Reviewerly/exHarmony huggingface dataset:
Robust and Fast Ship Detection In SAR Images With Complex Backgrounds Based on EfficientDet Model

Synthetic aperture radar (SAR) is one of the most important active imaging systems used in remote sensing. Thanks to SAR and deep learning methods, ship detection can be performed with high performances in recent years. However, using the images of different satellites with changing ship sizes and detecting the ships under complex backgrounds are two challenging tasks that decrease ship detection performance. Since the dimensions of the satellite images are quite high, it is also important to use a fast and lightweight deep learning model. In this paper, we propose the usage of EfficientDet-D0 model to provide a robust and fast solution to the above problems. Experiments were carried out on the Ship-Detection-Dataset that includes nearly 40,000 image patches from Sentinel-1 and Gaofen-3 satellites. EfficientDet-D0 model was compared with Faster R-CNN, RetinaNet, and SSD-MobileNetv2 in terms of 13 different performance metrics, computation times, and visual comparison. The results demonstrate that EfficienDet-D0 model provides the most robust solution to the complex background and multiscale ship size problems.
Monocular Visual Servo of Unmanned Surface Vehicles with View-field Constraints

In this article, a prescribed performance monocular visual servo (PPMVS) scheme is exclusively created such that an unmanned surface vehicle (USV) can be regulated to reach the desired configuration in the presence of both view-field constraints and unknown image depth. To satisfy view-field constraints, feasible specifications on system outputs are elaborately predefined by virtue of the prescribed performance control technique, and thereby resulting in an equivalent unconstrained visual servo system by incorporating barrier functions. To facilitate stable closed-loop system, the unknown image depth and its reciprocal are respectively accommodated by combining with the backstepping philosophy and Lyapunov synthesis. Completely independent of tuning parameters, the proposed PPMVS scheme makes not only feature points uniformly keep in the image plane, but also the USV asymptotically approach the desired pose. Simulation studies and comparisons on a prototype USV comprehensively demonstrate the effectiveness and superiority of the proposed PPMVS scheme.
Tight bound for estimating expectation values from a system of linear equations 

The System of Linear Equations Problem (SLEP) is specified by a complex invertible matrix $A$, the condition number $\kappa$ of $A$, a vector $b$, a Hermitian matrix $M$ and an accuracy $\epsilon$, and the task is to estimate $x^\dagger Mx$, where $x$ is the solution vector to the equation $Ax = b$. We aim to establish a lower bound on the complexity of the end-to-end quantum algorithms for SLEP with respect to $\epsilon$, and devise a quantum algorithm that saturates this bound. To make lower bounds attainable, we consider query complexity in the setting in which a block encoding of $M$ is given, i.e., a unitary black box $U_M$ that contains $M/\alpha$ as a block for some $\alpha \in \mathbb R^+$. We show that the quantum query complexity for SLEP in this setting is $\Theta(\alpha/\epsilon)$. Our lower bound is established by reducing the problem of estimating the mean of a black box function to SLEP. Our $\Theta(\alpha/\epsilon)$ result tightens and proves the common assertion of polynomial accuracy dependence (poly$(1/\epsilon)$) for SLEP, and shows that improvement beyond linear dependence on accuracy is not possible if $M$ is provided via block encoding.
Time-matching Recursive Extended Target Probability Hypothesis Density Filter for High Resolution Phased Array Radar

Extended target probability hypothesis density (ET -PHD) filter is a random finite set-based multi-target tracking (MTT) method. It implements the multi-target Bayesian filter approximately by propagating the first-order moment of target posterior, and achieves good real-time performance, attracting wide interest in the field of radar applications. This paper is devoted to proposing a time-matching recursive filtering method to improve the MTT ability of ET -PHD filter in high-resolution phased array radar (PAR) scenarios. Based on a pre-partitioning strategy and time matching framework, the proposed method calculates PHD for each sector independently, which can meet the requirements of PAR for flexibility. Furthermore, it also uses the recursive filtering method of multi-sensor MTT to update single-sector PHD by sequentially applying multi-scan measurements of the sector, and improves the accuracy of target state estimation. The simulation is implemented by using Gamma Gaussian inverse Wishart model, and validates the effectiveness of the proposed method.
Third Party Breaches - A Survey of Threats and Recommendations

