Reviewerly/exHarmony huggingface dataset:
Smart Projector with Wireless Mouse

This work is based on image processing using Raspberry pi 3 through Python programming that is interfaced with OpenCV tool and the processed image captured by camera. Haar cascade algorithm is available in OpenCV. It is used to process the image with Haar classifier that creates black spot on presenter face by considering distance and position to safe eyes by picking the coordinates as breadth, width, length and height. This black spot prevents the eyes from high intensity light beam of projector. The size of the black spots varies and continuously generated as presenter moves in front of projector. This does not affect the information displayed on projector screen. If two (or more) presenters are sharing the same projector, the eye protecting black spots are generated accordingly. Moreover wireless air mouse is incorporated in this project to detect the mouse movement using image processing. Cursor will move through laser device and click status will transmit to Raspberry pi via Bluetooth. Presenter can easily use the wireless mouse from the distance for presentation. It will be considered that this project prevent the eyes of presenter as well as reduce the size and power consumption from relatively available projectors attached with PC because of this project is embedded on Raspberry pi.
Research on Clothing Image Classification Models Based on CNN and Transfer Learning

Traditional clothing image classification method, which involves artificially extracting features of clothing images, are not effective owing to images having complex background and clothing distortion. With the purpose of improving the classification accuracy, eight models are designed based on convolutional neural network (CNN) and transfer learning. These CNN models are trained on the dataset named Street-FashionData having eight categories of clothing images, and their performances are evaluated and compared by conducting classification tests. The experimental results show that model ICM-C and model ECM-C have high classification accuracies of 97.2% and 97.8%, respectively. These two models have the same convolutional bases as pre-trained Inception-v3 and pre-trained EfficientNet-B0, respectively, moreover, their entire networks need to be trained. The experimental results have proved the efficiency of classification models described in this paper.
Movie Data Visualization Based on WebGL

Web 3D was initially realized by installing plug-ins on the browser, and WebGL can render interactive 3D graphics on web pages without using plug-ins. Three.js is the open-source framework of WebGL, a small amount of JavaScript code can create complex 3D scenes. This article uses Three.js to realize 3D data visualization, including 3D histograms, pie charts, maps, earth, and force-oriented graphs, and uses them for a visualized display of film data.
Reservoir computing with optical solitons

The need for faster and energy-efficient computing technologies has recently pushed for major developments on alternative computing paradigms to the common von Neumann architecture. Amongst those, reservoir computing framework is an emerging concept that leverages a simple training process and eases transference to hardware implementations, allowing any given nonlinear physical system to act as a computing platform. In this work, we explore how we can make use of a discrete chain of solitons to create an effective reservoir computing framework, investigating not only the ability to learn data but also to predict models depending on the strength of the nonlinear interaction of the media. Probing the role of the nonlinear separation for tasks involving nonlinear separable data, these results open new possibilities for a multitude of physical implementations in the context of optical sciences, from optical fibers to nonlinear crystals.
Real-time Object Tracking Method based on Multi-Core DSP

Abstract In this paper, we propose a fast object tracking method used in real-time application. The tracking method is based on compact kernelized correlation filters. Traditional, gray values are used to describe target feature. In order to achieve relatively fast performance, we adopt Principal Component Analysis (PCA) to simplify Histogram of Gradient (HOG) as feature descriptor, which can significantly reduce computation resources. Our method is implemented on Texas Instruments (TI) multi-core digital signal processor (DSP) TMS320 C6657, which can achieve 60 frame per-second (fps). Finally, we conduct both quantitative and qualitative experiment to show the robustness and real-time performance of our method.
Implementation of Encryption Techniques in Secure Communication Model

Wireless communication evolves the modern generation where communication has been possible to limit the distance without any physical connection between two parties. Basically, communication can be of two types namely wired and wireless where the second is preferred mostly than first. But we need a secure wireless communication in case of industries, companies, e-commerce and communication technologies, etc. In this research, we can transmit encrypted data wirelessly through the channel to provide data security using different encryption techniques. To do this we propose a model which comprises two parts namely transmitter and receiver. In the transmitter portion, the plaintext is encrypted and then after modulation, it is sent through the channel. The receiver at first receives the channel output and then demodulates and finally decrypts the encrypted data to get the original plaintext which is sent from the sender to the receiver. Here, we have considered two encryption techniques, i.e., Caesar and RSA, and compared them with no encryption in our proposed model. We implement our model using the MATLAB programming language. The overall investigation shows that the RSA algorithm deserves better performance than Caesar and no encryption techniques because RSA shows lower bit errors with signal-to-noise ratio compared to no encryption and Caesar cipher.
A Reliable significant message based Ranking Algorithm for online social Networks

