Reviewerly/exHarmony huggingface dataset:
Evaluation of Labeling Uncertainty in Multiple Target Tracking with Track-before-detect Radars

The labeling problem in Multiple Target Tracking represents the problem of dealing with joint multi-target position uncertainties. This problem can be tackled by probabilistically characterizing the assignment of positions to labels [1]. The goal of this paper is to provide a performance evaluation criterion for such labeling characterization. In particular, an optimal decomposition of the association-free filtering posterior is derived analytically in a detection-based context. A particle-based implementation of this decomposition provides a definition of optimality regarding the labeling of the targets. This definition of optimality is also relevant for the characterization of labeling uncertainty in the track-before-detect context. An algorithm is provided for practical implementation of the method and used to evaluate the labeling uncertainty characterization proposed in [1].
A Study on Trust Models in Cloud Computing

Information Security is an aggregation of people effort, processes and technology to help organizations at providing confidentiality, integrity and availability in their information assets. Nowadays, almost all technology services are heavily dependent on cloud computing under their various forms of service models. We realize there are weaknesses in cloud services concerning issues of performance, reliability, security, and privacy, among other issues. Cloud services consumers do not have enough information on these issues neither on compliance with laws and regulations. In this paper, we present a comprehensive mapping of works that deal with cloud trust aspects from user perspective. We aim at identifying how users or consumers of Cloud services measure trust on cloud computing services. Our main contributions are: (i) an updated study on the state of the art of cloud trust models; (ii) a taxonomy of characteristics and contributions to cloud trust; (iii) a discussion on works which present hard problems of research on cloud trust models and gaps of contributions in the literature.
The Path to the Future of Education

No AccessApr 2020The Path to the Future of EducationAuthors/Editors: Husein Abdul-Hamid, Mohamed YassineHusein Abdul-HamidSearch for more papers by this author, Mohamed YassineSearch for more papers by this authorhttps://doi.org/10.1596/978-1-4648-1546-1_ch6AboutView ChaptersFull TextPDF (0.8 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: Summarizes key findings from the analysis of the education system in Lebanon within six macro and intermediate dimensions that constitute the foundations of an education system: (1) standards and norms; (2) resources and expertise; (3) delivery of instruction, school and teaching services, and school management relationships; (4) governance and leadership; (5) information, evidence, and feedback; and (6) accountability and quality assurance. An important step to achieving higher learning outcomes in Lebanon is to strengthen the three weakest dimensions of the system—system governance and leadership; the culture of evidence, information flow, and feedback loops; and the accountability and quality assurance mechanisms. Reform angles should focus on energizing the ecological layers and political will to strengthen education; strengthening governance, institutionalization, and internal organization; reducing gaps and inequity within and between schools; evaluating and scaling up innovations servicing schools; strengthening the regulatory framework guiding school performance; strengthening data systems to track budgets, personnel, and infrastructure; and improving institutional capacity. ReferencesBronfenbrenner, U 2009. The Ecology of Human Development: Experiments by Nature and Design. Cambridge, MA: Harvard University Press. Google ScholarNewman, J L, E M King, and H Abdul-Hamid. 2016. “The Quality of Education Systems and Education Outcomes.” Background Paper, The Learning Generation: Investing in Education for a Changing World, International Commission on Financing Global Education Opportunity, New York. Google Scholar Previous chapterNext chapter FiguresreferencesRecommendeddetails View Published: March 2020ISBN: 978-1-4648-1546-1 Copyright & Permissions Related CountriesLebanonRelated TopicsEducation KeywordsTEACHER TRAININGEDUCATIONAL OUTCOMESPRIVATE EDUCATIONACCESS TO EDUCATIONQUALITY OF EDUCATIONEDUCATION REFORMEDUCATIONAL EQUALITYACCOUNTABILITYGAP BETWEEN POOR AND WEALTHYSCALING UPSCHOOL LAWEDUCATION FINANCINGINVESTMENT IN EDUCATIONCAPACITY BUILDING PDF DownloadLoading ...
Un-Paired Real World Super-Resolution with Degradation Consistency

With the development of deep convolutional neural networks, deep-learning based image super-resolution has achieved excellent performances with paired training samples and prior degradation models. Nevertheless, in real world, the information transportation and compression procedure are generally unknown and only the low-quality low-resolution images are available. In this case, un-paired real world image super-resolution task is far more challenging than the paired one. In this paper, we develop an efficient un-paired super-resolution method with degradation consistency (DCSR). Specifically, a multi-level aggregation network (MLAN) is developed for feature representation, and three degradation-consistency losses are introduced for synchronously retaining the inherit contents and generating desired photo-realistic details. The proposed methods show superior performance on benchmark datasets and achieve 2nd place on "Target Domain RWSR" track of the AIM RWSR Challenge [19].
Master’s students’ post-editing perception and strategies

