Reviewerly/exHarmony huggingface dataset:
A Remote Memory Sharing System for Virtualized Computing Infrastructures

Resource management is a critical issue in today’s virtualized computing infrastructures. Consolidation is the main technique used to optimize such infrastructure. Regarding memory management, it allows gathering overloaded and underloaded VM on the same server so that memory can be mutualized. However, because of infrastructures constraints and complexity of managing multiple resources, consolidation can hardly optimize memory management. In this article, we propose to rely on a remote memory sharing for mutualizing memory. We implemented a system which monitors the working set of virtual machines, reclaims unused memory and makes it available (as a remote swap device) for virtual machines which need memory. Our evaluations with HPC and Big Data benchmarks demonstrate the effectiveness of this approach. We show that remote memory can improve the performance of a standard Spark benchmark by up the 17 percent with an average performance degradation of 1.5 percent (for the providing application).
Support Constrained Generator Matrices of Gabidulin Codes in Characteristic Zero

Gabidulin codes over fields of characteristic zero were recently constructed by Augot et al., whenever the Galois group of the underlying field extension is cyclic. In parallel, the interest in sparse generator matrices of Reed-Solomon and Gabidulin codes has increased lately, due to applications in distributed computations. In particular, a certain condition pertaining to the intersection of zero entries at different rows, was shown to be necessary and sufficient for the existence of the sparsest possible generator matrix of Gabidulin codes over finite fields. In this paper we complete the picture by showing that the same condition is also necessary and sufficient for Gabidulin codes over fields of characteristic zero. Our proof builds upon and extends tools from the finite-field case, combines them with a variant of the Schwartz-Zippel lemma over automorphisms, and provides a simple randomized construction algorithm whose probability of success can be arbitrarily close to one. In addition, potential applications for low-rank matrix recovery are discussed.
Deep Learning Semantic Statistical Features’ Labeling Of SAR Image Based On K-MEANS Clustering And Wavelet Transform

Abstract [Purpose] Because Synthetic Aperture Radar (SAR) image contains much speckle, at present, it’s hard to obtain good results for SAR image with the traditional image classification methods. However, SAR image has rich texture information which facilitates SAR image segmentation. There is a Deep Learning method of K-means data clustering that is used to calculate the optimal k value to avoid more-segmentation or less-segmentation, the advantages of fuzzy theory and neural network are used to improve the classification accuracy of image processing.[Methods] According to the statistical characteristics of SAR images and the semantics of fuzzy neural networks analysis, an efficient image segmentation method based on Deep Learning Semantic analysis and wavelet transform is proposed to achieve precision of classification; Firstly, the texture features of SAR image are extracted by Deep Learning Semantic clustering; Secondly, the SAR image are characterized according to SAR semantics. Based on one of the Deep Learning Semantic k-means algorithms, the best k -value iterations of SSE(Sum of the Squared Errors)and SC(Silhouette Coefficient) are performed, and the suitable value k is selected; Finally, by Deep Learning segmentation based on the texture feature of the SAR image and the filtered gray component vector, and the SAR image is classified to facilitate the change detection of images.[Results] The experimental results show that a good k value detection is performed on the CPU/GPU platform. The SAR images is compared with two different results between the wavelet filtering and Deep Learning semantic method images which are single or multiple standard classified through detection-change SAR is better than the Empirical Approach, then the labels are classified for the accuracy and computational efficiency that are calculated. The classification results were accurately ameliorated. [Conclusions] The similarity calculation of k -value cluster is an important precondition, so it’s necessary to select the optimal k -value suitable for the segmentation condition. The method achieves are taken an adequate SAR image analysis of effect and improvement for performance by semantic classification of k-means clustering.
Mobile Device Forensics Investigation Process

One of the main topics that is discussed today is how can a person leverage technology in a positive and secure way in order to enhance their lives. However, with improvements in technology comes challenges; the concern that people have over their privacy and the safeguard of sensitive information being the greatest. In fact, one of the most used technologies is the mobile, which can take different forms, features, and shapes and create, store, delete, and transfer various types of data that can be evidence for the forensics fields. As such, this chapter proposes a different approach to this field by conglomerating and researching for all the information available and aiming at building a comprehensive systematic literature review on the topics of forensics, digital and mobile device forensics using the PRISMA methodology, with the intent of supporting and enhancing the mobile device forensics investigation process and allowing for a more robust and up-to-date knowledge base by breaking through the techniques available.
Luminance Channel Based Camera Model Identification

