Reviewerly/exHarmony huggingface dataset:
FutureOTC: An Intelligent Decentralized OTC Option Trading and E-contract Signing System

This paper proposes a decentralized, reliable and intelligent system - FutureOTC for China’s over-the-counter (OTC) option market based on Consortium Blockchain. Firstly, FutureOTC combines robust Byzantine Fault Tolerance (RBFT) with electronic Identity (EID) and electronic Business License (EBL) to provide solutions for personal and institutional authentication. Secondly, we apply smart contracts to bring OTC option trading more intelligent and reliable, which includes online enquiry, E-contract creation, E-contract signing, reporting and clearing. Last but not the least, we introduce penetrating supervision by setting administrative institution as a node on Consortium Blockchain. Regulators can conduct 7 * 24 h remote supervision based on blockchain address, which reduces institutions’ workloads to report daily and ensures the authenticity of OTC option market data.
Hindi Handwritten Character Recognition using Deep Convolution Neural Network

Convolution Neural Network (CNN) is turning out to be a very powerful tool for solving Machine Learning (ML) problems, especially in multiclass image classification. With the availability of a huge handwritten dataset, it is possible to achieve a never thought machine accuracy in image classification. In this paper, we have proposed a Deep Convolution Neural Network (DCNN) for Hindi handwritten character recognition. The idea is basically an expansion of LeNet-5 architecture. We have trained our model using 96000 character sets, obtaining a validation set accuracy of 95.72 per cent using Adam optimizer and 93.68 per cent using RMSprop optimizer.
Fuzzy Comprehensive Evaluation of Employee Production Safety Based on Fine Kinney

Domestic health and safety evaluations in the production process of employees are few and uneven. This paper intends to propose a perfect employee production evaluation system. By using the fuzzy comprehensive evaluation method and introducing the Fine Kinney method for the first time, the set of factors affecting employees' health and safety during production was established, the weights of factors were obtained, the evaluation level standard was established, the factor evaluation membership and evaluation matrix were obtained, and the evaluation results were obtained by fuzzy operation. Verify that the weight value can be applied to the Fine Kinney's risk level interval within a certain confidence interval. The experimental results are applied to fuzzy comprehensive evaluation, giving feedback and perfecting the evaluation system.
A Cybersecurity Framework for Classifying Non Stationary Data Streams Exploiting Genetic Programming and Ensemble Learning

Intrusion detection systems have to cope with many challenging problems, such as unbalanced datasets, fast data streams and frequent changes in the nature of the attacks (concept drift). To this aim, here, a distributed genetic programming (GP) tool is used to generate the combiner function of an ensemble; this tool does not need a heavy additional training phase, once the classifiers composing the ensemble have been trained, and it can hence answer quickly to concept drifts, also in the case of fast-changing data streams. The above-described approach is integrated into a novel cybersecurity framework for classifying non stationary and unbalanced data streams. The framework provides mechanisms for detecting drifts and for replacing classifiers, which permits to build the ensemble in an incremental way. Tests conducted on real data have shown that the framework is effective in both detecting attacks and reacting quickly to concept drifts.
Embodying Sound A work-in-progress on technologically expanded performance

Embodying Sound is a performing art project that integrates dance, music and digital technology, it explores a real-time sonification of human motion, captured by inertial sensors, using the XSens system. First and foremost, it is not a demonstration of technical virtuosity, but an attempt to put technology at the service of imagination and creativity. In a world dominated by computation - tending to a dystopic future of "artificial intelligences" [1], we tend to forget that present-day machines cannot really think or feel; Computers do not have purposes, do not love or understand reality the way a living organism does [2]. In this presentation, we explore how technology can act as a mediator between dance and music. The quest of this performative process is to investigate the sound signature of the body. Here we discuss how such phenomenological experience might challenge self-consciousness and the perception of identity. Furthermore, within a more general approach, we question how does this artistic and technological interaction stimulate the expansion of artistic expression, which might result in new aesthetics.
Research on Intelligent Question Answering System Based on Medical Knowledge Graph

With the development of artificial intelligence, smart medical systems play an increasingly important role. The traditional medical question answering system can only answer the preset questions. This paper introduces a model of intelligent question answering system based on knowledge graph. It analyzes how to construct a knowledge graph using the neo4j graph database, and uses convolutional neural network to semantically parse user questions. To a certain extent, the system has improved the understanding of user questions and can give better answers.
Incremental Kalman Estimation Algorithm Analysis