Nowdays, our daily lifeis much influenced by Social networks and significantly effects our wonted behaviours in day-to-day activities. Users desire to express their feelings through social networks mostly when they engage in joyful occasions. Users may be a participant in various groups in different social networks. In this fast-emerging world, it is extremely difficult to read all the messages in social networks. This paper has proposed a ranking algorithm to consolidate the significant messages in diverse groups of several social networks based on the interest and choice of users and also compare the performance of the algorithm with various social networks in respect of delay in reading messages, identifying significant messages of diverse groups.
The Star-Eaters: A 2019 Survey of Female and Gender-Non-Conforming Individuals Using Electronics for Music

Moving forward to the later twentieth and early twenty-first centuries, Chapter 14 provides a survey of contemporary female and gender-non-conforming artists using electronics for music. Margaret Schedel and Flannery Cunningham highlight how greater access to affordable means to manipulate digital sound from the autonomy of personal computers – away from difficult-to-access studios staffed by technicians and equipped with complex technology, which were previously largely the domain of male ‘experts’ – has opened up electronic music to a wider demographic of people (in terms of gender, race, and class). Taking an ethnographic approach which draws upon questionnaire material from twenty-four respondents variously identifying as composers, sound artists, instruments builders, and programmers, this chapter explores some of this diversity through the artists’ own words.
Application of soft computing techniques to calculation of medicine dose during the treatment of patient

For the last few decades, long medical treatments are entirely based on prescription of doctors which have been creating a significantly harmful impact on our society. Therefore, in this chapter, I am proposing a fuzzy logic–based intelligent system, which solves such types of problems. The application of fuzzy logic–based intelligent systems is capable of dealing with the calculation of drug dose in long medical treatment. The medicine doses play a vital role during the procedure and observation of the patient. In the patient's blood, many reactions have observed by the doctor, which may be high or low at some stage. Based on the observation, the number of medication doses is decided by the doctor to deal with the complications. The proposed method is capable of dealing with the calculation of medicine doses for chronic intestine illness symptoms like sedimentation and prostate antigen. Appropriate medicine dose requires for the long-duration treatment of any patient. For this experiment, I have used observational data of 20 patients. The experimental result has compared with the medicine dose provided by the doctors. Here, I have observed that the proposed intelligent system gives better results as compared to medicine dose recommended by the doctors or physicians.
Computer Vision in Android Application Development

This article is devoted to the application development for Android OS using computer vision technology. It demonstrates the implementation process step by step and also describes working with such development tools like: Java, ML Kit, Android Studio. The result of the study is application that the user interacts with by turning their head and blinking their eyes.
Two Camera 3D Time Series Reconstruction of a Mesh Subjected to Differential Excitation

The present study proposes a novel bio-inspired scheme for the 3D time series reconstruction of a Mesh. The key motivation of this study is to develop a cost effective and accessible system without compromising capabilities for the fundamental understanding in complicated external stimuli-induced vibration of a Mesh. A bio-inspired algorithm traces along the strands of the mesh to match a known control point to the next keypoint until the entire surface is matched for the critical reconstruction at the first frame/timestep. The match is then disseminated to the subsequent frames steps via digital image correlation and photogrammetric methods applied to effectively recover the time series response of the dynamic surface. This study offers significant and quantitative insight into the vibration and fluctuation of the entire excited surface. A 160mm×160mm mesh fluctuation is successfully re-constructing with a sampling density of approximately 1600 points at 24 frames per second. The proof-of-concept experiment was able to detect the undulation of mesh under an in-phase and an out-of-phase excitation, which result in the reconstruction of the point cloud with a 0.38mm error, as well as the frequency and phase angle accuracy attainments of about 99.1% and 86.0%, respectively. In short, the current reconstruction scheme may provide insights into the control and response of a piezoelectric mesh.
A Distributed Storage Middleware Based on HBase and Redis

In the era of big data, traditional relational databases face challenges in massive data storage. HBase is a non-relational database based on column storage which is used widely for big data storage. The writing performance of HBase is high, but the unbalanced load caused by its uneven data storage strategy is the bottleneck of its reading performance. HBase needs to access disks to get query results. Therefore, the efficiency of disk access has a great impact on the query performance of HBase. In view of the above problems, this paper proposes a storage middleware which utilizes HBase for load balancing and Redis for memory caching. Specifically, we improve HBase’s original load balancing algorithm for Regions and RegionServers and customize a Redis cache eviction algorithm according to the data’s query and update frequency. Furthermore, the coprocessor of HBase is used to synchronize data between HBase and Redis. Experiments on synthetic datasets show that the proposed storage middleware achieves better writing and reading performance than HBase. The load balancing algorithm employed in the middleware is better than HBase's original algorithm. The hit rate of the customized cache eviction algorithm is also higher than that of the LRU algorithm.
Fast Range Query of Fault Equipment Location Based on Breadth and Depth Ratio of the Binary Tree Model

