Reviewerly/exHarmony huggingface dataset:
3D SceneFlowNet: Self-Supervised 3D Scene Flow Estimation Based on Graph CNN

Despite deep learning approaches have achieved promising successes in 2D optical flow estimation, it is a challenge to accurately estimate scene flow in 3D space as point clouds are inherently lacking topological information. In this paper, we aim at handling the problem of self-supervised 3D scene flow estimation based on dynamic graph convolutional neural networks (GCNNs), namely 3D SceneFlowNet. To better learn geometric relationships among points, we introduce EdgeConv to learn multiple-level features in a pyramid from point clouds and a self-attention mechanism to apply the multi-level features to predict the final scene flow. Our trained model can efficiently process a pair of adjacent point clouds as input and predict a 3D scene flow accurately without any supervision. The proposed approach achieves superior performance on both synthetic ModelNet40 dataset and real LiDAR scans from KITTI Scene Flow 2015 datasets.
From pairwise to family-based generic analysis of delta-oriented model-based SPLs

One way to implement model-based software product lines (MBSPLs) is to use a transformational approach known as Delta Modeling (DM). Here, an MBSPL is implemented by one core model and a set of delta modules. Delta modules define model transformations using edit operations which add, remove or modify model elements. Editings of different delta modules can be in conflict or depend on each other, leading to conflict and dependency relations between delta modules. Conflicts and unfulfilled dependencies can cause the generation of a product to fail or to lead to invalid models. In order to spot such defects, one needs analysis tools for each modeling (sub-)language used. Existing generic approaches to statically detect such defects in a language-agnostic manner analyze pairs of delta modules. However, the pairwise approach can lead to false positives, i.e., conflicts and unfulfilled dependencies are reported although product generation does not fail. Following the idea of family-based analysis, this paper presents a new approach to detect pseudo defects resolved by "healing effects" implied by the network of dependencies. These effects typically occur when a delta module (partially) reverts the effect of a preceding delta module. We have implemented our approach within the SiPL framework and evaluated our family-based analysis using a realistic MBSPL known as Body Comfort System (BCS).
MAIRE - A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers

The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier's output. The human interpretable rule is defined as an axis-aligned hyper-cuboid containing the instance for which the classification decision has to be explained. The proposed procedure finds the largest (high coverage) axis-aligned hyper-cuboid such that a high percentage of the instances in the hyper-cuboid have the same class label as the instance being explained (high precision). Novel approximations to the coverage and precision measures in terms of the parameters of the hyper-cuboid are defined. They are maximized using gradient-based optimizers. The quality of the approximations is rigorously analyzed theoretically and experimentally. Heuristics for simplifying the generated explanations for achieving better interpretability and a greedy selection algorithm that combines the local explanations for creating global explanations for the model covering a large part of the instance space are also proposed. The framework is model agnostic, can be applied to any arbitrary classifier, and all types of attributes (including continuous, ordered, and unordered discrete). The wide-scale applicability of the framework is validated on a variety of synthetic and real-world datasets from different domains (tabular, text, and image).
Sentiment Analysis on Marketplace Review using Hybrid Lexicon and SVM Method

Nowadays, especially during the Covid-19 pandemic time, there is an enormous rise in online transactions. There exist some marketplaces that provide review facilities to help customers in choosing the right products or sellers. Sentiment analysis is a study to classify a review text to sentiment classes Tables. In this paper, we present a study of sentiment analysis on marketplace review text using a hybrid method developed based on lexicon from Sentiwordnet 3.0 and Support Vector Machine (SVM) method. The experimental results show that the hybrid method outperforms the lexicon approach and SVM approach.
Trapping Set Analysis of Finite-Length Quantum LDPC Codes

Iterative decoders for finite length quantum low-density parity-check (QLDPC) codes are impacted by short cycles, detrimental graphical configurations known as trapping sets (TSs) present in a code graph as well as symmetric degeneracy of errors. In this paper, we develop a systematic methodology by which quantum trapping sets (QTSs) can be defined and categorized according to their topological structure. Conventional definition of a TS from classical error correction is generalized to address the syndrome decoding scenario for QLDPC codes. We show that QTS information can be used to design better QLDPC code and decoder. For certain finite-length QLDPC codes, frame error rate improvements of two orders of magnitude in the error floor regime are demonstrated without needing any post-processing steps.
An Explainable Recurrent Neural Network for Solar Irradiance Forecasting

