Reviewerly/exHarmony huggingface dataset:
ABSSNet: Attention-Based Spatial Segmentation Network for Traffic Scene Understanding

The location information of road and lane lines is the supremely important thing for the automatic drive and auxiliary drive. The detection accuracy of these two elements dramatically affects the reliability and practicality of the whole system. In real applications, the traffic scene can be very complicated, which makes it particularly challenging to obtain the precise location of road and lane lines. Commonly used deep learning-based object detection models perform pretty well on the lane line and road detection tasks, but they still encounter false detection and missing detection frequently. Besides, existing convolution neural network (CNN) structures only pay attention to the information flow between layers, while it cannot fully utilize the spatial information inside the layers. To address those problems, we propose an attention-based spatial segmentation network for traffic scene understanding. We use the convolutional attention module to improve the network's understanding capacity of spatial location distribution. Spatial CNN (SCNN) obtains through the information flow within one single convolutional layer and improves the spatial relationship modeling ability of the network. The experimental results demonstrate that this method effectively improves the neural network's application ability of the spatial information, thereby improving the effect of traffic scene understanding. Furthermore, a pixel-level road segmentation dataset called NWPU Road Dataset is built to help improve the process of traffic scene understanding.
Mobile app recommendation via heterogeneous graph neural network in edge computing

As a new computing technology proposed with the development of 5G, IoT technologies and increasing requirement of mobile applications and services, edge computing enables mobile application developers and content providers to serve context-aware mobile services (e.g., mobile app recommendation). Mobile app recommendation is known as an effective solution to overcome the information overload in mobile app markets. Most existing models only consider user-app interaction and feature modeling, and neglect the structural information which actually is a crucial part in the scenario of app recommendation. To fully exploit both structural and feature information for app recommendation, this paper proposes a novel heterogeneous graph neural network framework (HGNRec) including one inner module and one outer module. Specifically, the inner module is able to use a node-level attention to learn the importance between a node and its meta-path based neighbors. The outer module with a path-level attention can learn the importance of different meta-paths. With the learned importance from two modules, the comprehensive embeddings for user and app nodes can be generated by integrating features from meta-path based neighbors. Extensive experiments on the real-world Google Play mobile app dataset demonstrate the effectiveness of HGNRec.
The CCSDS 123.0-B-2 “Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression” Standard: A comprehensive review

The Consultative Committee for Space Data Systems (CCSDS) published the CCSDS 123.0-B-2, “Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression” standard. This standard extends the previous issue, CCSDS 123.0-B-1, which supported only lossless compression, while maintaining backward compatibility. The main novelty of the new issue is support for near-lossless compression, i.e., lossy compression with user-defined absolute and/or relative error limits in the reconstructed images. This new feature is achieved via closed-loop quantization of prediction errors. Two further additions arise from the new near-lossless support: first, the calculation of predicted sample values using sample representatives that may not be equal to the reconstructed sample values, and, second, a new hybrid entropy coder designed to provide enhanced compression performance for low-entropy data, prevalent when nonlossless compression is used. These new features enable significantly smaller compressed data volumes than those achievable with CCSDS 123.0-B-1 while controlling the quality of the decompressed images. As a result, larger amounts of valuable information can be retrieved given a set of bandwidth and energy consumption constraints.
STGCN: A Spatial-Temporal Aware Graph Learning Method for POI Recommendation

Point-of-Interest (POI) recommendation helps users find their interested places to visit based on the time and user location. Unlike traditional recommendation tasks, POI recommendation is personalized, spatial-aware, and temporally dependent. Although many previous works have tried modeling spatial and temporal characteristics, most of them suffer from the following two limitations: For the spatial aspect, existing works only consider the user-POI distance or POI-POI distance. However, we find that a user prefers different regions at different times, which is known as user-region periodic pattern. For the temporal aspect, most works treat user and time as two independent factors. However, different users may prefer the same POI in different time periods, which is known as user-POI periodic pattern. To address the limitation of existing works, we propose a novel Spatial-Temporal aware Graph Convolutional Neural Network (STGCN) for POI recommendation. Specifically, we first design a user record multigraph to fuse all the context information into a unified graph. Then, we propose a time-based neighborhood sampling algorithm and take advantage of the flexible propagation mechanism of GCNs to learn the representations of each node at a specific time. Furthermore, multiple scoring functions are proposed to exploit user-region periodic pattern and user-POI periodic pattern, respectively. We also develop a time smoothing strategy to alleviate the data sparsity problem. Extensive experiments are conducted on two real-world datasets, and the experimental results demonstrate the effectiveness of our method.
Silicon nitride programmable photonic processor with folded heaters

