Reviewerly/exHarmony huggingface dataset:
Effective Transfer Learning for Identifying Similar Questions

People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them. This leaves many questions unanswered or inadequately answered. Many of these questions are not unique, and reliable identification of similar questions would enable more efficient and effective question answering schema. COVID-19 has only exacerbated this problem. Almost every government agency and healthcare organization has tried to meet the informational need of users by building online FAQs, but there is no way for people to ask their question and know if it is answered on one of these pages. While many research efforts have focused on the problem of general question similarity, these approaches do not generalize well to domains that require expert knowledge to determine semantic similarity, such as the medical domain. In this paper, we show how a double fine-tuning approach of pretraining a neural network on medical question-answer pairs followed by fine-tuning on medical question-question pairs is a particularly useful intermediate task for the ultimate goal of determining medical question similarity. While other pretraining tasks yield an accuracy below 78.7% on this task, our model achieves an accuracy of 82.6% with the same number of training examples, an accuracy of 80.0% with a much smaller training set, and an accuracy of 84.5% when the full corpus of medical question-answer data is used. We also describe a currently live system that uses the trained model to match user questions to COVID-related FAQs.
DPNet: Detail-preserving network for high quality monocular depth estimation

Existing monocular depth estimation methods are unsatisfactory due to the inaccurate inference of depth details and the loss of spatial information. In this paper, we present a novel detail-preserving network (DPNet), i.e., a dual-branch network architecture that fully addresses the above problems and facilitates the depth map inference. Specifically, in contextual branch (CB), we propose an effective and efficient nonlocal spatial attention module by introducing non-local filtering strategy to explicitly exploit the pixel relationship in spatial domain, which can bring significant promotion on depth details inference. Meanwhile, we design a spatial branch (SB) to preserve the spatial information and generate high-resolution features from input color image. A refinement module (RM) is then proposed to fuse the heterogeneous features from both spatial and contextual branches to obtain a high quality depth map. Experimental results show that the proposed method outperforms SOTA methods on benchmark RGB-D datasets.
Blockchain-Based Data Storage With Privacy and Authentication in Internet of Things

Internet of Things (IoT) composed of large number of sensing devices with a variety of features applicable for various applications. In such scenarios, due to low data handling capabilities, limited storage, and security aspects, it is quite challenging to protect networks against illegal information access and utilizes storage efficiently. Though researchers provide various solutions for security and data storage, but a few solutions are appropriate for wireless sensor networks (WSNs)-enabled IoTs. Therefore, a blockchain-based decentralized framework integrated with authentication and privacy-preserving schemes is developed for the secure communication in WSNs-enabled IoTs. Registration, certification, and revocation process are employed for the communication with sensor nodes and base station (BS) in a cloud computing environment. In this scheme cluster heads forward the collected information to the BS. Consequently, BS records all the key parameters on the distributed blockchain and large data is forwarded to clouds for the storage. The revoked certificates of all malicious nodes are eliminated from blockchain by BS. The performance of the proposed scheme is scrutinized in terms of detection accuracy, certification delay, computational, and communicational overheads. The simulated results, comparative analysis, and security validation support the superiority of the proposed solution over the existing approaches.
Hypergraph Convolutional Recurrent Neural Network

In this study, we present a hypergraph convolutional recurrent neural network (HGC-RNN), which is a prediction model for structured time-series sensor network data. Representing sensor networks in a graph structure is useful for expressing structural relationships among sensors. Conventional graph structure, however, has a limitation on representing complex structure in real world application, such as shared connections among multiple nodes. We use a hypergraph, which is capable of modeling complicated structures, for structural representation. HGC-RNN performs a hypergraph convolution operation on the input data represented in the hypergraph to extract hidden representations of the input, while considering the structural dependency of the data. HGC-RNN employs a recurrent neural network structure to learn temporal dependency from the data sequence. We conduct experiments to forecast taxi demand in NYC, traffic flow in the overhead hoist transfer system, and gas pressure in a gas regulator. We compare the performance of our method with those of other existing methods, and the result shows that HGC-RNN has strengths over baseline models.
Spectral–Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification

Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications. One of the limiting factors of CNN is it uses spatial image features only for classification. The spectral features from the transform domain have equivalent importance in the complex image classification algorithm. This paper proposes a new CNN structure to classify the histopathological cancer images based on integrating the spectral features obtained using a multi-resolution wavelet transform with the spatial features of CNN. In addition, batch normalization process is used after every layer in the convolution network to improve the poor convergence problem of CNN and the deep layers of CNN are trained with spectral-spatial features. The proposed structure is tested on malignant histology images of the breast for both binary and multi-class classification of tissue using the BreaKHis Dataset and the Breast Cancer Classification Challenge 2015 Datasest. Experimental results show that the combination of spectral-spatial features improves classification accuracy of the CNN network and requires less training parameters in comparison with the well known models (i.e., VGG16 and ALEXNET). The proposed structure achieves an average accuracy of 97.58% and 97.45% with 7.6 million training parameters on both datasets, respectively.
Assessment analysis of green development level based on S-type cloud model of Beijing-Tianjin-Hebei, China

As the environment suffers from various forms of destruction, especially irreversible destruction, the idea of green development is getting more and more attention by all countries over the world. Nevertheless, for the coexistence of fuzziness of evaluation grade thresholds' selecting and evaluation indexes' random error, the existing research on green development evaluation has not been involved. Therefore, aiming at this practical problem, this study proposes an evaluation method based on S-type cloud model, which be able to deal with the uncertainties of fuzziness and randomness concurrently. Furthermore, this method is adopted to assess the green development level of Beijing-Tianjin-Hebei (BTH) region, including 13 cities in China. The evaluation index system is established in terms of five dimensions: living environment, pollutant treatment and utilization, ecological efficiency, economic growth and innovative potential. The empirical results show that the overall level of pollutant treatment and utilization in Beijing-Tianjin-Hebei region is high while it is seemed to lack coordination between cities in other dimensions. This evaluation method proposed by this study can play the following key role: providing a new entry point for researches of green development, and also providing reference or guidance for the government in formulating public policies on green development.
Social media, protest, &amp; outrage communication in Ethiopia: toward fractured publics or pluralistic polity?

In 2018, Ethiopia experienced a tectonic political shift following the culmination of years of public outcry against the now defunct ruling party, the Ethiopian People’s Revolutionary Democratic Front (EPRDF). Protest groups, predominantly organized along ethnic identification, have used social networking sites such as Facebook and Twitter to disseminate strategies, recruit members, and galvanize support. Anchored on theories of collective identity and moral outrage, this study investigates the role of social media platforms in mobilizing Ethiopians toward political reform during the protest and post-protest periods demarcated by the ascent of Abiy Ahmed as the new Prime Minister of Ethiopia. Data generated from a mixed method approach consisting of an online survey and interviews indicate social media platforms played a crucial role by drawing Ethiopian youth to participate in political discourse, empowering formerly marginalized groups to influence policy, and fostering ingroup cultural/political cohesion. However, evidence indicates participation opportunities created by social media platforms also brought apprehension including the rise of outrage communication as manifested by inflammatory expressions, hate speech, and political extremism. By proposing outrage communication as a viable construct that captures this phenomenon, I argue, in the context of a polity embodying highly heterogeneous and competing collective identities – ethnic or otherwise – such as Ethiopia, social media platforms are likely to increase ingroup political participation but chronically diminish outgroup engagement.
What do adversarial images tell us about human vision?