Applying the projective theory, the incremental Kalman estimation algorithms including filter, predictor and smoother are presented based on the optimal estimation criterion of linear minimum variance. They solve the state estimation problem for the systems with unknown measurement errors, which don’t meet the requirements of classical Kalman filter. The proposed algorithms have the advantages of simple form and small computational burden so as to be easily used in the engineering practice. The simulation results show their effectiveness and feasibility.
A Multi-User Face Identification system with distributed tasks in a Big Data Environment

Computer vision is a field that handles how a computer can gain a high level of understanding from several kind of inputted information. In general, it tries to automate tasks that humans have the ability to do. Computer vision tasks contain a method to understand digital images and extract high dimensional in order to generate a piece of symbolic information, for example, in the forms of decisions. One of the open issues in image processing and computer vision nowadays is how we can handle the problem of quick identification in a multi-user identification system. In this paper, We propose an identification system in a big data environment that provide and an acceptance identification time while keeping a good performance, in uncontrolled conditions, in comparison with some compared classical systems.
Cryptosystem Based on Double Random Phase Encryption in Hybrid Transform Domain

In this paper, an encryption mechanism based on double random phase encryption in the hybrid transform domain is proposed. The hybrid transform is created by the blend of fractional Fourier transform of various orders, Walsh transform and Kekre transform. Here the fractional orders of the fractional Fourier transform expand the key-space of the proposed encryption mechanism in this way giving it additional power to dodge any brute-force attack. The proposed encryption mechanism is non-linear in nature. Numerical simulations have been carried out on grayscale images to authorize and authenticate the proposed mechanism. Results exhibit that the proposed encryption mechanism has higher sturdiness against noise and occlusion assaults.
Going Native with Your Web Dev Skills

This workshop will show how web development skills can be used to develop native mobile apps (iOS, Android, and Web) using a JavaScript library called React Native, an open-source framework developed by Facebook. React Native extends the popular React web framework with support for truly native mobile apps. After completing the workshop, participants will have a classroom-ready assignment to share with their students.
Listen to your Health: Reflections on Mobile Health Diagnostics through Audio Signals

Considerable research has been conducted into mobile and wearable systems for human health monitoring. This research concentrates on either devising sensing and systems techniques to effectively and efficiently collect data about users, and patients or in studying mechanisms to analyse the data coming from these systems accurately. In both cases, these efforts raise important technical as well as ethical issues.
Software Engineering and Human-Computer Interaction: Students' Perception of a Project-based Approach in a Postgraduate Course

Her focus is set on educational research, physics education, problem-solving, design of instructional material and teacher training
Vehicle Activity Recognition Using DCNN

This paper presents a novel Deep Convolutional Neural Network (DCNN) method for vehicle activity classification. We extend our previous approach to be able to classify a larger number of vehicle trajectories in a single network. We also highlight the flexibility of our approach in integrating further scenarios to our classifier. Firstly, a spatiotemporal calculus method is used to encode the relative movement between vehicles as a trajectory of QTC states. We then map the encoded trajectory to a 2D matrix using the one-hot vector mapping, this preserves the important positional data and order for each QTC state. To do this we associate the QTC sequences with pixels to form a 2D image texture. Afterwards, we adapted trained CNN architecture into our vehicles activity recognition task. Two separate types of driving data sets are used to evaluate our method. We demonstrate that the proposed method out-performs existing techniques. Along with the proposed approach we created a new dataset of vehicles interactions. Although the focus of this paper is on the automated analysis of vehicle interactions, the proposed technique is general and can be applied for pairwise analysis for moving objects.
Parallel Selective Sampling for Imbalance Data Sports Activities

Big data has a huge amount of large data set. Large data set has some major problems; the first one is size of the data and the second one is size of the data classes are strongly imbalanced. Several places where different devices produce the immense amount of data. Using this data nature, they are unable to determine the outcome from the data. To solve this problem, many numbers of applications and algorithms are used. Even though there are many applications and algorithms, many limitations occur while implementing the algorithm and using the applications. In the literature D’Addabbo and Maglietta [1] proposed a preprocessing technique method called parallel selective sampling (PSS). This preprocessing technique can be combined with any classification algorithm. In our research, we are combining PSS along with relevance vector machine (RVM). This PSS-RVM method provides the finest outcome and the existing Data Set algorithm for sporting activities.
4D Trajectory Planning of Unmanned Aerial Vehicle in Trajectory Based Operation Airspace

In the foreseeable future, UAV flight missions are more likely to be implemented independently. In Trajectory Based Operation (TBO) airspace, UAV will also fly according to 4D trajectory with time window. How to plan a safe 4D trajectory for UAV will become another requirement for UAV to integrate into airspace. In this paper, firstly, the navigation problem of UAV is modeled, and the constraints of UAV in the process of flying mission are given through the flight dynamics model of UAV. Combined with performance constraints, 4D waypoint constraints and obstacle constraints, the modeling of mixed integer linear programming (MILP) problem is carried out. Then the optimization of mixed integer programming problem is carried out. With the optimization of the trajectory for the acceleration change rate of the UAV, and using the method of multi-time scale decomposition, the computation complexity is greatly reduced. Finally, the solution of the case is analyzed.
Distributed Variational Inference-Based Heteroscedastic Gaussian Process Metamodeling

