Reviewerly/exHarmony huggingface dataset:
Attribute-based encryption for ciphertext in advanced encryption standard

With the confirmation of dispersed figuring, cell phones can spare/show signs of improvement singular actualities from everywhere at something component. Figure content material cloth fabric technique first-rate based encryption (CP-ABE) is probably a promising crypto logical approach for quality grained get to management of re-appropriated statistics inside the cloud. Be that as it can, a couple of dangers of key business enterprise disappointed the confirmation of its software. One impairment in crucial need of needs is that the important thing created potential problem. We exhibit that the front-quit gadgets of customers like practical phones quite a few the time have constrained warranty inclusion, so if individual keys are sincerely manage with the guide of them, clients peril key advent that isn't always in each case actually found yet typically existed in beyond exam. Moreover, huge purchaser deciphering overhead compels the realistic use of ABE. In those paintings, we will be inclined to prescribe an accommodating key company life-style in CP-ABE (CKM-CP-ABE). Our development recognizes passed on age, issue and functionality of character keys while no longer which includes any what's more repute quo. A fantastic grained and brief feature denial is proper key revive. The orchestrated beneficial component effectively settles no longer certainly key normal assertion downside at any charge conjointly key advent. In the duration in the center of, it lets in specifically lessen customer unscrambling overhead. A relationship with other agent CP-ABE designs shows that our association has in reality better execution as a long way as cloud-constructed re-appropriated realities taking an interest.
Rhabdomyosarcoma Histology Classification using Ensemble of Deep Learning Networks

A significant number of machine learning methods have been developed to identify major tumor types in histology images, yet much less is known about automatic classification of tumor subtypes. Rhabdomyosarcoma (RMS), the most common type of soft tissue cancer in children, has several subtypes, the most common being Embryonal, Alveolar, and Spindle Cell. Classifying RMS to the right subtype is critical, since subtypes are known to respond to different treatment protocols. Manual classification requires high expertise and is time consuming due to subtle variance in appearance of histopathology images. In this paper, we introduce and compare machine learning based architectures for automatic classification of Rhabdomyosarcoma into the three major subtypes, from whole slide images (WSI). For training purpose, we only know the class assigned to a WSI, having no manual annotations on the image, while most related work on tumor classification requires manual region or nuclei annotations on WSIs. To predict the class of a new WSI we first divide it into tiles, predict the class of each tile, then use thresholding with soft voting to convert tile level predictions to WSI level prediction. We obtain 94.87% WSI tumor subtype classification accuracy on a large and diverse test dataset. We achieve such accurate classification at 5X magnification level of WSIs, departing from related work, that uses 20X or 10X for best results. A direct advantage of our method is that both training and testing can be performed much faster computationally due to the lower image resolution.
Twitter data in Emotional Analysis - A study

As the innovation age encourages individuals to communicate their emotions via web-based media locales like Facebook, Instagram, Twitter, and so on. Numerous individuals share their musings and thoughts step by step in twitter similar to other tweets. This is considered as an exciting and attractive way to express ourselves as inspections are increasing bit by bit, due to which rundown of surveys are necessary for the job where summed up of text is required to give helpful data from the huge number of surveys. It is exceptionally hard for an individual to extricate helpful information or sum up it from the extremely enormous record. This paper focuses on comparison and analysis of different sentiment analysis techniques. It gives a far-reaching review about the recent and past examinations on sentiment analysis, challenges, and approaches for future angles.
A Throughput-Oriented NVMe Storage Virtualization with Workload-Aware Management

Storage virtualization is an important component of large-scale online services in multi-tenant clouds. It typically shares the physical storage among guest machines and performs transactional operations for high-performance data processing. However, even with the recent mediated pass-through virtualization optimization, the operations of multi-tenant storage I/O meet the bottleneck, and thus degrade the throughput performance of the cloud storage services. We observe that the root cause of the problem is the unawareness of varying and imbalanced workload inefficiency of resource management in the multi-tenant cloud storage setting. In this paper, we present FinNVMe, a new throughput-oriented NVMe storage virtualization management mechanism, that (1) passes-through I/O performance-critical resources and emulates privileged resources to provide high throughput in a workload-aware manner among multi-tenant VMs, (2) enables fine-grained scheduling for I/O resources to achieve promising flexibility and scalability with respective to virtualization, and (3) adopts the queue binding and the queue shuffling to reduce the virtualization and management overhead, and involves active polling for further I/O acceleration. This article subsequently evaluates FinNVMe with micro benchmarks on two typical scenarios (both balanced and imbalanced workload) and the real-world storage workloads to show its high throughput performance, along with the flexibility and scalability of virtualization and resource management. For example, FinNVMe achieves up to 20 percent throughput improvement with more stable latency in the varying and imbalanced workload.
A Modified Joint Geometrical and Statistical Alignment Approach for Low-Resolution Face Recognition