Abstract The present article aims at presenting the results of an exploratory post-editing process study carried out in a Belgian university, the University of Mons. For this experiment, 64 final-year translation students with no post-editing experience post-edited from English into French parts of five different institutional texts from the Directorate-General for Translation (DGT) of the European Commission. They were additionally asked to fill in a prospective questionnaire and a retrospective one, related to their post-editing perception and strategies. Four students took part in the experiment on a separate computer equipped with an eye-tracking device, so that eye-tracking data could be collected and compared with these students’ questionnaires. We found that results related to eye-tracking data correlate well with previous research, and that students’ perceptions of post-editing depend on each university’s particular context.
Hardware Based Detection, Recovery, and Tamper Evident Concept to Protect from Control Flow Violations in Embedded Processing

The ubiquitous presence of embedded devices coupled with their low processing power and finite energy creates unique challenges in the security of embedded systems. Software attacks targeted at maliciously modifying the control flow of an executing embedded program negatively affect real-time response. Hardware-based control flow violation detectors have been researched and tested, but still lack the means to recover from control flow attacks and provide meaningful data for post cyber-incident analysis. Proposed is a hardware-based system on a chip (SoC) concept to protect low-end embedded processors from control flow attacks. The concept provides an end-to-end protection combination of detection, response, recovery, and tamper evident techniques against control flow violations in the presence of interrupts, real-time operating systems, and exceptional functions. The implemented detection portion of the solution shows promise to detect most CRAs on low-end embedded systems without added cycle latency at the cost of increased area.
Methodology for the Production of Learning Objects Enriched with Augmented Reality by University Students

The Augmented Reality is currently considered one of the most promising emerging technologies; it has entered the educational process in an accelerated way as an innovative element that simplifies the interaction through mobile devices. This investigation analyzes an university innovation experience of Information Technology (IT) Docentship students, in the Human Sciences and Education Faculty of the Technical University of Ambato. It accomplishes the objective of designing learning objects enriched with Augmented Reality (AR), to measure the level of technological acceptance (TAM), the impact on academic performance, the proof of the training process and the production tools used. The results showed that the use of AR in university teaching has aroused a high degree of acceptance and motivation, an improvement in the academic performance and the advantages and disadvantages of the tools used for the production of objects enriched with Augmented Reality were determined.
NNBench-X: A Benchmarking Methodology for Neural Network Accelerator Designs

The tremendous impact of deep learning algorithms over a wide range of application domains has encouraged a surge of neural network (NN) accelerator research. Prior NN accelerator research reveals that the software-hardware co-design method is promising to achieve significant performance improvement and energy savings. To guide future co-designs, an evolving benchmark suite and its associated benchmark method are needed to characterize NN workloads and incorporate emerging NN compression techniques. However, the co-design method has not been well covered by existing NN benchmark work. In this paper, we propose a novel benchmarking methodology, which decouples the benchmarking process into three stages. First, we select the most representative applications from a user-customized candidate pool by analyzing their performance features. Second, we compress selected applications according to customized model compression techniques to generate a benchmark suite. Finally, we evaluate a variety of accelerator designs on the generated benchmark suite. To demonstrate the effectiveness of our benchmarking methodology, we conduct a case study of designing a general NN benchmark from TensorFlow Model Zoo and with various NN compression methods. We finally evaluate the benchmark on various representative architectures.
Recent Advances in Information-Theoretically Secure Data Outsourcing

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This tutorial focuses on secret-sharing-based techniques that allow data outsourcing and provides a review of recent secret-sharing-based techniques based on the security they offer (revealing or hiding access-patterns). Then, we discuss database systems developed using such techniques and, particularly, discuss a recent secret-sharing based system, namely Obscure.
Ranking-Based Cited Text Identification with Highway Networks

In recent years, content-based citation analysis (CCA) has attracted great attention, which focuses on citation texts within full-text scientific articles to analyze the meaning of each citation. However, citation texts often lack the appropriate evidence and context from cited papers and are sometimes even inaccurate. Thus it is necessary to identify the corresponding cited text from a cited paper and examine which part of the content of the paper was cited in a citation. In this study, we proposed a novel ranking-based method to identify cited texts. This method contains two stages: similarity-based unsupervised ranking and deep learning-based supervised ranking. A novel listwise ranking model was developed with the use of 36 similarity features and 11 section position features. Firstly, top-5 sentences were selected for each citation text according to a modified Jaccard similarity metric. Then the selected sentences were ranked using the trained listwise ranking model, and top-2 sentences were selected as cited sentences. The experiments showed that the proposed method outperformed other classification-based and voting-based identification methods on the test set of the CL-SciSumm 2017.
Tensor-valued random fields for continuum physics, 1st edition