Camera model identification is an active research problem because of its importance in investigating the source and the authenticity of an image. Traditional camera model identification methods are based on strategies to extract the low-level traces left by the image acquisition pipeline of a camera on an image. One such intrinsic and camera-specific trace is the sensor pattern noise (SPN). The SPN is roughly approximated from the noise-residual obtained by performing high-pass filtering on an image. The noise-residual of an image also contains information about other types of noises. The extraction of the noise-residuals is generally performed on a single primary color channel, like the green channel of an image. However, the performance of a channel in the YCbCr color space is never explored. In this paper, we have proposed a novel camera model identification method based on convolutional neural network, where the noise-residuals are extracted from the luminance (Y) channel of the images. A constrained convolutional layer learns data-driven high-pass filters to extract the noise-residuals and the following layers learn a feature representation for the classification task. We have conducted experiments with multiple class combinations from the Dresden image database. The experimental results show the effectiveness of the Y channel for camera model identification both in terms of classification accuracy and convergence of the network.
Performing Reengineering using Scrum Agile Framework

Software reengineering is an approach of improving the maintainability of the existing software. Reengineering enhances the quality of the software and keeps the software alive for a more extended period. For the last four decades, researchers used a reengineering approach to transform the legacy system, to adopt new requirements as well as to reduce the faults of the system. The proposed work reduces the complexity of the software by applying the reengineering process. Scrum agile approach has been used for implementing the process of reengineering. Work also validates the improvement in the software after performing reengineering. The internal design complexity of the software is measured and reengineering is performed to reduce software complexity.
The Time Series Prediction Algorithm Based on Improved GSA-ELM

Aiming at the random selection of the input weight and threshold of the extreme learning machine (ELM), the stability is poor. This paper proposes an extreme learning machine model optimized based on the gravitational search algorithm (GSA). The chaotic sequence is used to initialize the population position, and Levi's flight updates the object position to avoid falling into a local optimum, and improves the gravitational search algorithm. Then it optimizes the parameters of the extreme learning machine. Results show that the model has good prediction effect on time series.
Robust 3D Object Detection from LiDAR Point Cloud Data with Spatial Information Aggregation

Current 3D object detectors from Bird's Eye View (BEV) LiDAR point cloud data rely on Convolutional Neural Networks (CNNs), which have originally been designed for camera images. Therefore, they look for the same target features, regardless of the position of the objects with respect to the sensor. Discarding this spatial information makes 3D object detection unreliable and not robust, because objects in LiDAR point clouds contain distance dependent features. The position of a group of points can be decisive to know if they represent an object or not. To solve this, we propose a network extension called FeatExt operation that enables the model to be aware of both the target objects features and their spatial location. FeatExt operation expands a group of feature maps extracted from a BEV representation to include the distance to a specific position of interest in the scene, in this case the distance with respect to the LiDAR. When adding the proposed operation to a baseline network in an intermediate fusion fashion, it shows up to an 8.9 average precision boost in the KITTI BEV benchmark. Our proposal can be easily added to improve existing object detection networks.
Mediating effects of academic self-efficacy in the relationship between academic stress and sleep quality among academic high school students

Purpose: This study aimed to confirm mediating effects of academic self-efficacy in the relationship between academic stress and sleep quality among academic high school students.Methods: The participants included 195 academic high school students who were attending a private educational institution in the Gwangju or Pusan metropolitan city. Data were collected from September 1 to September 30, 2019. The data were analyzed using PROCESS macro SPSS/WIN 3.2 and bootstrapping was used to test the mediating effects.Results: There were significant relationships between sleep quality and academic stress (r=-.55, &lt;i&gt;p&lt;/i&gt;&lt;.001), sleep quality and academic self-efficacy (r=.35, &lt;i&gt;p&lt;/i&gt;&lt;.001), and academic stress and academic self-efficacy (r=-.16, &lt;i&gt;p&lt;/i&gt;&lt;.020). Academic self-efficacy showed partial mediating effects in the relationship between academic stress and sleep quality.Conclusion: These findings suggest that the development of a program that improves the academic self-efficacy of academic high school students who are experiencing academic stress is a way to improve the quality of their sleep.
Target Recognition Method of Rehabilitation Robot Based on Image Local Features