The factors affecting solar irradiance are usually complex and diverse, making it difficult to accurately predict the photovoltaic power generation. In this paper, an explainable recurrent neural network (ExRNN) algorithm is proposed based on deep recurrent neural network (RNN) and additive index model for solar irradiance forecasting problems. The proposed ExRNN is designed as an ante-hoc explainable algorithm with cyclic units by linearly combining single-feature models to learn explainable features of solar irradiances, and the ridge function is used as an activation function to extract and explain mapping correlations between meteorological features and solar irradiances. Furthermore, the RNN is used with memory characteristics to discover the time correlation hidden in the solar irradiance data sequence and retain the explainability. Therefore, the factors affecting solar irradiances can be quantified by the proposed ExRNN, and a legible explanation on the relationship between meteorological inputs and solar irradiances can be provided. Solar irradiance samples from Lyon France are used to evaluate the prediction accuracy and explainability of the proposed ExRNN.
STATE: A Clustering Algorithm Focusing on Edges Instead of Centers

With the expansion of data scale and the increase in data complexity, it is particularly important to accurately identify clusters and efficiently save clustering results. To address this, we propose a novel clustering algorithm, Shape clustering based on data field (STATE), which can quickly identify clusters of arbitrary shapes and greatly reduce the storage space of clustering results in any datasets without reducing the accuracy. STATE mainly focuses on finding the edges of clusters and directions of edges instead of clustering centers through the data field. The results of STATE are presented as the edges of clusters without data objects inside clusters and without noise. Extensive experiments show that STATE can recognize complex data distribution in noisy environments without discrimination and greatly save the storage space of clustering results. When it is applied in a real-world scene, facial feature extraction, STATE can recognize eyes, nose, mouth, eyebrows and facial contours automatically without calibrating key features or training. Using the extracted facial features, we achieve facial recognition with high accuracy.
Evaluation of enterprise technology innovation capability in prefabricated construction in China

Purpose This study aims to evaluate enterprise technology innovation capability in prefabricated construction (PC) from an input-output perspective, using six integrated enterprises in China as cases. Design/methodology/approach An evaluation system for enterprise technology innovation capability in PC was constructed, including total input, technology output (TO) and project output. All the evaluation indexes were quantified, and the subject and object indexes weights were determined using the fuzzy cognitive map and information entropy, respectively. The final scores and ranks were evaluated through gray relational analysis (GRA) based on the combined weights. Findings It was found that enterprise technology innovation capability in PC was low in China, with its unbalanced development in different dimensions and the poorest performance in TO, currently. Originality/value This research has developed an evaluation system for technology innovation capability in PC at the enterprise level and scientifically quantified all the indexes, which is a breakthrough over existing studies. The GRA model based on the combined weights proposed in this study can be applied to other comparable fields and regions, with its easy operation.
Athlete Rehabilitation Evaluation System Based on Internet of Health Things and Human Gait Analysis Algorithm

In order to improve the effect of athlete’s injury recognition and rehabilitation evaluation, this paper studies the traditional rehabilitation evaluation method and proposes a new athlete rehabilitation evaluation system combining the Internet of Health Things technology and human gait analysis algorithm. Moreover, this paper combines sports characteristics to improve the algorithm of human gait analysis. In addition, through the study of the athlete’s human body modeling and movement process, a human gait analysis algorithm that can be applied to multiple sports is proposed, and the gait parameter analysis and algorithm reliability research are carried out through simulation analysis. After confirming that the algorithm is effective, this paper combines the Internet of Health Things technology to construct a system model, obtains the system function module architecture with the support of the Internet of Health Things technology, and conducts experiments to verify the system performance. From the experimental research, it can be seen that the model constructed in this paper meets the theoretical and practical needs, and the system in this paper can be applied to practice in the future. The human gait recognition algorithm constructed in this article has a good effect and can play an important role in sports rehabilitation of athletes. At the same time, the system constructed in this article has certain advantages over traditional sports rehabilitation systems with the support of algorithms.
Improving Handwritten Arabic Text Recognition Using an Adaptive Data-Augmentation Algorithm

Deep learning has increased the performance of classification and object detection, but it generally requires large amounts of labeled data for training. In this paper, we introduce a new data augmentation algorithm that promotes diversity between classes, representing the characters of the Arabic script, and can balance samples between different classes. This algorithm gives each word in the lexicon a weight. The weight of a word is based on the occurrence probabilities of the characters constituting the word. Minority classes are given higher weight as compared to the classes frequently occurring in the text. The data augmentation technique was evaluated on a handwritten word recognition task using the publicly available IFN/ENIT and AHDB datasets. We see significant improvement in results by employing our data augmentation technique, and we achieve state-of-the-art results on both datasets.
The Implementation Strategies of Smart Teaching in College Oral English Court Based on Artificial Intelligence

