for more comprehensive modeling of panoramic video features. Experimental results show that our method is superior to the existing full-reference and no-reference methods in solving the task of panoramic video quality assessment.
The UX of Interactive Machine Learning

Machine Learning (ML) has been a prominent area of research within Artificial Intelligence (AI). ML uses mathematical models to recognize patterns in large and complex data sets to aid decision making in different application areas, such as image and speech recognition, consumer recommendations, fraud detection and more. ML systems typically go through a training period in which the system encounters and learns about the data; further, this training often requires some degree of human intervention. Interactive machine learning (IML) refers to ML applications that depend on continuous user interaction. From an HCI perspective, how humans interact with and experience ML models in training is the main focus of this workshop proposal. In this workshop we focus on the user experience (UX) of Interactive Machine Learning, a topic with implications not only for usability but also for the long-term success of the IML systems themselves.
A Blockchain-Based Storage System With Financial Incentives for Load-balancing

Most storage systems adopt distributed architecture to reach high reliability. In these distributed systems, a well-balanced data distribution can improve storage reliability. However, existing schemes rely on dealers to distribute data, bring back the risk of single-point failure again. In this paper, we propose a blockchain-based storage system with financial incentives for load-balancing. Nodes in the system are rational and compete for data to earn a reward. The only source of storage rewards is from users' payment for leasing storage. To reach load-balancing, we design a new incentive scheme, which contains an income function to reward the nodes who own proper data and punish the nodes who own excessive data. In the system, each node continuously generates a proof of storage. We present a chain structure using the proofs to detect node failures and record data distribution. The state of distribution could be taken as input to the income function for reward allocation. We decouple the role of nodes from miners to reduce their workload, making the system more compatible. Our simulation experiments show efficient performance in the data distribution. The system can always recover to a balanced status as the blockchain grows up.
Black-Box Non-interactive Non-malleable Commitments

There has been recent exciting progress on building non-interactive non-malleable commitments from judicious assumptions. All proposed approaches proceed in two steps. First, obtain simple "base" commitment schemes for very small tag/identity spaces based on a various sub-exponential hardness assumptions. Next, assuming sub-exponential non-interactive witness indistinguishable proofs (NIWIs), and variants of keyless collision resistant hash functions, construct non-interactive compilers that convert tag-based non-malleable commitments for a small tag space into tag-based non-malleable commitments for a larger tag space. We propose the first black-box construction of non-interactive non-malleable commitments. Our key technical contribution is a novel implementation of the non-interactive proof of consistency required for tag amplification. Prior to our work, the only known approach to tag amplification without setup and with black-box use of the base scheme (Goyal, Lee, Ostrovsky and Visconti, FOCS 2012) added multiple rounds of interaction. Our construction satisfies the strongest known definition of non-malleability, i.e., CCA (chosen commitment attack) security. In addition to being black-box, our approach dispenses with the need for sub-exponential NIWIs, that was common to all prior work. Instead of NIWIs, we rely on sub-exponential hinting PRGs which can be obtained based on a broad set of assumptions such as sub-exponential CDH or LWE.