Reviewerly/exHarmony huggingface dataset:
A GA-Based Multi-View, Multi-Learner Active Learning Framework for Hyperspectral Image Classification

This paper introduces a novel multi-view multi-learner (MVML) active learning method, in which the different views are generated by a genetic algorithm (GA). The GA-based view generation method attempts to construct diverse, sufficient, and independent views by considering both inter- and intra-view confidences. Hyperspectral data inherently owns high dimensionality, which makes it suitable for multi-view learning algorithms. Furthermore, by employing multiple learners at each view, a more accurate estimation of the underlying data distribution can be obtained. We also implemented a spectral-spatial graph-based semi-supervised learning (SSL) method as the classifier, which improved the performance of the classification task in comparison with supervised learning. The evaluation of the proposed method was based on three different benchmark hyperspectral data sets. The results were also compared with other state-of-the-art AL-SSL methods. The experimental results demonstrated the efficiency and statistically significant superiority of the proposed method. The GA-MVML AL method improved the classification performances by 16.68%, 18.37%, and 15.1% for different data sets after 40 iterations.
Locating splicing forgery by adaptive-SVD noise estimation and vicinity noise descriptor

Splicing forgery in digital images is a common form of photographic manipulation which composites two or more images into a single picture. Detection of splicing forgery remains a challenging task in image forensics. Base on the fact that images from different origins should have different amount of noise produced by image sensors or post processing by software, in this paper we proposed a novel detection method by analyzing noise discrepancy to expose and locate splicing forgery in digital images. To improve the accuracy of noise estimation, we proposed the Adaptive Singular Value Decomposition (Adaptive-SVD) to estimate the local noise. By combining local and global noise clues, we proposed the Vicinity Noise Descriptor to locate splicing forgery. Regional forensic information is inferred via machine learning method - Support Vector Machine (SVM). To evaluate the proposed method, we constructed splicing forgery databases which include various scenarios and different spliced objects with artificially added or camera-generated noise. Experimental results show that our proposed method is able to locate multi-objects spliced from different origins and comparing to state-of-the-art noise difference based methods, our method improves detection accuracy especially the value of precision, which significantly affects observers’ judgement.
Multi-Scale Weight Sharing Network for Image Recognition

In this paper, we explore the idea of weight sharing over multiple scales in convolutional networks. Inspired by traditional computer vision approaches, we share the weights of convolution kernels over different scales in the same layers of the network. Although multi-scale feature aggregation and sharing inside convolutional networks are common in practice, none of the previous works address the issue of convolutional weight sharing. We evaluate our weight sharing scheme on two heterogeneous image recognition datasets – ImageNet (object recognition) and Places365-Standard (scene classification). With approximately 25% fewer parameters, our shared weight ResNet model provides similar performance compared to baseline ResNets. Shared-weight models are further validated via transfer learning experiments on four additional image recognition datasets – Caltech256 and Stanford 40 Actions (object-centric) and SUN397 and MIT Inddor67 (scene-centric). Experimental results demonstrate significant redundancy in the vanilla implementations of the deeper networks, and also indicate that a shift towards increasing the receptive field per parameter may improve future convolutional network architectures.
An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images

The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and adaptive non-local means (FANLM), which combined the adaptive non-local means (NLM) with fuzzy c-means (FCM), as a novel method to reduce noise in the study.The algorithm chose the optimal size of search window automatically based on the noise variance which was estimated by the improved estimator of the median absolute deviation (MAD) for Rician noise. Meanwhile, it solved the problem that the traditional NLM algorithm had to use a fixed size of search window. Considering the distribution characteristics for each pixel, we designed three types of search window sizes as large, medium and small instead of using a fixed size. In addition, the combination with the FCM algorithm helped to achieve better denoising effect since the improved the FCM algorithm divided the membership degrees of images and introduced the morphological reconstruction to preserve the image details.The experimental results showed that the proposed algorithm (FANLM) can effectively remove the noise. Moreover, it had the highest peak signal-noise ratio (PSNR) and structural similarity (SSIM), compared with other three methods: non-local means (NLM), linear minimum mean square error (LMMSE) and undecimated wavelet transform (UWT). Using the FANLM method, the image details can be well preserved with the noise being mostly removed.Compared with the traditional denoising methods, the experimental results showed that the proposed approach effectively suppressed the noise and the edge details were well retained. However, the FANLM method took an average of 13 s throughout the experiment, and its computational cost was not the shortest. Addressing these can be part of our future research.
Startup Initiative Response Analysis (SIRA) Framework for Analyzing Startup Initiatives on Twitter

Social Media (SM) platforms, particularly Twitter, have become useful tools for startup companies (henceforth startups) which use the latter to support most of their business activities. As a result, there is a need to gauge the performance of specific business initiatives vis-à-vis public sentiment, or more specifically the spread of such initiatives based on Twitter user-generated content. Previous research which makes use of Twitter analysis to analyze the business activities of startups is minimal, especially for Twitter user content in the Arabic language. Consequently, this paper proposes an analytics-based framework called Startup Initiatives Response Analysis (SIRA) designed to assess the performance of initiatives launched by startups via text classification, sentiment analysis, and statistical analysis techniques. To provide empirical evidence for the viability of the proposed research framework, this paper examined the case of an Arab transportation network startup, carrying out a SIRA analysis of an initiative undertaken by Careem to empower women by encouraging them to work for the company. The results confirm the effectiveness of the proposed framework for statistically measuring the initiative spread and the public feedback based on the user-generated content on the Twitter social platform.
Dynamic Weighted Cross Entropy for Semantic Segmentation with Extremely Imbalanced Data

The most common loss function in semantic segmentation is the cross entropy. In the most data-balanced scene, the cross entropy can be used as a loss function to achieve good results. However, in the real scene, there are many cases where the data are extremely imbalanced. In these cases, it is difficult to obtain ideal results by using the cross entropy as the loss function. In order to solve the aforementioned problem, we propose a dynamic weighted cross entropy as the loss function for semantic segmentation. Firstly, we count the number of each category in each training batch and global data. And then a weighting method is designed to weight the cross entropy. The object which is extremely hard to classify is removed by the hard truncation. Finally, we iterate the weight in every train step. The experiment results demonstrate that our method can effectively improve the segmentation accuracy with extremely imbalanced data.
A Parallel Architecture of Age Adversarial Convolutional Neural Network for Cross-Age Face Recognition

Cross-Age Face Recognition (CAFR) remains one of the most challenging tasks in the field of face recognition, as the aging process significantly affects the facial appearance. Another limitation is the insufficient special datasets that cover a wide range of ages. The key to tackle this problem is to separate the variations caused by aging process from facial features and obtain stable person-specific features. Specifically, we proposed a novel end-to-end CNN method called Age Adversarial Convolutional Neural Network (AA-CNN) with parallel network architecture. By adversarial training in Age Discrimination Network (ADN), the features extracted by AA-CNN are invariant to age variation, while remaining identity discriminative via joint training in Identity Recognition Network (IRN). Furthermore, we adopt a pyramid architecture of feature fusion to assist the ADN in adversarial training to obtain effective age-related information. The training datasets of AA-CNN are labeled by identity or age, and there is no need to search for the datasets with both identity and age labels. Extensive experiments have been conducted on the challenging aging face datasets, including FG-NET dataset, MORPH Album 2 dataset, Cross-Age Celebrity dataset, and Cross-Age LFW dataset, which demonstrates the superiority and effectiveness of the AA-CNN model.
Testing Regex Generalizability And Its Implications: A Large-Scale Many-Language Measurement Study

The regular expression (regex) practices of software engineers affect the maintainability, correctness, and security of their software applications. Empirical research has described characteristics like the distribution of regex feature usage, the structural complexity of regexes, and worst-case regex match behaviors. But researchers have not critically examined the methodology they follow to extract regexes, and findings to date are typically generalized from regexes written in only 1-2 programming languages. This is an incomplete foundation. Generalizing existing research depends on validating two hypotheses: (1) Various regex extraction methodologies yield similar results, and (2) Regex characteristics are similar across programming languages. To test these hypotheses, we defined eight regex metrics to capture the dimensions of regex representation, string language diversity, and worst-case match complexity. We report that the two competing regex extraction methodologies yield comparable corpuses, suggesting that simpler regex extraction techniques will still yield sound corpuses. But in comparing regexes across programming languages, we found significant differences in some characteristics by programming language. Our findings have bearing on future empirical methodology, as the programming language should be considered, and generalizability will not be assured. Our measurements on a corpus of 537,806 regexes can guide data-driven designs of a new generation of regex tools and regex engines.
The price and liquidity impact of China forbidding initial coin offerings on the cryptocurrency market

In this article, we empirically examine the cryptocurrency market reaction to china prohibiting initial coin offerings, on the 4 September 2007 for the 100 largest cryptocurrencies. The announcement has a significant negative but temporary impact on cryptocurrency returns and liquidity.
Online Motion Planning for Deforming Maneuvering and Manipulation by Multilinked Aerial Robot Based on Differential Kinematics