Tensor-valued random fields for continuum physics is a meticulously written book with a firm theoretical basis in mathematics, physics, and engineering mechanics. This book covers a number of gener...
Analysis of Object Identification Using Quadrature Bank Filter in Wavelet Transforms

Nowadays guided missiles are widely used in military airbase. Weaponry advancements are developing in the last few decades. It has become an aggressive threat from the enemies in the battlefield. By using the effective electronic countermeasure (ECM) toward the targeted missile, it helps to miss the approaching target. For the missile tracking, parameters like phase, frequency, and intensity of the signal play a vital role to guide the signal toward the approaching target. In this paper, analysis of approaching missile has been done through quadrature bank filter (QBF), and it identifies the parameters using wavelet transform. It helps to improve the precision of the input signal by compressing the image through QBF. In addition, the simulation result has been evaluated, and the effectiveness of parameters in each stage of decomposition, filtration, and reconstruction has been done through wavelet transform.
Dynamic fusion of human and machine decisions for efficient cost-sensitive biometric authentication

Despite growing interest in human-machine collaboration for enhanced decision-making, little work has been done on the optimal fusion of human and machine decisions for cost-sensitive biometric authentication. An elegant and robust protocol for achieving this objective is proposed. The merits of the protocol is illustrated by simulating a scenario where a workforce of human experts and a score-generating machine are available for the authentication of handwritten signatures on, for example, bank cheques. The authentication of each transaction is determined by its monetary value and the quality of the claimed author's signature. A database with 765 signatures is considered, and an experiment that involves 24 human volunteers and two different machines is conducted. When a reasonable number of experts are kept in the loop, the average expected cost associated with the workforce-machine hybrid is invariably lower than that of the unaided workforce and that of the unaided machine.
An Examination on Motion Planning for Mobile Robot using Deep Q-learning with RPLidar Sensor

In this paper, we investigate about deep Q-learning for autonomous motion planning of mobile robot in indoor environment. Additionally, the proposed method utilizes an RPLidar sensor to deal with training issue. The deep Q-learning is adopted in this work, which is the combination between Q-learning and deep neural network. We propose a neural network model to learn the Q-function in order to solve the planning task, the proposed model is implemented in a simulation environment by using gazebo robot simulation. Our propose studies a new approach on motion planning for mobile robot. The new approach based on the deep Q-learning, which is start-of-art for robotic application. The approach does not based on a model of environment to navigation robot toward a given goal, therefore, it is independent with the scale of environment. As a result, the planning time is able to be real time to execute. Our model can directly map the range finder sensor data to a motion action such as the angular velocity, the result in section 3 points out our model well execute on a static indoor environment and successfully learn to reach a goal after some thousand training episodes. The research achieves two improvements. Firstly, the motion planning task is able to be real time planning comparing with the previous research. Secondly, the proposed model help robot reach to a given goal without the model of map.Keywords: Navigation, Obstacle avoidance, Deep Q-learning, RPLidar ,Deep neural network
Architecture Development of Cloud-Based Fail-Safe Privacy Data Storage and Processing System

The article describes the development of an architecture for cloud storage of melons based on a Residue Number System (RNS). The developed architecture is evaluated in terms of the reliability of the stored data, and its comparative analysis is carried out with existing data storage methods. The architecture of a multi-cloud big data storage and processing system based on the principles of modular arithmetic is proposed. The proposed modification of the cloud storage and data processing system allows increasing the reliability and fault tolerance of the stored and processed data. Single-level and two-level models for reliable storage of big data were developed based on the simulation results. The comparative analysis showed the advantage of models based on modular arithmetic over models based on traditional arithmetic.
Generating Myanmar News Headlines using Recursive Neural Network

