Reviewerly/exHarmony huggingface dataset:
Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm

Acute myeloid leukemia (AML) is a fatal blood cancer that progresses rapidly and hinders the function of blood cells and the immune system. The current AML diagnostic method, a manual examination of the peripheral blood smear, is time consuming, labor intensive, and suffers from considerable inter-observer variation. Herein, a machine learning model to detect and classify immature leukocytes for efficient diagnosis of AML is presented. Images of leukocytes in AML patients and healthy controls were obtained from a publicly available dataset in The Cancer Imaging Archive. Image format conversion, multi-Otsu thresholding, and morphological operations were used for segmentation of the nucleus and cytoplasm. From each image, 16 features were extracted, two of which are new nucleus color features proposed in this study. A random forest algorithm was trained for the detection and classification of immature leukocytes. The model achieved 92.99% accuracy for detection and 93.45% accuracy for classification of immature leukocytes into four types. Precision values for each class were above 65%, which is an improvement on the current state of art. Based on Gini importance, the nucleus to cytoplasm area ratio was a discriminative feature for both detection and classification, while the two proposed features were shown to be significant for classification. The proposed model can be used as a support tool for the diagnosis of AML, and the features calculated to be most important serve as a baseline for future research.
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving

Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view (> 180 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> ). However, they come at the cost of strong non-linear distortions which require more complex algorithms. In this paper, we explore Euclidean distance estimation on fisheye cameras for automotive scenes. Obtaining accurate and dense depth supervision is difficult in practice, but self-supervised learning approaches show promising results and could potentially overcome the problem. We present a novel self-supervised scale-aware framework for learning Euclidean distance and ego-motion from raw monocular fisheye videos without applying rectification. While it is possible to perform piece-wise linear approximation of fisheye projection surface and apply standard rectilinear models, it has its own set of issues like re-sampling distortion and discontinuities in transition regions. To encourage further research in this area, we will release our dataset as part of the WoodScape project [1]. We further evaluated the proposed algorithm on the KITTI dataset and obtained state-of-the-art results comparable to other self-supervised monocular methods. Qualitative results on an unseen fisheye video demonstrate impressive performance <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A time–frequency comovement and causality relationship between Bitcoin hashrate and energy commodity markets

This study examines time–frequency relationship between Bitcoin prices and Bitcoin mining based on daily data from January 2013 to October 2018. Bitcoin mining is measured through Bitcoin hashrate, which represents the completion speed of the Bitcoin code. We also include three energy commodities, i.e. oil, coal, and gas in a multivariate model employing time–frequency wavelet extensions in the form of partial and multivariate models. Results of our study suggest that both oil and gas lead Bitcoin returns from mid 2014 till 2016 across 64– 128 days' period. Under the investment period of 64– 256, hashrate and Bitcoin returns share significant comovement in the presence of oil and natural gas however exhibit no comovement when the effect of coal market is considered. Our results of wavelet decomposition suggest that the magnitude of comovement ranging from short- to long-run is time varying. Finally, results of the causality on quantile test suggest that Bitcoin returns cause changes in Bitcoin hashrate mostly during median quantiles with an asymmetric pattern. Our work entail implications for investors in the Bitcoin and energy market and is also helpful in forecasting the pricing behavior of Bitcoin using the hashrate and vice versa.
Blockchain and Demand Response: Zero-Knowledge Proofs for Energy Transactions Privacy

Nowadays, the adoption of demand response programs is still lagging due to the prosumers' lack of awareness, fear of losing control and privacy of energy data, etc. Programs decentralization, by adopting promising technologies such as blockchain, may bring significant advantages in terms of transparency, openness, improved control, and increased active participation of prosumers. Nevertheless, even though in general the transparency of the public blockchain is a desirable feature in the energy domain, the prosumer energy data is sensitive and rather private, thus, a privacy-preserving solution is required. In this paper, we present a decentralized implementation of demand response programs on top of the public blockchain which deals with the privacy of the prosumer's energy data using zero-knowledge proofs and validates on the blockchain the prosumer's activity inside the program using smart contracts. Prosumer energy data is kept private, while on the blockchain it is stored a zero-knowledge proof that is generated by the prosumer itself allowing the implementation of functions to validate potential deviations from the request and settle prosumer's activity. The solution evaluation results are promising in terms of ensuring the privacy of prosumer energy data stored in the public blockchain and detecting potential data inconsistencies.
Chatbot usage in restaurant takeout orders: A comparison study of three ordering methods

