Reviewerly/exHarmony huggingface dataset:
Radar-camera Fusion for Road Target Classification

This paper presents a radar and camera sensor fusion framework as a vulnerable road user (VRU) perception system that can automatically detect, track and classify different targets on the road. The first module of the system performs a spatial-temporal alignment on a common plane of detections provided by the radar signal processing and video processing modules. The second module is dedicated to data association of the aligned detections. A centralized fusion algorithm takes the current aligned detection set (locations and labels) as inputs from both sensors and performs multi-object tracking with a joint probabilistic data association (JPDAF) algorithm underlying the Kalman filter. The proposed radar/camera fusion system is experimentally evaluated through multi-object tracking scenarios. The experimental results demonstrate its reliability and effectiveness compared to a single sensor system.
Raw Image Deblurring

Deep learning-based blind image deblurring plays an essential role in solving image blur since all existing kernels are limited in modeling the real world blur. Thus far, researchers focus on powerful models to handle the deblurring problem and achieve decent results. For this work, in a new aspect, we discover the great opportunity for image enhancement (e.g., deblurring) directly from RAW images and investigate novel neural network structures benefiting RAW-based learning. However, to the best of our knowledge, there is no available RAW image deblurring dataset. Therefore, we built a new dataset containing both RAW images and processed sRGB images and design a new model to utilize the unique characteristics of RAW images. The proposed deblurring model, trained solely from RAW images, achieves the state-of-art performance and outweighs those trained on processed sRGB images. Furthermore, with fine-tuning, the proposed model, trained on our new dataset, can generalize to other sensors. Additionally, by a series of experiments, we demonstrate that existing deblurring models can also be improved by training on the RAW images in our new dataset. Ultimately, we show a new venue for further opportunities based on the devised novel raw-based deblurring method and the brand-new Deblur-RAW dataset.
Energy-Efficient and QoS-Optimized Adaptive Task Scheduling and Management in Clouds

The enormous energy consumed by clouds becomes a significant challenge for cloud providers and smart grid operators. Due to performance concerns, applications typically run in different clouds located in multiple sites. In different clouds, many factors, including electricity prices, available servers, and task service rates, exhibit spatial variations. Therefore, it is important to manage and schedule tasks among multiple clouds in a high-quality-of-service and low-energy-cost manner. This work proposes a task scheduling method to jointly minimize energy cost and average task loss possibility (ATLP) of clouds. A problem is formulated and tackled with an adaptive biobjective differential evolution based on simulated annealing to determine a real-time and near-optimal set of solutions. A final knee solution is further chosen to specify suitable servers in clouds and task allocation among web portals. Simulation results based on realistic data prove that less average loss possibility of tasks, and smaller energy cost is obtained with it than its widely used peers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work considers joint optimization of both ATLP and average energy cost of all clouds. It is of great significance to execute tasks among multiple clouds by jointly allocating all tasks among multiple web portals and specifying suitable servers in different clouds. Yet, it is challenging to achieve joint optimization in a market where factors, including prices of electricity and available servers, show spatial variations. Current studies are coarse-grained and fail to jointly achieve average energy cost minimization and quality-of-service optimization of tasks. In this work, a novel algorithm named adaptive simulated-annealing-based biobjective differential evolution is proposed for an energy cost and quality-of-service-optimized task scheduling strategy in a real-time manner. Experiments prove that it realizes lower energy cost and ATLP compared with its typical widely used peers. It can also be applied to other industrial areas, including smart manufacturing, Internet of Things, and smart city.
Digital image correlation for the analysis of in-plane tested unreinforced masonry walls

In the experimental assessment of the in-plane behaviour of unreinforced masonry (URM) walls containing openings, accurate measurement of the deformations occurring in the pier and spandrel substructures is important. Eight full-scale perforated URM walls were tested under in-plane cyclic loading and constant vertical pre-compression load. To measure the deformation of the walls (pier-spandrel substructures) during testing and to capture the full-field cracking of the masonry, a two dimensional (2D) Digital Image Correlation (DIC) technique was used in parallel with traditional hard-wired instrumentation (Linear Variable Differential Transformers, commonly referred to as LVDTs for short). This paper describes the detailed procedures of using DIC in large scale masonry wall testing. Firstly, the digital images at the first peak of each displacement amplitude in both push and pull directions for one specimen were analysed using the VIC-2D software to validate the DIC results against the LVDT recorded measurements. After confirming the accuracy of the DIC measurements for the specimen, the pier-spandrel movements and the cracking analyses of all the walls were performed in order to better understand the integrity of these elements during cyclic in-plane loading.
Efficient simulatability of continuous-variable circuits with large Wigner negativity

