Reviewerly/exHarmony huggingface dataset:
A Framework of Callbot for Dialogue Process in Seperated Network

Recently, customer services based on automated dialogue system have used commonly in many fields such as public, financial, and distribution. The introduction of dialogue systems as customer services for existing call channels are becoming increasingly important. In this paper, we introduce a framework for implementing callbot systems in a separate network environment. In general, customer service operated on a separate network from external networks such as the Internet, and structured approaches are needed to operate callbot and chatbot services in such the environment.
Multiquadratic Rings and Walsh-Hadamard Transforms for Oblivious Linear Function Evaluation

The Ring Learning with Errors (RLWE) problem has become one of the most widely used cryptographic assumptions for the construction of modern cryptographic primitives. Most of these solutions make use of power-of-two cyclotomic rings mainly due to its simplicity and efficiency. This work explores the possibility of substituting them for multiquadratic rings and shows that the latter can bring about important efficiency improvements in reducing the cost of the underlying polynomial operations. We introduce a generalized version of the fast Walsh-Hadamard Transform which enables faster degree-n polynomial multiplications by reducing the required elemental products by a factor of O(log n). Finally, we showcase how these rings find immediate application in the implementation of OLE (Oblivious Linear Function Evaluation) primitives, which are one of the main building blocks used inside Secure Multiparty Computation (MPC) protocols.
Exploring Research on Intelligent Management and Control of Power Production Operators’ Personal Injury Risk Based on Big Data

Based on the actual situation of power production in hydropower station, this paper explores how to manage and control various possible risks that may exist in the field operation of power production in advance to avoid on-site personnel injury. During the research, all kinds of risks existing in the power production site of the hydropower plant and the relevant work to be carried out in the production process are all quantified with data. Through the application of certain review process and risk algorithm, the risk value and safety protection measures taken by the on-site operators in operation process are dynamically analyzed to see whether they are sufficient, and the operations beyond the risk value range will be given early warned in time to remind operators and management personnel that relevant measures need to be taken in a timely manner striving to achieve the goal of zero personal injury for on-site operators and ensure the safety in production.
Locally Aggregated Hierarchical Decomposition Based Ensemble Learning for Robust Face Recognition

For the detection and extraction of features, a number of hierarchical layering methods have been explored extensively. This is followed by content-based image indexing and retrieval implemented by taking local aggregation of the extracted features. Through a clustering process, a mapping between the numerous patches in the image to the centres of the learned clusters is made. A histogram representation reflecting each of the independent features together form the vectorized encoded image. A global feature vector is then obtained. A comparative study is made with the traditional feature extraction and aggregation techniques following which we extensively explore various learning algorithms to enhance the stability and performance. In preference to using the existing hypothesis space from which the learning models are derived from, we make use of a hybridized hypothesis space which is a combination of the existing hypothesis spaces to get an enhanced hypothesis set. The key idea behind fusing multiple models is to decrease bias which arises from the spurious assumptions that might be concluded by the model, decrease the variance owing to the sensitivity due to minor aberrations that creep in the training phase and to improve predictions. We extend this concept of ensemble system by combining conceptually dissimilar base classifiers and use the majority vote for final prediction. This is performed to balance out limitations of the individual classifiers. A five-fold cross-validation technique was employed on the standard datasets like Grimace and Faces96. Our proposed model can achieve the best accuracy of 99.7 percent on the Grimace dataset and constantly hit in excess of 95 percent for far more complex datasets which are a direct implication of the high tolerance to variances in scale, rotation, pose and expression.
Research on Deep Learning and Other Methods Based on Abnormal Traffic Detection in Complex Network Environment

With the increasingly complex structure of complex network environments, how to use intelligent methods to diagnose network faults has attracted more and more attention from operators. Self-organizing network (SON) self-configuration, self-optimization and self-healing functions can be used to improve the self-organization capability of wireless networks, replace the manual intervention of high-cost network operators, and realize automatic anomaly detection and diagnosis, thereby effectively reducing the network Deployment and operating costs. In this paper, based on multivariate time series analysis and self-organizing neural network deep learning method, an abnormal traffic prediction and abnormal detection method for complex network environment is proposed. This method uses wireless channel quality indicator (CQI) to determine the traffic performance status and can respond to Reasonable system changes (concept drift). It can be seen from the application results in the actual operating LTE network that this method can effectively predict the traffic behavior trend and detect the irregular traffic behavior from a large amount of data in a self-organizing manner.
Vidyasetu - A Digital Disruption In Engineering Education System In India As An Impact Of Industry 4.0