Domain Adaptation (DA) or Transfer Learning (TL) makes use of the already available source domain information for training the target domain classifier. Traditional ML algorithms require abundant amount of labeled data for training the model, and also they assume that both training and testing data follow similar distributions. However, in a real-world scenario, this does not always work. The scarcity of labeled data in the target domain is a big issue. Also, the source and the target domains have distinct data distributions. So, lessening the gap between the distributions of the two domains is very important so that a model that is trained using source domain information can be deployed to classify the target domain information efficiently. The already existing domain adaptation technique tries to reduce this distribution interval statistically and geometrically to an extent. Nevertheless, it requires some important components such as Laplacian regularization and maximizing source domain variance. Hence, we propose a Modified Joint Geometrical and Statistical Alignment (MJGSA) approach for Low-Resolution Face Recognition that enhances the previous transfer learning methods by incorporating all the necessary objectives that are useful for diminishing the distribution gap between the domains. Rigorous experiments on several real-world datasets verify that our proposed MJGSA approach surpasses other state-of-the-art existing methods.
Design of Authoring Tool for Static and Dynamic Projection Mapping

This study introduces the design details of a tool to create interactive projection-mapping content in a convenient manner.For the proposed tool design, a homography-based camera-projector calibration method was applied with the use of red-green-blue-depth images from a Kinect V2 sensor that did not require accurate camera calibration prerequisites.In addition, the proposed tool simultaneously achieved static projection mapping that projected the image content onto a fixed object, and dynamic projection mapping that projected the image content onto a user's body, by tracing the moving user.To verify the effectiveness of the proposed content-creation tool, users with no programming capabilities were employed to create contents that were projected onto various objects in fixed positions and a user's body in various poses, thereby analyzing the tool's completeness.Moreover, the projection accuracy was analyzed at different depth positions, and the projection-mapping accuracy was verified with the use of the proposed method.
Supply Chain Malware Targets SGX: Take Care of what you Sign

Malware attacks represent a significant part of today's security threats. Software guard extensions (SGX) are a set of hardware instructions introduced by Intel in their recent lines of processors that are intended to provide a secure execution environment for user-developed applications. To our knowledge, there was no serious attempt yet to overcome the SGX protection by leveraging the software supply chain infrastructure, such as weaknesses in the development, build or signing servers. While SGX protection does not specifically take into consideration such threats, we show in the current paper that a simple malware attack exploiting a separation between the build and signing processes can have a serious damaging impact, practically nullifying the SGX integrity protection measures. Finally, we also suggest some possible mitigations against the attack.
Space-variant Color Point Cloud Measurement System - Enomous Data Reduction using Saliency Map -

This paper aims at reducing enormous data amount of color point cloud measured from a 3D-LIDAR sensor by combining with a visible-ray color camera. A huge data amount of the points often keep us away from saving the data storage device and computing by a real-time processing. In this paper, the authors propose a measurement system, i.e., Space-variant Color Point Cloud Measurement System, in order to achieve enormous data reduction. A 2D-LIDAR sensor and a stepping motor are combined (i.e., playing a role of the 3D-LIDAR sensor) to compose a proto-type of our experimental device. A space-variant color point cloud generation method is also proposed using the above measurement system. The region of interest (ROI) map, generated from the saliency map of the input image from the CMOS color camera, plays an important role for the data reduction in our proposed method. This paper explains also how a pixel with RGB color data in each input image corresponds to each of the distance data, by paying attention to a disparity calculated from the distance. Experimental results proved good performance of data reduction using our proposed method by 60.59 percent and 40.82 percent in different reduction conditions compared to the original data amount, respectively.
Personalized Quality of Experience (QOE) Management using Data Driven Architecture in 5G Wireless Networks