The requirement for detecting faulty equipment is widespread in the industry. In today's big data era, using big data features to detect equipment is facing new opportunities and challenges. In order to improve the efficiency of faulty equipment detection, we found the characteristics of the binary tree that are suitable for manufacturing processes of the current status. In practice, this paper establishes a binary tree model for the manufacturing process. Then, in order to analyze and study the breadth traversal process and depth traversal process of bad nodes in binary tree, this paper proposes a location method of breadth and depth ratio. Finally, the location range of the faulty equipment (bad node) is quickly found due to the influence of breadth and depth ratio. The experimental results show the difference between the existing mechanisms of detection and position. In terms of accuracy and efficiency, both methods of deep traversal and breadth traversal have their own advantages in different devices and processes. To compare with the two methods, the detection method of breadth and depth ratio in this paper has the characteristics of higher efficiency, feasibility and accuracy.
Categorizing the feature space for two-class imbalance learning

Class imbalance limits the performance of most learning algorithms, resulting in a low recognition rate for samples belonging to the minority class. Although there are different strategies to address this problem, methods that generate ensemble of classifiers have proven to be effective in several applications. This paper presents a new strategy to construct the training set of each classifier in the ensemble by exploiting information in the feature space that can give rise to unreliable classifications, which are determined by a novel algorithm here introduced. The performance of our proposal is compared against multiple standard ensemble approaches on 25 publicly available datasets, showing promising results.
Privacy Preserving Serial Publication of Trajectory Data

Sharing trajectory data with researchers or any organization is very challenging due to individual privacy risk. Specially it becomes more challenging when trajectory data have to share serially after a specific interval. All the existing methods are not applicable for serial publication. Methods for serial publication is required because of dynamic behaviour of trajectory data. In a serial publication of trajectory data, two types of privacy guards must be provided. Firstly, privacy guards is to be used in each release. Secondly, there must be a privacy guard between two releases, so that individual trajectory is not identified. To fulfill these two objectives, we propose a model that consists of space shifting and spatiotemporal points clustering. In our model, two types of clustering approaches have been used. Each cluster meets the concept of k-anonymity that prevent record linkage attack in each release. After applying different clustering algorithm in each release, experimental result shows that our propose approach is able to mitigate intersection attack.
A Computational Efficient Architecture for Extremely Sparse Stereo Network

CNN-based stereo matching methods achieve great performance but come with high computational requirements. Pruning a CNN can reduce the complexity but may in turn lead to memory conflicts, lowering throughput. Our proposed architecture and memory mapping technique aim at reducing conflicts to exploit extremely sparse stereo matching networks. To maintain a high utilization of processing elements, we decompose the de-convolution operation into several convolution operations. The proposed architecture provides a 2.1 x speed up over SCNN. Compared to the software implementation, only 0.01% performance drop is observed, so that the proposed architecture obtains state-of-the-art accuracy compared to existing sparsity aware hardware implementations.
Empirical Study of Software Adoption Process in the Bitcoin Network

Managing the flood of devices is a modern challenge in distributed networks as it is becoming increasingly difficult to manage the software updates needed to protect the devices from malicious attacks. Bitcoin, which is a blockchain-based system that relies on approximately 10,000 volunteer nodes to manage its ledger, faces similar problems. A centralized system mitigates this problem by enforcing update policies through one authority. In contrast, a distributed system with different update policies for different users could enter a worst-case scenario of sparse deployment due to forgotten updates or refusal to apply them. Nevertheless, we find that Bitcoin peer-software updates have been surprisingly successful in terms of evading this scenario. Therefore, we investigate the modeling of the impact caused by software version history and the remaining challenges, such as the inability to work well with update cycles. Consequently, we report that previous vulnerable software versions are still being used, and the share growth decelerates as the number of versions increases. Moreover, we clarify that the probability of recovering the adoption rate to previous levels is small, while surviving older versions increases the vulnerability of the network.
Design of Slam Robot Based on Internet of Things