Discriminating between quantum computing architectures that can provide quantum advantage from those that cannot is of crucial importance. From the fundamental point of view, establishing such a boundary is akin to pinpointing the resources for quantum advantage; from the technological point of view, it is essential for the design of non-trivial quantum computing architectures. Wigner negativity is known to be a necessary resource for computational advantage in several quantum-computing architectures, including those based on continuous variables (CVs). However, it is not a sufficient resource, and it is an open question under which conditions CV circuits displaying Wigner negativity offer the potential for quantum advantage. In this work we identify vast families of circuits that display large, possibly unbounded, Wigner negativity, and yet are classically efficiently simulatable, although they are not recognized as such by previously available theorems. These families of circuits employ bosonic codes based on either translational or rotational symmetries (e.g., Gottesman-Kitaev-Preskill or cat codes), and can include both Gaussian and non-Gaussian gates and measurements. Crucially, within these encodings, the computational basis states are described by intrinsically negative Wigner functions, even though they are stabilizer states if considered as codewords belonging to a finite-dimensional Hilbert space. We derive our results by establishing a link between the simulatability of high-dimensional discrete-variable quantum circuits and bosonic codes.
Balanced Feature Pyramid Network for Ship Detection in Synthetic Aperture Radar Images

Ship detection in Synthetic Aperture Radar (SAR) images is a fundamental but challenging task. Nowadays, given that the huge imbalance between sparse-distribution ships and complex backgrounds in training process, most existing deep-learning-based SAR ship detectors often face great difficulty in further improving accuracy. Therefore, to solve this problem, in this paper, a novel Balanced Feature Pyramid Network (B-FPN) is applied to enhance detection accuracy. Different from the raw Feature Pyramid Network (FPN), B-FPN utilizes the same-deep integration balanced semantic features to strengthen the multi-level features in the feature pyramid, by means of four steps, namely rescaling, integrating, refining and strengthening, which do not increase too much network parameter quantity. Experimental results on the open SAR Ship Detection Dataset (SSDD) shows that B-FPN can make a 7.15% mean Average Precision (mAP) improvement than FPN.
Adaptive ensemble of classifiers with regularization for imbalanced data classification

The dynamic ensemble selection of classifiers is an effective approach for processing label-imbalanced data classifications. However, such a technique is prone to overfitting, owing to the lack of regularization methods and the dependence on local geometry of data. In this study, focusing on binary imbalanced data classification, a novel dynamic ensemble method, namely adaptive ensemble of classifiers with regularization (AER), is proposed, to overcome the stated limitations. The method solves the overfitting problem through a new perspective of implicit regularization. Specifically, it leverages the properties of stochastic gradient descent to obtain the solution with the minimum norm, thereby achieving regularization; furthermore, it interpolates the ensemble weights by exploiting the global geometry of data to further prevent overfitting. According to our theoretical proofs, the seemingly complicated AER paradigm, in addition to its regularization capabilities, can actually reduce the asymptotic time and memory complexities of several other algorithms. We evaluate the proposed AER method on seven benchmark imbalanced datasets from the UCI machine learning repository and one artificially generated GMM-based dataset with five variations. The results show that the proposed algorithm outperforms the major existing algorithms based on multiple metrics in most cases, and two hypothesis tests (McNemar's and Wilcoxon tests) verify the statistical significance further. In addition, the proposed method has other preferred properties such as special advantages in dealing with highly imbalanced data, and it pioneers the researches on regularization for dynamic ensemble methods.
Self-Attention Networks for Human Activity Recognition Using Wearable Devices

