Reviewerly/exHarmony huggingface dataset:
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product

Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product recommendations, and product retrieval. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, we propose a multimodal method to jointly predict product attributes and extract values from textual product descriptions with the help of the product images. We argue that product attributes and values are highly correlated, e.g., it will be easier to extract the values on condition that the product attributes are given. Thus, we jointly model the attribute prediction and value extraction tasks from multiple aspects towards the interactions between attributes and values. Moreover, product images have distinct effects on our tasks for different product attributes and values. Thus, we selectively draw useful visual information from product images to enhance our model. We annotate a multimodal product attribute value dataset that contains 87,194 instances, and the experimental results on this dataset demonstrate that explicitly modeling the relationship between attributes and values facilitates our method to establish the correspondence between them, and selectively utilizing visual product information is necessary for the task. Our code and dataset are available at https://github.com/jd-aig/JAVE.
An efficient data prediction model using hybrid Harris Hawk Optimization with random forest algorithm in wireless sensor network

Wireless Sensor Networks (WSNs) are consistently gathering environmental weather data from sensor nodes on a random basis. The wireless sensor node sends the data via the base station to the cloud server, which frequently consumes immoderate power consumption during transmission. In distribution mode, WSN typically produces imprecise measurable or missing data and redundant data that influence the whole network of WSN. To overcome this complexity, an effective data prediction model was developed for decentralized photovoltaic plants using hybrid Harris Hawk Optimization with Random Forest algorithm (HHO-RF) primarily based on the ensemble learning approach. This work is proposed to predict the precise data and minimization of error in WSN Node. An efficient model for data reduction is proposed based on the Principal Component Analysis (PCA) for processing data from the sensor network. The datasets were gathered from the Tamil Nadu photovoltaic power plant, India. A low cost portable wireless sensor node was developed for collecting PV plant weather data using Internet of Things (IoT). The experimental outcomes of the proposed hybrid HHO-RF approach were compared with the other four algorithms, namely: Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and Long Short Term Memory (LSTM) algorithm. Results show that the determination coefficient (R2), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of the HHO-RF model are 0.9987, 0.0693, 0.2336 and 0.15881, respectively. For the prediction of air temperature, the RMSE of the proposed model is 3.82 %, 3.84% and 6.92% model in the lowest, average and highest weather days. The experimental outcomes of the proposed hybrid HHO-RF model have better performance compared to the existing algorithms.
Lightweight Fault Detection Strategy for Wireless Sensor Networks Based on Trend Correlation

In this work, we propose a fault detection strategy for wireless sensor networks (WSNs) called Trend Correlation based Fault Detection strategy (TCFD).This strategy can detect the faulty sensor nodes through analyzing the trend correlation and the median value of neighboring nodes.On this basis, aiming to avoid the excessive routing overhead caused by over-frequent fault detection, a fault detection self-starting mechanism is designed based on the cubic exponential smoothing method.Since the detection results in TCFD are determined by historical data at consecutive times and do not rely on the comparison of instantaneous sensed values at a single moment, it can significantly reduce the impact of fault detection time on detection accuracy.The simulation results have indicated that compared with referenced strategies, the proposed TCFD can obtain better fault detection accuracy for four common fault types of sensor nodes; in the case where the real fault rate of the network reaches 0.5, at least 70% of the faulty nodes can be detected by TCFD and the false alarm rate can still be kept below 30%; with the help of fault detection self-starting mechanism, the response time of sensor nodes to faults can be significantly shortened.
Non-Profiled Side-Channel Attack Based on Deep Learning Using Picture Trace