Abstract Engineering education in India facing challenges from very past ages, but the most recent calls of Industry 4.0 may cause huge disruption for the ongoing education system. This change will not cause greater transparency or accountability of the education system, but it will reflect the role of universities, colleges on higher education, and also how people learn. Industry 4.0 described as an automation process for manufacturing in a smart way. So the time has arrived to create a new wave of the education system that only solve bookish problems but can educate the people to solve global issues. This study will discuss the problems of engineering education in India and the required change in the educational process to meet the new millennium needs of Industry 4.0.
Study on Intelligent Wiper Based on STC89C51 Microcontroller

In this paper, an intelligent wiper is designed base on STC89C51 Microcontroller. This intelligent wiper system can detect raindrops in real time with high sensitivity and automatically adjust the wiper speed according to the actual situation of rain. At the same time, the system can be manually switched by three wiper speeds: fast, medium and slow. The software system is based on the programming code and realizes the control of STC89C51 single chip microcomputer. In the whole measurement process, the system can work normally and respond to various instructions perfectly. After optimization and improvement, it finally shows a very high performance and fully meets the initial design requirements.
Group Targets Tracking Using Maximum Entropy Fuzzy Based on Fire-fly Algorithm and Particle Filter

In the complex tracking scenes, such as the formation of the aircrafts and the battleships, the ground moving tanks, the ballistic missiles and so on, the tracked objects are formed by targets moving in an interacting fashion which shows the characteristics of the group. The research on group targets tracking plays an important role in theoretical significance and military value. In order to solve the problem of complex data correlation and particle degradation, a novel algorithm, called fire-fly algorithm and particle filter (FA-PF), is proposed based on maximum entropy fuzzy (MEF) for fine tracking of group targets. The FA-PF algorithm can estimate not only the uncertainty of the group structure but also the group target states. The MEF clustering data association algorithm is used to improve the accuracy and efficiency of data association. The improved intelligent fire-fly algorithm is combined with the particle filter to improve the tracking accuracy. Therefore, the proposed algorithm can reduce the complexity of data association and improve the accuracy and efficiency of the group targets tracking compared with the existing fine tracking algorithm of group targets. The simulation results verified the effectiveness of the proposed algorithm.
Impact of Dynamic Metrics on Maintainability of System Using Fuzzy Logic Approach

In software development life cycle, software maintainability is the most important and crucial phase as it requires more effort as compared to other phases. To minimize the cost of software maintenance, it is necessary to predict software maintainability during the early phases of software development life cycle. Most of the metrics available in existing literature to calculate maintainability factor were static, i.e., based on design patterns. Therefore, in this paper, authors propose a fuzzy logic model to measure maintainability of software that takes static and dynamic metrics set as input and maintainability as output. Static metrics were collected using CodeMR tool, i.e., an eclipse plugin whereas dynamic event tracing was done by AspectJ, an implementation of aspect oriented programming on an eclipse platform. Further, the proposed fuzzy model was applied on 15 java classes to calculate maintainability at compile time and run time based on static and dynamic metrics. Lastly, proposed model was validated using analytical hierarchy processing (AHP) technique and it is concluded that proposed fuzzy model gives satisfactory result in case of dynamic metrics. Hence, it is concluded that dynamic metrics are better predictor of maintainability factor as compared to static metrics that helps the software industry to measure maintainability of software early in advance.
Dynamic Risk Management

This chapter focuses on dynamic risk management and its ability to accurately drive decisions to minimize an organization's exposure to threats. Starting from the framework presented by NIST, a brief explanation is provided on the risk management process. Regarding the risk assessment, risk factors are explained and their integration in the risk assessment process. This chapter provides an introduction in the use of probabilistic graphical security models into a risk management framework. Furthermore, to set up the environment for graphical models, the use of scoring systems is explained. Incorporating attack graphs in the dynamic risk assessment process, the risk is interpreted as a probability metric and according to the network topology, Bayesian inference is conducted to address the need for dynamic risk assessment. However, this procedure is scaling exponentially for larger graphs and thus, a solution is given by introducing factor graphs and the Belief Propagation (BP) algorithm. The algorithm's use is limited to trees. This situation leads to the use of other implementations of the BP called Loopy BP, estimating the risk approximately. Finally, the rest of the chapter is focused on the mitigation strategies by categorizing proactive mitigation actions.
OMNISCIENT: A Branch Monitoring System for Large-scale Organizations

