Reviewerly/exHarmony huggingface dataset:
Tree-Structured Regional CNN-LSTM Model for Dimensional Sentiment Analysis

Dimensional sentiment analysis aims to recognize continuous numerical values in multiple dimensions such as the valence-arousal (VA) space. Compared to the categorical approach that focuses on sentiment classification such as binary classification (i.e., positive and negative), the dimensional approach can provide a more fine-grained sentiment analysis. This article proposes a tree-structured regional CNN-LSTM model consisting of two parts: regional CNN and LSTM to predict the VA ratings of texts. Unlike a conventional CNN which considers a whole text as input, the proposed regional CNN uses a part of the text as a region, dividing an input text into several regions such that the useful affective information in each region can be extracted and weighted according to their contribution to the VA prediction. Such regional information is sequentially integrated across regions using LSTM for VA prediction. By combining the regional CNN and LSTM, both local (regional) information within sentences and long-distance dependencies across sentences can be considered in the prediction process. To further improve performance, a region division strategy is proposed to discover task-relevant phrases and clauses to incorporate structured information into VA prediction. Experimental results on different corpora show that the proposed method outperforms lexicon-, regression-, conventional NN and other structured NN methods proposed in previous studies.
Personality-Assisted Multi-Task Learning for Generic and Personalized Image Aesthetics Assessment

Traditional image aesthetics assessment (IAA) approaches mainly predict the average aesthetic score of an image. However, people tend to have different tastes on image aesthetics, which is mainly determined by their subjective preferences. As an important subjective trait, personality is believed to be a key factor in modeling individual's subjective preference. In this paper, we present a personality-assisted multi-task deep learning framework for both generic and personalized image aesthetics assessment. The proposed framework comprises two stages. In the first stage, a multi-task learning network with shared weights is proposed to predict the aesthetics distribution of an image and Big-Five (BF) personality traits of people who like the image. The generic aesthetics score of the image can be generated based on the predicted aesthetics distribution. In order to capture the common representation of generic image aesthetics and people's personality traits, a Siamese network is trained using aesthetics data and personality data jointly. In the second stage, based on the predicted personality traits and generic aesthetics of an image, an inter-task fusion is introduced to generate individual's personalized aesthetic scores on the image. The performance of the proposed method is evaluated using two public image aesthetics databases. The experimental results demonstrate that the proposed method outperforms the state-of-the-arts in both generic and personalized IAA tasks.
News sentiment in the cryptocurrency market: An empirical comparison with Forex

We use high frequency intra-day data to investigate the influence of unscheduled currency and Bitcoin news on the returns, volume and volatility of the cryptocurrency Bitcoin and traditional currencies over the period from January 2012 to November 2018. Results show that Bitcoin behaves differently to traditional currencies. Traditional currencies typically experience a decrease in returns after negative news arrivals and an increase in returns following positive news whereas Bitcoin reacts positively to both positive and negative news. This suggests investor enthusiasm for Bitcoin irrespective of the sentiment of the news. This phenomenon is exacerbated during bubble periods. Conversely, cryptocurrency cyber-attack news and fraud news dampen this effect, decreasing Bitcoin returns and volatility. Our results contribute to the discussion on the nature of Bitcoin as a currency or an asset. They further inform practitioners about the characteristics of cryptocurrencies as a financial asset and inform regulators about the influence of news on Bitcoin volatility, particularly during bubble periods.
Exploring the influence of culture on tourist experiences with robots in service delivery environment

Robots and artificial intelligence represent a newly emerging trend in tourism and hospitality. However, studies examining how cultural perceptions influence tourists’ experiences interacting with service robots are lacking. In response to the industrial trend, the experiential components of robot-staffed hotels are assessed in this study. A qualitative approach is adopted to compare the semantic networks of Japanese and non-Japanese tourists’ online reviews, using 1,498 reviews from nine robot-staffed hotels in Japan. The results indicate that hotel guests’ interaction with robots is one of the main experiential components in robot-staffed hotels. The semantic network analysis results demonstrate noticeable differences, with Japanese reviews demonstrating more emotional responses to human-robot interaction and non-Japanese reviews valuing the functional and technical aspects of robot-provided services more.
Holistic++ Scene Understanding: Single-View 3D Holistic Scene Parsing and Human Pose Estimation With Human-Object Interaction and Physical Commonsense