In this paper, we generalize the variational Bayesian inference-based Gaussian process (VBGP) modeling approach for handling large-scale heteroscedastic datasets. VBGP is suitable for simultaneously estimating the underlying mean and variance functions with a single simulation output available at each design point. To improve the scalability of VBGP, we consider building distributed VBGP (DVBGP) models and their hierarchical versions by partitioning a dataset and aggregating individual subset VBGP predictions based on the idea of transductive combination of GP experts. Numerical evaluations are performed to demonstrate the performance of the DVBGP models from which some insights are derived.
Describing Image Using Neural Networks

Automatic generationKumar, Atul of textualKumar, Ratnesh description of an imageShrivastava, Shailesh Kumar is a challenging problem of the computer vision domain. This can be attributed to the fact that generating textual description of an image requires the application of image processing techniques in conjunction with natural language processing methods. This paper proposes a model to automate the process of generating textual description of an image using the concepts of neural networks. The proposed model can generate human-like sentence for expressing an image. The proposed model is validated using the MSCOCO dataset. The obtained results clearly indicate that the performance of the proposed system increases with an increase in convolution layers as well as increase in depth.
Local search-based dynamically adapted bat algorithm in image enhancement domain

Bat algorithm (BA) is a new metaheuristic optimisation algorithm, which has already proved its supreme performance on many optimisation fields. However, it is possible to increase its efficiency when solving complex optimisation problems. This study concentrates on improving the efficiency of BA by incorporating different types of local search strategies and novel self-adaption strategies of parameters such as loudness, pulse rate and frequency. Comparative analysis of three different proposed local search strategies has been performed to find the best one. The proposed modified BAs with local search strategies are employed to solve five popular image enhancement models. Experimental results prove that self-adaption of parameters enhances the capability of standard BA. But the addition of efficient local search technique with self-adaption increases the effectiveness of the standard BA to a great extent.
Scalable kernel k-means clustering with Nyström approximation

Kernel k-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear k-means clustering algorithm. However, kernel k- means clustering is ...
Two-Factor Authentication Using Mobile OTP and Multi-dimensional Infinite Hash Chains

Hash chains are often used to implement One Time Password based authentication systems. Some use finite hash chains that require frequent system re-initialization. Some use computationally-intensive public-key algorithm to achieve infiniteness. Eldefrawy et al. proposed a hash-based infinite chain but has limited ability to resist pre-play and guessing attack. This paper provides a smartphone-based two-factor authentication system nRICH that uses both knowledge (password) and possession (seed) based information. The OTP is generated perpetually from a multi-dimensional infinite hash chain that eliminates the limitations of other techniques. It is superior to resist pre-play attack. The hard challenge is a random path from origin to a random point inside a multi-dimensional moving hypercube. We have rigorously performed the security analysis and compared with other techniques w.r.t. various metrics and found suitable to be implemented in even low-end devices. The only drawback is the increased length of the challenge to be typed by the user. We propose to use QR code to avoid this problem.
Evaluating the Performance of the Deep Active Imitation Learning Algorithm in the Dynamic Environment of FIFA Player Agents

Deep Learning is a state-of-the-art approach for machine learning using real-world or realist data. FIFA is a soccer simulation game that provides a very realistic environment, but which has been relatively poorly explored in the context of learned game-playing agents. This paper explores the Deep Active Imitation (DAI) learning strategy applied to a dynamic environment in FIFA game. DAI is a segment of Imitation Learning, which consists of a supervised Deep Learning training strategy where the agents learn by observing and replicating human experts' behavior. Noteworthy here is that such learning strategy has only been validated in static navigation scenarios in the sense that the environment is changed only through the actions of the agent. In this way, the main objective of the present work is to investigate the efficacy of DAI to cope with a dynamic FIFA scenario named confrontation mode. The agents were evaluated in terms of in-game score through tournaments against FIFA's engine. The results show that DAI performs well in the confrontation mode. Thus, this work indicates that such learning strategy can be used to solve complex problems.
Blockchain Interfaced Sacure E-Voting System