Deep convolutional neural networks (DCNNs) are frequently described as the best current models of human and primate vision. An obvious challenge to this claim is the existence of adversarial images that fool DCNNs but are uninterpretable to humans. However, recent research has suggested that there may be similarities in how humans and DCNNs interpret these seemingly nonsense images. We reanalysed data from a high-profile paper and conducted five experiments controlling for different ways in which these images can be generated and selected. We show human-DCNN agreement is much weaker and more variable than previously reported, and that the weak agreement is contingent on the choice of adversarial images and the design of the experiment. Indeed, we find there are well-known methods of generating images for which humans show no agreement with DCNNs. We conclude that adversarial images still pose a challenge to theorists using DCNNs as models of human vision.
DeepSigns: A predictive model based on Deep Learning for the early detection of patient health deterioration

Early diagnosis of critically ill patients depends on the attention and observation of medical staff about different variables, as vital signs, results of laboratory tests, among other. Seriously ill patients usually have changes in their vital signs before worsening. Monitoring these changes is important to anticipate the diagnosis in order to initiate patients’ care. Prognostic indexes play a fundamental role in this context since they allow to estimate the patients’ health status. Besides, the adoption of electronic health records improved the availability of data, which can be processed by machine learning techniques for information extraction to support clinical decisions. In this context, this work aims to create a computational model able to predict the deterioration of patients’ health status in such a way that it is possible to start the appropriate treatment as soon as possible. The model was developed based on Deep Learning technique, a Recurrent Neural Networks, the Long Short-Term Memory, for the prediction of patient’s vital signs and subsequent evaluation of the patient’s health status severity through Prognostic Indexes commonly used in the health area. Experiments showed that it is possible to predict vital signs with good precision (accuracy > 80%) and, consequently, predict the Prognostic Indexes in advance to treat the patients before deterioration. Predicting the patient’s vital signs for the future and use them for the Prognostic Index’ calculation allows clinical times to predict future severe diagnoses that would not be possible applying the current patient’s vital signs (50%–60% of cases would not be identified).
A deep learning framework for text-independent writer identification

Handwriting Writer Identification (HWI) refers to the process of handwriting text image analysis to identify the authorship of the documents. It has yielded promising results in various applications, including digital forensics, criminal purposes, exploring the writer of historical documents, etc. The complexity of the text image, especially in images with various handwriting makes the writer identification difficult. In this work, we propose an end-to-end system that relies on a straightforward yet well-designed deep network and very efficient feature extraction, emphasizing feature engineering. Our system is an extended version of ResNet by conjugating deep residual networks and a new traditional yet high-quality handwriting descriptor towards handwriting analysis. Our descriptor analyzes the handwriting thickness as a preliminary and essential feature for human handwriting characteristics. Our approach can also provide text-independent writer identification that we do not need to have the same handwriting content for learning our model. The proposed approach is evaluated and achieved consistent results on four public and well-known datasets of IAM, Firemaker, CVL, and CERUG-EN. We empirically demonstrate that our conjugated network outperforms the original ResNet, and it can work well for real-world applications in which patches with few letters exist.
Automatic Bug Triage in Software Systems Using Graph Neighborhood Relations for Feature Augmentation

Bug triaging is the process of prioritizing bugs based on their severity, frequency, and risk in order to be assigned to appropriate developers for validation and resolution. This article introduces a graph-based feature augmentation approach for enhancing bug triaging systems using machine learning. A new feature augmentation approach that utilizes graph partitioning based on neighborhood overlap is proposed. Neighborhood overlap is a quite effective approach for discovering relationships in social graphs. Terms of bug summaries are represented as nodes in a graph, which is then partitioned into clusters of terms. Terms in strong clusters are augmented to the original feature vectors of bug summaries based on the similarity between the terms in each cluster and a bug summary. We employed other techniques such as term frequency, term correlation, and topic modeling to identify latent terms and augment them to the original feature vectors of bug summaries. Consequently, we utilized frequency, correlation, and neighborhood overlap techniques to create another feature augmentation approach that enriches the feature vectors of bug summaries to use them for bug triaging. The new modified vectors are used to classify bug reports into different priorities. Bug Triage in this context is to correctly recognize the priority of new bugs. Several classification algorithms are tested using the proposed methods. Experimental results on a data set with Eclipse bug reports extracted from the Bugzilla tracking system have shown that our approach outperformed the existing bug triaging systems including modern techniques that utilize deep learning.
Cybertrust: From Explainable to Actionable and Interpretable Artificial Intelligence