The purpose of this study was to explore customers' perceptions and behaviors when using chatbots in restaurant takeout orders. Built on the social presence theory, this study conducted a lab experiment to examine and compare three ordering methods in quick-service and full-service restaurants. Results revealed that phone ordering and online ordering were both better than chatbot ordering in terms of satisfaction and behavioral outcomes. The phone ordering method elicited best social presence and cognitive attitudes, while the online ordering method generated highest order amounts. Chatbot ordering is better suited for use in quick-service restaurants due to their simpler menus. In terms of order items, chatbot method was used for simple menu items and core products, phone method for specials and more complicated items, while online method for more expensive items and add-ons. The findings offer new insight for restaurant practitioners into designing and adopting chatbots.
Black Box Attacks on Explainable Artificial Intelligence(XAI) methods in Cyber Security

Cybersecurity community is slowly leveraging Machine Learning (ML) to combat ever evolving threats. One of the biggest drivers for successful adoption of these models is how well domain experts and users are able to understand and trust their functionality. As these black-box models are being employed to make important predictions, the demand for transparency and explainability is increasing from the stakeholders.Explanations supporting the output of ML models are crucial in cyber security, where experts require far more information from the model than a simple binary output for their analysis. Recent approaches in the literature have focused on three different areas: (a) creating and improving explainability methods which help users better understand the internal workings of ML models and their outputs; (b) attacks on interpreters in white box setting; (c) defining the exact properties and metrics of the explanations generated by models. However, they have not covered, the security properties and threat models relevant to cybersecurity domain, and attacks on explainable models in black box settings.In this paper, we bridge this gap by proposing a taxonomy for Explainable Artificial Intelligence (XAI) methods, covering various security properties and threat models relevant to cyber security domain. We design a novel black box attack for analyzing the consistency, correctness and confidence security properties of gradient based XAI methods. We validate our proposed system on 3 security-relevant data-sets and models, and demonstrate that the method achieves attacker's goal of misleading both the classifier and explanation report and, only explainability method without affecting the classifier output. Our evaluation of the proposed approach shows promising results and can help in designing secure and robust XAI methods.
Exploring the use of AR technology for co-creative product and packaging design

Extended Reality technologies, including Virtual Reality (VR) and Augmented Reality (AR), are being applied in a wide variety of industrial applications, but their use within design practice remains very limited, despite some promising research activities in this area over the last 20 years. At the same time, design practice has been evolving to place greater emphasis on the role of the client or end-user in the design process through ‘co-creative design’ activities. Whilst offering many benefits, co-creative design activities also present challenges, notably in the communication between designers and non-designers, which can hinder innovation. In this paper, we investigate the potential of a novel, projection-based AR system for the creation of design representations to support co-creative design sessions. The technology is tested through benchmarking experiments and in-situ trials conducted with two industrial partners. Performance metrics and qualitative feedback are used to evaluate the effectiveness of the new technology in supporting co-creative design sessions. Overall, AR technology allows quick, real-time modifications to the surfaces of a physical prototype to try out new ideas. Consequently, designers perceive the possibility to enhance the collaboration with the end-users participating in the session. Moreover, the quality and novelty of ideas generated whilst using projection-based AR outperform conventional sessions or handheld display AR sessions. Whilst the results of these early trials are not conclusive, the results suggest that projection-based AR design representations provide a promising approach to supporting co-creative design sessions.
Dissector

