Reviewerly/exHarmony huggingface dataset:
A Non-Blind Deconvolution Semi Pipelined Approach to Understand Text in Blurry Natural Images for Edge Intelligence

Text understanding from natural scene images has progressively gained much interest in computer vision due to the frequent emergence of handheld or wearable devices. Sometimes, these devices capture low-quality images. As a result, the image can be disrupted with unpredictable blur caused by text movement and camera shake. Further, the problem is integrated with edge intelligence, an emerging paradigm that drives the computing services, applications, and tasks from mainstream cloud to network edge. The purpose is to minimize the bandwidth cost and latency and improves privacy. Hence, this research introduces a novel semi pipelined technique to address such challenging issue for edge intelligence. The fundamental contributions of this work are as given: (i) after text image enhancement, the natural text images are blurred to create a synthetic dataset; (ii) we introduced image-based 2D Radix-4 DIT FFT and the inverse to deblur the blurred images; (iii) after that, text understanding process is applied on the recovered images. Firstly, the text localization and segmentation are taken out using a novel open contour and 4-connected edge-based region approach. Secondly, the recovered images are classified into text and non-text classes adopting multimodal feature representation. Thirdly, Character Labeling Convolution Neural Network (CL-CNN) model is introduced for character labeling by extracting deep features to work fine on discriminative and ambiguous text. Finally, the experiments validated that the proposed framework achieved promising results on ICDAR 2003, SVT, and IIIT5K compared with standard techniques substantially and from blur images efficiently and flexibly.
An Advanced Noise Reduction and Edge Enhancement Algorithm

Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments, especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised one from a noisy input image. Second, the produced images are passed through a proposed image fusion (IF) module based on Laplacian pyramid decomposition to combine them and prevent noise amplification and color shift. Finally, we introduce a progressive refinement (PR) module, which adopts the summed-area tables to take advantage of spatially correlated information for edge and image quality enhancement. Qualitative and quantitative evaluations demonstrate the efficiency, superiority, and robustness of our proposed method.
Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance

To evaluate if artificial intelligence (AI) can discriminate recalled benign from recalled malignant mammographic screening abnormalities to improve screening performance.A total of 2257 full-field digital mammography screening examinations, obtained 2011-2013, of women aged 50-69 years which were recalled for further assessment of 295 malignant out of 305 truly malignant lesions and 2289 benign lesions after independent double-reading with arbitration, were included in this retrospective study. A deep learning AI system was used to obtain a score (0-95) for each recalled lesion, representing the likelihood of breast cancer. The sensitivity on the lesion level and the proportion of women without false-positive ratings (non-FPR) resulting under AI were estimated as a function of the classification cutoff and compared to that of human readers.Using a cutoff of 1, AI decreased the proportion of women with false-positives from 89.9 to 62.0%, non-FPR 11.1% vs. 38.0% (difference 26.9%, 95% confidence interval 25.1-28.8%; p < .001), preventing 30.1% of reader-induced false-positive recalls, while reducing sensitivity from 96.7 to 91.1% (5.6%, 3.1-8.0%) as compared to human reading. The positive predictive value of recall (PPV-1) increased from 12.8 to 16.5% (3.7%, 3.5-4.0%). In women with mass-related lesions (n = 900), the non-FPR was 14.2% for humans vs. 36.7% for AI (22.4%, 19.8-25.3%) at a sensitivity of 98.5% vs. 97.1% (1.5%, 0-3.5%).The application of AI during consensus conference might especially help readers to reduce false-positive recalls of masses at the expense of a small sensitivity reduction. Prospective studies are needed to further evaluate the screening benefit of AI in practice.• Integrating the use of artificial intelligence in the arbitration process reduces benign recalls and increases the positive predictive value of recall at the expense of some sensitivity loss. • Application of the artificial intelligence system to aid the decision to recall a woman seems particularly beneficial for masses, where the system reaches comparable sensitivity to that of the readers, but with considerably reduced false-positives. • About one-fourth of all recalled malignant lesions are not automatically marked by the system such that their evaluation (AI score) must be retrieved manually by the reader. A thorough reading of screening mammograms by readers to identify suspicious lesions therefore remains mandatory.
INTROSPECTRE: A Pre-Silicon Framework for Discovery and Analysis of Transient Execution Vulnerabilities

