Reviewerly/exHarmony huggingface dataset:
Liquidations: DeFi on a Knife-Edge

The trustless nature of permissionless blockchains renders overcollateralization a key safety component relied upon by decentralized finance (DeFi) protocols. Nonetheless, factors such as price volatility may undermine this mechanism. In order to protect protocols from suffering losses, undercollateralized positions can be liquidated. In this paper, we present the first in-depth empirical analysis of liquidations on protocols for loanable funds (PLFs). We examine Compound, one of the most widely used PLFs, for a period starting from its conception to September 2020. We analyze participants’ behavior and risk-appetite in particular, to elucidate recent developments in the dynamics of the protocol. Furthermore, we assess how this has changed with a modification in Compound’s incentive structure and show that variations of only 3% in an asset’s dollar price can result in over 10 m USD becoming liquidable. To further understand the implications of this, we investigate the efficiency of liquidators. We find that liquidators’ efficiency has improved significantly over time, with currently over 70% of liquidable positions being immediately liquidated. Lastly, we provide a discussion on how a false sense of security fostered by a misconception of the stability of non-custodial stablecoins, increases the overall liquidation risk faced by Compound participants.
Privacy-Preserved Electronic Medical Record Exchanging and Sharing: A Blockchain-Based Smart Healthcare System

The digitization of Electronic Medical Record (EMR) provides potential access to a wealth of medical information, but also presents new challenges in privacy-preserved EMR exchanging and sharing. In this paper, we propose a blockchain-based smart healthcare system with fine-grained privacy protection for reliable data exchanging and sharing among different users. We design a blockchain-enabled dynamic access control framework combined with Local Differential Privacy (LDP) strategies to provide the attribute-based privacy protection in transaction workflow. We design four types of smart contracts in the framework to meet the requirements of anonymous transaction, dynamic access control, beneficial matching decision, and evaluation of published data in an open network. To satisfy fine-grained privacy protection, we classify sensitive attributes of EMRs into different levels and set differential privacy budgets to randomize attributes before data publishing. Also, we design data quality function to depict the disturbance incurred by LDP-based privacy preferences at the requester view, and present appropriate many-to-many matching decisions among participants for beneficial transactions. Finally, we develop a prototype system and test our approach using 200,000 real-world EMRs. Experimental results show that the proposed privacy-preserved scheme can make stable and reliable transactions between EMR publishers and requesters. The prototype system achieves individual-centric privacy configuration at the patient site, while providing error-guaranteed statistics at the requester site. Additionally, the access control policies, logs of anonymous transaction are kept in the blockchain to provide system-level traceability.
Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management

Deep reinforcement learning (DRL) has proven to be an effective, general-purpose technology to develop ‘good’ replenishment policies in inventory management. We show how transfer learning from existing, well-performing heuristics may stabilize the training process and improve the performance of DRL in inventory control. While the idea is general, we specifically implement potential-based reward shaping to a deep Q-network algorithm to manage inventory of perishable goods that, cursed by dimensionality, has proven to be notoriously complex. The application of our approach may not only improve inventory cost performance and reduce computational effort, the increased training stability may also help to gain trust in the policies obtained by black box DRL algorithms.
Antecedents and moderators of consumer adoption toward AR‐enhanced virtual try‐on technology: A stimulus‐organism‐response approach

Abstract This study aimed to investigate consumers’ adoption intention toward augmented reality (AR)‐enhanced virtual try‐ons (VTOs) by examining the effects of consumers’ evaluations of technology attributes on their immersive experiences, perceived value, and adoption intention based on the stimulus–organism–response (SOR) framework. To explain variations in consumers’ evaluations and adoption of AR‐enhanced VTOs, two important personality traits that pertain to the use of AR‐enhanced VTOs, sensation‐seeking tendency and technology anxiety, were incorporated into the model as moderators. Data for this study were collected via a self‐administered survey of students enrolled in 13 classes of a southeastern university in the United States. A total of 398 respondents were recruited to participate in this study via a convenience sampling approach. A sample of 352 was retained for data analysis. Structural equation modeling and multigroup comparisons were conducted to test the proposed hypotheses. The finding revealed that the two salient technology attributes of AR‐enhanced VTOs exerted significant positive influences on telepresence, which, in turn, influenced consumers’ perceived values and adoption intention toward AR‐enhanced VTOs. Also, the two personality traits moderated the proposed relationships among technology attributes, perceived values, and adoption intentions. The findings add to empirical support for the relationship proposed in the SOR framework. Further, the results of this study provide insights that can guide companies in improving and marketing VTOs.
Universal Limitations on Quantum Key Distribution over a Network