With the advent of the information age, traditional and single teaching methods can no longer meet the needs of modern education. Artificial intelligence, as a promising development direction of computing, has played a great role in the practice of teaching systems. This paper takes college English speaking teaching as an example and studies the role of FIF app in college English teaching to verify that artificial intelligence can help students correct their spoken pronunciation., improve teachers' teaching effectiveness and efficiency and increase students' interest in online learning, thus promoting intelligent teaching in college English speaking courses.
HyperGI: Automated Detection and Repair of Information Flow Leakage

Maintaining confidential information control in soft-ware is a persistent security problem where failure means secrets can be revealed via program behaviors. Information flow control techniques traditionally have been based on static or symbolic analyses — limited in scalability and specialized to particular languages. When programs do leak secrets there are no approaches to automatically repair them unless the leak causes a functional test to fail. We present our vision for HyperGI, a genetic improvement framework that detects, localizes and repairs information leakage. Key elements of HyperGI include (1) the use of two orthogonal test suites, (2) a dynamic leak detection approach which estimates and localizes potential leaks, and (3) a repair component that produces a candidate patch using genetic improvement. We demonstrate the successful use of HyperGI on several programs with no failing functional test cases. We manually examine the resulting patches and identify trade-offs and future directions for fully realizing our vision.
A GPU Architecture Aware Fine-Grain Pruning Technique for Deep Neural Networks

The model size and computation requirement of Deep Convolutional Neural Networks (DNNs) have ever increased as their applications to various real-life use-cases, e.g., autonomous driving, are getting more pervasive and popular. While DNN workloads are executed on Graphics Processing Units (GPUs) in many cases, it is not trivial to improve the inference speed through the conventional DNN weight pruning techniques, due to the parallel architecture of GPUs. On the other hand, the coarse-grain pruning, also known as structured sparsity or structured pruning, can speedup the inference, but cause significant losses of accuracy. In this paper, we propose two fine-grain DNN pruning techniques that are aware of the underlying GPU architecture. For that, we analyze the hierarchical architecture of parallel processing elements and memory of GPU to identify the finest possible pruning where the removed weights can be safely skipped during the inference. The effectiveness of the proposed techniques has been evaluated with VGG16. Compared to the existing pruning techniques, the proposed methods result in significantly improved inference speed with less accuracy drop.
Modernizing Medical Museums Through the 3D Digitization of Pathological Specimens

The anatomical collections at the National Museum of Health and Medicine (NMHM) contain skeletal specimens that highlight the history of military and civilian medicine dating from the American Civil War and the founding of the museum as the Army Medical Museum in 1862. Today, NMHM curates over 6400 gross skeletal specimens consisting primarily of pathological or anomalous single bone elements that display a variety of pathological conditions, including congenital anomalies, neoplasms, healed and unhealed trauma and infectious diseases, and surgical interventions such as amputations and excisions. In an effort to increase accessibility to these pathological specimens, NMHM is collaborating with Virginia Commonwealth University (VCU) and the Laboratory Division of the Federal Bureau of Investigation (FBI) to digitize and disseminate high-quality 3D models via online portals, enabling scholars and educators to manipulate, analyze, and 3D print the models from anywhere in the world. Many institutions with courses in paleopathology and forensic anthropology do not have reference collections or access to museum collections for hands-on teaching. Therefore a digital repository of osteological specimens can provide an unprecedented and unique resource of exemplars for scholars and educators. The sharing of these military medical assets improves historical knowledge and diagnostic capabilities in the fields of medicine and anthropology. This chapter outlines the digitization processes that are being utilized to increase access to these pathological skeletal specimens through multimodal 3D capture.
Assessing Domain Adaptation Techniques for Mitosis Detection in Multi-scanner Breast Cancer Histopathology Images

Breast cancer is the most commonly diagnosed cancer worldwide, with over two million new cases each year. During diagnostic tumour grading, pathologists manually count the number of dividing cells (mitotic figures) in biopsy or tumour resection specimens. Since the process is subjective and time-consuming, data-driven artificial intelligence (AI) methods have been developed to automatically detect mitotic figures. However, these methods often generalise poorly, with performance reduced by variations in tissue types, staining protocols, or the scanners used to digitise whole-slide images. Domain adaptation approaches have been adopted in various applications to mitigate this issue of domain shift. We evaluate two unsupervised domain adaptation methods, CycleGAN and Neural Style Transfer, using the MIDOG 2021 Challenge dataset. This challenge focuses on detecting mitotic figures in whole-slide images digitised using different scanners. Two baseline mitosis detection models based on U-Net and RetinaNet were investigated in combination with the aforementioned domain adaptation methods. Both baseline models achieved human expert level performance, but had reduced performance when evaluated on images which had been digitised using a different scanner. The domain adaptation techniques were each found to be beneficial for detection with data from some scanners but not for others, with the only average increase across all scanners being achieved by CycleGAN on the RetinaNet detector. These techniques require further refinement to ensure consistency in mitosis detection.
Efficient Identity-Based Encryption With Revocation for Data Privacy in Internet of Things