State-of-the-art work on deformable multirotor aerial robots has developed a strong maneuvering ability in such robots, whereas there is no versatile aerial robot that can perform both deforming maneuvering and aerial manipulation yet. However, a novel multilinked aerial robot presented in our previous work, called DRAGON, has both potential because of its serial-link structure. Therefore, an online motion planning method for such a multilinked aerial robot is required. In this letter, we first reveal the general statics model of the multilinked aerial robot, which involves the influence of joint torque, rotor thrust force, external wrench, and gravity, and further discuss the necessary rotor thrust force and joint torque required to compensate for external force and gravity under the quasi-static assumption. Then, we propose a real-time motion planning method, which sequentially solves the differential kinematics problem. This method considers the limitations of rotor thrust force and joint torque, as well as kinematics constraints. Furthermore, we introduce the integrated control framework, which can follow a quasi-static multilinks' trajectory and compensate for the external wrench. Finally, experiments to squeeze a virtual hatch covered by a movable plate are performed with quad-type DRAGON to demonstrate the feasibility of the proposed motion planning method in real-time.
ResCoNN: Resource-Efficient FPGA-Accelerated CNN for Traffic Sign Classification

Precise classification and detection of (distorted and normal) traffic signs in real-time is one of the non-trivial requirements for safe autonomous driving. The state-of-the-art convolutional neural networks (CNNs) for traffic sign detection though accurate are resource-hungry due to their inherent structure with massive networks with millions of full-precision parameters making them infeasible for low-end FPGA platforms leading to higher implementation costs. The existing works employing low-precision bits and similar techniques though hardware friendly leads to degradation in accuracy. In this work, we propose a resource-efficient CNN (ResCoNN) architecture with a small number of weights (only 60,000 compared to a few million in state-of-the-art CNNs) and employ for traffic sign detection and classification. For increased efficiency, the network takes advantage of binary weights and integer activations, rather than employing complex computations like Batch Normalization and Exponential Linear Units. ResCoNN achieves a classification accuracy of > 96% on real-world images at a framerate of 36fps on a Zynq SoC (xc7z020clg484-1) with 90% reduced weights compared to state-of-the-art CNNs.
Recovery of original individual person data (IPD) inferences from empirical IPD summaries only: Applications to distributed computing under disclosure constraints

There are many settings where individual person data (IPD) are not available, due to privacy or technical reasons, and one must work with IPD proxies, such as summary statistics, to approximate original IPD inferences, that is, the results of statistical analyses that would ideally have been performed on individual‐level data. For instance, in a distributed computing setting, as implemented in the DataSHIELD software framework, different centers can only share IPD proxies to obtain pooled IPD inferences. Such privacy requirements limit the scope of statistical investigation. For example, it can be challenging to perform between‐center random‐effect regression models. To increase modeling freedom we propose a method that only uses simple nondisclosive summaries of the original IPD as input, such as empirical marginal moments and correlation matrices, and generates artificial data compatible with those summary features. Specifically, data are generated from a Gaussian copula with marginal and joint components specified by the above summaries. The goal is to reproduce original IPD features in the artificial data, such that original IPD inferences are recovered from the artificial data. In an application example, and through simulations, we show that we can recover estimates of a multivariable IPD random‐effect logistic regression, from artificial data generated via the Gaussian copula using the above IPD summaries, suggesting the proposed approach provides a generally applicable strategy for distributed computing settings with data protection constraints.
QoE-Driven Coupled Uplink and Downlink Rate Adaptation for 360-Degree Video Live Streaming

360-degree video provides an immersive 360-degree viewing experience and has been widely used in many areas. The 360-degree video live streaming systems involve capturing, compression, uplink (camera to video server) and downlink (video server to user) transmissions. However, few studies have jointly investigated such complex systems, especially the rate adaptation for the coupled uplink and downlink in the 360-degree video streaming under limited bandwidth constraints. In this letter, we propose a quality of experience (QoE)-driven 360-degree video live streaming system, in which a video server performs rate adaptation based on the uplink and downlink bandwidths and information concerning each user's real-time field-of-view (FoV). We formulate it as a nonlinear integer programming problem and propose an algorithm, which combines the Karush-Kuhn-Tucker (KKT) condition and branch and bound method, to solve it. The numerical results show that the proposed optimization model can improve users' QoE significantly in comparison with other baseline schemes.
Hopfield neural networks using Klein four-group