Over the years, deep learning algorithms have advanced a lot and any innovation in the algorithms are demonstrated and benchmarked for image classification.Several other field including sidechannel analysis (SCA) have recently adopted deep learning with great success.In SCA, the deep learning algorithms are typically working with 1-dimensional (1-D) data.In this work, we propose a unique method to improve deep learning based side-channel analysis by converting the measurements from raw-trace of 1-dimension data based on float or byte data into picture-formatted trace that has information based on the data position.We demonstrate why ''Picturization'' is more suitable for deep learning and compare how input and hidden layers interact for each raw (1-D) and picture form.As one potential application, we use a Binarized Neural Network (BNN) learning method that relies on a BNN's natural properties to improve variables.In addition, we propose a novel criterion for attack success or failure based on statistical confidence level rather than determination of a correct key using a ranking system.
Linear Velocity-Free Visual Servoing Control for Unmanned Helicopter Landing on a Ship With Visibility Constraint

In this article, a constrained image-based visual servoing control method for the shipboard landing problem of unmanned helicopters is proposed. First, the pitch and roll motion of ship are predicted by an autoregressive (AR) model to determine an appropriate period for landing. Subsequently, a novel robust sliding mode controller without linear velocity measurements is developed on the basis of the perspective image feature in a virtual image plane. Meanwhile, a modified Chebyshev neural network (CNN) is proposed to estimate the uncertainties, including the linear acceleration of ship and translational perturbation, while an adaptive law is employed to compensate the influence of rotational disturbances. The whole controller only requires the measurements feedback of a vision sensor and an inertial measurement unit (IMU). Ulteriorly, to prevent the visual target on the ship from going beyond the field of view of camera, the constrained controller is developed by a control barrier function and a quadratic programming, where the unknown relative velocity is estimated by a velocity observer. Finally, simulations are implemented to substantiate the capability of the presented shipboard landing control method.
The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study

Research has shown the feasibility of human activity recognition using wearable accelerometer devices. Different studies have used varying numbers and placements for data collection using sensors.This study aims to compare accuracy performance between multiple and variable placements of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults.In total, 93 participants (mean age 72.2 years, SD 7.1) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary versus nonsedentary, locomotion versus nonlocomotion, and lifestyle versus nonlifestyle activities (eg, leisure walk vs computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on 5 different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used to develop random forest models to assess activity category recognition accuracy and MET estimation.Model performance for both MET estimation and activity category recognition were strengthened with the use of additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03-0.09 MET increase in prediction error compared with wearing all 5 devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for the detection of locomotion (balanced accuracy decrease range 0-0.01), sedentary (balanced accuracy decrease range 0.05-0.13), and lifestyle activities (balanced accuracy decrease range 0.04-0.08) compared with all 5 placements. The accuracy of recognizing activity categories increased with additional placements (accuracy decrease range 0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition.Additional accelerometer devices slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.
A blockchain empowered and privacy preserving digital contact tracing platform

The spread of the COVID-19 virus continues to increase fatality rates and exhaust the capacity of healthcare providers. Efforts to prevent transmission of the virus among humans remains a high priority. The current efforts to quarantine involve social distancing, monitoring and tracking the infected patients. However, the spread of the virus is too rapid to be contained only by manual and inefficient human contact tracing activities. To address this challenge, we have developed Connect, a blockchain empowered digital contact tracing platform that can leverage information on positive cases and notify people in their immediate proximity which would thereby reduce the rate at which the infection could spread. This would particularly be effective if sufficient people use the platform and benefit from the targeted recommendations. The recommendations would be made in a privacy-preserving fashion and contain the spread of the virus without the need for an extended period of potential lockdown. Connect is an identity wallet platform which will keep user digital identities and user activity trace data on a blockchain platform using Self-Sovereign Identity(SSI) proofs. User activities include the places he/she has travelled, the country of origin he/she came from, travel and dispatch updates from the airport etc. With these activity trace records, Connect platform can easily identify suspected patients who may be infected with the COVID-19 virus and take precautions before spreading it. By storing digital identities and activity trace records on blockchain-based SSI platform, Connect addresses the common issues in centralized cloud-based storage platforms (e.g. lack of data immutability, lack of traceability).
C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams

Streaming Machine Learning (SML) studies single-pass learning algorithms that update their models one data item at a time given an unbounded and often non-stationary flow of data (a.k.a., in presence of concept drift). Online class imbalance learning is a branch of SML that combines the challenges of both class imbalance and concept drift. In this paper, we investigate the binary classification problem of rebalancing an imbalanced stream of data in the presence of concept drift, accessing one sample at a time. We propose Continuous Synthetic Minority Oversampling Technique (C-SMOTE), a novel rebalancing meta-strategy to pipeline with SML classification algorithms. C-SMOTE is inspired by the popular SMOTE algorithm but operates continuously. We benchmark C-SMOTE pipelines on ten different groups of data streams. We bring empirical evidence that models learnt with C-SMOTE pipelines outperform models trained on imbalanced data stream without losing the ability to deal with concept drifts. Moreover, we show that they outperform other stream balancing techniques from the literature.
SoK: Enabling Security Analyses of Embedded Systems via Rehosting

Closely monitoring the behavior of a software system during its execution enables developers and analysts to observe, and ultimately understand, how it works. This kind of dynamic analysis can be instrumental to reverse engineering, vulnerability discovery, exploit development, and debugging. While these analyses are typically well-supported for homogeneous desktop platforms (e.g., x86 desktop PCs), they can rarely be applied in the heterogeneous world of embedded systems. One approach to enable dynamic analyses of embedded systems is to move software stacks from physical systems into virtual environments that sufficiently model hardware behavior. This process which we call "rehosting" poses a significant research challenge with major implications for security analyses. Although rehosting has traditionally been an unscientific and ad-hoc endeavor undertaken by domain experts with varying time and resources at their disposal, researchers are beginning to address rehosting challenges systematically and in earnest. In this paper, we establish that emulation is insufficient to conduct large-scale dynamic analysis of real-world hardware systems and present rehosting as a firmware-centric alternative. Furthermore, we taxonomize preliminary rehosting efforts, identify the fundamental components of the rehosting process, and propose directions for future research.
BRAT: A BRidge Attack Tool for Cyber Security Assessments of Maritime Systems

: Today’s shipping industry is largely digitalized and networked, but by no means immune to cyber attacks. As recent incidents show, attacks, particularly those targeting on the misleading of navigation, not only pose a serious risk from an economic perspective when disrupting maritime value chains, but can also cause collisions and endanger the environment and humans. However, cyber defense has not yet been an integral part of maritime systems engineering, nor are there any automated tools to systematically assess their security level as well-established in other domains. In this paper, we therefore present a holistic BRidge Attack Tool (BRAT) that interactively offers various attack implementations targeting the communication of nautical data in maritime systems. This provides system engineers with a tool for security assessments of integrated bridge systems, enabling the identification of potential cyber vulnerabilities during the design phase. Moreover, it facilitates the development and validation of an effective cyber defense.
The Choice Strategy of Authentication Technology for Luxury E-Commerce Platforms in the Blockchain Era

Blockchain technology, as an emerging information technology, has attracted much attention. Some luxury e-commerce platforms have focused on the adoption of blockchain technology to provide electronic certificates for consumers, which helps to increase consumers’ trust in luxury goods. However, due to the high authentication cost of blockchain technology, some platforms still adopt manual technology with lower authentication costs to provide paper certificates for consumers. So whether platforms choose to adopt manual technology or blockchain technology for authentication is a problem worthy of attention. In this article, we investigate the choice strategy of authentication technology for luxury e-commerce platform(s) in the monopoly and duopoly markets, respectively. The results show that the choice of authentication technology for platform(s) depends on the cost difference between manual technology authentication and blockchain technology authentication. When the cost difference is small (large), platform(s) should adopt blockchain (manual) technology. In particular, in the duopoly market, the competition between the two platforms will improve their motivation to adopt blockchain technology, but the two platforms may trap into a prisoner's dilemma when they both adopt blockchain technology. Moreover, in the monopoly and duopoly markets, adopting blockchain technology is likely to improve consumer surplus and social welfare at the same time.
Multi-Task Retrieval for Knowledge-Intensive Tasks

Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oguz, Veselin Stoyanov, Gargi Ghosh. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at S\~ao Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.
GilBERT: Generative Vision-Language Pre-Training for Image-Text Retrieval

Given a text/image query, image-text retrieval aims to find the relevant items in the database. Recently, visual-linguistic pre-training (VLP) methods have demonstrated promising accuracy on image-text retrieval and other visual-linguistic tasks. These VLP methods are typically pre-trained on a large amount of image-text pairs, then fine-tuned on various downstream tasks. Nevertheless, due to the natural modality incompleteness in image-text retrieval, i.e., the query is either image or text rather than an image-text pair, the naive application of VLP to image-text retrieval results in significant inefficiency. Moreover, existing VLP methods cannot extract comparable representations for a single-modal query and multi-modal database items. In this work, we propose a generative visual-linguistic pre-training approach, termed as GilBERT, to simultaneously learn generic representations of image-text data and complete the missing modality for incomplete pairs. In testing phase, the proposed GilBERT facilitates efficient vector-based retrieval by providing unified feature embedding for query and database items. Moreover, the generative training not only makes GilBERT compatible with non-parallel text/image corpus, but also enables GilBERT to model the image-text relationships without suffering massive randomly-sampled negative samples, leading to superior experimental performances. Extensive experiments demonstrate the advantages of GilBERT in image-text retrieval, in terms of both efficiency and accuracy.
Fast Multiqubit Gates through Simultaneous Two-Qubit Gates

Near-term quantum computers are limited by the decoherence of qubits to only being able to run low-depth quantum circuits with acceptable fidelity. This severely restricts what quantum algorithms can be compiled and implemented on such devices. One way to overcome these limitations is to expand the available gate set from single- and two-qubit gates to multi-qubit gates, which entangle three or more qubits in a single step. Here, we show that such multi-qubit gates can be realized by the simultaneous application of multiple two-qubit gates to a group of qubits where at least one qubit is involved in two or more of the two-qubit gates. Multi-qubit gates implemented in this way are as fast as, or sometimes even faster than, the constituent two-qubit gates. Furthermore, these multi-qubit gates do not require any modification of the quantum processor, but are ready to be used in current quantum-computing platforms. We demonstrate this idea for two specific cases: simultaneous controlled-Z gates and simultaneous iSWAP gates. We show how the resulting multi-qubit gates relate to other well-known multi-qubit gates and demonstrate through numerical simulations that they would work well in available quantum hardware, reaching gate fidelities well above 99 %. We also present schemes for using these simultaneous two-qubit gates to swiftly create large entangled states like Dicke and Greenberg-Horne-Zeilinger states.
Graph Attention Multi-Layer Perceptron

Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications under industrial scenarios. Although some scalable GNNs are proposed for large-scale graphs, they adopt a fixed K-hop neighborhood for each node, thus facing the over-smoothing issue when adopting large propagation depths for nodes within sparse regions. To tackle the above issue, we propose a new GNN architecture --- Graph Attention Multi-Layer Perceptron (GAMLP), which can capture the underlying correlations between different scales of graph knowledge. We have deployed GAMLP in Tencent with the Angel platform, and we further evaluate GAMLP on both real-world datasets and large-scale industrial datasets. Extensive experiments on these 14 graph datasets demonstrate that GAMLP achieves state-of-the-art performance while enjoying high scalability and efficiency. Specifically, it outperforms GAT by 1.3% regarding predictive accuracy on our large-scale Tencent Video dataset while achieving up to 50x training speedup. Besides, it ranks top-1 on both the leaderboards of the largest homogeneous and heterogeneous graph (i.e., ogbn-papers100M and ogbn-mag) of Open Graph Benchmark.