Reviewerly/exHarmony huggingface dataset:
Design and Analysis of a Rural Accurate Poverty Alleviation Platform Based on Big Data

Poverty alleviation has always been the focus of China's work. According to the survey, the poverty population in rural areas has been reduced to a large extent, and the unemployed have had the lowest historical record in history. Big data technology is a new technology that has slowly emerged in recent years. The use of big data technology to create a visual platform for rural poverty alleviation is a relatively new idea at this stage. And we use the Map-reducebased big data missing value filling algorithm, which is designed to solve the data loss phenomenon in the query process. It greatly improves search quality and improves a series of problems in the current platform for accurate poverty alleviation and visualization.
Implementation of FM-Index Based Pattern Search on a Multi-FPGA System

Pattern matching is a versatile task which has a variety of applications including genome sequencing as a major application. During the analysis, short read mapping technique is used where short DNA sequences are mapped relative to a known reference sequence. This paper discusses the use of reconfigurable hardware to accelerate the short read mapping problem. The proposed design is based on the FM-index algorithm. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for genome sequencing due to the fastest mapping from known indices. In order to make use of inherent parallelism, a multi-FPGA system called Flow-in-Cloud (FiC) is used. FiC consists of multiple boards, mounting middle scale Xilinx’s FPGAs and SDRAMs, which are tightly coupled with high speed serial links. By distributing the input data transfer with I/O ring network and broadcasting I-Table, C-Table and Suffix-Array with the board-to-board interconnection network, about 10 times performance improvement was achieved when compared to the software implementation. Since the proposed method is scalable to the number of boards, we can obtain the required performance by increasing the number of boards.
Implementable hybrid entanglement witness

Hybrid encoding of quantum information is a promising approach towards the realisation of optical quantum protocols. It combines advantages of continuous variables encoding, such as high efficiencies, with those of discrete variables, such as high fidelities. In particular, entangled hybrid states were shown to be a valuable ressource for quantum information protocols. In this work, we present a hybrid entanglement witness that can be implemented on currently available experiments and is robust to noise currently observed in quantum optical set-ups. The proposed witness is based on measurements of genuinely hybrid observables. The noise model we consider is general. It is formally characterised with Kraus operators since the considered hybrid system can be expressed in a finite dimension basis. A practical advantage of the witness is that it can be tested by measuring just a few experimentally available observables.
MobiGyges: A mobile hidden volume for preventing data loss, improving storage utilization, and avoiding device reboot

Sensitive data protection is essential for mobile users. Plausibly Deniable Encryption (PDE) systems provide an effective manner to protect sensitive data by hiding them on the device. However, existing PDE systems can lose data due to overriding the hidden volume, waste physical storage owing to the “reserved area” for avoiding data loss, and require device reboot when using the hidden volume. This paper presents MobiGyges, a hidden volume-based mobile PDE system, to fill the gap. MobiGyges addresses the problem of data loss by restricting each storage block used only by one volume, and it improves storage utilization by eliminating the “reserved area”. MobiGyges also avoids device reboot by mounting the hidden volume dynamically on-demand with the Dynamic Mounting service. Moreover, we identify two novel PDE oriented attacks, the capacity comparison attack and the fill-to-full attack. MobiGyges can defend against them by jointly leveraging the Shrunk U-disk method and multi-level deniability. We implement the MobiGyges proof-of-concept system on a real mobile phone Google Nexus 6P with LineageOS 13. Experimental results show that MobiGyges prevents data loss, avoids device reboot, improves storage utilization by over 30% with acceptable performance overhead compared with current solutions.
FAURAS: A Proxy-Based Framework for Ensuring the Fairness of Adaptive Video Streaming over HTTP/2 Server Push

HTTP/2 video streaming has gotten a lot of attention in the development of multimedia technologies over the last few years. In HTTP/2, the server push mechanism allows the server to deliver more video segments to the client within a single request in order to deal with the requests explosion problem. As a result, recent research efforts have been focusing on utilizing such a feature to enhance the streaming experience while reducing the request-related overhead. However, current works only optimize the performance of a single client, without necessary concerns of possible influences on other clients in the same network. When multiple streaming clients compete for a shared bandwidth in HTTP/1.1, they are likely to suffer from unfairness, which is defined as the inequality in their bitrate selections. For HTTP/1.1, existing works have proven that the network-assisted solutions are effective in solving the unfairness problem. However, the feasibility of utilizing such an approach for the HTTP/2 server push has not been investigated. Therefore, in this paper, a novel proxy-based framework is proposed to overcome the unfairness problem in adaptive streaming over HTTP/2 with the server push. Experimental results confirm the outperformance of the proposed framework in ensuring the fairness, assisting the clients with avoiding rebuffering events and lowering bitrate degradation amplitude, while maintaining the mechanism of the server push feature.
Exploring entanglement in open cavity parametric oscillators: From triply to doubly resonant cavities