General-purpose programmable photonic processors rely on the large-scale integration of beamsplitters and reconfigurable phase shifters, distributed within unit cells or photonic gates. With their future evolution threatened by several hardware constrains, including the integration density that can be achieved with current mesh topologies, in this work, we present a unit cell topology design to increase the integration density of waveguide mesh arrangements based on folded Mach-Zehnder Interferometers. We report the design details of a 40-unit cell waveguide mesh integrated in a 11mm x 5.5 mm silicon nitride chip achieving, to the best of our knowledge, the highest integration density reported to date for a general-purpose photonic processor. The chip is electrically interfaced to a PCB and we report examples of reconfigurable optical beamsplitters, basic tunable microwave photonic filters with high peak rejection (40 dB approx.), as well as the dynamic interconnection and routing of 5G digitally modulated signals within the photonic mesh.
Generating Synthetic ECGs Using GANs for Anonymizing Healthcare Data

In personalized healthcare, an ecosystem for the manipulation of reliable and safe private data should be orchestrated. This paper describes an approach for the generation of synthetic electrocardiograms (ECGs) based on Generative Adversarial Networks (GANs) with the objective of anonymizing users’ information for privacy issues. This is intended to create valuable data that can be used both in educational and research areas, while avoiding the risk of a sensitive data leakage. As GANs are mainly exploited on images and video frames, we are proposing general raw data processing after transformation into an image, so it can be managed through a GAN, then decoded back to the original data domain. The feasibility of our transformation and processing hypothesis is primarily demonstrated. Next, from the proposed procedure, main drawbacks for each step in the procedure are addressed for the particular case of ECGs. Hence, a novel research pathway on health data anonymization using GANs is opened and further straightforward developments are expected.
DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data

Detecting persons using a 2D LiDAR is a challenging task due to the low information content of 2D range data. To alleviate the problem caused by the sparsity of the LiDAR points, current state-of-the-art methods fuse multiple previous scans and perform detection using the combined scans. The downside of such a backward looking fusion is that all the scans need to be aligned explicitly, and the necessary alignment operation makes the whole pipeline more expensive - often too expensive for real-world applications. In this paper, we propose a person detection network which uses an alternative strategy to combine scans obtained at different times. Our method, Distance Robust SPatial Attention and Auto-regressive Model (DR-SPAAM), follows a forward looking paradigm. It keeps the intermediate features from the backbone network as a template and recurrently updates the template when a new scan becomes available. The updated feature template is in turn used for detecting persons currently in the scene. On the DROW dataset, our method outperforms the existing state-of-the-art, while being approximately four times faster, running at 87.2 FPS on a laptop with a dedicated GPU and at 22.6 FPS on an NVIDIA Jetson AGX embedded GPU. We release our code in PyTorch and a ROS node including pre-trained models.
Measuring topic network centrality for identifying technology and technological development in online communities

Online communities are a rapidly growing knowledge repository that provides scholarly research, technical discussion, and social interactivity. This abundance of online information increases the difficulty of keeping up with new developments difficult for researchers and practitioners. Thus, we introduced a novel method that analyses both knowledge and social sentiment within the online community to discover the topical coverage of emerging technology and trace technological trends. The method utilizes the Weibull distribution and Shannon entropy to measure and link social sentiment with technological topics. Based on question-and-answer and social sentiment data from Zhihu, which is an online question and answer (Q&A) community with high-profile entrepreneurs and public intellectuals, we built an undirected weighting network and measured the centrality of nodes for technology identification. An empirical study on artificial intelligence technology trends supported by expert knowledge-based evaluation and cognition provides sufficient evidence of the method's ability to identify technology. We found that the social sentiment of hot technological topics presents a long-tailed distribution statistical pattern. High similarity between the topic popularity and emerging technology development trends appears in the online community. Finally, we discuss the findings in various professional fields that are widely applied to discover and track hot technological topics.
A Mechanism to Detect and Prevent Ethereum Blockchain Smart Contract Reentrancy Attacks