The hindrance evasion is utilized for recognizing obstacles and evading the impact of collision. This is autonomousrobot used to clean the useless objects in and around the orbit in the earth. The plan of obstacles shirking requires the coordination of numerous sensors as indicated by their presentation task it gets the data from encompassing zone through mounted sensors. Ultrasonic sensor is generally appropriate for snag location and it is of ease and high going capacity. Vacuum cleaner is used to remove the space debris in an efficient manner. Concurrent Localization and Mapping gauges provide the posture of a robot and the guide of the climate simultaneously; this sort of robot manufactures a guide speaking to its spatial climate while monitoring its situation inside the constructed map. SLAM is centralto a range of indoor, outside, in-air and submerged applications for both monitored and self-sufficient vehicles.
Recent Tools for Computer and Mobile-Assisted Foreign Language Learning Edited by Alberto Andujar

Recent Tools for Computer and Mobile-Assisted Foreign Language Learning Edited by Alberto Andujar Hershey PA: IGI Global US $ 156.00 ISBN 9781799810971 378 pages 2019
Local-Global Interactive Network For Face Age Transformation

Face age transformation aims to generate a face image in the past or future and has been receiving increasing attention for its significance recently. This paper proposes a local-global interactive framework for long-span face age transformation. We divide a face image into five local parts and design a local generative network for each of them to learn the local changes of a face image. Meanwhile a global generative network is utilized to learn the global changes. We introduce an interactive network and an age classification network, which are respectively used to integrate the local and global features and maintain the corresponding age features in different age groups. Given a face image at a certain age, our network can produce a realistic image of face aging or rejuvenation. We test and evaluate the model on complex datasets. Extensive qualitative comparison experiments prove the effectiveness and potential of our proposed method.
Operator appointed for cybersecurity innovation hotbed DISH

The Council has selected a consortium led by Barclays Eagle Labs to operate the new Manchester Digital Innovation and Security Hub (DISH).
A Multibit Left-Shift Modular Inverse Hardware Algorithm and its Implementation

Modular inverse calculation has critical influence on the efficiency of public-key cryptographic algorithms such as RSA and elliptic curve cryptography. In this work, based on the original single bit left-shift modular inverse algorithm, a multibit left-shift modular inverse hardware algorithm and its implementation are proposed. Our proposed algorithm makes the operands able to be left-shifted by at most 8 bits within one clock cycle as depending on the output bits of the leading zero counting module. This can produce a reduction on the average computation cycles and absolute execution time. Simulations show that the proposed algorithm can reduces to 0.8n cycles from original 2n cycles for two n-bit operands and gains a 40% decrease in execution time, compared with the original algorithm.
A Strategy for Advancing Research and Impact in New Computing Paradigms

In the domain of information technology, new paradigms, technologies, development approaches, and application scenarios continue to emerge rapidly presenting new opportunities for researchers to pursue. Emerging paradigms are often considered to be high risk for academic research due to the immaturity of research foundation and ambiguity in fundamentals supporting the paradigm. A pragmatic and holistic strategy would be needed to systematically steer continuous development and evaluation of research in the area and to accelerate impact and adoption. Drawing on our own a decade-long experience in establishing strong and impactful research in cloud computing, we document and reflect on our strategy for advancing research and building a strong community in what was believed to be a high risk emerging/new computing research area for researchers to embark on. The strategy consists of nine key elements which include creation of a roadmap, development of simulators and supporting tools, and creation of application demonstrators that confirm feasibility and effectiveness of applications of the new research findings. We discuss how development of simulators can be cost-effective in accelerating design of real systems and outline strategic role played by different types of publications or conference organization, and development and delivery of educational programs that form part of the research strategy. We illustrate effectiveness of the recommended strategy with our cloud computing research work, which is globally recognized for its impact.
Method of Multispectral Image Denoising Based on Whole and Sub-Sparsity

Multi-Spectral Image(MSI) denoising is an important preprocessing procedure to improve the performance of high-level processing. Tensor-based approach is one of the most popular methods for MSI denoising, since MSIs can be seen as multi-dimension arrays containing both spatial and spectral information. There are two main information in MSI, Global Correlation along Spectrum(GCS) and Nonlocal Self Similarity across space(NSS). Most tensor based approaches exploited these two characteristics by low-rank regularizations, mainly based on CANDERCOMP/PARAFAC(CP) decomposition and Tucker decomposition. However, they did not show a clear physical meaning. In this paper, we exploit the fact that pixels in MSI often cover several different materials and so that tensor data is mixed. Based on this, we divide tensor into several sub-tensors and propose a novel low rank regularization called Whole and Sub-Sparsity(WSS): GCS is modeled in the sub-tensors and NSS is modeled in the original tensor, which shows a clear physical meaning. Besides, to solve our model, we develop the corresponding algorithm by employing alternating direction method of multipliers(ADMM) framework. Experiment results show that our method is competitive compared to all state of the art MSI denoising methods.