We propose a new 3D holistic <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> scene understanding problem, which jointly tackles two tasks from a single-view image: (i) holistic scene parsing and reconstruction-3D estimations of object bounding boxes, camera pose, and room layout, and (ii) 3D human pose estimation. The intuition behind is to leverage the coupled nature of these two tasks to improve the granularity and performance of scene understanding. We propose to exploit two critical and essential connections between these two tasks: (i) human-object interaction (HOI) to model the fine-grained relations between agents and objects in the scene, and (ii) physical commonsense to model the physical plausibility of the reconstructed scene. The optimal configuration of the 3D scene, represented by a parse graph, is inferred using Markov chain Monte Carlo (MCMC), which efficiently traverses through the non-differentiable joint solution space. Experimental results demonstrate that the proposed algorithm significantly improves the performance of the two tasks on three datasets, showing an improved generalization ability.
Probabilistic Object Detection: Definition and Evaluation

We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections. Given the lack of methods capable of assessing such probabilistic object detections, we present the new Probability-based Detection Quality measure (PDQ). Unlike AP-based measures, PDQ has no arbitrary thresholds and rewards spatial and label quality, and foreground/background separation quality while explicitly penalising false positive and false negative detections. We contrast PDQ with existing mAP and moLRP measures by evaluating state-of-the-art detectors and a Bayesian object detector based on Monte Carlo Dropout. Our experiments indicate that conventional object detectors tend to be spatially overconfident and thus perform poorly on the task of probabilistic object detection. Our paper aims to encourage the development of new object detection approaches that provide detections with accurately estimated spatial and label uncertainties and are of critical importance for deployment on robots and embodied AI systems in the real world.
Incremental learning for the detection and classification of GAN-generated images

Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images, hardly distinguishable from real ones. In particular, human face generation achieved a stunning level of realism, opening new opportunities for the creative industry but, at the same time, new scary scenarios where such content can be maliciously misused. Therefore, it is essential to develop innovative methodologies to automatically tell apart real from computer generated multimedia, possibly able to follow the evolution and continuous improvement of data in terms of quality and realism. In the last few years, several deep learning-based solutions have been proposed for this problem, mostly based on Convolutional Neural Networks (CNNs). Although results are good in controlled conditions, it is not clear how such proposals can adapt to real-world scenarios, where learning needs to continuously evolve as new types of generated data appear. In this work, we tackle this problem by proposing an approach based on incremental learning for the detection and classification of GAN-generated images. Experiments on a dataset comprising images generated by several GAN-based architectures show that the proposed method is able to correctly perform discrimination when new GANs are presented to the network.
On the performance of hybrid search strategies for systematic literature reviews in software engineering

Context: When conducting a Systematic Literature Review (SLR), researchers usually face the challenge of designing a search strategy that appropriately balances result quality and review effort. Using digital library (or database) searches or snowballing alone may not be enough to achieve high-quality results. On the other hand, using both digital library searches and snowballing together may increase the overall review effort. Objective: The goal of this research is to propose and evaluate hybrid search strategies that selectively combine database searches with snowballing. Method: We propose four hybrid search strategies combining database searches in digital libraries with iterative, parallel, or sequential backward and forward snowballing. We simulated the strategies over three existing SLRs in SE that adopted both database searches and snowballing. We compared the outcome of digital library searches, snowballing, and hybrid strategies using precision, recall, and F-measure to investigate the performance of each strategy. Results: Our results show that, for the analyzed SLRs, combining database searches from the Scopus digital library with parallel or sequential snowballing achieved the most appropriate balance of precision and recall. Conclusion: We put forward that, depending on the goals of the SLR and the available resources, using a hybrid search strategy involving a representative digital library and parallel or sequential snowballing tends to represent an appropriate alternative to be used when searching for evidence in SLRs.
Distributed Off-Chain Storage of Patient Diagnostic Reports in Healthcare System Using IPFS and Blockchain

The healthcare industry electronically maintains medical data which includes patients' information such as patients' personal information, diagnostic reports, and doctor prescriptions. However, the centralized storage model is currently used for storing such sensitive information. One main disadvantage of the centralized model is the difficulty in preserving user privacy. Threats relating to user (patient) privacy include unauthorized access of critical information such as identity details and diseases from which a patient is suffering, and misuse of patients' data and their medical reports. To address this issue, we propose a distributed off-chain storage of medical data using IPFS (Interplanetary File System) and blockchain technology. The proposed framework while preserving patient privacy facilitates easy access of medical data by authorized entities such as healthcare providers (e.g., doctors and nurses). Moreover, it achieves consistency, integrity, and availability.
Addressing class imbalance in deep learning for small lesion detection on medical images