Target recognition technology is an important topic in the field of artificial intelligence, which is widely used in fields such as medicine, robot vision, highway traffic, and VR technology. In the robot vision system, the target recognition technology requires high precision, real-time, and practicality, and it must be able to cope with the harsh recognition environment on site. This paper mainly studies the target recognition method of rehabilitation robot based on image local features. In this paper, a rehabilitation robot recognition system is established based on the local features of the image and the target recognition technology. Firstly, after collecting or processing the initially prepared images, feature extraction is performed, and the feature vectors extracted from the features are classified and recognized and then transferred to the recognition model of the rehabilitation robot. Then, according to the functional requirements of the rehabilitation robot recognition system, the “one-to-many” rehabilitation mode and real-time system monitoring are realized to accurately identify the target state. Finally, the Harris algorithm is used to convert the predetermined image set into a grayscale image, and the Gaussian distribution is used uniformly to determine the location and proportion of the feature points, and extract the corresponding image. The Harris matching method used in this article is very effective and has the highest accuracy. The number of correct matches accounted for 58.3%. Compared with the SIFT algorithm and the Harris+SIFT algorithm, the accuracy rate increases by 12.3% and 14.4%, respectively. The recall rate increased by 13.2% and 8.9% respectively. The experimental results show that the target recognition technology of rehabilitation robot based on image local features is more accurate than general technology.
FaultDroid

Fault attacks belong to a potent class of implementation-based attacks that can compromise a crypto-device within a few milliseconds. Out of the large numbers of faults that can occur in the device, only a very few are exploitable in terms of leaking the secret key. Ignorance of this fact has resulted in countermeasures that have either significant overhead or inadequate protection. This article presents a framework, referred to as FaultDroid, for automated vulnerability analysis of fault attacks. It explores the entire fault attack space, identifies the single/multiple fault scenarios that can be exploited by a differential fault attack, rank-orders them in terms of criticality, and provides design guidance to mitigate the vulnerabilities at low cost. The framework enables a designer to automatically evaluate the fault attack vulnerabilities of a block cipher implementation and then incorporate efficient countermeasures. FaultDroid uses a formal model of fault attacks on a high-level specification of a block cipher and hence is equally applicable to both software and hardware implementation of the cipher. As case studies, we employ FaultDroid to comprehensively evaluate the fault scenarios in several common ciphers—AES, CLEFIA, CAMELLIA, SMS4, SIMON, PRESENT, and GIFT—and assess their vulnerability.
Safety Protection Design of Virtual Machine Drift Flow in Cloud Data Center Based on VXLAN Technology

Based on the analysis of the security problems existing in the cloud platform of the data center, this paper proposes a set of cloud platform security protection scheme being with virtualization technology.This paper focuses on the overall architecture of cloud platform as well as the design of virtualization security architecture.Meantime, it introduces the key technologies of VXLAN in detail.The scheme realizes flexible scheduling of security resources through virtual pooling of independent security gateway and virtual machine isolation through VXLAN technology.Moreover, it guides all horizontal traffic to independent security gateway for processing, unified management of security gateway through cloud platform by using Huawei NSH business chain technology.This scheme effectively solves the horizontal transmission of security threat among virtual machines, and realizes the fine security control and protection for the campus data center.
A Hybrid Deep Neural Networks for Sensor-based Human Activity Recognition

Human activity recognition is playing a significant role to health-care. Existing methods to classify human activity are mainly based on machine learning algorithm combined with features selection method and deep neural network models. Due to the complexity and diversity of sensors signal data of human activities, there exists challenge to design a suitable model. In this paper, we designed a hybrid deep neural network model, which makes use of several kinds of deep neural networks to learning features of sensors data for human activities. However, the periodicity and transferability of human action require model is able to know spatial-temporal information and the vanishing gradient problem need the rationality for a design of model. In view of the abovementioned factors, proposed model is designed by convolutional layers, bidirectional LSTM layers and attention modules. Experimental results on UCI-HAR dataset show, proposed model achieved an accuracy of up to 95.58%, which is the best performance among the classic classification methods.
Data Cleaning of Raw Tweets for Sentiment Analysis