Complex-valued Hopfield neural networks have been extended to several high-dimensional Hopfield neural networks (HDHNNs) using hypercomplex numbers. However, the extensions by hypercomplex numbers are limited. For example, the dimensions of Clifford and Cayley-Dickson algebras are power of two. Group theory provides more various algebras since the orders of groups are any. In this work, Klein four-group is introduced to HDHNNs. It is an abelian group whose order is 4. A Klein Hopfield neural network employs the twin-multistate activation function. We compare the noise tolerance with that of quaternion-valued and commutative quaternion-valued twin-multistate Hopfield neural networks by computer simulations.
medExtractR: A targeted, customizable approach to medication extraction from electronic health records

Abstract Objective We developed medExtractR, a natural language processing system to extract medication information from clinical notes. Using a targeted approach, medExtractR focuses on individual drugs to facilitate creation of medication-specific research datasets from electronic health records. Materials and Methods Written using the R programming language, medExtractR combines lexicon dictionaries and regular expressions to identify relevant medication entities (eg, drug name, strength, frequency). MedExtractR was developed on notes from Vanderbilt University Medical Center, using medications prescribed with varying complexity. We evaluated medExtractR and compared it with 3 existing systems: MedEx, MedXN, and CLAMP (Clinical Language Annotation, Modeling, and Processing). We also demonstrated how medExtractR can be easily tuned for better performance on an outside dataset using the MIMIC-III (Medical Information Mart for Intensive Care III) database. Results On 50 test notes per development drug and 110 test notes for an additional drug, medExtractR achieved high overall performance (F-measures &amp;gt;0.95), exceeding performance of the 3 existing systems across all drugs. MedExtractR achieved the highest F-measure for each individual entity, except drug name and dose amount for allopurinol. With tuning and customization, medExtractR achieved F-measures &amp;gt;0.90 in the MIMIC-III dataset. Discussion The medExtractR system successfully extracted entities for medications of interest. High performance in entity-level extraction provides a strong foundation for developing robust research datasets for pharmacological research. When working with new datasets, medExtractR should be tuned on a small sample of notes before being broadly applied. Conclusions The medExtractR system achieved high performance extracting specific medications from clinical text, leading to higher-quality research datasets for drug-related studies than some existing general-purpose medication extraction tools.
Integrated Functional Safety and Security Diagnosis Mechanism of CPS Based on Blockchain

This paper proposes a functional safety and information security protection mechanism based on blockchain technology.The design of the basic level and integration level blockchain structure of CPS distributed architecture and related functional safety and information security measures are introduced.An effective communication judgment mechanism based on functional safety error threshold is proposed, which is stored and judged by smart contract.And a refund transaction with a clock is proposed to ensure the effective execution of the functional safety error threshold mechanism.The article takes cyber physical machine tool system as an example to describe the SIL judgment method of CPS physical equipment and functional safety loop and combines SIL with fault diagnosis and risk protection.Finally, the rationality of our proposed mechanism is proved by information security, functional security, real-time and maintainability.
The Semantic Web: Two decades on

More than two decades have passed since the establishment of the initial cornerstones of the Semantic Web. Since its inception, opinions have remained divided regarding the past, present and potential future impact of the Semantic Web. In this paper – and in light of the results of over two decades of development on both the Semantic Web and related technologies – we reflect on the current status of the Semantic Web, the impact it has had thus far, and future challenges. We first review some of the external criticism of this vision that has been put forward by various authors; we draw together the individual critiques, arguing both for and against each point based on the current state of adoption. We then present the results of a questionnaire that we have posed to the Semantic Web mailing list in order to understand respondents’ perspective(s) regarding the degree to which the original Semantic Web vision has been realised, the impact it can potentially have on the Web (and other settings), its success stories thus far, as well as the degree to which they agree with the aforementioned critiques of the Semantic Web in terms of both its current state and future feasibility. We conclude by reflecting on future challenges and opportunities in the area.
Efficient quantum key distribution against collective noise using polarization and transverse spatial mode of photons

Channel noise is the main issue which reduces the efficiency of quantum communication. Here we present an efficient scheme for quantum key distribution against collective-rotation channel noise using polarization and transverse spatial mode of photons. Exploiting the two single-photon Bell states and two-photon hyperentangled Bell states in the polarization and the transverse spatial mode degrees of freedom (DOFs), the mutually unbiased bases can be encoded for logical qubits against the collective-rotation noise. Our scheme shows noiseless subspaces can be made up of two DOFs of two photons instead of multiple photons, which will reduce the resources required for noiseless subspaces and depress the photonic loss sensitivity. Moreover, the two single-photon Bell states and two-photon hyperentangled Bell states are symmetrical to the two photons, which means the relative order of the two photons is not required in our scheme, so the receiver only needs to measure the state of each photon, which makes our protocol easy to execute in experiment than the previous works.