We consider the distribution of secret keys, both in a bipartite and a multipartite (conference) setting, via a quantum network and establish a framework to obtain bounds on the achievable rates. We show that any multipartite private state--the output of a protocol distilling secret key among the trusted parties--has to be genuinely multipartite entangled. In order to describe general network settings, we introduce a multiplex quantum channel, which links an arbitrary number of parties, where each party can take the role of sender only, receiver only, or both sender and receiver. We define asymptotic and non-asymptotic LOCC-assisted secret-key-agreement (SKA) capacities for multiplex quantum channels and provide strong and weak converse bounds. The structure of the protocols we consider, manifested by an adaptive strategy of secret key and entanglement [Greenberger-Horne-Zeilinger (GHZ state)] distillation over an arbitrary multiplex quantum channel, is generic. As a result, our approach also allows us to study the performance of quantum key repeaters and measurement-device-independent quantum key distribution (MDI-QKD) setups. For teleportation-covariant multiplex quantum channels, we get upper bounds on the SKA capacities in terms of the entanglement measures of their Choi states. We also obtain bounds on the rates at which secret key and GHZ states can be distilled from a finite number of copies of an arbitrary multipartite quantum state. We are able to determine the capacities for MDI-QKD setups and rates of GHZ-state distillation for some cases of interest.
Atoll

With user-facing apps adopting serverless computing, good latency performance of serverless platforms has become a strong fundamental requirement. However, it is difficult to achieve this on platforms today due to the design of their underlying control and data planes that are particularly ill-suited to short-lived functions with unpredictable arrival patterns. We present Atoll, a serverless platform, that overcomes the challenges via a ground-up redesign of the control and data planes. In Atoll, each app is associated with a latency deadline. Atoll achieves its per-app request latency goals by: (a) partitioning the cluster into (semi-global scheduler, worker pool) pairs, (b) performing deadline-aware scheduling and proactive sandbox allocation, and (c) using a load balancing layer to do sandbox-aware routing, and automatically scale the semi-global schedulers per app. Our results show that Atoll reduces missed deadlines by ~66x and tail latencies by ~3x compared to state-of-the-art alternatives.
A First Look into DeFi Oracles

Recently emerging Decentralized Finance (DeFi) takes the promise of cryptocurrencies a step further, leveraging their decentralized networks to transform traditional financial products into trustless and transparent protocols that run without intermediaries. However, these protocols often require critical external information, like currency or commodity exchange rates, and in this respect they rely on special oracle nodes. In this paper, we present a comprehensive measurement study of DeFi price oracles deployed in practice. First, we investigate designs of mainstream DeFi platforms that rely on data from oracles. We find that these designs, surprisingly, position oracles as trusted parties with no or low accountability. Then, we present results of large-scale measurements of deployed oracles. We find and report that prices reported by oracles regularly deviate from current exchange rates, oracles are not free from operational issues, and their reports include anomalies. Finally, we compare the oracle designs and propose potential improvements.
From statistical‐ to machine learning‐based network traffic prediction

Abstract Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large‐scale, heterogeneous, and complex networks, the volume of transferred data, as big data , is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical‐ and Machine Learning (ML)‐based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.
Practical Guide for Building Superconducting Quantum Devices