In Ethereum blockchain, smart contracts are immutable, public, and distributed. However, they are subject to many vulnerabilities stemming from coding errors made by developers. Seven cybersecurity incidents occurred in Ethereum smart contracts between 2016 and 2018, which led to financial losses estimated to be over US$ 289 million. Reentrancy vulnerability was the cause of two of these incidents, and the impacts went far beyond financial loss. Several reentrancy countermeasures are available, which are based on predefined patterns that are used to prevent vulnerability exploitation before the deployment of a smart contract; however, several limitations have been identified in these countermeasures. Motivated by all these issues, the objective of this article is to help developers improve the cybersecurity of smart contracts by proposing a solution that calculates the difference between the contract balance and the total balance of all participants in a smart contract before and after any operation in a transaction that changes its state. Proof-of-concept implementations show that this solution can provide a detection and prevention mechanism against reentrancy attacks during the execution of any smart contract.
Image Classification Algorithm Based on Improved AlexNet

Abstract Aiming at the problems that the traditional CNN has many parameters and a large proportion of fully connected parameters, a image classification method is proposed, which based on improved AlexNet. This method adds deconvolution layer to traditional AlexNet and classifies the images by full connection layer. Using Cifar-10 data set to test the classification algorithm. The results indicate that the method not only reduces the number of parameters and parameters proportion of the full connection layer, but also improves the classification accuracy compared with AlexNet.
Resource-Constrained Federated Learning with Heterogeneous Labels and Models for Human Activity Recognition

One of the most significant applications in pervasive computing for modeling user behavior is Human Activity Recognition (HAR). Such applications necessitate us to characterize insights from multiple resource-constrained user devices using machine learning techniques for effective personalized activity monitoring. On-device Federated Learning proves to be an extremely viable option for distributed and collaborative machine learning in such scenarios, and is an active area of research. However, there are a variety of challenges in addressing statistical (non-IID data) and model heterogeneities across users. In addition, in this paper, we explore a new challenge of interest – to handle heterogeneities in labels (activities) across users during federated learning. To this end, we propose a framework with two different versions for federated label-based aggregation, which leverage overlapping information gain across activities – one using Model Distillation Update, and the other using Weighted \(\alpha \)-update. Empirical evaluation on the Heterogeneity Human Activity Recognition (HHAR) dataset (with four activities for effective elucidation of results) indicates an average deterministic accuracy increase of at least \(\sim \)11.01% with the model distillation update strategy and \(\sim \)9.16% with the weighted \(\alpha \)-update strategy. We demonstrate the on-device capabilities of our proposed framework by using Raspberry Pi 2, a single-board computing platform.
Autonomous Data Collection With Timed Communication Constraints for Unmanned Underwater Vehicles

This letter studies the problem of data collection where an autonomous underwater vehicle (AUV) is required to inspect several goals while staying within the communication range of an unmanned surface vehicle (USV) moving along a predefined path. A reward is associated with each inspected goal, and, so the overall objective is to increase the sum of the rewards. The proposed planner takes the AUV dynamics and the obstacles into account in order to generate collision-free and dynamically-feasible trajectories that enable the AUV to inspect multiple goals while maintaining communication with the USV. When not all the goals can be inspected, the planner generates trajectories that increase the sum of the rewards. The proposed planner constructs a roadmap equipped with time information in order to facilitate navigation and maintain communication in complex 3D environments. A crucial aspect is an effective coupling of discrete search with sampling-based motion planning. Specifically, a multi-goal discrete search is proposed to effectively compute roadmap paths that increase the sum of the rewards. These paths then guide the sampling-based expansion of a motion tree. Experiments using complex 3D environments and an AUV model defined by nonlinear second-order differential equations indicate the efficiency of the planner to inspect an increasing number of goals.
An Empirical Study of Refactorings and Technical Debt in Machine Learning Systems

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today's data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source software, and the technical debt issues they alleviate. We analyzed 26 projects, consisting of 4.2 MLOC, along with 327 manually examined code patches. The results indicate that developers refactor these systems for a variety of reasons, both specific and tangential to ML, some refactorings correspond to established technical debt categories, while others do not, and code duplication is a major cross-cutting theme that particularly involved ML configuration and model code, which was also the most refactored. We also introduce 14 and 7 new ML-specific refactorings and technical debt categories, respectively, and put forth several recommendations, best practices, and anti-patterns. The results can potentially assist practitioners, tool developers, and educators in facilitating long-term ML system usefulness.
A hierarchical sampling based triplet network for fine-grained image classification