Preparation of data prior to information retrieval is an important task to perform so as to gather accurate results efficiently. Preprocessing is an approach that helps to make data ready for mining algorithms. Aim of this research is to club all the techniques of cleaning for preprocessing of opinion bearing text in one single model. Besides, entire process of preprocessing for textual data is furnished in two steps for this work. First phase is of data collection and the second includes cleaning of data. Further, the paper endows insight of all the functionalities incorporated for cleaning process.
Examination hall access control system using Radio Frequency Identification

Conducting of examination is a crucial and complex job for most universities and at times this exercise comes with threads of security breaches and fraudulent activities. Hence, the purpose of this paper is to provide information about the design of an examination hall access control system as well as an Information Service System that is used by university institutions. The proposed technology aims to manage controlled and authorized access into examination halls, and to manage student's examination attendance records and to provide capabilities of monitoring student absentees during the examination process, as well as to provide supporting information services including but not limited to the provision of student examination timetable, examination attendance register, and examination attendance notifications. To minimize the safety and security concerns, as well as reducing the rate of corruption in the educational sector, an extensive research with a quantitative approach is undertaken and a time and motion study is conducted to identify the most time-efficient and robust examination hall access control technology. The time and motion study performed reveals that the implementation of this solution will reduce examination documentation efforts by 73% and decrease the time spent to manually grant access to students into examination halls by 35 seconds per student. This study shows that the proposed solution is time-effective, and the adoption of the examination hall access control technology will achieve lower threads of security breaches and fraudulent activities when compared to other technologies or manual paper-based processes.
Preventing the generation of inconsistent sets of crisp classification rules

In recent years, the interest in interpretable classification models has grown. One of the proposed ways to improve the interpretability of a classification model based on collections of crisp rules is to use sets (unordered collections) instead of lists (ordered collections). One of the problems associated with sets is that multiple rules may cover a single instance but predict different classes for it, thus requiring a conflict resolution strategy. In this work, we propose two algorithms capable of finding feature-space regions inside which any created rule would be consistent with the already existing rules, preventing inconsistencies from arising. Our algorithms, named CFSGS and CFSBE, do not generate classification rules nor classification models but are instead meant to enhance algorithms that do so, such as Learning Classifier Systems. We analyzed both algorithms from a theoretical perspective and conducted experiments with a proof of concept evolutionary algorithm that employs CFSBE. The experiments suggest that using CFSBE as an embedded tool does incur a computational overhead, but such cost is not prohibitive.
Segmentation, Detection and Classification of Cell Nuclei on Oral Cytology Samples Stained with Papanicolaou

Although oral cancer is considered a global health issue with 350.000 people diagnosed over a year it can successfully be treated if diagnosed at early stages. Papanicolaou is an unexpensive and non-invasive method, generally applied to detect cervical cancer, but it can also be useful to detect cancer on oral cavities. The manual process of analyzing cells to detect abnormalities is a time-consuming cell analysis and is subject to variations in perceptions from different professionals. This paper compares models for three different deep learning approaches: segmentation, object detection and image classification. Our results show that the binary object detection with Faster R-CNN is the best approach for nuclei detection and localization (0.76 IoU). Since ResNet 34 had a good performance on abnormal nuclei classification (0.88 accuracy, 0.86 F_1 score) we concluded that these two models can be used in combination to make a localization and classification pipeline. This work reinforces that the automated analysis of oral cytology to make a pipeline for nuclei classification and localization using deep learning can help to minimize the subjectivity of the human analysis and also to detect cancer at early stages.
At Home with Alexa: A Tale of Two Conversational Agents

Voice assistants in mobile devices and smart speakers offer the potential of conversational agents as storytelling peers of children, especially those who may have limited proficiency in spelling and grammar. Despite their prevalence, however, the built-in automatic speech recognition features of voice interfaces have been shown to perform poorly on children's speech, which may affect child-agent interaction. In this paper, we describe our experiments in deploying a conversational storytelling agent on two popular commercial voice interfaces - Google Assistant and Amazon Alexa. Through post-validation feedback from children and analysis of the captured conversation logs, we compare the challenges encountered by children when sharing their stories with these voice assistants. We also used the Bilingual Evaluation Understudy to provide a quantitative assessment of the text-to-speech transcription quality. We found that voice assistants' short waiting time and the frequent yet misplaced interruptions during pauses disrupt the thinking process of children. Furthermore, disfluencies and grammatical errors that naturally occur in children's speech affected the transcription quality.