Quantum computing offers a powerful new paradigm of information processing that has the potential to transform a wide range of industries. In the pursuit of the tantalizing promises of a universal quantum computer, a multitude of new knowledge and expertise has been developed, enabling the construction of novel quantum algorithms as well as increasingly robust quantum hardware. In particular, we have witnessed rapid progress in the circuit quantum electrodynamics (cQED) technology, which has emerged as one of the most promising physical systems that is capable of addressing the key challenges in realizing full-stack quantum computing on a large scale. In this Tutorial, we present some of the most crucial building blocks developed by the cQED community in recent years and a précis of the latest achievements towards robust universal quantum computation. More importantly, we aim to provide a synoptic outline of the core techniques that underlie most cQED experiments and offer a practical guide for a novice experimentalist to design, construct, and characterize their first quantum device.11 MoreReceived 9 October 2020DOI:https://doi.org/10.1103/PRXQuantum.2.040202Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum information with solid state qubitsQuantum Information
Accurate segmentation of breast tumors using AE U-net with HDC model in ultrasound images

Breast cancer poses a great threat on women health due to its high malignant rate. In China, ultrasound screening is the commonly-used method for breast cancer diagnosis, and the localization and segmentation of the lesions in ultrasound images are helpful for breast cancer detection. In this paper, an Attention Enhanced U-net with hybrid dilated convolution (AE U-net with HDC) model was proposed and employed to segment the breast tumors in ultrasound images. First, based on Attention U-net, we added a new loss function to update the weight matrix in the AGs module, in order to enhance the weight of the lesion area. Combined with fine-tuning training method, the precision of breast ultrasound image lesion region segmentation was improved from 82.38% to 86.28% and the M-IOU was improved from 76.27% to 81.81%. Second, three groups of HDC with expansion rates of [1,2,5] were integrated into AE U-net to replace the four convolution operations. HDC module brought larger receptive field and reduced the loss of spatial information. The experimental results proved that HDC module was helpful to improve the Acc of image segmentation results from 94.18% to 95.81% and the Recall from 78.69% to 80.48%. Combined with U-net, the F1 score, AUC, Acc and M-IOU of the network proposed in this paper had significantly improved. It proved that AE U-net with HDC model would have very important research value and application prospect for modern medicine.
Arguing in Favor of Revising the Simulator Sickness Questionnaire Factor Structure When Assessing Side Effects Induced by Immersions in Virtual Reality

Two issues are increasingly of interest in the scientific literature regarding unwanted virtual reality (VR) induced side effects: (1) whether the latent structure of the Simulator Sickness Questionnaire (SSQ) is comprised of two or three factors, and (2) if the SSQ measures symptoms of anxiety that can be misattributed to unwanted negative side effects induced by immersions in VR. Study 1 was conducted with a sample of 876 participants. A confirmatory factor analysis clearly supported a two-factor model composed of nausea and oculomotor symptoms instead of the 3-factor structure observed in simulators. To tease-out symptoms of anxiety from unwanted negative side effects induced by immersions in VR, Study 2 was conducted with 88 participants who were administered the Trier Stress Social Test in groups without being immersed in VR. A Spearman correlation showed that 11 out of 16 side effects correlated significantly with anxiety. A factor analysis revealed that items measuring general discomfort, difficulty concentrating, sweating, nausea, and vertigo loaded significantly on the anxiety factor comprised of items from the State-Trait Anxiety Inventory. Finally, a multiple regression indicated that the items measuring general discomfort and difficulty concentrating significantly predicted increases in anxiety. The overall results support the notion that side effects associated with immersions in VR consist mostly of a nausea and an oculomotor latent structure and that a few items are confounding anxiety and cybersickness. The data support the suggestion to revise the scoring procedures of the Simulator Sickness Questionnaire when using this instrument with immersions in VR.
Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumors. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted much attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic review of the literature in this area, covering 128 papers. The goal of this review paper is to analyze the relation between anomaly detection techniques and types of GANs, to identify the most common application domains for GAN-assisted and GAN-based anomaly detection, and to assemble information on datasets and performance metrics used to assess them. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area. In summary, GANs are used in anomaly detection to address the problem of insufficient amount of data for the anomalous behaviour, either through data augmentation or representation learning. The most commonly used GAN architectures are DCGANs, standard GANs, and cGANs. The primary application domains include medicine, surveillance and intrusion detection.
Self-supervised Video Transformer