Omniscient is a system that enables higher-level management of massive organizations to remotely monitor and scrutinize the activities that take place in the branches from the head office itself by providing exclusive insight in the form of detailed reports on the employees' behaviour and performance daily, weekly and monthly. The system further monitors the branch and provides reports on any suspicious behaviour and also on the customers' activity within the branch premises. Omniscient rates the customer's level of satisfaction by capturing the customer's facial expressions and analyzing their emotions while they are being served. The employee face and dress recognition models have accuracies of 90.90% and 87.00% respectively while, employee activity detection has an accuracy of 89.00%. Customer emotion and miscellaneous activities detection models have the accuracies of 91.50% and 83.00% respectively. All of the aforementioned procedures were made possible by systematically analyzing the IP camera video footage obtained throughout the day to analyze the work productivity and performance of the branch as accurately as possible using deep learning and modern visual computing techniques like CNN, OpenCV, Haar Cascade classifier, face recognition, Dlib and Darknet.
NavTuner: Learning a Scene-Sensitive Family of Navigation Policies

The advent of deep learning has inspired research into end-to-end learning for a variety of problem domains in robotics. For navigation, the resulting methods may not have the generalization properties desired let alone match the performance of traditional methods. Instead of learning a navigation policy, we explore learning an adaptive policy in the parameter space of an existing navigation module. Having adaptive parameters provides the navigation module with a family of policies that can be dynamically reconfigured based on the local scene structure and addresses the common assertion in machine learning that engineered solutions are inflexible. Of the methods tested, reinforcement learning (RL) is shown to provide a significant performance boost to a modern navigation method through reduced sensitivity of its success rate to environmental clutter. The outcomes indicate that RL as a meta-policy learner, or dynamic parameter tuner, effectively robustifies algorithms sensitive to external, measurable nuisance factors.
WITHDRAWN: DBN deep learning of edge cloud data analytics using markov decision process

Basically, Deep learning has become more popular in present day generation. This is very cheaper and easier to use. In this paper the design of DBN (Dynamic Bayesian Network) deep learning architecture is implemented for edge cloud computing analytics by using markov decision process. The main intent of edge and cloud computing is to overcome the problem of over traffic that is occurred while data is transmitting. The data analytics while analyze the complexity occurred in the system. Here data reduction block is introduced on the edge with ML on the cloud. Hence this paper will investigate the edge cloud computing for data analytics and introduces an deep learning approach for reducing data on the ML on edge cloud computing from the simulation results, it can observe that comparison of normal and DBN server communication power efficiency. Similarly, the speed of server communication also shown.
Resource-Constrained Project Scheduling of Cloud Platform Based on Column Generation Algorithm

This paper focuses on how to realize efficient deployment in the cloud platform server resource pool. Aiming at this problem, a server resource allocation model and a shared resource-constrained project scheduling algorithm (CGS) based on column generation algorithm were designed. This algorithm improves the CG algorithm instability, approximate solution, and library updating to ensure that the multiprocessor can complete the task by sharing a certain number of resources. Finally, experiments are carried out by comparing with the traditional algorithms ILP, LR, and GA. Experimental results show that this method has good performance and is better than the traditional algorithm under a large task condition.
Extraction of Key Frame from Random Videos Based On Discrete Cosine Transformation

Video Analysis has rooted tremendously in many applications with the development of new technologies. Video Analysis plays vital role in many real time event applications where the small content from video gives a full story of the whole video. In such case the analysis of video becomes important to study the data contents present in the videos rather than its attributes. Key frame extraction is a method used for video indexing and in video retrieval applications. The proposed work presents the extraction of key frames from a given video using Discrete Cosine Transform (DCT). The expirmenatal result obtained show competative outcomes with 82.75% completeness of video with 32 × 32 pixel coefficent as compared to 64 × 64 and 128 × 128 coefficients of frames by good time and space complexity compared to results reported in literature.
Improving Attention-based End-to-end ASR by Incorporating an N-gram Neural Network