We argue that artificial intelligence (AI) systems should be designed with features that build trust by bringing decision-analytic perspectives into AI. Actionable and interpretable AI will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
Cryptanalysis of Image Ciphers With Permutation-Substitution Network and Chaos

In recent decades, the introduction of chaos to image encryption has drawn worldwide attention. The permutation-substitution architecture has been widely applied, and chaotic systems are generally employed to produce the required encryption elements. Although many security assessment tests have been conducted, some chaotic image ciphers are being cryptanalyzed. In this article, we evaluate the security of a family of image ciphers whose encryption kernel consists of a bit-level or pixel-level permutation and a bit-wise exclusive OR substitution. After investigating the intrinsic linearity inside the outfitted structures and encryption techniques, we find that each ciphertext-plaintext pair can be represented as a combination of a set of ciphertext-plaintext bases. A chosen-ciphertext attack is proposed to construct the ciphertext-plaintext bases rather than the traditional solution to retrieve equivalent encryption elements. We further reveal that such weakness cannot be remedied by common enhancements such as more chaotic dynamics, complex permutation methods, and random pixel insertion during encryption. In addition, applications of the proposed attack to break 12 ciphers are theoretically presented and experimentally verified.
Overview of Touché 2020: Argument Retrieval

This paper is a condensed report on Touché: the first shared task on argument retrieval that was held at CLEF 2020. With the goal to create a collaborative platform for research in argument retrieval, we run two tasks: (1) supporting individuals in finding arguments on socially important topics and (2) supporting individuals with arguments on everyday personal decisions.
Co-clustering optimization using Artificial Bee Colony (ABC) algorithm

This paper presents an Artificial Bee Colony (ABC) optimization based algorithm for co-clustering of high-dimensional data. The ABC algorithm is used for optimization problems including data clustering. We incorporate aspects of co-clustering by embedding it into the objective function used for clustering by the ABC algorithm. Instead of a linear metric, such as the Euclidean distance, we propose the use of higher order correlations to build similarity between rows and columns, each based on the other. This measure uses co-evolving similarities which when embedded into the objective function results in optimizing the co-clusters. The search space is also explored in the vicinity of the solutions produced by the ABC algorithm using three local search methods — the first is a heuristic based on computing the cluster means; the second uses the analytical gradient of the objective with respect to a centroid to find lower cost solutions in the vicinity; and, the third is a hybrid of the first two methods. Numerical experiments show significant improvement in the search for optimal clustering by incorporating new similarity metric and optimized local search method. Finally, the algorithm is shown to be highly scalable for parallel architectures for both distributed and shared memory systems. Theoretically, the best iso-efficiency function of Θ (p log p) for fully connected network with p processors is also computed for the parallel algorithm.
Is blockchain a solution for logistics and freight transportation problems?

The fragmented market of freight transportation imposes several inefficiency costs as a result of delay, double-spending, disputes, and cancellations. While in such competitive market, every player seeks its own profit, optimisation at individual level also becomes harder due to invisibility and disconnectivity. Although tackling some of these issues are doable by synchronization, encryption and integration, it seems that blockchain not only incorporates all these solutions in one package, but also may provide other economic benefits. This paper discusses blockchain’ s potential in improving the inefficiencies in maritime supply chain and logistics, as well as limitations and challenges.
HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media

We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and $2$ male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. Moreover, a spatio-temporally aligned scanned and rigged 3D character complements HUMAN4D to enable joint research on time-varying and high-quality dynamic meshes. We provide evaluation baselines by benchmarking HUMAN4D with state-of-the-art human pose estimation and 3D compression methods. For the former, we apply 2D and 3D pose estimation algorithms both on single- and multi-view data cues. For the latter, we benchmark open-source 3D codecs on volumetric data respecting online volumetric video encoding and steady bit-rates. Furthermore, qualitative and quantitative visual comparison between mesh-based volumetric data reconstructed in different qualities showcases the available options with respect to 4D representations. HUMAN4D is introduced to the computer vision and graphics research communities to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues. The dataset and its code are available https://tofis.github.io/myurls/human4d.