The Internet of Things (IoT) is making the world around us smarter and more convenient. However, its extensive application has rendered security problems, such as the privacy of sensitive data, increasingly serious. Encryption provides an effective and important means of protecting data privacy in the IoT. Because of resource limitations, to achieve high efficiency IoT devices require an encryption scheme that ensures that the encryption phase does not incur a heavy data transmission overhead. By virtue of its many advantages, the use of public-key encryption in current applications is widespread. Identity-based public-key encryption (IBE) removes the obstacle raised by the sophisticated certificate management required by other schemes, and its efficiency renders it more suitable for application in the IoT. However, a problem that needs to be solved in IBE is the revocation of a user whose private key may have been exposed. In this article, we present an efficient and practical IBE scheme having a revocation functionality to preserve data privacy in IoT applications. Elements in the system, such as sensors and actuators, can exchange encrypted data directly or via a cloud server. If a private key is compromised, the private key generator can revoke its user. The security of our proposed scheme can be proved based on SM9 encryption and the bilinear Diffie–Hellman problem.
Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions

This article develops theoretical, algorithmic, perceptual, and interaction aspects of script legibility enhancement in the visible light spectrum for the purpose of scholarly editing of papyri texts. Novel legibility enhancement algorithms based on color processing and visual illusions are compared to classic methods in a user experience experiment. (1) The proposed methods outperformed the comparison methods. (2) Users exhibited a broad behavioral spectrum, under the influence of factors such as personality and social conditioning, tasks and application domains, expertise level and image quality, and affordances of software, hardware, and interfaces. No single enhancement method satisfied all factor configurations. Therefore, it is suggested to offer users a broad choice of methods to facilitate personalization, contextualization, and complementarity. (3) A distinction is made between casual and critical vision on the basis of signal ambiguity and error consequences. The criteria of a paradigm for enhancing images for critical applications comprise: interpreting images skeptically; approaching enhancement as a system problem; considering all image structures as potential information; and making uncertainty and alternative interpretations explicit, both visually and numerically.The online version contains supplementary material available at 10.1007/s10032-021-00386-0.
Fundamental Limits of Loss Sensing over Bosonic Channels

We consider the problem of estimating unknown loss η over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> uses of single-mode lossy thermal noise bosonic channel under an average photon number constraint per mode. We prove that a product of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> two-mode squeezed vacuum (TMSV) states achieves minimal quantum Cramér-Rao bound (QCRB) over Gaussian quantum states in this scenario, and characterize the optimal receiver structure. We show that TMSV minimizes QCRB over all quantum states in the limit of low input photon number. Finally, we compare the performance of our optimal receiver for TMSV to other receivers.
How Far Deep Learning Systems for Text Detection and Recognition in Natural Scenes are Affected by Occlusion?

With the rise of deep learning, significant advances in scene text detection and recognition in natural images have been made. However, the severe impact threat to the algorithm’s performance caused by occlusion still consists of an open issue due to the lack of consistent real-world datasets, richer annotations, and evaluations in the specific occlusion problem. Therefore, unlike previous works in this field, our paper addresses occlusions in scene text recognition. The goal is to evaluate the effectiveness and efficiency of existing deep architectures for scene text detection and recognition in various occlusion levels. First, we investigated state-of-the-art scene text and recognition methods and evaluated these current deep architectures performances on ICDAR 2015 dataset without any generated occlusion. Second, we created a methodology to generate large datasets of scene text in natural images with ranges of occlusion between 0 and 100%. From this methodology, we produced the ISTD-OC, a dataset derivated from the ICDAR 2015 database to evaluate deep architectures under different levels of occlusion. The results demonstrated that these existing deep architectures that have achieved state-of-the-art are still far from understanding text instances in a real-world scenario. Unlike the human vision systems, which can comprehend occluded instances by contextual reasoning and association, our extensive experimental evaluations show that current scene text recognition models are inefficient when high occlusions exist in a scene. Nevertheless, for scene text detection, segmentation-based methods, such as PSENet and PAN, are more robust in predicting higher levels of occluded texts. In contrast, methods that detect at the character level, such as CRAFT, are unsatisfactory to heavy occlusions. When it comes to recognition, attention-based methods that benefit contextual information have performed better than CTC-based methods.