&#x0D; National elections are a big deal which has a major role in the development and progress of a country. The security of an election is in fact the matter of national security. Current voting systems in the context of Nepal and other parts of the world are still traditional and insecure. From the dawn of democracy, elections are still based on pen and papers. While there are new electronic voting machines, these systems have a high risk of rigging and are prone to failure, crashes, and attacks from intruders. The stated problems can be addressed through the implementation of new technology namely Blockchain for a transparent and reliable voting process. A blockchain based voting platform has been built to make the voting process digital, secure, reliable and intuitive. The votes casted by voters are stored in the blockchain, thus making it immutable and secure. The system can be scaled based on the requirements of an election. Voters can login to the system with the help of encrypted identification. This encrypted identification will lead to secure voting process. A separate portal for both voters and the election commission reduces the election hassle and time. So, in this system, it is demonstrated how the use of blockchain platform could contribute to modern elections using Ethereum, smart contracts and distributed systems to provide voters with secure, transparent and reliable voting platform.
Optimization and Implementation of Synthetic Basis Feature Descriptor on FPGA

Feature detection, description, and matching are crucial steps for many computer vision algorithms. These steps rely on feature descriptors to match image features across sets of images. Previous work has shown that our SYnthetic BAsis (SYBA) feature descriptor can offer superior performance to other binary descriptors. This paper focused on various optimizations and hardware implementation of the newer and optimized version. The hardware implementation on a field-programmable gate array (FPGA) is a high-throughput low-latency solution which is critical for applications such as high-speed object detection and tracking, stereo vision, visual odometry, structure from motion, and optical flow. We compared our solution to other hardware designs of binary descriptors. We demonstrated that our implementation of SYBA as a feature descriptor in hardware offered superior image feature matching performance and used fewer resources than most binary feature descriptor implementations.
Generalized Product Quantization Network for Semi-supervised Hashing.

Image retrieval methods that employ hashing or vector quantization have achieved great success by taking advantage of deep learning. However, these approaches do not meet expectations unless expensive label information is sufficient. To resolve this issue, we propose the first quantization-based semi-supervised image retrieval scheme: Generalized Product Quantization (GPQ) network. We design a novel metric learning strategy that preserves semantic similarity between labeled data, and employ entropy regularization term to fully exploit inherent potentials of unlabeled data. Our solution increases the generalization capacity of the quantization network, which allows overcoming previous limitations in the retrieval community. Extensive experimental results demonstrate that GPQ yields state-of-the-art performance on large-scale real image benchmark datasets.
Using Governance and Adaptive Normative Multiagent Systems for Dynamic Vehicle Platoon Formation

Autonomous vehicles (AVs) are vehicles able to sense their environments and guide themselves with little or no human input. AVs have the potential to provide a wide variety of benefits to road traffic, such as improving traffic flow, alleviating traffic congestion and reducing car accidents. However, there are scenarios where AVs require governance systems capable of performing collaborative actions in order to keep traffic flowing smoothly and safely. This paradigm shift will support moving from isolated to collaborative autonomous vehicles. Our goal is to create a collaborative governance-based autonomous vehicle approach using adaptive normative multiagent systems to reduce congestion time. With this collaborative perspective in mind, steps should be taken for AVs to improve their collective mobility while not undermining the ‘social’ goals of vehicle platoons and be able to provide benefits such as reduced congestion, increased travel choice and equity, and reduced pollutant emissions.
Learning Bounds for Moment-Based Domain Adaptation.

Domain adaptation algorithms are designed to minimize the misclassification risk of a discriminative model for a target domain with little training data by adapting a model from a source domain with a large amount of training data. Standard approaches measure the adaptation discrepancy based on distance measures between the empirical probability distributions in the source and target domain. In this setting, we address the problem of deriving learning bounds under practice-oriented general conditions on the underlying probability distributions. As a result, we obtain learning bounds for domain adaptation based on finitely many moments and smoothness conditions.
GPU-Based Dynamic Solar Potential Estimation Tool Using 3D Plans

Estimations of the solar potential from the building design files may affect placement considerations in favor of more sunlight reception that reduces the energy costs and saves the environment. In this study, a GPU based system (GPU-DSRM) is proposed to estimate direct and diffuse solar radiation aggregated on 3D structures at urban or individual scale. In the proposed approach, finite element method, back-face detection and ray-tracing algorithms are customized to run in parallel to reduce the execution time. Thus, real-time shadow analysis with adjustable sampling rate and time scale can be performed without compromising precision and accuracy of the estimations. The most important novel aspect of the study is that it can be used anywhere in the world without the need for meteorological data. Some of the test results obtained from a site with 10 buildings are presented in this paper that shows a speedup value of 45 with the new GPU-based implementation compared to the CPU-based model. The GPU-DSRM tool has also been compared with geometric tools in the literature. Solar energy potential analysis of building designs or existing urban formations can be completed faster and more precisely with this new approach.