the aim of this paper is to Personalized Quality of Experience (QOE) Management using Data driven Architecture in 5G Wireless Networks that consume less resources. The proposed research will be the part of the overall research project, which focuses on addressing a problem that many organizations experience that introduce an Enterprise Architecture to support the integration of different services across the enterprise. With the rapid growth in mobile network usage and video streaming being the most popular service, Quality of Experience of video in mobile networks is of extreme importance to both service providers and their customers. The ability to effectively predict Quality of Experience of video is key for QoE adaptation and higher levels of customer satisfaction. In this work machine learning algorithms were used to create models that predict QoE with network QoS parameters, including wireless-specific and 5Gspecific parameters. An 5G simulation that reflects the current mobile traffic landscape was created to obtain the data set for training. An objective tool for video QoE evaluation was used to gather QoE data necessary to train the prediction models. Support Vector Machines, Random Forest, Gradient Boosted Trees and Neural Networks were chosen as the machine learning algorithms for Quality of Experience prediction, and it was shown that they achieve high accuracy. Influence of wireless-specific parameters on QoE prediction was also investigated, and it was discovered that they are suitable for use in Quality of Experience prediction models.The problem is that; organizations do not know where they either have or may encounter weaknesses in their Enterprise Architecture with Data Driven Architecture (DDA). The framework presented is based on concepts from Wireless Networks with Driven Architecture will be designed to support both Transitional Gap Analysis (TGA) and Comparative Gap Analysis (CGA). TGA is supported by comparing a baseline Data Driven Architecture (DDA) to a target QoE where both DDA have been defined from the management perspective. DDA is facilitated by mapping a QoE to two or more 5G networks. The research methodology used in the paper is design science research for the QoE management based 5G network. The QOE for implementation of 5th generation network and apply it in many different real-world organizations. The goal of the paper is to present a framework in the form an implementation and management model, called QOE, that visualizes the gaps (weaknesses) in proposed or existing enterprise architectures and to support a comparative analysis process for different a5Grnative solution approaches. a set of requirements on the QOE management can be presented and the frameworks are applied on Matlab for implementation.
Recognition Method of Important Words in Korean Text Based on Reinforcement Learning

The manual labeling work for constructing the Korean corpus is too time-consuming and laborious. It is difficult for low-minority languages to integrate resources. As a result, the research progress of Korean language information processing is slow. From the perspective of representation learning, reinforcement learning was combined with traditional deep learning methods. Based on the Korean text classification effect as a benchmark, and studied how to extract important Korean words in sentences. A structured model Information Distilled of Korean (IDK) was proposed. The model recognizes the words in Korean sentences and retains important words and deletes non-important words. Thereby transforming the reconstruction of the sentence into a sequential decision problem. So you can introduce the Policy Gradient method in reinforcement learning to solve the conversion problem. The results show that the model can identify the important words in Korean instead of manual annotation for representation learning. Furthermore, compared with traditional text classification methods, the model also improves the effect of Korean text classification.
Usability study of Abhilekh Patal, National Archives of India (NAI), India

This study contains the results of a research conducted to know the user's perception about a national digital library system in India called Abhilekh Patal of National Archive of India, New Delhi. This paper aims to gain an insight about the broad scenario of usability studies in India and the researcher has collected necessary data using a structured questionnaire based on a modified criterion containing eight usability attributes. This research also aims to know the problem area of digital platforms developed for users of National Archive of India.
Modeling nonstationary lens blur using eigen blur kernels for restoration

Images acquired through a lens show nonstationary blur due to defocus and optical aberrations. This paper presents a method for accurately modeling nonstationary lens blur using eigen blur kernels obtained from samples of blur kernels through principal component analysis. Pixelwise variant nonstationary lens blur is expressed as a linear combination of stationary blur by eigen blur kernels. Operations that represent nonstationary blur can be implemented efficiently using the discrete Fourier transform. The proposed method provides a more accurate and efficient approach to modeling nonstationary blur compared with a widely used method called the efficient filter flow, which assumes stationarity within image regions. The proposed eigen blur kernel-based modeling is applied to total variation restoration of nonstationary lens blur. Accurate and efficient modeling of blur leads to improved restoration performance. The proposed method can be applied to model various nonstationary degradations of image acquisition processes, where degradation information is available only at some sparse pixel locations.
Architecture of an end-to-end energy consumption model for a Cloud Data Center

Estimates show that a significant proportion of future ICT related energy consumption will be from Cloud Computing. Based on detail analysis and survey of energy consumption and optimization trends in cloud computing, this research presents a comprehensive end-to-end energy consumption model of a cloud facility extending from the end-user equipment to the data center facility. The model is subdivided into three planes and four associated layers and depicts the cross-plane and cross-layer relationships between the components in terms of energy consumption and potential optimization areas and provides a reference framework for planning power optimization strategies at a cloud facility.
Evaluation of the Efficacy and Performance of a New Nano-Drug Carrier in the Treatment of Recurrent Oral Ulcerper