Text summarization in the form of Headline prediction for written articles becomes a popular research recently. This paper presents a headline prediction model using Recursive Recurrent Neural Network (Recursive RNN) for Myanmar articles and evaluates the performance by comparing with sequence-to-sequence models. Recursive RNN model use encoder-decoder LSTM model that generates a single word forecast and call it recursively to text summarization in the form of news headlines for Myanmar news articles. A Recursive RNN, which takes input as the sequence of variable length and has to generate the variable length output by taking into account the previous input. 5000 articles of Myanmar news were collected to train headline prediction model. The performance of Recursive RNN, Seq2Seq with one-hot encoding and Seq2Seq with word embedding (GloVe) were evaluated in terms of ROUGE score values. The experimental results show that Recursive RNN model significantly better than other two models.
Comparative Study on Different Local Adaptive Thresholding Techniques for Detection of Sickle Cell Anaemia from Microscopic Blood Images

Modern image processing techniques are playing an important role in the detection and diagnosis of several medical abnormalities nowadays. In the process of analysis of any image, thresholding is a significant stage. Various researchers have developed different tools for image thresholding depending on the type of applications. Adaptive thresholding is a powerful tool in processing medical images for diagnosis of various diseases. In this work several adaptive image thresholding techniques are being used in processing the blood images of patients suffering from Sickle Cell Disease (SCD). The Sickle cell anaemia is a disorder of inherited type where defective haemoglobin distorts the shape of the red blood cell. It is essential to accurately identify the distorted red blood cells (RBCs) for faster diagnosis of this disease. Although different adaptive thresholding techniques like Niblack, Bernsen, Sauvola and NICK have extensively been used for segmentation purpose, this paper focuses mainly on applying these tools for image thresholding for detection of Sickle cell anaemia from microscopic blood images and providing a comparative analysis.
Study of distribution network fault location based on IACA

Abstract The probability of the distribution network fault is the highest in power network. Correct fault location of distribution network is very important for the reliability of power supply. A simplified model of distribution network is established in the paper. Then the switching function and evaluation function are introduced for distribution network fault location. Because of some defects in fault section location of distribution network in ACA(Ant Colony Algorithm), the IACA(Improvement Ant Colony Algorithm) method is proposed. The results of an example shows that the method was correct and effective.
Real-Time Suspicious Activity Detection

Sarang, Shefali Shinde, Harshal Raut, Vaishnavi Sonje, Shubham Phadke, GargiToday, we live in a less secure world. That being said we are constantly under some threat, be it accidents on road or robbery, etc. There are number of security systems which are installed to tackle these problems. Instead, they record video and consume memory. It does not give any implication about the incident. To tackle these problems a real-time suspicious activity detection system should be developed. This system will have an advantage over conventional system as it will continuously monitor the frame from particular camera. This can be implemented in any field using less amount of hardware. The system which we are designing is used to monitor the events taking place in frame of camera using image processing. In this proposed method, we are using a Raspberry Pi as our main processor to which camera will be interfaced.
Retrieving Quantum Backscattered Signals in the Presence of Noise

Quantum sensing based on entangled photon pairs is gradually establishing itself as a cornerstone in modern communication networks. The unrivalled capability of quantum sensing techniques in distilling signals plagued by noise, renders them suitable for deployment in backscatter communication networks. Several attempts have been made recently to utilize pairs of entangled signal- idler photons, to enhance the sensitivity of photo- detection in backscatter networks. However, these efforts have always assumed the lossless retention of the idler mode, which is a challenging task from a practical perspective. In this study we examine the extent to which quantum correlations remain after retaining the idler mode in a lossy memory element, while the signal photon propagates through a lossy thermal channel as usual. We also examine briefly two different detection methods, and estimate the received signal-to-noise ratio for them both. This new proposed model is one step further towards realizing quantum backscatter communication.
Comparative Analysis of the Usage of Neural Networks for Sound Processing

Main task of this article is to describe a problem of sound processing automation and to offer a solution to this problem. Visual presentation of audio data was processed with artificial neural network to determine sound fragments to a noise class. Flexibility and training possibility of neural networks are used for applying a single algorithm for difficult to classify data.
The Modified Traditional Motion Compensation Method in Video Compression Applications

This paper is dedicated to the research and development of the modified traditional motion compensation method. The paper proposes a new motion compensation method with the use of approximation functions with additional parameters. For applying the considered approximation functions in the considered wavelet-based video codec Dirac, the subtasks associated with the accuracy and the transmission method of additional parameters have been solved. Based on the processing results of different video with the approximation functions studied, the best suitable function has been chosen. Compared with the traditional motion compensation method, the usage of the proposed method in the considered video codec can reduce bitrate up to 20% for good reconstruction quality PSNR = 40 dB and up to 33.5% for excellent quality PSNR = 42 dB for frame splitting case 16×16. In the case of block size 32×32, the proposed method can reduce bitrate from 3% to 35% for quality PSNR = 40 dB and from 2% to 40% for quality PSNR = 42 dB.