Human activity recognition has gained a lot of attention in recent years as it has many potential applications, such as in smart homes, healthcare and sport monitoring. Sensors in wearable devices and smartphones are widely used not only because they are low cost but also because they are not invasive to users and are easy to deploy. However, accurately predicting human activities using wearable devices is challenging as the generated data provides only indirect information about the activities being performed. In this study, we propose a self-attention network that processes data from inertial measurement unit sensors of smartphones to classify common human activities. Self-attention networks are able to extract useful information from time-dependent signals by carefully allocating their focus among relevant input features. Our method was tested along several popular human activity recognition algorithms using two datasets, including a new human activity dataset that is publicly released in this study. Our method consistently obtains state-of-the-art results predicting the activities of the tested datasets with an average accuracy of 97%.
Distance Education Gamification in Social Virtual Reality: A Case Study on Student Engagement

Sustaining students' interest and engagement in semester-long courses in distance learning settings is a tangible challenge in Higher Education. Social Virtual Reality platforms feature richer, spatial affordances in comparison to 2D, web-based synchronous and asynchronous systems. Gamification is a promising method which adds an additional, affective layer to learning in the direction of the enhancement of motivation. In this article the results of an exploratory study on student active engagement applying gamification in a postgraduate distance education course taught at a UK university are presented. Based on the design principles of a gamified course, student experiences and recommendations to practitioners are discussed.
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving

For realizing safe autonomous driving, the end-to-end delays of real-time object detection systems should be thoroughly analyzed and minimized. However, despite recent development of neural networks with minimized inference delays, surprisingly little attention has been paid to their end-to-end delays from an object's appearance until its detection is reported. With this motivation, this paper aims to provide more comprehensive understanding of the end-to-end delay, through which precise best- and worst-case delay predictions are formulated, and three optimization methods are implemented: (i) on-demand capture, (ii) zero-slack pipeline, and (iii) contention-free pipeline. Our experimental results show a 76% reduction in the end-to-end delay of Darknet YOLO (You Only Look Once) v3 (from 1070 ms to 261 ms), thereby demonstrating the great potential of exploiting the end-to-end delay analysis for autonomous driving. Furthermore, as we only modify the system architecture and do not change the neural network architecture itself, our approach incurs no penalty on the detection accuracy.
Using Stacked Long Short Term Memory with Principal Component Analysis for Short Term Prediction of Solar Irradiance based on Weather Patterns

Energy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.
Smart Contract Data Feed Framework for Privacy-Preserving Oracle System on Blockchain

As blockchain-based applications and research such as cryptocurrency increase, an oracle problem to bring external data in the blockchain is emerging. Among the methods to solve the oracle problem, a method of configuring oracle based on TLS, an existing internet infrastructure, has been proposed. However, these methods currently have the disadvantage of not supporting privacy protection for external data, and there are limitations in configuring the process of a smart contract based on external data verification for automation. To solve this problem, we propose a framework consisting of middleware of external source server, data prover, and verification contract. The framework converts the data signed in the web server into a proof that the owner can prove with zk-SNARKs and provides a smart contract that can verify this. Through these procedures, data owners not only protect their privacy by proving themselves, but they can also automate on-chain processing through smart contract verification. For the proposed framework, we create a proof using libsnark for server data and show the performance and cost to verify with Solidity the smart contract language of the Ethereum platform.
Real-Time clustering and LiDAR-camera fusion on embedded platforms for self-driving cars

3D object detection and classification are crucial tasks for perception in Autonomous Driving (AD). To promptly and correctly react to environment changes and avoid hazards, it is of paramount importance to perform those operations with high accuracy and in real-time. One of the most widely adopted strategies to improve the detection precision is to fuse information from different sensors, like e.g. cameras and LiDAR. However, sensor fusion is a computationally intensive task, that may be difficult to execute in real-time on an embedded platforms. In this paper, we present a new approach for LiDAR and camera fusion, that can be suitable to execute within the tight timing requirements of an autonomous driving system. The proposed method is based on a new clustering algorithm developed for the LiDAR point cloud, a new technique for the alignment of the sensors, and an optimization of the Yolo-v3 neural network. The efficiency of the proposed method is validated comparing it against state-of-the-art solutions on commercial embedded platforms.
Intermediate fused network with multiple timescales for anomaly detection