Deep learning methods utilizing Convolutional Neural Networks (CNNs) have led to dramatic advances in automated understanding of medical images. However, in many medical image classification tasks, lesions occupy only a few pixels of the image. This results in a significant class imbalance between lesion and background. From recent literature, it is known that class imbalance may negatively affect the performance of CNN classification. However, very few research exists in the context of lesion detection. In this work, we propose a two-stage deep learning framework able to deal with the high class imbalance encountered during training of small lesion detectors. First, we train a deep cascade (DC) of long sequences of decision trees with an algorithm designed to handle unbalanced data that also drastically reduces the number of background samples reaching the final stage. The remaining samples are fed to a CNN, whose training benefits from both rebalance and hard mining done by the DC. We evaluated DC-CNN on two severely unbalanced classification problems: microcalcification detection and microaneurysm detection. In both cases, DC-CNN outperformed the CNNs trained with commonly used methods for addressing class imbalance such as oversampling, undersampling, hard mining, cost sensitive learning, and one-class classification. The DC-CNN was also ∼ 10x faster than CNN at test time.
A Survey of Data-driven and Knowledge-aware eXplainable AI

We are witnessing a fast development of Artificial Intelligence (AI), but it becomes dramatically challenging to explain AI models in the past decade. “Explanation” has a flexible philosophical concept of “satisfying the subjective curiosity for causal information”, driving a wide spectrum of methods being invented and/or adapted from many aspects and communities, including machine learning, visual analytics, human-computer interaction and so on. Nevertheless, from the view-point of data and knowledge engineering (DKE), a best explaining practice that is cost-effective in terms of extra intelligence acquisition should exploit the causal information and explaining scenarios which is hidden richly in the data itself. In the past several years, there are plenty of works contributing in this line but there is a lack of a clear taxonomy and systematic review of the current effort. To this end, we propose this survey, reviewing and taxonomizing existing efforts from the view-point of DKE, summarizing their contribution, technical essence and comparative characteristics. Specifically, we categorize methods into data-driven methods where explanation comes from the task-related data, and knowledge-aware methods where extraneous knowledge is incorporated. Furthermore, in the light of practice, we provide survey of state-of-art evaluation metrics and deployed explanation applications in industrial practice.
Quantum secure direct communication based on single-photon Bell-state measurement

Abstract Security loopholes exploiting the flaws of practical apparatus, especially non-ideal photon detectors, are pressing issues in practical quantum communication. We propose a simple quantum secure direct communication protocol based on single-photon Bell-state measurement and remove side-channel attacks on photon detectors. This quantum communication protocol in principle works in a deterministic way, and it does not require the two-photon interference of photons from independent sources. The single-photon Bell-state measurement with a unity efficiency can be constructed with only linear optics, which significantly simplifies its experimental implementation. Furthermore, we prove that our quantum secure direct communication protocol is immune to general detector-side-channel attacks.
Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors

Decoding human activity accurately from wearable sensors can aid in applications related to healthcare and context awareness. The present approaches in this domain use recurrent and/or convolutional models to capture the spatio-temporal features from time-series data from multiple sensors. We propose a deep neural network architecture that not only captures the spatio-temporal features of multiple sensor time-series data but also selects, learns important time points by utilizing a self-attention mechanism. We show the validity of the proposed approach across different data sampling strategies on six public datasets and demonstrate that the self-attention mechanism gave a significant improvement in performance over deep networks using a combination of recurrent and convolution networks. We also show that the proposed approach gave a statistically significant performance enhancement over previous state-of-the-art methods for the tested datasets. The proposed methods open avenues for better decoding of human activity from multiple body sensors over extended periods of time. The code implementation for the proposed model is available at https://github.com/isukrit/encodingHumanActivity.
Riskio: A Serious Game for Cyber Security Awareness and Education

