Reviewerly/exHarmony huggingface dataset:
An Autonomous Lane-Changing System With Knowledge Accumulation and Transfer Assisted by Vehicular Blockchain

Inappropriate lane following and changing behaviors of connected and autonomous vehicles (CAVs) can result in accidents, such as rear-end collision and side collision. To remedy that, the use of deep reinforcement learning (DRL) for autonomous driving decisions is currently a widely used promising solution. In this case, the accuracy and effectiveness of such a machine learning (ML) model is quite essential for this artificial intelligence (AI)-enabled CAVs. This article proposes a blockchain-based collective learning (BCL) framework for autonomous lane-changing systems. Four key issues, namely, learning efficiency, data security, users' privacy, as well as communication burden, are addressed by applying collective learning, vehicular blockchain, and knowledge transfer. First, we model the lane-changing problem as a DRL process and learn the autonomous lane-changing strategy through the deep deterministic policy gradient (DDPG) algorithm. Second, a single CAV involves a limited number of driving scenarios, and the independent learning method has the problem of inefficiency. Therefore, we propose a collective learning framework to utilize the “collective intelligence” shared by CAVs. Third, a vehicular blockchain is then applied to ensure the security and privacy of the user and data. In addition, the introduction of the blockchain can incentivize more users to participate in collective learning. Finally, in order to accelerate the learning process and achieve higher level performance while further reducing the communication burden, we use the corresponding knowledge extracted from the ML model such as human learning, as privileged information for sharing instead of directly sharing local ML models. Extensive simulation results validate the effectiveness and efficiency of our proposal in terms of learning efficiency, driving safety, as well as system security and robustness.
Computer assisted recognition of breast cancer in biopsy images via fusion of nucleus-guided deep convolutional features

Background and objective: Breast cancer is a commonly detected cancer among women, resulting in a high number of cancer-related mortality. Biopsy performed by pathologists is the final confirmation procedure for breast cancer diagnosis. Computer-aided diagnosis systems can support the pathologist for better diagnosis and also in reducing subjective errors. Methods: In the automation of breast cancer analysis, feature extraction is a challenging task due to the structural diversity of the breast tissue images. Here, we propose a nucleus feature extraction methodology using a convolutional neural network (CNN), ‘NucDeep’, for automated breast cancer detection. Non-overlapping nuclei patches detected from the images enable the design of a low complexity CNN for feature extraction. A feature fusion approach with support vector machine classifier (FF + SVM) is used to classify breast tumor images based on the extracted CNN features. The feature fusion method transforms the local nuclei features into a compact image-level feature, thus improving the classifier performance. A patch class probability based decision scheme (NucDeep + SVM + PD) for image-level classification is also introduced in this work. Results: The proposed framework is evaluated on the publicly available BreaKHis dataset by conducting 5 random trials with 70-30 train-test data split, achieving average image level recognition rate of 96.66 ± 0.77%, 100% specificity and 96.21% sensitivity. Conclusion: It was found that the proposed NucDeep + FF + SVM model outperforms several recent existing methods and reveals a comparable state of the art performance even with low training complexity. As an effective and inexpensive model, the classification of biopsy images for breast tumor diagnosis introduced in this research will thus help to develop a reliable support tool for pathologists.
Explainable Deep-Fake Detection Using Visual Interpretability Methods

Deep-Fakes have sparked concerns throughout the world because of their potentially explosive consequences. A dystopian future where all forms of digital media are potentially compromised and public trust in Government is scarce doesn't seem far off. If not dealt with the requisite seriousness, the situation could easily spiral out of control. Current methods of Deep-Fake detection aim to accurately solve the issue at hand but may fail to convince a lay-person of its reliability and thus, lack the trust of the general public. Since the fundamental issue revolves around earning the trust of human agents, the construction of interpretable and also easily explainable models is imperative. We propose a framework to detect these Deep-Fake videos using a Deep Learning Approach: we have trained a Convolutional Neural Network architecture on a database of extracted faces from FaceForensics' DeepFakeDetection Dataset. Furthermore, we have tested the model on various Explainable AI techniques such as LRP and LIME to provide crisp visualizations of the salient regions of the image focused on by the model. The prospective and elusive goal is to localize the facial manipulations caused by Faceswaps. We hope to use this approach to build trust between AI and Human agents and to demonstrate the applicability of XAI in various real-life scenarios.
Fibre-direction strain measurement in a composite ply under quasi-static tensile loading using Digital Volume Correlation and in situ Synchrotron Radiation Computed Tomography