Trust is a very important character trait and takes on many forms. People trust one another to develop relationships, organizations trust their supply chains to provide and deliver necessities to stay in business, and data consumers trust cloud service providers to protect their data and provide services as agreed upon. Trust however inherently comes with a certain amount of risk; “It’s difficult to learn how to trust because we know all too well the risk that comes along with it.” (Abood, 2018). In the cybersecurity world, these risks manifest themselves through breaches. According to the Ponemon institute, sixty-one percent of U.S. respondents reported their organizations experienced a data breach caused by a third-party affiliate (Editorial, 2020). This is a 5% increase from 2017 and a 7% increase from the year prior (Ponemon, 2017). This paper will discuss three examples of third-party breaches from different verticals, specific threats associated with these breaches, and actions parties should take to mitigate these threats. Finally, the paper will discuss additional recommendations for improving security while interacting with third parties.
A Check on WHO Protocol Implementation for COVID-19 Using IoT

The lockdowns imposed by the governments worldwide is proving to be one of the most taxing repercussion of COVID 19 pandemic. One of the efficacious precautionary methods recommended by the World Health Organization (WHO) is to wear a face mask when in close contact with other people. In this chapter, Raspberry Pi 3 has been used to implement a TensorFlow-based Deep Learning Neural Network model that tries to recognize if a person is wearing a face mask or not using Raspberry Pi camera module. It also notes his/her body temperature using a non-contact infrared temperature sensor. The proposed solution is mitigating the problem where people casually avoid wearing a mask on their face by detecting face mask and measuring body temperature which should fall in the normal human body temperature range simultaneously. Various examples have been used to train the model to make it robust. The dataset consisted of 1509 images in which 755 images were with mask and 754 images were without mask. The proposed deep learning model has an accuracy of 97.8% for face mask detection after training on TensorFlow GPU 2.0.0 which is best achieved till date as per authors' knowledge.
Garbling, Stacked and Staggered

Stacked Garbling (SGC) is a Garbled Circuit (GC) improvement that efficiently and securely evaluates programs with conditional branching. SGC reduces bandwidth consumption such that communication is proportional to the size of the single longest program execution path, rather than to the size of the entire program. Crucially, the parties expend increased computational effort compared to classic GC.
An Evaluation of Methods for Manipulating Virtual Objects at Different Scales

Immersive Virtual Reality enables users to experience 3D models and other virtual content in ways that cannot be achieved on a flat screen, and several modern Virtual Reality applications now give users the ability to include or create their own content and objects. With user-generated content however, objects may come in all shapes and sizes. This necessitates the use of object manipulation methods that are effective regardless of object size. In this work we evaluate two methods for manipulating virtual objects of varying sizes. World Pull enables the user to directly manipulate and scale the virtual environment, while Pivot Manipulation enables the user to rotate objects around a set of predefined pivot points. The methods were compared to a traditional 6 degree of freedom manipulation method during a user study and the results showed that World Pull performed better in terms of precision for small and large objects, while Pivot Manipulation performed better for large objects.
Design and Implementation of Digital Museum System Based on AR-VR Hybrid Technology

research-article Design and Implementation of Digital Museum System Based on AR-VR Hybrid Technology Share on Authors: Lehui Huang Jiangxi Science & Technology Normal University, China Jiangxi Science & Technology Normal University, ChinaView Profile , Na Lv Jiangxi Science & Technology Normal University, China Jiangxi Science & Technology Normal University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2675–2679https://doi.org/10.1145/3482632.3487493Online:22 November 2021Publication History 0citation31DownloadsMetricsTotal Citations0Total Downloads31Last 12 Months31Last 6 weeks13 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
iScreen: A Pure Software-based Screen Privacy Protection System for Mobile Devices

With the explosive popularity of mobile devices, people tend to use them in different areas of daily life including shopping online, searching information, social networking and etc.. In spite of great convenience, the wide-use of mobile devices also brings about serious risk of privacy leakage through screen peeking. Even though various methods have been proposed to address this problem, they have different types of shortcomings such as additional hardware cost, not applicable to long texts and etc.. To alleviate screen peeking in a smarter way, we propose a pure software-based screen privacy protection system named iScreen for commercial mobile devices, which can effectively reduce peeper’s readability but without affecting a valid user’s. The key idea of iScreen is to make use of the differences of viewing angles and distances between the valid user and shoulder surfer, and accordingly adjust the font size, color scheme and screen brightness. By this means, iScreen can obviously sacrifice the shoulder surfer’s readability with little degradation of the user’s. We build a prototype on Android smartphones and conduct real-world experiments to evaluate its performance.
PBFL: Communication-Efficient Federated Learning via Parameter Predicting