We use a versatile model to evaluate the multipartite entanglement and the nonclassical light generation in optical parametric oscillators, exploring the differences between doubly and triply resonant cavity configurations. We demonstrate the entanglement of the pump mode with converted fields in both situations, and the fundamental differences of oscillators using parametric down conversion and four wave mixing processes as the intracavity amplification technique. The strong correlations involving the sidebands of the pump and converted fields give the signatures of a rich dynamic of multipartite entanglement.
Comparison of OpenStack Orchestration Procedures

OpenStack is a cloud computing platform, where controller node interacts with other compute nodes to create virtual server instances. Using this OpenStack platform it is possible to create any node on demand and provide the required functionality. Using OpenStack we can create or delete a node in four ways, such as server creation using simple openstack command, server creation using Heat, server creation using Tacker and server creation using Mistral. In this work, first we experimentally find the creation time of a single server considering various orchestration procedure available in OpenStack platform. Then we extend the same procedure for creation of multiple servers (vnfs) and measure the performance of server creation time and command execution time. Further we compare these procedures to show the advantage and disadvantage with respect to some practical aspects.
Knot selection in sparse Gaussian processes with a variational objective function

Abstract Sparse, knot‐based Gaussian processes have enjoyed considerable success as scalable approximations of full Gaussian processes. Certain sparse models can be derived through specific variational approximations to the true posterior, and knots can be selected to minimize the Kullback‐Leibler divergence between the approximate and true posterior. While this has been a successful approach, simultaneous optimization of knots can be slow due to the number of parameters being optimized. Furthermore, there have been few proposed methods for selecting the number of knots, and no experimental results exist in the literature. We propose using a one‐at‐a‐time knot selection algorithm based on Bayesian optimization to select the number and locations of knots. We showcase the competitive performance of this method relative to optimization of knots simultaneously on three benchmark datasets, but at a fraction of the computational cost.
Applying Convolutional Neural Networks For Image Detection

Machine Learning “Data Science”. Its goal is to program a computer in order to use a provided data for learning from its characteristics and to create a model which used later to predict a new unseen data. Many Successful applications were developed by using Machine learning and it really acts in our life daily activities, like recommended systems, disease detection, sales predictions, robot's development, and many others that affects in our life. Machine learning uses many approaches like supervised learning, unsupervised learning and reinforcement learning, also every approach use a state of art algorithm. One of the wildly powerful approaches is the Artificial Neural Networks which use its own algorithm. During this research we will cover a part of the supervised learning which is “CNN” Convolutional Neural Networks, which is a subsidiary of Artificial neural networks. Convolutional neural networks are wildly used to analyze visual images. During this research we will introduce CNN architect, starting from the model construction techniques that targets best fit with the best selection of the hyper parameter for the model with optimal accuracy, targeting the best images classifications predictions.
TeeRex: Discovery and Exploitation of Memory Corruption Vulnerabilities in SGX Enclaves

Intel's Software Guard Extensions (SGX) introduced new instructions to switch the processor to enclave mode which protects it from introspection. While the enclave mode strongly protects the memory and the state of the processor, it cannot withstand memory corruption errors inside the enclave code. In this paper, we show that the attack surface of SGX enclaves provides new challenges for enclave developers as exploitable memory corruption vulnerabilities are easily introduced into enclave code. We develop TeeRex to automatically analyze enclave binary code for vulnerabilities introduced at the host-to-enclave boundary by means of symbolic execution. Our evaluation on public enclave binaries reveal that many of them suffer from memory corruption errors allowing an attacker to corrupt function pointers or perform arbitrary memory writes. As we will show, TeeRex features a specifically tailored framework for SGX enclaves that allows simple proof-of-concept exploit construction to assess the discovered vulnerabilities. Our findings reveal vulnerabilities in multiple enclaves, including enclaves developed by Intel, Baidu, and WolfSSL, as well as biometric fingerprint software deployed on popular laptop brands.
Testing Distributed Cloud: A Case Study