Deep learning (DL) applications are becoming increasingly popular. Their reliabilities largely depend on the performance of DL models integrated in these applications as a central classifying module. Traditional techniques need to retrain the models or rebuild and redeploy the applications for coping with unexpected conditions beyond the models' handling capabilities. In this paper, we take a fault tolerance approach, Dissector, to distinguishing those inputs that represent unexpected conditions (beyond-inputs) from normal inputs that are still within the models' handling capabilities (within-inputs), thus keeping the applications still function with expected reliabilities. The key insight of Dissector is that a DL model should interpret a within-input with increasing confidence, while a beyond-input would probably cause confused guesses in the prediction process. Dissector works in an application-specific way, adaptive to DL models used in applications, and extremely efficiently, scalable to large-size datasets from complex scenarios. The experimental evaluation shows that Dissector outperformed state-of-the-art techniques in the effectiveness (AUC: avg. 0.8935 and up to 0.9894) and efficiency (runtime overhead: only 3.3--5.8 milliseconds). Besides, it also exhibited encouraging usefulness in defensing against adversarial inputs (AUC: avg. 0.9983) and improving a DL model's actual accuracy in use (up to 16% for CIFAR-100 and 20% for ImageNet).
Deformable Spatial Propagation Networks For Depth Completion

Depth completion has attracted extensive attention recently due to the development of autonomous driving, which aims to recover dense depth map from sparse depth measurements. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art methods in this task, which adopt a linear propagation model to refine coarse depth maps with local context. However, the propagation of each pixel occurs in a fixed receptive field. This may not be the optimal for refinement since different pixel needs different local context. To tackle this issue, in this paper, we propose a deformable spatial propagation network (DSPN) to adaptively generates different receptive field and affinity matrix for each pixel. It allows the network obtain information with much fewer but more relevant pixels for propagation. Experimental results on KITTI depth completion benchmark demonstrate that our proposed method achieves the state-of-the-art performance.
A disentangled generative model for disease decomposition in chest X-rays via normal image synthesis

The interpretation of medical images is a complex cognition procedure requiring cautious observation, precise understanding/parsing of the normal body anatomies, and combining knowledge of physiology and pathology. Interpreting chest X-ray (CXR) images is challenging since the 2D CXR images show the superimposition on internal organs/tissues with low resolution and poor boundaries. Unlike previous CXR computer-aided diagnosis works that focused on disease diagnosis/classification, we firstly propose a deep disentangled generative model (DGM) simultaneously generating abnormal disease residue maps and “radiorealistic” normal CXR images from an input abnormal CXR image. The intuition of our method is based on the assumption that disease regions usually superimpose upon or replace the pixels of normal tissues in an abnormal CXR. Thus, disease regions can be disentangled or decomposed from the abnormal CXR by comparing it with a generated patient-specific normal CXR. DGM consists of three encoder-decoder architecture branches: one for radiorealistic normal CXR image synthesis using adversarial learning, one for disease separation by generating a residue map to delineate the underlying abnormal region, and the other one for facilitating the training process and enhancing the model’s robustness on noisy data. A self-reconstruction loss is adopted in the first two branches to enforce the generated normal CXR image to preserve similar visual structures as the original CXR. We evaluated our model on a large-scale chest X-ray dataset. The results show that our model can generate disease residue/saliency maps (coherent with radiologist annotations) along with radiorealistic and patient specific normal CXR images. The disease residue/saliency map can be used by radiologists to improve the CXR reading efficiency in clinical practice. The synthesized normal CXR can be used for data augmentation and normal control of personalized longitudinal disease study. Furthermore, DGM quantitatively boosts the diagnosis performance on several important clinical applications, including normal/abnormal CXR classification, and lung opacity classification/detection.
Improving Speech Emotion Recognition With Adversarial Data Augmentation Network

When training data are scarce, it is challenging to train a deep neural network without causing the overfitting problem. For overcoming this challenge, this article proposes a new data augmentation network-namely adversarial data augmentation network (ADAN)- based on generative adversarial networks (GANs). The ADAN consists of a GAN, an autoencoder, and an auxiliary classifier. These networks are trained adversarially to synthesize class-dependent feature vectors in both the latent space and the original feature space, which can be augmented to the real training data for training classifiers. Instead of using the conventional cross-entropy loss for adversarial training, the Wasserstein divergence is used in an attempt to produce high-quality synthetic samples. The proposed networks were applied to speech emotion recognition using EmoDB and IEMOCAP as the evaluation data sets. It was found that by forcing the synthetic latent vectors and the real latent vectors to share a common representation, the gradient vanishing problem can be largely alleviated. Also, results show that the augmented data generated by the proposed networks are rich in emotion information. Thus, the resulting emotion classifiers are competitive with state-of-the-art speech emotion recognition systems.
Deep Feature-Based Text Clustering and its Explanation