Digital Volume Correlation (DVC), in concert with in situ Synchrotron Radiation Computed Tomography (SRCT), has been applied to Carbon-Fibre Reinforced Polymers (CFRPs) under quasi-static tensile loading. DVC represents a relatively novel tool for quantifying full-field volumetric displacements and implicit strain fields. The highly anisotropic and somewhat regular/self-similar microstructures found in well-aligned unidirectional (UD) materials at high volume fractions are shown to be intrinsically challenging for DVC, especially along the fibre direction. To permit the application of DVC to displacement and/or strain measurements parallel to the fibre orientation, the matrix was doped with a sparse population of sub-micrometre barium titanate particles to act as displacement trackers (i.e. fiducial markers). For the novel materials systems we have developed, measurement noise is considered along with the spatial filtering intrinsic to DVC data processing. Compared to volume images acquired through Micro-focus Computed Tomography (µCT), hold-at-load artefacts are mitigated through scan times on the order of seconds using SRCT, as opposed to hours. Instances of individually fractured fibres evolving into clusters of breaks are presented, together with the associated strain redistribution (imaged at a voxel resolution of 0.65 µm). It is shown that the distance over which strain is recovered in the broken fibres not only increases with the applied force, but also with the number of broken fibres, delineating aspects of the load shedding phenomenon. The study demonstrates that unprecedented, mechanistically-consistent three-dimensional (3D) strain measurements may be made in relation to fibre failure events, that can be used to validate micromechanical models for predicting UD tensile failure. We believe this work presents the first application of DVC to the SRCT imaging of failure in CFRPs, achieving significantly higher resolution than reported previously within the literature.
Electronic Medical Record Search Engine (EMERSE): An Information Retrieval Tool for Supporting Cancer Research

PURPOSE The Electronic Medical Record Search Engine (EMERSE) is a software tool built to aid research spanning cohort discovery, population health, and data abstraction for clinical trials. EMERSE is now live at three academic medical centers, with additional sites currently working on implementation. In this report, we describe how EMERSE has been used to support cancer research based on a variety of metrics. METHODS We identified peer-reviewed publications that used EMERSE through online searches as well as through direct e-mails to users based on audit logs. These logs were also used to summarize use at each of the three sites. Search terms for two of the sites were characterized using the natural language processing tool MetaMap to determine to which semantic types the terms could be mapped. RESULTS We identified a total of 326 peer-reviewed publications that used EMERSE through August 2019, although this is likely an underestimation of the true total based on the use log analysis. Oncology-related research comprised nearly one third (n = 105; 32.2%) of all research output. The use logs showed that EMERSE had been used by multiple people at each site (nearly 3,500 across all three) who had collectively logged into the system &gt; 100,000 times. Many user-entered search queries could not be mapped to a semantic type, but the most common semantic type for terms that did match was “disease or syndrome,” followed by “pharmacologic substance.” CONCLUSION EMERSE has been shown to be a valuable tool for supporting cancer research. It has been successfully deployed at other sites, despite some implementation challenges unique to each deployment environment.
Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs.
A novel system architecture for secure authentication and data sharing in cloud enabled Big Data Environment

With the rapid growth of data sources, Big data security in Cloud is a big challenge. Different issues have ascended in the area of Big data security such as infrastructure security, data privacy, data management and data integrity. Currently, Big data processing, analytics and storage is secured using cryptography algorithms, which are not appropriate for Big data protection over Cloud. In this paper, we present a solution for addressing the main issues in Big data security over Cloud. We propose a novel system architecture called the Secure Authentication and Data Sharing in Cloud (SADS-Cloud). There are three processes involved in this paper including (i). Big Data Outsourcing, (ii). Big Data Sharing and (iii). Big Data Management. In Big data outsourcing, the data owners are registered to a Trust Center using SHA-3 hashing algorithm. The MapReduce model is used to split the input file into fixed-size of blocks of data and SALSA20 encryption algorithm is applied over each block. In Big data sharing, data users participate in a secure file retrieval. For this purpose, user's credentials (ID, password, secure ID, and current timestamp, email id) are hashed and compared with that stored in a database. In Big data management, there are three important processes implemented to organize data. They are as follows: Compression using Lemperl Ziv Markow Algorithm (LZMA), Clustering using Density-based Clustering of Applications with Noise (DBSCAN), and Indexing using Fractal Index Tree. The proposed scheme for these processes are implemented using Java Programming and performance tested for the following metrics: Information Loss, Compression Ratio, Throughput, Encryption Time and Decryption Time.
Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images

Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on public datasets; however, their performance in segmenting overlapped nuclei are limited. To address the issue, we propose the bending loss regularized network for nuclei segmentation. The proposed bending loss defines high penalties to contour points with large curvatures, and applies small penalties to contour points with small curvature. Minimizing the bending loss can avoid generating contours that encompass multiple nuclei. The proposed approach is validated on the MoNuSeg dataset using five quantitative metrics. It outperforms six state-of-the-art approaches on the following metrics: Aggregate Jaccard Index, Dice, Recognition Quality, and Panoptic Quality.
Caledonian (Early Paleozoic) veins overprinted by Yanshanian (Late Mesozoic) gold mineralization in the Jiangnan Orogen: A case study on gold deposits in northeastern Hunan, South China

The Jiangnan Orogen, situated between the Yangtze and Cathaysia blocks in South China, is an important gold-polymetallic metallogenic belt in China. Like many gold deposits hosted in Precambrian rocks in eastern China, the timing of gold mineralization in the Jiangnan Orogen, mostly hosted by Neoproterozoic rocks, has been controversial. A number of previous geochronological studies have suggested that mineralization ages range from Neoproterozoic to Cretaceous, with Caledonian (Early Paleozoic) and Yanshanian (Jurassic – Cretaceous) being considered most important. As Caledonian is the most important period of regional metamorphism, and Yanshanian is the most important period of granitic magmatism, the timing of mineralization is closely related to the genetic nature (orogenic versus post-orogenic) of the gold mineralization. This paper addresses these problems by studying the ages and characteristics of ore-forming fluids of two major gold deposits (Wangu and Huangjindong) in the central part of the Jiangnan Orogen, in northeastern Hunan. Muscovite Ar-Ar dating suggests that the bulk of the quartz veins in the Wangu and Huangjindong deposits were formed at ca. > 399 – 397 Ma, i.e., during the Caledonian orogeny, whereas gold mineralization (and associated veining) took place at ca. 142 – 130 Ma, i.e., during the Yanshanian orogeny. Fluid inclusions in the barren “Caledonian veins” are characterized by the H2O-NaCl-CO2 system, homogenization temperatures of 266 – 276 °C and salinities of 5 – 5.4 wt% NaCl equivalent, similar to those found in many orogenic-type gold deposits, whereas the fluid inclusions in the gold-bearing “Yanshanian veins” (with abundant sulfides) overprinting the barren veins have a H2O-NaCl composition system, homogenization temperatures of 169 to 256 °C, and salinities from 8.7 to 15.2 wt% NaCl equivalent. It is proposed that both the Caledonian and Yanshanian veining events are controlled by the same fracture systems, with the former being formed in a relatively deep, orogenic deformation-metamorphic environment, and the latter in a relatively shallow, extensional environment accompanied by extensive granitic magmatism. Reactivation of the Caledonian structures during the Yanshanian orogeny, together with availability of sulfur and gold, possibly associated with granitic intrusions, played a key role in the formation of the gold deposits in northeastern Hunan and possibly elsewhere in the Jiangnan Orogen.
Who Are the Bitcoin Investors? Evidence from Indirect Cryptocurrency Investments

Cryptocurrencies have received growing attention from individuals, the media, and regulators. However, little is known about the investors whom these financial instruments attract. Using administrative data, we describe the investment behavior of individuals who invest in cryptocurrencies with structured retail products. We find that cryptocurrency investors are active traders who are prone to investment biases and hold risky portfolios. Cryptocurrency investors are more likely to invest in stocks with high media sentiment and more likely to employ heuristics from technical analysis. In line with attention effects and anticipatory utility, we find that the average cryptocurrency investor substantially increases account logins and trading activity after his or her first cryptocurrency purchase. Furthermore, cryptocurrency investors tend to tilt their portfolios towards even more risky securities after cryptocurrency adoption. Our results document which investors are more likely to adopt new financial products and help inform regulators about investorsâ€™ vulnerability to cryptocurrency investments.
RLSK: A Job Scheduler for Federated Kubernetes Clusters based on Reinforcement Learning

Job scheduling in cluster is often considered as a difficult online decision-making problem, and its solution depends largely on the understanding of the workload and environment. People usually first propose a simple heuristic scheduling algorithm, and then perform repeated and tedious manual tests and adjustments based on the characteristics of the workload to gradually improve the algorithm. In this work, focusing on multi-cluster environments, load balancing and efficient scheduling, we present RLSK, a deep reinforcement learning based job scheduler for scheduling independent batch jobs among multiple federated cloud computing clusters adaptively. By directly specifying high-level scheduling targets, RLSK interacts with the system environment and automatically learns scheduling strategies from experience without any prior knowledge assumed over the underlying multi-cluster environment and human instructions, which avoids people's tedious testing and tuning work. We implement our scheduler based on Kubernetes, and conduct simulations to evaluate the performance of our design. The results show that, RLSK can outperform traditional scheduling algorithms.
Beyond 5G: Leveraging Cell Free TDD Massive MIMO Using Cascaded Deep Learning