In this paper, we propose self-supervised training for video transformers using unlabeled video data. From a given video, we create local and global spatiotemporal views with varying spatial sizes and frame rates. Our self-supervised objective seeks to match the features of these different views representing the same video, to be invariant to spatiotemporal variations in actions. To the best of our knowledge, the proposed approach is the first to alleviate the dependency on negative samples or dedicated memory banks in Self-supervised Video Transformer (SVT). Further, owing to the flexibility of Transformer models, SVT supports slow-fast video processing within a single architecture using dynamically adjusted positional encoding and supports longterm relationship modeling along spatiotemporal dimensions. Our approach performs well on four action recognition benchmarks (Kinetics-400, UCF-101, HMDB-51, and SSv2) and converges faster with small batch sizes. Code is available at: https://git.io/J1juJ.
Automated classification of acute leukemia on a heterogeneous dataset using machine learning and deep learning techniques

Today, artificial intelligence and deep learning techniques constitute a prominent part in the area of medical sciences. These techniques help doctors detect diseases early and reduce their burden as well as chances of errors. However, experiments based on deep learning techniques require large and well-annotated dataset. This paper introduces a novel dataset of 500 peripheral blood smear images, containing normal, Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia images. The dataset comprises almost 1700 cancerous blood cells. The size of the dataset is increased by adding images of a publicly available dataset and forming a heterogeneous dataset. The heterogeneous dataset is used for the automated binary classification task, which is one of the major tasks of the proposed work. The proposed work perform binary as well as three-class classification tasks involving state-of-the-art techniques based on machine learning and deep learning. For binary classification, the proposed work achieved an accuracy of 97% when fully connected layers along with the last three convolutional layers of VGG16 are fine tuned and 98% for DenseNet121 along with support vector machine. For three-class classification task, an accuracy of 95% is obtained for ResNet50 along with support vector machine. The preparation of the novel dataset is done under the opinion of various expertise that will help the scientific community for medical research supported by machine learning models.
Enhanced the moving object detection and object tracking for traffic surveillance using RBF-FDLNN and CBF algorithm

For traffic surveillance systems (TSS), Moving object detection and tracking is the key technology. On account of moving object-orientation variation, changing weather conditions, moving objects’ appearance, and non-target objects in the background, it's very hard to detection some objects in a traffic sequence of the video frame. Though many methods were introduced to detect and track moving objects in a traffic environment, they could not attain a desirable output. Hence, this work proposes an enhanced system for automatic moving object detection (MOD) and object tracking (OT) using RBF-FDLNN and CFR algorithms. The noises existent in a video frame are initially filtered using a GESFA filter. Next, the conversion of the RGB frame to HSL is done, and the background regions are eradicated from the frame using IGMM. After background removal (BGR), the Motion Estimation (ME) of object and OT are performed using GCS and CFR algorithm, respectively. Subsequently, the required features in the frames are extracted and fed into the RBF-FDLNN classifier for performing object detection (OD). The performance of the proposed method RBF-FDLNN classifier is better than other existing methods in the given video frame. Lastly, the outcomes are proffered to corroborate the proposed method’s effectiveness.
Hierarchical Modular Network for Video Captioning

Video captioning aims to generate natural language descriptions according to the content, where representation learning plays a crucial role. Existing methods are mainly developed within the supervised learning framework via word-by-word comparison of the generated caption against the ground-truth text without fully exploiting linguistic semantics. In this work, we propose a hierarchical modular network to bridge video representations and linguistic semantics from three levels before generating captions. In particular, the hierarchy is composed of: (I) Entity level, which highlights objects that are most likely to be mentioned in captions. (II) Predicate level, which learns the actions conditioned on highlighted objects and is supervised by the predicate in captions. (III) Sentence level, which learns the global semantic representation and is supervised by the whole caption. Each level is implemented by one module. Extensive experimental results show that the proposed method performs favorably against the state-of-the-art models on the two widely-used benchmarks: MSVD 104.0% and MSR-VTT 51.5% in CIDEr score. Code will be made available at https://github.com/MarcusNerva/HMN.