Deep metric learning leverages well-designed distance measurement and a sample selection strategy to learn a discriminative feature space. Among the various deep metric learning formulations, triplet loss is built based on a 3-tuple that can simultaneously minimise the distance between the items in the positive pair and maximise the distance between those in the negative pair. However, this endeavour requires a critical selection of triplet samples to guide the training process. In this paper, we propose a layered Triplet loss to solve the fine-grained image classification problem. Unlike the existing triplet loss, which selects samples from only a single criterion, we construct the loss function with the 'coarse to fine' scheme. This scheme can separate the coarse-level classes while clustering the fine-level samples within a certain margin. An ontology-based sampling method is proposed to enable the network to mine more reasonable hard triplets. Semantic knowledge is employed to assign the visually similar classes to the same learning task, from which hard triplets can be generated. Finally, the softmax tree classifier is used to classify the hierarchical features. The experimental results on multiple datasets demonstrate the effectiveness of the proposed method.
Leveraging Deep Learning for Visual Odometry Using Optical Flow

In this paper, we study deep learning approaches for monocular visual odometry (VO). Deep learning solutions have shown to be effective in VO applications, replacing the need for highly engineered steps, such as feature extraction and outlier rejection in a traditional pipeline. We propose a new architecture combining ego-motion estimation and sequence-based learning using deep neural networks. We estimate camera motion from optical flow using Convolutional Neural Networks (CNNs) and model the motion dynamics using Recurrent Neural Networks (RNNs). The network outputs the relative 6-DOF camera poses for a sequence, and implicitly learns the absolute scale without the need for camera intrinsics. The entire trajectory is then integrated without any post-calibration. We evaluate the proposed method on the KITTI dataset and compare it with traditional and other deep learning approaches in the literature.
Toward Blockchain-Enabled IoV with Edge Computing: Efficient and Privacy-Preserving Vehicular Communication and Dynamic Updating

By virtue of intelligent data processing and vehicular network, Internet of Vehicle (IoV) provides the transport system with real-time traffic information, which greatly improves the traffic conditions of the city. Moreover, to ease the computing and storage burden of the increasing number of the vehicle, edge computing is introduced to offload computing tasks on the local with low latency. But it still suffers from data integrity and privacy concerns. To meet these challenges, this paper proposes an efficient and conditional privacy-preserving authentication protocol using blockchain-enabled IoV with edge computing. Specifically, edge computing and consortium blockchains are combined to support efficient computation and storage capabilities with low communication delay while providing data auditability. Also, a pseudonym mechanism and identity-based signature are utilized to achieve identity privacy-preserving conditionally and messages authentication of vehicles, respectively. Moreover, for the dynamic change of vehicles, a clustering selection algorithm and a key updating algorithm based on Chinese remainder theorem are given, which guarantees the forward and backward security of transmission information. Security analysis and experiment evaluation with respect of communication and computation cost demonstrate the effectiveness of the proposed protocol.
Decentralized Privacy-Preserving Fair Exchange Scheme for V2G Based on Blockchain

With the fast development of the electric vehicle (EV) technology, EVs are expected to be the mainstream in future. The large number of EVs facilitate development of the emerging vehicle-to-grid (V2G) technology, which realizes two-way electricity flows between EVs and the power grid. How to achieve fairness and privacy for EVs during electricity/service exchanges remains a challenging problem for V2G. In this article, we propose a privacy-preserving fair exchange scheme <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V2GEx</i> for V2G based on the blockchain. V2GEx is composed of an extended blockchain that supports zero-knowledge funds, a fair exchange smart contract based on the hashchain micropayment mechanism, and a privacy-preserving protocol for V2G. We further propose a simpler and more efficient scheme called Uni-V2GEx, which preserves privacy for only one party. We also provide a rigorous security proof under the universal composability (UC) model to prove V2GEx's security. To evaluate its efficiency, we implement V2GEx and conduct comprehensive experiments to test its performance in terms of computation cost and processing delay. The experiment results show that V2GEx is highly efficient in that verification of V2GEx transactions costs only 20 ms and the average transaction processing latency is around 6 seconds in a 200-node blockchain network.