Cloud computing offers the potential to improve productivity and reduce costs but it was faced with an overhead on high latency. As a result, a decentralized cloud paradigm was introduced for distributing cloud services known as Distributed Cloud. In this paper, we present a comprehensive survey on distributed cloud, and then we suggest a new architecture for testing such new environment. Finally, we illustrate our approach trough a case study.
Toward RSA-OAEP Without Random Oracles

We show new partial and full instantiation results under chosen-ciphertext security for the widely implemented and standardized RSA-OAEP encryption scheme of Bellare and Rogaway (EUROCRYPT 1994) and two variants. Prior work on such instantiations either showed negative results or settled for “passive” security notions like IND-CPA. More precisely, recall that RSA-OAEP adds redundancy and randomness to a message before composing two rounds of an underlying Feistel transform, whose round functions are modeled as random oracles (ROs), with RSA. Our main results are: In particular, our full instantiations evade impossibility results of Shoup (J. Cryptology 2002), Kiltz and Pietrzak (EUROCRYPT 2009), and Bitansky et al. (STOC 2014). Moreover, our results for s-clear RSA-OAEP yield the most efficient RSA-based encryption scheme proven IND-CCA2 in the standard model (using bold assumptions on cryptographic hashing) to date.
Deep <scp>OCR</scp> for Arabic script‐based language like Pastho

Abstract Developing cursive script recognition systems have always been a challenging task for researchers. This article proposes a ligature‐based recognition system for the cursive Pashto script using four pre‐trained CNN models using a fine‐tuned approach. The SqueezeNet, ResNet, MobileNet and DenseNet models have been observed for the classification and the recognition of Pashto sub‐word (ligature). Overall, the proposed system is divided into two domains (Source and Target). The source domain contains the pre‐trained models used on the ImageNet Dataset. These models are later fine‐tuned using the transfer learning approach to be used for the Pashto ligature recognition. The data augmentation techniques of negative and contour are used to increase the representation of ligature images and the dataset size. The CNN models have been evaluated on the benchmarks Pashto ligatures FAST‐NU dataset. The proposed system achieved the highest recognition rate of up to 99.31% using the DenseNet architecture of Convolutional Neural Network for Pashto ligature.
Large-Scale Network-Traffic-Identification Method with Domain Adaptation

With the continuous evolution of the Internet, a variety of web applications, such as video streaming and social network services, are widely used, significantly increasing the amount of traffic. Classifying the types of network traffic in detail is becoming more important from the perspectives of resource management and security analysis. In an Internet service provider (ISP) level large-scale network, details of packet payload cannot be collected due to the huge amount of traffic. Alternatively, network flow data representing the statistics of a sequence of packets are collected from devices. However, due to the lack of packet details, the granularity of classification is limited to the protocol level. For better understanding of the types of traffic in detail, we propose an application-level traffic identification method combining both packet data of small-scale networks and flow data of large-scale networks. With our proposed method, we adapt an identification model trained from small-scale network data to large-scale network data with domain adaptation.
Photo-Voltaic (PV) Monitoring System, Performance Analysis and Power Prediction Models in Doha, Qatar

This study aims developing customized novel data acquisition for photovoltaic systems under extreme climates by utilizing off-the-shelf components and enhanced with data analytics for performance evaluation and prediction. Microcontrollers and sensors are used to measure meteorological and electrical parameters. Customized signal conditioning, which can withstand high-temperature along with microcontrollers’ development boards enhanced with appropriate interfacing shields and wireless data transmission to iCloud IoT platforms, is developed. In addition, an automatically controllable in-house electronic load of the PV system was developed to measure the maximum power possible from the system. LabVIEW™ program was used to allow ubiquitous access and processing of the recorded data over the used IoT. Furthermore, machine learning algorithms are utilized to predict the PV output power by utilizing data collected over a two-year span. The result of this study is the commissioning of original hardware for PV study under extreme climates. This study also shows how the use of specific ML algorithms such as Artificial Neural Network (ANN) can successfully provide accurate predictions with low root-mean-squared error (RMSE) between the predicted and actual power. The results support reliable integration of PV systems into smart-grids for efficient energy planning and management, especially for arid and semi-arid regions.
Robot Vision System for Human Detection and Action Recognition

Mobile robots equipped with camera sensors are required to perceive humans and their actions for safe autonomous navigation. For simultaneous human detection and action recognition, the real-time performance of the robot vision is an important issue. In this paper, we propose a robot vision system in which original images captured by a camera sensor are described by the optical flow. These images are then used as inputs for the human and action classifications. For the image inputs, two classifiers based on convolutional neural networks are developed. Moreover, we describe a novel detector (a local search window) for clipping partial images around the target human from the original image. Since the camera sensor moves together with the robot, the camera movement has an influence on the calculation of optical flow in the image, which we address by further modifying the optical flow for changes caused by the camera movement. Through the experiments, we show that the robot vision system can detect humans and recognize the action in real time. Furthermore, we show that a moving robot can achieve human detection and action recognition by modifying the optical flow.
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping.