Transient execution vulnerabilities originate in the extensive speculation implemented in modern high-performance microprocessors. Identifying all possible vulnerabilities in complex designs is very challenging. One of the challenges stems from the lack of visibility into the transient micro-architectural state of the processor. Prior work has used covert channels to identify data leakage from transient state, which limits the systematic discovery of all potential leakage sources.This paper presents INTROSPECTRE, a pre-silicon framework for early discovery of transient execution vulnerabilities. IN- TROSPECTRE addresses the lack of visibility into the micro- architectural processor state by integrating into the register transfer level (RTL) design flow, gaining full access to the internal state of the processor. Full visibility into the processor state enables INTROSPECTRE to perform a systematic leakage analysis that includes all micro-architectural structures, allowing it to identify potential leakage that may not be reachable with known side channels. We implement INTROSPECTRE on an RTL simulator and use it to perform transient leakage analysis on the RISC-V BOOM processor. We identify multiple transient leakage scenarios, most of which had not been highlighted on this processor design before.
Timing and synchronisation for high‐loss free‐space quantum communication with Hybrid de Bruijn Codes

Satellite-based, long-distance free-space quantum key distribution has the potential to realise global quantum secure communication networks. Detecting faint quantum optical pulses sent from space requires highly accurate and robust classical timing systems to pick out signals from the noise and allow for reconciliation of sent and received key bits. For such high-loss applications, a fault-tolerant synchronisation signal coding and decoding scheme based on de Bruijn sequences is proposed. A representative synchronisation timing system was tested in laboratory conditions and it demonstrated high fault tolerance for the error-correction algorithm even under high loss. The performance limitations of this solution are also discussed, and the maximum error tolerance of the scheme and the estimated computational overhead are analysed, allowing for the possibility of implementation on a real-time system-on-chip. This solution not only can be used for synchronisation of high-loss channels such as channels between satellites and ground stations but can also be extended to applications with low loss, high bit error rate, but require reliable synchronisation such as quantum and non-quantum communications over terrestrial free space or fibre optic channels.
IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data

Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence of data.As one of the effective imputation approaches, generative adversarial networks (GANs) are implicit generative models that can be used for data imputation, which is formulated as an unsupervised learning problem.This work introduces a novel iterative GAN architecture, called Iterative Generative Adversarial Networks for Imputation (IGANI), for data imputation.IGANI imputes data in two steps and maintains the invertibility of the generative imputer, which will be shown to be a sufficient condition for the convergence of the proposed GAN-based imputation.The performance of our proposed method is evaluated on (1) the imputation of traffic speed data collected in the city of Guangzhou in China, and the training of short-term traffic prediction models using imputed data, and (2) the imputation of multi-variable traffic data of highways in Portland-Vancouver metropolitan region which includes volume, occupancy, and speed with different missing rates for each of them.It is shown that our proposed algorithm mostly produces more accurate results compared to those of previous GAN-based imputation architectures.
RF-Badge: Vital Sign-Based Authentication via RFID Tag Array on Badges

Nowadays, authentication systems are usually required to provide continuous, contactless, and non-intrusive services. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RF-Badge</i> , a vital sign-based authentication scheme on human subjects to meet the above requirements by using RFID technology. We consider two biometric features with individual diversity to characterize the vital sign of users, including the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">movement effect</i> from respiration and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reflection effect</i> from organs, especially the heart. To derive the movement effect from respiration, we build a phase-based geometric model to restore the fine-grained badge moving trace as the feature. To derive the reflection effect from human internal organs, we extract the reflection signal from the original signal and generate the spectrum as the feature. Besides, to deal with the feature deviation in different physical conditions of users, we propose a multi-condition network (MCNet) to further guarantee the generalization of RF-Badge. We implement a prototype system and evaluate the performance in real environments. The experiment results show that our system achieves the average false positive rate (FPR) of 3.9 percent and false negative rate (FNR) of 3.3 percent for continuous authentication within four signal cycles.
An interactive greeting system using convolutional neural networks for emotion recognition

The convolutional neural network (CNN) has been progressively applied in face recognition and various applications. This paper presents an interactive greeting system that combines face identification, emotion recognition, and funny 3D animation. The proposed system recognizes a user by a face-CNN and interacts with the user according to the recognized mood. The animated 3D girl plays a motion-dependent video according to the recognized user’s emotion. First, we employ a Viola-Jones algorithm to segment the areas around the face, eyes, and mouth in a captured image. The face-CNN recognizes a known person. An eye-CNN and a mouth-CNN recognize the emotion. Finally, a 3D animation is played according to the identified emotion. Experimental results show that the proposed interactive greeting system recognizes target persons well, identifies the emotion adequately, and provides users to enjoy funny interaction.
Blockchain in medical informatics

Aiming at the problems of information storage security and information sharing among various medical information systems, combined with blockchain consensus mechanism, encryption mechanism, peer-to-peer network, and other technologies. A medical information system scheme based on blockchain is proposed to realize the storage and sharing of medical information. The program has the characteristics of non-tampering and decentralization. This paper has designed a Practical Byzantine fault tolerance (PBFT) medical blockchain information management system. This system is multi-node maintenance and shared management system that can prevent medical information data from being tampered with and medical information leakage. It can be used to solve Medical information data management problems. Compared with the existing medical blockchain system, this medical information management system has certain advantages and better applicability.
3D Detection and Pose Estimation of Vehicle in Cooperative Vehicle Infrastructure System

Three-dimensional (3D) object detection is of great significance for avoiding collisions between vehicles and obstacles in autonomous driving. In particular, the recent 3D object detection methods based on supervised learning are widely studied to achieve excellent performance. However, the 3D labels for training in such methods are expensive and often difficult to be collected. To solve this issue, we propose a monocular 3D vehicle detection method. First, we propose a general mathematical K-means-like method for clustering arbitrary object contours into linear equations. Second, the position, orientation and dimensions of the vehicle can be estimated by applying K-means-like method without the need for 3D labels in the contour of the vehicle. Finally, given the 2D object detection, we maximize a posterior probability of vehicle position, orientation and dimensions to improve the accuracy of the 3D object detection based on the results of K-means-like method. We evaluate the proposed algorithm on the dataset collected by the vehicle-side and road-side cameras in the cooperative vehicle infrastructure system (CVIS). Compared with the state-of-art Deep3DBox and SMOKE methods, the evaluated results show that the detection accuracy of 3D object of our method is 1.4% higher than that of Deep3DBox in the vehicle-side system, while for the road-side camera, the proposed method has 3.86% and 4.37% higher accuracy than Deep3DBox and SMOKE, respectively. Thus, the proposed method can be seen as an effective 3D object detection method in the intelligent transportation system and CVIS.
Information security vulnerability prediction based on business process model using machine learning approach

Identifying information security vulnerabilities of a new business process resulting from a business process redesign (BPR) must occur as early as possible. Organisations frequently initiate a BPR, and vulnerability identification that involves information security experts should follow. In developing countries, many organisations have insufficient experts, causing problems related to the experts' workload. In this study, we propose a new method called Task-based Vulnerability Prediction (TbVP), which uses a machine-learning approach to predict information security vulnerabilities of the business process model. The method utilises the type and label of tasks in the model to predict vulnerabilities in implementing applications. Vulnerabilities data are taken from the Common Weakness Enumeration (CWE) dictionary. Our method consists of two main stages. First, we developed clusters using classification and clustering methods. Second, we built an automatic system to predict vulnerabilities using the clusters obtained from the first stage. Business processes of public universities were used as case studies to evaluate the method. We evaluated the automatic prediction system using the reliability test and comparing vulnerabilities our system predicted with actual vulnerabilities that materialised in the applications. The evaluation result shows that the system is a reliable predictor of application vulnerabilities, which our method can automatically predict based on a business process model, before implementing supporting applications.
Simple-Encoded evolving convolutional neural network and its application to skin disease image classification

Automatically searched architectures by neural architecture search (NAS) methods have shown promising performance in various visual recognition tasks. Among NAS methods, evolutionary neural architecture methods are popular because of their potential to find the global optimal convolutional neural networks (CNNs). These methods usually use an individual to represent a CNN, while often facing two challenges: 1) since a CNN has numerous encoded parameters and weights, the length of each individual is long, which causes a large search space, and 2) due to the unknown optimal depth of a CNN, it is necessary to deal with a variable-length optimization problem, which leads to the search in a messy way since search spaces with different dimensions may have different optimal solutions. In this paper, we propose a genetic algorithm with a simple encoding scheme (SEECNN) for evolving CNNs to address image classification problems. In our encoding scheme, the parameters and weights of each layer are encoded into an individual and the whole population represents an entire CNN. Over the course of evolution, three offspring subpopulations are separately produced by genetic operators on three subpopulations. In each subpopulation, the lengths of individuals are short and equal. Afterward, we design a stable search strategy to update the population based on the performance improvement, where we only insert, replace, and remove one individual to generate candidate populations. SEECNN is compared with 24 well-known algorithms on nine benchmark datasets and five state-of-the-art methods on a real-world skin disease image classification case. The results demonstrate its effectiveness.
Adversarial Attacks and Defenses in Deep Learning: A Survey

In recent years, researches on adversarial attacks and defense mechanisms have obtained much attention. It's observed that adversarial examples crafted with small perturbations would mislead the deep neural network (DNN) model to output wrong prediction results. These small perturbations are imperceptible to humans. The existence of adversarial examples poses great threat to the robustness of DNN-based models. It is necessary to study the principle behind it and develop their countermeasures. This paper provides a survey to evaluate the existing attacking methods and defense techniques. Meanwhile, we discuss the relationship between the defense schemes and the robustness of the model in detail.
A novel two-step approach for optimal groundwater remediation by coupling extreme learning machine with evolutionary hunting strategy based metaheuristics

We propose a simulation-optimization (SO) model based on a novel two-step strategy for the optimal design of groundwater remediation systems. The SO models are developed by coupling simulation models directly or through the extreme learning machine (ELM) with evolutionary hunting strategy based metaheuristics (EHSMs). In the first step, EHSMs with a combinatorial optimization technique are used to obtain optimal pumping locations by minimizing the percentage of contaminant mass that remained in the aquifer while keeping the pumping strategy as constant. In the second step, the optimal pumping locations are directly used as input, and a composite function is employed to minimize the sum of the water extraction rates and the percentage of extracted contaminant mass by constraining hydraulic heads and contaminant concentrations. The performance of the two-step strategy is found to be slightly better and computationally more efficient than the alternate approach. Moreover, various statistical measures suggest the superiority of EHSMs over other metaheuristics for groundwater remediation.
A Comprehensive Review of Automated Essay Scoring (AES) Research and Development

Automated Essay Scoring (AES) is a service or software that can predictively grade essay based on a pre-trained computational model. It has gained a lot of research interest in educational institutions as it expedites the process and reduces the effort of human raters in grading the essays as close to humans’ decisions. Despite the strong appeal, its implementation varies widely according to researchers’ preferences. This critical review examines various AES development milestones specifically on different methodologies and attributes used in deriving essay scores. To generalize existing AES systems according to their constructs, we attempted to fit all of them into three frameworks which are content similarity, machine learning and hybrid. In addition, we presented and compared various common evaluation metrics in measuring the efficiency of AES and proposed Quadratic Weighted Kappa (QWK) as standard evaluation metric since it corrects the agreement purely by chance when estimate the degree of agreement between two raters. In conclusion, the paper proposes hybrid framework standard as the potential upcoming AES framework as it capable to aggregate both style and content to predict essay grades Thus, the main objective of this study is to discuss various critical issues pertaining to the current development of AES which yielded our recommendations on the future AES development.
Dual-graph regularized subspace learning based feature selection

Feature selection has attracted widespread attention with the massive growth of high-dimensional data. In recent years, all kinds of unsupervised feature selection methods have been presented. However, most of these methods can not fully explore the local geometric structure of the original data, which has been proven very important in unsupervised feature selection. To tackle this problem, we present a novel feature selection algorithm called dual-graph subspace learning based feature selection (DGSLFS). Specifically, on one hand, DGSLFS conducts feature selection procedures based on subspace learning, which can guarantee the useful information hidden in the original space be well exploited. On the other hand, we develop two novel graphs on samples and features, respectively, which can well preserve the local geometric structures. In addition, we impose an ℓ2,1-norm to constrain the reconstruction error term and the feature selection matrix. Thus, DGSLFS is robust to outliers and noises, and can guarantee the sparsity of features. The experimental results on several popular datasets show that our proposed algorithm can obtain encouraging results in comparison with some state-of-the-art algorithms.