This paper proposes an intermediate fused network with multiple timescales to predict future video segments for video anomaly detection. Video prediction technique for anomaly detection requires to derive an anomaly-distinguishable future frame from normal distributions provided by training data. Then by measuring the difference between generated frames and reference frames, the model can tell which frames represent anomalous events in certain video sequence. In order to synthesize more distinctive future frames, we propose to boost image predictions by integrating several input video segments in multiple timescales. Specifically, the diversity in timescales means the diversity in sampling frequencies of video frames. In addition, we introduce a novel intermediate fusion strategy by concatenating feature maps from intermediate layers to better preserve different characteristics from different timescales. We evaluate the proposed method on some public video surveillance datasets and achieve competitive results with respect to the state-of-the-art approaches.
Personalized Chatbot Trustworthiness Ratings

This article contains examples of offensive and abusive language as a necessary part of illustrating its research findings. In this article, we address a setting where a conversation agent, also known as a chatbot, cannot be modified and its training data cannot be accessed, and yet a neutral party wants to assess and communicate its trustworthiness to a user in a way that is tailored to the user's priorities over the various trust issues (such as bias, abusive language, information leakage, or inappropriate communication complexity). Such a rating can help users choose among alternative chatbots, developers test their systems, business leaders price their offerings, and regulators set policies. We describe a chatbot rating methodology that relies on separate rating modules for each trust issue, and on users' priority orderings among the relevant trust issues, to generate an aggregate personalized rating for the trustworthiness of a chatbot. The method is independent of the specific trust issues and is parametric to the aggregation procedure, thereby allowing for seamless generalization. We illustrate its general use, integrate it with a live chatbot, and evaluate it on four dialog datasets and representative user profiles, validated with a user survey.
High-speed and high-accuracy fringe projection profilometry without phase unwrapping

High-speed and high-accuracy measurement is the ultimate objective of fringe projection profilometry(FPP). However, improving measurement speed, especially without the reduce of measurement accuracy, poses a great challenge for the development of FPP. In contrast to the existing methods, which typically use phase unwrapping method, this paper proposes a high-speed and high-accuracy FPP without phase unwrapping. This approach is a specially designed speckle matching function based on digital image correlation, instead of phase unwrapping, to improve the matching speed. At the same time, the binary defocusing method can speed up the projection speed and the cyclic method can reduce the total number of gratings needed for reconstruction. In order to improve the accuracy, six-step phase shift method is used instead of the three-step phase shift method. Dynamic 3D measurement experiments demonstrate that this approach realizes high-speed measurement by 320 fps and high-accuracy measurement by a mean standard deviation of approximately 45 μm. This approach provides a welcome boost to the development of high-speed and high-accuracy FPP.
Few-Shot Learning of Signal Modulation Recognition based on Attention Relation Network

Most of existing signal modulation recognition methods attempt to establish a machine learning mechanism by training with a large number of annotated samples, which is hardly applied to the real-world electronic reconnaissance scenario where only a few samples can be intercepted in advance. Few-Shot Learning (FSL) aims to learn from training classes with a lot of samples and transform the knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, a novel FSL framework called Attention Relation Network (ARN) is proposed, which introduces channel and spatial attention respectively to learn a more effective feature representation of support samples. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust to low signal-to-noise-ratio conditions.
Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks

Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data, and reducing the ingress bandwidth demand beyond the edge. However, for a given DNN, identifying the optimal partition configuration for distributing the DNN that maximizes performance is a significant challenge. This is because the combination of potential target hardware resources that maximizes performance and the sequence of layers of the DNN that should be distributed across the target resources needs to be determined, while accounting for user-defined objectives/constraints for partitioning. This paper presents Scission, a tool for automated benchmarking of DNNs on a given set of target device, edge and cloud resources for determining optimal partitions that maximize DNN performance. The decision-making approach is context-aware by capitalizing on hardware capabilities of the target resources, their locality, the characteristics of DNN layers, and the network condition. Experimental studies are carried out on 18 DNNs. The decisions made by Scission cannot be manually made by a human given the complexity and the number of dimensions affecting the search space. The benchmarking overheads of Scission allow for responding to operational changes periodically rather than in real-time. Scission is available for public download <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .