Reviewerly/exHarmony huggingface dataset:
Spatial frequency domain imaging for determining absorption and scattering properties of bruised pears based on profile corrected diffused reflectance

Curved surface of fruit has a great influence on the measurement of absorption (μa) and reduced scattering coefficients (μ's) by using the spatial frequency domain imaging (SFDI) technique. In this study, a developed SFDI system was used to conduct phase-measuring profilometry (PMP) to obtain the surface height of tested object. Monte Carlo (MC) simulation was conducted on planar and spherical geometric models, and the correction formula for diffuse reflectance (Rd) was then determined by simulation results and validated by tilt standard reflector. The Rd correction effect on estimating μa and μ's was revealed by five semi-spherical phantoms, which turned out that both the accuracy and uniformity were substantially improved, with the relative error range of 0.78 %-11.84 % and 2.83 %-8.95 %, and standard deviation range of 1.89 × 10−5 mm-1-1.42 × 10-3 mm-1 and 1.76 × 10-4 mm-1-8.17 × 10-3 mm-1 for corrected μa and μ's respectively. Finally, the proposed Rd correction method was applied to the bruise detection in pears, the comparisons of μa and μ's maps between normal and bruised pears were conducted. The results revealed that the area around the highest point of normal pear contour was easily to be identified as bruised area before Rd correction; the μa and μ's maps for normal pear were more uniform, and for bruised pears after the correction, and the injured area was highlighted for bruised pears. The profile correction method proposed in this study, coupled with the SFDI technique, was essential for precise measurement of μa and μ's of pear tissues, resulting in less misclassification for bruise detection of pears.
BRIM: Bistable Resistively-Coupled Ising Machine

Physical Ising machines rely on nature to guide a dynamical system towards an optimal state which can be read out as a heuristical solution to a combinatorial optimization problem. Such designs that use nature as a computing mechanism can lead to higher performance and/or lower operation costs. Quantum annealers are a prominent example of such efforts. However, existing Ising machines are generally bulky and energy intensive. Such disadvantages may be acceptable if these designs provide some significant intrinsic advantages at a much larger scale in the future, which remains to be seen. But for now, integrated electronic designs of Ising machines allow more immediate applications. We propose one such design that uses bistable nodes, coupled with programmable and variable strengths. The design is fully CMOS compatible for on-chip applications and demonstrates competitive solution quality and significantly superior execution time and energy.
Compact joints encoding for skeleton-based dynamic hand gesture recognition

With the development of 3D hand pose estimation technologies, skeleton-based dynamic hand gesture recognition has attracted widespread attention. In this paper, we propose a novel framework for skeleton-based dynamic hand gesture recognition. In the spatial perception stream (SP-Stream), we design a compact joints encoding method. It can adaptively select compact joints based on the convex hull of the hand skeleton and encode them into a skeleton image for fully extracting spatial features. Besides, we present a global enhancement module (GEM) to enhance key feature maps. In the temporal perception stream (TP-Stream), we propose a motion perception module (MPM) to enhance the notable movement of hand gestures on X/Y/Z coordinate axes. Experimental results show that the proposed framework performs better than the state-of-the-art methods on two benchmark datasets.
Extractive Automatic Text Summarization using SpaCy in Python &amp; NLP

Propulsion of the everchanging technological innovations, has led to consider the data generated in the present era very crucial with significant roles both in technical & nontechnical fields. In the digital world, as the amount of data produced at every instance is very huge; there is an ultimate need to develop a machine that can reduce the length of the texts automatically. Moreover, applying text summarization gears up the procedure of researching, reduces reading time, and increases the amount of important information being generated in the specific field. The main agenda is to develop a meaningful and coherent summary to recapitulate highlights of the text. From the collection of fascinating problems, we have opted for the Automatic Text Summarization. The solution to this problem unlike doing manually has proved to be essential in accurately summarizing voluminous texts in a cost and time efficient manner.
Dirty Pixels: Towards End-to-end Image Processing and Perception

Real-world, imaging systems acquire measurements that are degraded by noise, optical aberrations, and other imperfections that make image processing for human viewing and higher-level perception tasks challenging. Conventional cameras address this problem by compartmentalizing imaging from high-level task processing. As such, conventional imaging involves processing the RAW sensor measurements in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping, and compression. This pipeline is optimized to obtain a visually pleasing image. High-level processing, however, involves steps such as feature extraction, classification, tracking, and fusion. While this silo-ed design approach allows for efficient development, it also dictates compartmentalized performance metrics without knowledge of the higher-level task of the camera system. For example, today’s demosaicking and denoising algorithms are designed using perceptual image quality metrics but not with domain-specific tasks such as object detection in mind. We propose an end-to-end differentiable architecture that jointly performs demosaicking, denoising, deblurring, tone-mapping, and classification (see Figure 1). The architecture does not require any intermediate losses based on perceived image quality and learns processing pipelines whose outputs differ from those of existing ISPs optimized for perceptual quality, preserving fine detail at the cost of increased noise and artifacts. We show that state-of-the-art ISPs discard information that is essential in corner cases, such as extremely low-light conditions, where conventional imaging and perception stacks fail. We demonstrate on captured and simulated data that our model substantially improves perception in low light and other challenging conditions, which is imperative for real-world applications such as autonomous driving, robotics, and surveillance. Finally, we found that the proposed model also achieves state-of-the-art accuracy when optimized for image reconstruction in low-light conditions, validating the architecture itself as a potentially useful drop-in network for reconstruction and analysis tasks beyond the applications demonstrated in this work. Our proposed models, datasets, and calibration data are available at https://github.com/princeton-computational-imaging/DirtyPixels .
Online-Extractability in the Quantum Random-Oracle Model

We show the following generic result: When a quantum query algorithm in the quantum random-oracle model outputs a classical value t that is promised to be in some tight relation with H(x) for some x, then x can be efficiently extracted with almost certainty. The extraction is by means of a suitable simulation of the random oracle and works online, meaning that it is straightline, i.e., without rewinding, and on-the-fly, i.e., during the protocol execution and (almost) without disturbing it. The technical core of our result is a new commutator bound that bounds the operator norm of the commutator of the unitary operator that describes the evolution of the compressed oracle (which is used to simulate the random oracle above) and of the measurement that extracts x. We show two applications of our generic online extractability result. We show tight online extractability of commit-and-open $$\Sigma $$ -protocols in the quantum setting, and we offer the first complete post-quantum security proof of the textbook Fujisaki-Okamoto transformation, i.e., without adjustments to facilitate the proof, including concrete security bounds.
A CNN-BiLSTM based Approach for Detection of SQL Injection Attacks

Web application attacks concerned with Structured Query Language Injection(SQLI) have been a major threat in the field of cybersecurity. SQLI attacks majorly lead to leakage of user's data leading to data manipulation, updation and deletion in database management system. Traditional techniques used to prevent SQLI injections include rule-based matching and other related methods that are limited to a few number of SQL injections. Major concern regarding SQLI attacks relates to invention of new malicious SQL queries by hackers to perform SQLI attacks. The problem can be effectively dealt with use of machine learning algorithms for prediction of SQLI attacks. Paper proposes a hybrid CNN-BiLSTM based approach for SQLI attack detection. The proposed CNN-BiLSTM model had significant accuracy of 98% and superior performance compared to other machine learning algorithms. Also, paper presents a comparative study of different types of machine learning algorithms used for the purpose of SQLI attack detection. The study shows the performance of various algorithms based on accuracy, precision, recall, and F1 score with respect to proposed CNN-BiLSTM model in detection of SQL injection attacks.
Internet of Samples (iSamples): Toward an interdisciplinary cyberinfrastructure for material samples

Abstract Sampling the natural world and built environment underpins much of science, yet systems for managing material samples and associated (meta)data are fragmented across institutional catalogs, practices for identification, and discipline-specific (meta)data standards. The Internet of Samples (iSamples) is a standards-based collaboration to uniquely, consistently, and conveniently identify material samples, record core metadata about them, and link them to other samples, data, and research products. iSamples extends existing resources and best practices in data stewardship to render a cross-domain cyberinfrastructure that enables transdisciplinary research, discovery, and reuse of material samples in 21st century natural science.
Distributed Blockchain-Based Authentication and Authorization Protocol for Smart Grid

Authentication and authorization (A &amp; A) mechanisms are critical to the security of Internet of Things (IoT) applications. Smart grid system processing and exchanging data without human intervention, known as smart grids, are well-known as IoT scenarios. Entities in such smart grid systems need to identify and validate one another and ensure the integrity of data exchange mechanisms. However, at present, most commonly used A &amp; A protocols are centralized, resulting in security risks such as information leaks, illegal access, and identity theft. In this study, we propose a new distributed A &amp; A protocol for smart grid networks based on blockchain technology to address with these risks. The proposed protocol integrates the decentralized authentication and immutable ledger characteristics of blockchain architectures suitable for power systems with a novel blockchain technique to realize both identity authentication and resource authorization for smart grid systems. We discuss the security of and threat models for prior A &amp; A protocols and demonstrate how our protocol protects against these threats. We further demonstrate an approach to a real deployment of our A &amp; A protocol using the FISCO consortium platform, applying algorithms from smart contract systems. Finally, we present the results of experimental simulations showing the efficacy and efficiency of our proposed protocol.
Document image classification: Progress over two decades

Document image classification plays a vital role in the document image processing system. Thus it is of great importance to have a clear understanding of the state-of-the-art of the document image classification field, especially in this deep learning era, which will facilitate the development of effective document image processing systems. In this paper, we provide a comprehensive survey of the progress that has been made in the field of document image classification over the past two decades. We categorize the document images into non-mobile images and mobile images according to the way they are acquired. The existing document image classification methods for these two types of images are reviewed, which are classified as textual-based methods, structural-based methods, visual-based methods and hybrid methods. We further compare the performance of different classification methods on several public benchmark datasets. Finally, we highlight some open issues and recommend promising directions for future research.
Social network sentiment classification method combined Chinese text syntax with graph convolutional neural network

In view of most current studies on text sentiment classification focus on the deep learning model to obtain the sentimental characteristics of English text. Chinese text sentiment analysis is rarely involved, and only the context information of the statement is considered, but the syntax information of the statement is rarely considered. In this paper, a novel sentiment classification model is proposed (Dependency Tree Graph Convolutional Network, DTGCN) combined Chinese syntactically dependent tree with graph convolution. Firstly, the Bi-GRU (Bi-directional Gated Recurrent Unit) model is used to learn the contextual feature representation of a given text. Secondly, the syntax-dependent tree structure of a given text is constructed, then obtain its adjacency matrix according to the syntax-dependent tree, with the initial features extracted from the bidirectional gate control network, input into the graph convolutional neural network (GCN) to extract the sentimental features of the text; the obtained sentimental characteristics are then input into the classifier SoftMax for text sentimental polarity classification. Finally, the data set is compared with the mainstream neural network model. The experimental results show that the accuracy of the proposed DTGCN model proposed on the data set is 90.51% and the recall rate is 90.34%. Compared with the benchmark models (LSTM, CNN, TextCNN and Bi-GRU), the proposed DTGCN model shows a 4.45% advantage in accuracy. It shows that the proposed DTGCN model can effectively use the grammatical information of Chinese text to mine the hidden relationship in statements, it can improve the accuracy of Chinese text sentiment classification. In addition, the proposed DTGCN model not only improves the performance of sentiment classification in the essay, it also provides a new research method for social network public opinion identification.
Why Security Defects Go Unnoticed During Code Reviews? A Case-Control Study of the Chromium OS Project

Peer code review has been found to be effective in identifying security vulnerabilities. However, despite practicing mandatory code reviews, many Open Source Software (OSS) projects still encounter a large number of post-release security vulnerabilities, as some security defects escape those. Therefore, a project manager may wonder if there was any weakness or inconsistency during a code review that missed a security vulnerability. Answers to this question may help a manager pinpointing areas of concern and taking measures to improve the effectiveness of his/her project's code reviews in identifying security defects. Therefore, this study aims to identify the factors that differentiate code reviews that successfully identified security defects from those that missed such defects. With this goal, we conduct a case-control study of Chromium OS project. Using multi-stage semi-automated approaches, we build a dataset of 516 code reviews that successfully identified security defects and 374 code reviews where security defects escaped. The results of our empirical study suggest that the are significant differences between the categories of security defects that are identified and that are missed during code reviews. A logistic regression model fitted on our dataset achieved an AUC score of 0.91 and has identified nine code review attributes that influence identifications of security defects. While time to complete a review, the number of mutual reviews between two developers, and if the review is for a bug fix have positive impacts on vulnerability identification, opposite effects are observed from the number of directories under review, the number of total reviews by a developer, and the total number of prior commits for the file under review.
Security Notifications in Static Analysis Tools: Developers’ Attitudes, Comprehension, and Ability to Act on Them

Static analysis tools (SATs) have the potential to assist developers in finding and fixing vulnerabilities in the early stages of software development, requiring them to be able to understand and act on tools' notifications. To understand how helpful such SAT guidance is to developers, we ran an online experiment (N=132) where participants were shown four vulnerable code samples (SQL injection, hard-coded credentials, encryption, and logging sensitive data) along with SAT guidance, and asked to indicate the appropriate fix. Participants had a positive attitude towards both SAT notifications and particularly liked the example solutions and vulnerable code. Seeing SAT notifications also led to more detailed open-ended answers and slightly improved code correction answers. Still, most SAT (SpotBugs 67%, SonarQube 86%) and Control (96%) participants answered at least one code-correction question incorrectly. Prior software development experience, perceived vulnerability severity, and answer confidence all positively impacted answer accuracy.
Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning

Leukaemia is diagnosed conventionally by observing the peripheral blood and bone marrow smear using a microscope and with the help of advanced laboratory tests. Image processing-based methods, which are simple, fast, and cheap, can be used to detect and classify leukemic cells by processing and analysing images of microscopic smear. The proposed study aims to classify Acute Lymphoblastic Leukaemia (ALL) by Deep Learning (DL) based techniques. The study used Deep Convolutional Neural Networks (DNNs) to classify ALL according to WHO classification scheme without using any image segmentation and feature extraction that involves intense computations. Images from an online image bank of American Society of Haematology (ASH) were used for the classification. A classification accuracy of 94.12% is achieved by the study in isolating the B-cell and T-cell ALL images using a pretrained CNN AlexNet as well as LeukNet, a custom-made deep learning network designed by the proposed work. The study also compared the classification performances using three different training algorithms. The paper detailed the use of DNNs to classify ALL, without using any image segmentation and feature extraction techniques. Classification of ALL into subtypes according to the WHO classification scheme using image processing techniques is not available in literature to the best of the knowledge of the authors. The present study considered the classification of ALL only, and detection of other types of leukemic images can be attempted in future research.
Lung Cancer Detection and Classification from Chest CT Scans Using Machine Learning Techniques

Lung cancer is one of the key causes of death amongst humans globally, with a mortality rate of approximately five million cases annually. The mortality rate is even higher than breast cancer and prostate cancer combination. However, early detection and diagnosis can improve the survival rate. Different modalities are used for lung cancer detection and diagnosis, while Computed Tomography (CT) scan images provide the most significant lung infections information. This research's main contribution is the detection and classification of different kinds of lung cancers such as Adenocarcinoma, Large cell carcinoma, and Squamous cell carcinoma. A novel lung cancer detection technique has been developed using machine learning techniques. The technique comprises feature extraction, fusion using patch base LBP (Local Binary Pattern) and discrete cosine transform (DCT). The machine learning technique such as support vector machine (SVM) and K-nearest neighbors (KNN) evaluated chest CT scan images dataset for texture feature classification. The proposed technique's performance achieves better accuracy of 93% and 91% for support vector machine and K-nearest neighbors, respectively, than state-of-the-art techniques.
Feedback-driven loss function for small object detection

In the recent years, Convolutional Neural Network-based object detection has experienced impressive progress. Despite these improvements, the performance on the small object detection leaves much to be desired, small objects are often missed or wrongly detected. The reasonable explanation is that the supervisory signals on the small objects are insufficient, and through the analysis of loss distribution over different scales in the iterations, there is a significant gap between the loss provided by the small objects and large objects. In order to balance the loss distribution and alleviate the insufficient supervisory on the small objects, a Feedback-driven loss function is presented in this paper. The Feedback-driven loss function uses the loss distribution information as the feedback signal, compared with the original loss function, the Feedback-driven loss function can supervise small objects more effectively, and train the detectors in a more balanced way. Experiments have been conducted on various detectors, backbones, training periods and datasets. Compared to the current state-of-the-art method, the novel Feedback-driven loss function can achieve 2.3% relative improvement on the Mean Average Precision, and especially 3.5% improvement on the detection of small objects, with nearly no additional computation both in training and testing stages.
Continual Learning With Structured Inheritance for Semantic Segmentation in Aerial Imagery

With the rapid update and iteration of current aerial image data, the continual learning scenarios and catastrophic forgetting problem attracted increased attention, especially in the semantic segmentation task. However, the existing methods mainly focus on the class continual learning in a single task and are not satisfactory when extended to multiple tasks. In this article, we consider more realistic and complicated settings, namely task continual learning. We revisit the characteristics of semantic segmentation and knowledge distillation (KD) strategy, then propose a general and effective framework, named structured inheritance, to learn new tasks while retaining high performance on old tasks. Specifically, we present two structure-preserving penalties: pixel affinity structure loss and representation consistency structure loss. The former breaks the isolation of pixels and retains the pixel interactive information learned by the old tasks. At the same time, the latter protects high-frequency stationary information between sequence semantic segmentation tasks. Our approach does not need to add extra parameters nor does it need to access the data stream of the old tasks. Therefore, it can be applied in practical applications with strict computational burden, memory cost, and storage budget. Extensive continual learning experiments on four semantic segmentation datasets of Vaihingen, Potsdam, DeepGlobe, and Gaofen challenge semantic segmentation dataset (GCSS) prove the effectiveness of our proposed framework, which outperforms the current state-of-the-art methods and even exceeds the theoretical upper-bound performance of multitask learning. The code and models will be made publicly available.