Recurrent oral ulcer is a common oral mucosal disease. Due to its periodic and recurrent characteristics, the onset of burning pain is unbearable, which brings great inconvenience to the patient’s life and seriously affects the patient’s quality of life. There are certain limitations to conventional drug therapy. With the rapid development of nanotechnology, the obvious advantages of nanotechnology such as targeting, controlled release, biocompatibility is obviously shown. The combination of nanotechnology and medical research has led to the emergence of polymer nanoparticles and ligands. Nano-drugs for gene therapy, and many other new nano-drug carriers, polymer micelle is a new type of nano drug carrier that has appeared in recent years. It has both a hydrophilic shell and a hydrophobic core, and has a variety of excellent properties, such as higher stability in vivo and in vitro , and poorly soluble drugs. In this paper, a new method for treating recurrent oral ulcers based on a new nano-drug carrier was studied. Because of the hydrophilicity and biocompatibility of oral cell surface proteins, the research progress of nanopharmaceutical carrier in the treatment of recurrent oral ulcers is reviewed. The experimental results show that the method has good reproducibility and high efficiency in the treatment of recurrent oral ulcers. It is used to explore the application and progress of nanotechnology in the diagnosis and treatment of recurrent oral ulcers, and to provide new ideas for the clinical diagnosis and treatment of recurrent oral ulcers. This new technical method has wide practical application value.
Trajectory Sampling and Fitting Restoration Based on Machine Vision for Robot Fast Teaching

Industrial robot teaching movement methods can be roughly divided into two types: direct teaching and offline teaching. Because the offline teaching method requires modeling of the robot and its surroundings, the technical requirements of the operator are high and the process is time-consuming and energy-consuming, the direct teaching method is commonly used. However, when the trajectory of the robot movement is complicated, a large number of points need to be taught to ensure the accuracy of the movement. In order to improve the teaching efficiency, a rapid teaching method for industrial robots based on machine vision technology is proposed. This method requires the robot to move on a fixed plane and achieves rapid teaching by sampling and fitting the robot's end trajectory. Firstly, a robot visual teaching system composed of a robot movement control subsystem and a visual image acquisition and processing subsystem is set up, which solved the communication problem between B & R real-time operating system and Windows operating system. It realizes direct observation of movement trajectory by method of "eye-in-hand". Then, using the data points obtained by manual sampling, a cubic B-spline curve fitting algorithm is used to fit and restore a continuous and smooth trajectory curve. Using the camera's internal and external parameters and the hand-eye calibration results, the target set point sequence to which the robot end should move can be inversely calculated. Finally, the experiment discusses the influence of different sampling methods on the trajectory restoration result and obtains the optimal sampling method to improve the accuracy of trajectory restoration. Although this method cannot be applied to scenarios with strict location accuracy requirements, it can be applied to most welding and spraying tasks with general location accuracy requirements. At the same time, the binocular camera or RGB-D depth camera can be used to replace the monocular camera in this paper, so that the method can be extended to three-dimensional stereo application scenarios.
S-RASTER: contraction clustering for evolving data streams

Abstract Contraction Clustering (RASTER) is a single-pass algorithm for density-based clustering of 2D data. It can process arbitrary amounts of data in linear time and in constant memory, quickly identifying approximate clusters. It also exhibits good scalability in the presence of multiple CPU cores. RASTER exhibits very competitive performance compared to standard clustering algorithms, but at the cost of decreased precision. Yet, RASTER is limited to batch processing and unable to identify clusters that only exist temporarily. In contrast, S-RASTER is an adaptation of RASTER to the stream processing paradigm that is able to identify clusters in evolving data streams. This algorithm retains the main benefits of its parent algorithm, i.e. single-pass linear time cost and constant memory requirements for each discrete time step within a sliding window. The sliding window is efficiently pruned, and clustering is still performed in linear time. Like RASTER, S-RASTER trades off an often negligible amount of precision for speed. Our evaluation shows that competing algorithms are at least 50% slower. Furthermore, S-RASTER shows good qualitative results, based on standard metrics. It is very well suited to real-world scenarios where clustering does not happen continually but only periodically.
Performance Evaluation of a Secure Microkernel on a Single-Board Computer

Safety-critical systems require the underlying Integrated Circuits (ICs) to be resilient against security and reliability failures. At the kernel level, there have been efforts to use micro-kernels to isolate components and limit the capabilities of wouldbe attackers. These implementations might incur additional overheads on the system that may affect the performance. This paper tests the overall performance of a formally-verified secure microkernel, seL4, in order to ascertain whether it is acceptable for real-time embedded applications. Various benchmark suites are used to compare an seL4-enabled Jetson TK1 and native Jetson TK1 computer. Results show that there are impacts on performance due to the use of SeL4 microkernel.