RMPflow is a recently proposed policy-fusion framework based on differential geometry. While RMPflow has demonstrated promising performance, it requires the user to provide sensible subtask policies as Riemannian motion policies (RMPs: a motion policy and an importance matrix function), which can be a difficult design problem in its own right. We propose RMPfusion, a variation of RMPflow, to address this issue. RMPfusion supplements RMPflow with weight functions that can hierarchically reshape the Lyapunov functions of the subtask RMPs according to the current configuration of the robot and environment. This extra flexibility can remedy imperfect subtask RMPs provided by the user, improving the combined policy's performance. These weight functions can be learned by back-propagation. Moreover, we prove that, under mild restrictions on the weight functions, RMPfusion always yields a globally Lyapunov-stable motion policy. This implies that we can treat RMPfusion as a structured policy class in policy optimization that is guaranteed to generate stable policies, even during the immature phase of learning. We demonstrate these properties of RMPfusion in imitation learning experiments both in simulation and on a real-world robot.
Receding Horizon Curiosity.

Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion. A principled treatment of the problem of optimal input synthesis for system identification is provided within the framework of sequential Bayesian experimental design. In this paper, we present an effective trajectory-optimization-based approximate solution of this otherwise intractable problem that models optimal exploration in an unknown Markov decision process (MDP). By interleaving episodic exploration with Bayesian nonlinear system identification, our algorithm takes advantage of the inductive bias to explore in a directed manner, without assuming prior knowledge of the MDP. Empirical evaluations indicate a clear advantage of the proposed algorithm in terms of the rate of convergence and the final model fidelity when compared to intrinsic-motivation-based algorithms employing exploration bonuses such as prediction error and information gain. Moreover, our method maintains a computational advantage over a recent model-based active exploration (MAX) algorithm, by focusing on the information gain along trajectories instead of seeking a global exploration policy. A reference implementation of our algorithm and the conducted experiments is publicly available.
Pandemic underpins need for SOAR

Network SecurityVol. 2020, No. 5 RegularsPandemic underpins need for SOARColin TankardColin TankardDigital PathwaysSearch for more papers by this authorColin TankardPublished Online:3 Nov 2021https://doi.org/10.1016/S1353-4858(20)30057-XAboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 May 2020Volume 2020Issue 5 ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 3 November 2021 Published in print 1 May 2020 Information© MA Business Ltd.PDF download
Measure of the density of quantum states in information geometry and quantum multiparameter estimation

Recently, there is a growing interest in study quantum mechanics from the information geometry perspective, where a quantum state is depicted with a point in the projective Hilbert space. By taking quantum Fisher information (QFI) as the metric of projective Hilbert spaces, estimating a small parameter shift is equivalent to distinguishing neighboring quantum states along a given curve. Henceforth, information geometry plays a significant role in the single parameter estimation. However, the absence of high dimensional measures limits its applications in studying the multi-parameter systems. In this paper, we will discuss the physical implications of the volume element of QFI. It measures the intrinsic density of quantum states (IDQS) in projective Hilbert spaces, which is, then, a measure to define the (over) completeness relation of a class of quantum states. As an application, IDQS can be used in quantum measurement and multi-parameter estimation. We find the density of distinguishable states (DDS) for a set of efficient estimators is measured by the invariant volume of the classical Fisher information, which is the classical counterpart of QFI and serves as the metric of statistical manifolds. Correspondingly, a determinant form of quantum Cram\'{e}r-Rao inequality is proposed to quantify the ability to infer the IDQS via quantum measurement. As a result, we find a gap between IDQS and maximal DDS over the measurements. The gap has tight connections with the uncertainty relationship. Exemplified with the three-level system with two parameters, we find the maximal DDS attained via the \emph{vertex measurements} (MvDDS) equals the square root of the quantum geometric tensor's determinant. It indicates the square gap between IDQS and MvDDS is proportional to the square of Berry curvature.