Abstract Federated learning (FL) is an emerging privacy-preserving technology for machine learning, which enables end devices to cooperatively train a global model without uploading their local sensitive data. Because of limited network bandwidth and considerable communication overhead, communication efficiency has become an essential bottleneck for FL. Existing solutions attempt to improve this situation by reducing communication rounds while usually come with more computation resource consumption or model accuracy deterioration. In this paper, we propose a parameter Prediction-Based DL (PBFL). In which an extended Kalman filter-based prediction algorithm, a practical prediction error threshold setting mechanism and an effective global model updating strategy are included. Instead of collecting all updates from participants, PBFL takes advantage of predicting values to aggregate the model, which substantially reduces required communication rounds while guaranteeing model accuracy. Inspired by the idea of prediction, each participant checks whether its prediction value is out of the tolerance threshold limits and only uploads local updates that have an inaccurate prediction value. In this way, no additional local computational resources are required. Experimental results on both multilayer perceptrons and convolutional neural networks show that PBFL outperforms the state-of-the-art methods and improves the communication efficiency by &amp;gt;66% with 1% higher model accuracy.
Foundations of Brain Image Segmentation: Pearls and Pitfalls in Segmenting Intracranial Blood on Computed Tomography Images

Not only the time-dependent varying of signal intensity (i.e. haematoma evolution) characteristics of the intracranial blood in computed tomography images, but also the fluctuating image quality, the distortions introduced after medical interventions, and the brain deformations and intensity profile variations due to underlying pathologies make the segmentation of intracranial blood a challenging task. In addition to describing various challenges with blood segmentation, this chapter also reviews the following: (1) the general concept of segmentation-explaining why a proper segmentation is a critical step when creating machine learning algorithms for image detection purposes, (2) the different segmentation types and how different medical conditions and technical issues can further complicate this task, (3) how to choose a proper software to facilitate the segmentation task, and (4) useful tips that may be applied before launching a similar segmentation project.
Emotion-Core: An Open Source framework for emotion detection research

Identifying emotions from text is crucial for a variety of real world tasks. We describe Emotion-Core, an Open-Source framework for training, evaluating, and showcasing textual Emotion Detection models. Our framework is composed of two components: Emotion Classification and EmotionUI, which allow researchers to easily extend and reuse existing emotion detection solutions. We discuss the potential impact of our software project, including a recent publication in the findings of the International conference on Empirical Methods in Natural Language Processing (EMNLP 2021). Our code is available and free to use for interested researchers.
A Quantum Computing Programming Language for Transparent Experiment Descriptions

We present a new quantum programming language called ‘Qualia’ that enables transparent programming of quantum hardware. Qualia allows seamless integration of abstraction layers such as the digital circuit layer and the analog control pulse waveform layer. Additionally, the language supports user-issued low-level hardware instructions like FPGA actions. Mid-circuit measurements and branching decision logic support real-time, adaptive programs. This flexibility allows users to write code for everything from quantum error correction to analog quantum simulation. The combination of a user-facing calibration database and a powerful symbolic algebra framework provides users with an unprecedented level of expressiveness and transparency. We display the salient characteristics of the language structure and describe how the accompanying compiler can translate programs written in any abstraction layer into precisely timed hardware commands. Qualia provides a fully transparent programming interface that allows simultaneous circuit level and hardware level control. There is no "behind-the-scenes" circuit compilation or hardware calibration. Every command is executed exactly as the the user dictates.
Comparison between the maximum likelihood and the bayesian estimation methods for logistic regression model (case study: risk of low birth weight in Indonesia)

Abstract This study aims to compare the efficacy of logistic regression model for identifying the risk factors of low-birth-weight babies in Indonesia using the maximum likelihood estimation (MLE)and the Bayesian estimation methods. The data used in this study is secondary data derived from the 2017 Indonesian Demographic Health Survey with a total sample of 16,344 newborn babies. Selection of the best logistic regression model was based on the smaller Bayesian Schwartz Information Criterion (BIC) value. The logistic regression model with the Bayesian estimation method has a smaller BIC value than the MLE method. Twin births, baby girl, maternal age at risk, birth spacing that is too close, iron deficiency, low education, low economy, inadequate drinking water sources have provided a higher risk of low-birth-weight incidence.
qopt: An experiment-oriented Qubit Simulation and Quantum Optimal Control Package

Realistic modelling of qubit systems including noise and constraints imposed by control hardware is required for performance prediction and control optimization of quantum processors. We introduce qopt, a software framework for simulating qubit dynamics and robust quantum optimal control considering common experimental situations. To this end, we model open and closed qubit systems with a focus on the simulation of realistic noise characteristics and experimental constraints. Specifically, the influence of noise can be calculated using Monte Carlo methods, effective master equations or with the efficient filter function formalism, which enables the investigation and mitigation of auto-correlated noise. In addition, limitations of control electronics including finite bandwidth effects can be considered. The calculation of gradients based on analytic results is implemented to facilitate the efficient optimization of control pulses. The software is published under an open source license, well-tested and features a detailed documentation.
Arabic Sentiment Analysis towards Feelings among Covid-19 Outbreak Using Single and Ensemble Classifiers