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community.Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information.Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results.In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks.This model, which is based on sequence representations, breaks the dependency on supervision.The experimental results show that our model outperforms classic text clustering algorithms and the state-of-the-art pretrained language model, i.e., BERT, on almost all the considered datasets.In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach.Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results.
PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

In soccer, passing is the most frequent interaction between players and plays a significant role in creating scoring chances. Experts are interested in analyzing players' passing behavior to learn passing tactics, i.e., how players build up an attack with passing. Various approaches have been proposed to facilitate the analysis of passing tactics. However, the dynamic changes of a team's employed tactics over a match have not been comprehensively investigated. To address the problem, we closely collaborate with domain experts and characterize requirements to analyze the dynamic changes of a team's passing tactics. To characterize the passing tactic employed for each attack, we propose a topic-based approach that provides a high-level abstraction of complex passing behaviors. Based on the model, we propose a glyph-based design to reveal the multi-variate information of passing tactics within different phases of attacks, including player identity, spatial context, and formation. We further design and develop PassVizor, a visual analytics system, to support the comprehensive analysis of passing dynamics. With the system, users can detect the changing patterns of passing tactics and examine the detailed passing process for evaluating passing tactics. We invite experts to conduct analysis with PassVizor and demonstrate the usability of the system through an expert interview.
Cascade Reasoning Network for Text-based Visual Question Answering

We study the problem of text-based visual question answering (T-VQA) in this paper. Unlike general visual question answering (VQA) which only builds connections between questions and visual contents, T-VQA requires reading and reasoning over both texts and visual concepts that appear in images. Challenges in T-VQA mainly lie in three aspects: 1) It is difficult to understand the complex logic in questions and extract specific useful information from rich image contents to answer them; 2) The text-related questions are also related to visual concepts, but it is difficult to capture cross-modal relationships between the texts and the visual concepts; 3) If the OCR (optical character recognition) system fails to detect the target text, the training will be very difficult. To address these issues, we propose a novel Cascade Reasoning Network (CRN) that consists of a progressive attention module (PAM) and a multimodal reasoning graph (MRG) module. Specifically, the PAM regards the multimodal information fusion operation as a stepwise encoding process and uses the previous attention results to guide the next fusion process. The MRG aims to explicitly model the connections and interactions between texts and visual concepts. To alleviate the dependence on the OCR system, we introduce an auxiliary task to train the model with accurate supervision signals, thereby enhancing the reasoning ability of the model in question answering. Extensive experiments on three popular T-VQA datasets demonstrate the effectiveness of our method compared with SOTA methods. The source code is available at https://github.com/guanghuixu/CRN_tvqa.
EdgeRec

Recommender system (RS) has become a crucial module in most web-scale applications. Recently, most RSs are in the waterfall form based on the cloud-to-edge framework, where recommended results are transmitted to edge (e.g., user mobile) by computing in advance in the cloud server. Despite effectiveness, network bandwidth and latency between cloud server and edge may cause the delay for system feedback and user perception. Hence, real-time computing on edge could help capture user preferences more preciously and thus make more satisfactory recommendations. Our work, to our best knowledge, is the first attempt to design and implement the novel Recommender System on Edge (EdgeRec), which achieves Real-time User Perception and Real-time System Feedback. Moreover, we propose Heterogeneous User Behavior Sequence Modeling and Context-aware Reranking with Behavior Attention Networks to capture user's diverse interests and adjust recommendation results accordingly. Experimental results on both the offline evaluation and online performance in Taobao home-page feeds demonstrate the effectiveness of EdgeRec.
Crisis and extended realities: remote presence in the time of COVID-19

The transformative influence of the COVID-19 pandemic on remote forms of communication has been a frequent theme in popular discourse during 2020, but any lingering transformation of what we do at a distance will rely on convincing and accessible forms of remote presence and interaction. Embodied communication is difficult to simulate, and this discussion examines current and emerging extended reality (XR)–based communication tools in a range of contexts to discover what role they may play in a future where crises of mobility are likely to grow more frequent and protracted. We define XR and its current uses, then examine key terms used to conceptualise it such as ‘presence’ and ‘social presence’, before highlighting social challenges of remote presence and ethical considerations that accompany its use, particularly how the technology might (or fail to) address important social problems, support education and have relevance to the future of work.