In attention-based end-to-end ASR, the intrinsic LM is modeled by an RNN and it forms the major part of the decoder. Comparing with external LMs, the intrinsic LM is considered as modest as it is only trained with the transcription associated with the speech data. Although it is a common practise to interpolate the scores of the end-to-end model and the external LM, the need of an external model hurts the novelty of end-to-end. Therefore, researchers are investigating different ways of improving the intrinsic LM of the end-to-end model. By observing the fact that N-gram LMs and RNN LMs can complement each other, we would like to investigate the effect of implementing an N-gram neural network inside the end-to-end model. In this paper, we examine two implementations of N-gram neural network in the context of attention-based end-to-end ASR. We find that both implementations improve the baseline and CBOW (Continuous Bag-of-Words) performs slightly better. We further propose a way to minimize the size of the N-gram component by utilizing the coda information of the modeling units. Experiments on LibriSpeech dataset show that our proposed method achieves obvious improvement with only a slight increase in model parameters.
SCT-CC: A Supply Chain Traceability System Based on Cross-chain Technology of Blockchain

In the traditional supply chain traceability model, a centralized management method is prevalent in supply chain traceability system. Problems such as falsification of production data, difficulty in information sharing, and opaque data have become more and more prominent. Blockchain technology has the characteristics of decentralization, non-tampering, openness, transparency and traceability, and can overcome many shortcomings of centralized traceability systems. Building supply chain traceability system based on blockchain technology provides an effective way solving the problems arousing in the supply chain safety domain. We propose SCT-CC, a supply chain traceability system based on cross-chain technology of blockchain. First, we use cross-chain technology to design a multi-chain architecture. Then, we design smart contracts for different agencies in the supply chain. Finally, we use Hyperledger fabric as a development framework and built an experimental environment using Docker technology. We also test the query interface and the write interface. Through the analysis of the test results, the system can meet the needs of actual applications.
A single-stage, Deep Neural Network detector for the detection and tracking of moving objects in real time

Recent breakthroughs in parallel computing have brought back into attention the application of Deep Neural Networks (DNN) to machine vision tasks. Indeed, thanks to the parallel cores of the Graphics Processor Unit (GPU) inside it, a desktop computer can perform the basic machine vision tasks of object detection, localization and classification with surprising accuracy and speed. This paper investigates the feasibility and the efficiency of a low cost object detection and tracking system that exploits state-of-the-art algorithms and operates in real time, under realistic conditions. Specifically, the ‘You Only Look Once’ (YOLO) single stage detector is selected and employed after suitable training for the detection and tracking of an object moving on the 2D plane. A prototype, proof-of-concept system is built of readily available, low-cost components, to experimentally evaluate the proposed solution, with successful detection and localization results. Practical considerations arising chiefly from the latency introduced by the sensor (camera) are discussed and an algorithmic solution is proposed.
Design of Intelligent Lighting Monitoring System Based on Polling Algorithm in Internet of Things

Based on the research of large quantity, multi data and real-time intelligent monitoring of lighting equipment in the environment of Internet of things, this paper proposes a segmented lighting equipment monitoring system based on polling algorithm. With the help of effective communication protocol, the system designs a polling algorithm for data acquisition to realize long-term continuous and real-time tracking of lighting equipment, which is conducive to timely detection of lighting equipment operation faults in the monitoring process, and improves the monitoring efficiency and level.
Enhanced Content-Based Image Retrieval Using Information Oriented Angle-Based Local Tri-Directional Weber Patterns

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image’s basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing.
Quantum Circuits and Modules

This chapter is devoted to the quantum circuits and quantum modules. We first describe basic single-qubit gates and provide Bloch sphere representation of the single-qubit. Next, we describe binary and ternary quantum gates. We then provide the generalization to N-qubit gates and quantum information processing fundamentals. The quantum measurements circuits are provided then, and the principle of deferred measurement and the principle of implicit measurement are formulated. We also formulate Gottesman-Knill and Solovay-Kitaev theorems. Further, various sets of universal quantum gates are introduced. We then describe Bell state preparation circuit and quantum relay. We also describe basics concepts of quantum teleportation. Finally, we briefly discuss the computation-in-place concept.