The need to study and analyze public opinions about the Corona virus (COVID-19) pandemic or about those preventive measures that are imposed, led to the emergence of many studies. These conducted studies have concerned the analysis of public feelings and opinions, known as sentiment analysis (SA). Taking a benefit of social media platforms such as Twitter a dataset of Arab people feelings, especially fear and anxiety, towards Covid-19 was built through surveying the Arabic content in this platform. A machine learning (ML) model was applied to analyze and categorize the tweets related to fear and anxiety regarding Covid-19 outbreak. In this model, the word2vec was employed for word embedding to form the vector of features with two CBOW pre-trained models CC.AR.300 and Arabic.news. Moreover, the effect of the sampling technique that is called Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTENN) was investigated in this study. In addition, the performance of several single-based and ensemble classifiers were evaluated and discussed. The experimental results show that applying word embedding and SMOTENN with both single and ensemble classifiers achieve a good improvement in terms of F1 average score compared to the baseline, single and ensemble classifiers without SMOTENN.
Amazon Chess Based on UCT-PVS Hybrid Algorithm

Amazon Chess is a game with complexity between Go and Chinese Chess. Its complexity is mainly due to its extremely large branching factor, which makes it difficult to reach a high depth in the search process. This paper adopts the hybrid search algorithm combines with Upper Confidence bounds Trees search algorithm (UCT) and Principal Variation Search (PVS), which is called UCT-PVS hybrid search algorithm. The UCT algorithm is used at the opening stage to enable the game player to obtain a more advantageous opening. The PVS combines with Historical Heuristics and Transposition Table, by reducing the pruning window, the pruning efficiency is effectively increased and greatly improves the search depth. The Amazon Chess software is developed by using this technology and is effectively improved its game level.
How to Enhance the Effectiveness of the Learning Process during Distance Learning. Psychologist Recommendations

Abstract Effective management of the learning/teaching process in distance learning is one of the major challenges facing education, especially now in the Covid-19 pandemic. The list of problems mainly combines social and interactive aspects. This article provides tips to help the student increase motivation, overcome feelings of social isolation, find alternative ways of communicating and, develop communication skills. The aim of the research is to identify the pros and cons of online learning, thus finding out how to improve the effectiveness of the learning process during distance learning. The research was obtained through a focus group, the research was conducted in Tbilisi public schools, students, teachers and parents participated in the focus groups. After analyzing the data, it was found that participants positively see such a form of teaching, which in turn would have a positive impact on students’ academic performance, but they also talk about small negative aspects.
Evaluation Model of New English Talents Training Model Based on AHP-Entropy Method

The current talent training model evaluation model uses the AHP method to calculate the evaluation index weights, which leads to the calculation of the evaluation index weights of the new English talent training model, which is difficult to pass the consistency test. To solve this problem, a new type of English talent training based on the AHP-entropy weight method is proposed. Model evaluation model. Based on the five principles of evaluation indicators, the evaluation indicators of the new English talent training model are divided into four levels: target level, criterion level, first-level index factor level and second-level index factor level, and establish an evaluation index system; The evaluation index system is divided into five levels and assigned to determine the set of evaluation system comments; The change of evaluation index is controlled between 0 and 1, and it is divided into positive index and negative index; Entropy weight method and AHP method are used to calculate the weight of evaluation index respectively. According to the calculation results, the weight of comprehensive evaluation index is obtained, and the final evaluation result is obtained through multi-level and step-by-step evaluation. The experimental results show that: the calculated evaluation index weight, the consistency test result is less than 0.1, and the consistency test is passed.
Sentiment Analysis using Machine Learning and Deep Learning Models on Movies Reviews

The huge amount of data being generated and transferred each day on the Internet leads to an increase of the need to automate knowledge-extraction tasks. Sentiment analysis is an ongoing research field in knowledge extraction that faces many challenges. In this paper, different machine learning, neural networks, deep learning models were evaluated over the IMDB benchmark dataset for movies reviews. Moreover, various word-embedding techniques were tested. Among all the presented models, the results of this work showed that the Long Short-Term Memory (LSTM) model with Bidirectional Encoder Representations from Transformer (BERT) embeddings has achieved the highest results with an accuracy of 93%.