This letter deals with the calibration of Time Division Duplexing (TDD) reciprocity in an Orthogonal Frequency Division Multiplexing (OFDM) based Cell Free Massive MIMO system where the responses of the (Radio Frequency) RF chains render the end to end channel non-reciprocal, even though the physical wireless channel is reciprocal. We further address the non-availability of the uplink channel estimates at locations other than pilot subcarriers and propose a single-shot solution to estimate the downlink channel at all subcarriers from the uplink channel at selected pilot subcarriers. We propose a cascade of two Deep Neural Networks (DNN) to achieve the objective. The proposed method is easily scalable and removes the need for relative reciprocity calibration based on the cooperation of antennas, which usually introduces dependency in Cell Free Massive MIMO systems.
Paraphrase identification using collaborative adversarial networks

The paper presents a Collaborative Adversarial Network (CAN) model for paraphrase identification, which is a collaborative network holding generator that is pitted against an adversarial network called discriminator. There has been tremendous research work and countless examinations done on sentenc e similarity demonstration. Learning and identifying the constant highlights, specifically in various areas and domains is the main focus of paraphrase identification. It Involves the capture of regular highlights between two sentences and the community-oriented learning upon traditional ill-disposed and adversarial learning for common feature extraction. The model outperforms the MaLSTM model, which is the baseline model, and also proves to be comparable to many of the state-of-the-art techniques.
Who contributes what? Scrutinizing the activity data of 4.2 million Zhihu users via immersion scores

Studies of knowledge communities have focused predominantly on contributors who ask questions and/or post replies, while little research has examined the contributions of those who neither pose questions nor suggest answers in knowledge communities. To illuminate member contributions of various sorts, this study evaluated user contribution to knowledge community from three dimensions (influence, content-contribution, and activeness) of immersion. Based on the user activity data of more than 4 million users from Zhihu, the largest online knowledge community in China, we calculated the immersion level for the four user groups (Lurkers, Questioners, Answerers, and Questioner-Answerers) in line with their question-asking and question-answering behaviors in Zhihu. The research findings revealed that Lurkers (members who posted nothing) showed higher community-immersion score than Questioners who asked questions only. The latter, Questioners, had the lowest community-immersion score, while Questioner-Answerers, who posted both questions and answers, exhibited the greatest contribution in the case knowledge community. We further made horizontal comparison of immersion score among the four different user groups, and found that when immersion scores of the four different user groups are above a certain threshold, the immersion scores of the four different user groups display a consistent distinguishing pattern. This result highlights the similarity of tendencies in behavioral orientation among different users in knowledge communities. Theoretical contributions and practical implications to be gleaned from this research are discussed.
EYEditor

On-the-go text-editing is difficult, yet frequently done in everyday lives. Using smartphones for editing text forces users into a heads-down posture which can be undesirable and unsafe. We present EYEditor, a heads-up smartglass-based solution that displays the text on a see-through peripheral display and allows text-editing with voice and manual input. The choices of output modality (visual and/or audio) and content presentation were made after a controlled experiment, which showed that sentence-by-sentence visual-only presentation is best for optimizing users' editing and path-navigation capabilities. A second experiment formally evaluated EYEditor against the standard smartphone-based solution for tasks with varied editing complexities and navigation difficulties. The results showed that EYEditor outperformed smartphones as either the path OR the task became more difficult. Yet, the advantage of EYEditor became less salient when both the editing and navigation was difficult. We discuss trade-offs and insights gained for future heads-up text-editing solutions.
New fractional-order shifted Gegenbauer moments for image analysis and recognition

Orthogonal moments are used to represent digital images with minimum redundancy. Orthogonal moments with fractional-orders show better capabilities in digital image analysis than integer-order moments. In this work, the authors present new fractional-order shifted Gegenbauer polynomials. These new polynomials are used to define a novel set of orthogonal fractional-order shifted Gegenbauer moments (FrSGMs). The proposed method is applied in gray-scale image analysis and recognition. The invariances to rotation, scaling and translation (RST), are achieved using invariant fractional-order geometric moments. Experiments are conducted to evaluate the proposed FrSGMs and compare with the classical orthogonal integer-order Gegenbauer moments (GMs) and the existing orthogonal fractional-order moments. The new FrSGMs outperformed GMs and the existing orthogonal fractional-order moments in terms of image recognition and reconstruction, RST invariance, and robustness to noise.