Cyber attacks are increasing in number and sophistication, causing organisations to continuously adapt management strategies for cyber security risks. As a key risk mitigation policy, organisations are investing in professional training courses for their employees to raise awareness on cyber attacks and related defences. Serious games have emerged as a new approach that can complement instruction-led or computer-based security training by providing a fun environment where players learn and practice cyber security concepts through the game. In this paper we propose Riskio, a tabletop game to increase cyber security awareness for people with no-technical background working in organisations. Riskio provides an active learning environment where players build knowledge on cyber security attacks and defences by playing both the role of the attacker and the defender of critical assets in a fictitious organisation.
Using a Deep Neural Network and Transfer Learning to Bridge Scales for Seismic Phase Picking

Abstract The important task of tracking seismic activity requires both sensitive detection and accurate earthquake location. Approximate earthquake locations can be estimated promptly and automatically; however, accurate locations depend on precise seismic phase picking, which is a laborious and time‐consuming task. We adapted a deep neural network (DNN) phase picker trained on local seismic data to mesoscale hydraulic fracturing experiments. We designed a novel workflow, transfer learning‐aided double‐difference tomography, to overcome the 3 orders of magnitude difference in both spatial and temporal scales between our data and data used to train the original DNN. Only 3,500 seismograms (0.45% of the original DNN data) were needed to retrain the original DNN model successfully. The phase picks obtained with transfer‐learned model are at least as accurate as the analyst's and lead to improved event locations. Moreover, the effort required for picking once the DNN is trained is a small fraction of the analyst's.
Context-Aware Document Term Weighting for Ad-Hoc Search

Bag-of-words document representations play a fundamental role in modern search engines, but their power is limited by the shallow frequency-based term weighting scheme. This paper proposes HDCT, a context-aware document term weighting framework for document indexing and retrieval. It first estimates the semantic importance of a term in the context of each passage. These fine-grained term weights are then aggregated into a document-level bag-of-words representation, which can be stored into a standard inverted index for efficient retrieval. This paper also proposes two approaches that enable training HDCT without relevance labels. Experiments show that an index using HDCT weights significantly improved the retrieval accuracy compared to typical term-frequency and state-of-the-art embedding-based indexes.
The use of artificial intelligence, machine learning and deep learning in oncologic histopathology

Abstract Background Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast new opportunities to improve health care and has introduced a new wave of heightened precision in oncologic pathology. The impact of AI on oncologic pathology has now become apparent, and its use with respect to oral oncology is still in the nascent stage. Discussion A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine learning and computational pathology will be presented. This paper provides a focused review on the recent advances in AI and deep learning in oncologic histopathology and oral oncology. In addition, specific emphasis on recent studies that have applied these technologies to oral cancer prognostication will also be discussed. Conclusion Machine and deep learning methods designed to enhance prognostication of oral cancer have been proposed with much of the work focused on prediction models on patient survival and locoregional recurrences in patients with oral squamous cell carcinomas (OSCC). Few studies have explored machine learning methods on OSCC digital histopathologic images. It is evident that further research at the whole slide image level is needed and future collaborations with computer scientists may progress the field of oral oncology.
A Deep Convolutional Neural Network Approach for Static Hand Gesture Recognition

The communication barrier and the hearing majority are the key social concerns of the deaf-dumb community that prevent them from accessing the basic and essential services of the life. Eventhough the problem has been addressed with the innovations in automatic sign language recognition, an adequate solution has not yet been attained due to a number of challenging factors. Most of the existing works try to develop vision based recognizers through classical pattern analysis approach by deriving complex hand crafted feature descriptors from the captured images of the gestures. But the efficiency of those methods are very limited to work with large sign vocabulary captured in complex and uncontrolled background conditions. This paper proposes a methodology for the recognition of hand gestures, which is the prime component in sign language vocabulary, based on an efficient deep convolutional neural network (CNN) architecture. The method has been tested on two publicly available datasets (NUS hand posture dataset and American fingerspelling A dataset) and achieved better recognition accuracies.
Weakly Supervised Video Anomaly Detection via Center-Guided Discriminative Learning

Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. In this paper, we consider video anomaly detection as a regression problem with respect to anomaly scores of video clips under weak supervision. Hence, we propose an anomaly detection framework, called Anomaly Regression Net (ARNet), which only requires video-level labels in training stage. Further, to learn discriminative features for anomaly detection, we design a dynamic multiple-instance learning loss and a center loss for the proposed AR-Net. The former is used to enlarge the inter-class distance between anomalous and normal instances, while the latter is proposed to reduce the intra-class distance of normal instances. Comprehensive experiments are performed on a challenging benchmark: ShanghaiTech. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset.