Reviewerly/exHarmony huggingface dataset:
GC-Net: Gridding and Clustering for Traffic Object Detection With Roadside LiDAR

The emerging intelligent transportation systems puts higher demands on the collection and analysis of the traffic data. LiDAR can provide high-precision point clouds of traffic objects, making it a promising choice for the surveillance device. This article focuses on the traffic object detection with roadside LiDAR: estimating both positions and categories of them. To overcome the challenges posed by point clouds, we propose GC-net, which is based on a three-stage pipeline, including gridding, clustering, and classification. First, we design a one-to-one mapping on raw point cloud as data preprocessing, which transforms the data structure from the graph to the grid. Then, we propose an efficient clustering algorithm: Grid- Density-Based Spatial Clustering of Applications with Noise to search the traffic objects. It exploits index information in the grid data to simplify the computational complexity. Last, we train a CNN-based classifier to categorize the found objects by extracting the local features, which performs well even the global shapes are defective. It only employs object-wise supervision, which reduces the difficulty of creating datasets. Based on the point clouds collected in real urban traffic scenarios, comparative experiences show that the proposed GC-net achieves a superior performance both in detection accuracy and computational speed, which are significant indicators for the real-time traffic surveillance systems.
Semantic Image Segmentation with Improved Position Attention and Feature Fusion

Encoder–decoder structure is an universal method for semantic image segmentation. However, some important information of images will lost with the increasing depth of convolutional neural network (CNN), and the correlation between arbitrary pixels will get worse. This paper designs a novel image segmentation model to obtain dense feature maps and promote segmentation effects. In encoder stage, we employ ResNet-50 to extract features, and then add a spatial pooling pyramid (SPP) to achieve multi-scale feature fusion. In decoder stage, we provide an improved position attention module to integrate contextual information effectively and remove the trivial information through changing the construction way of attention matrix. Furthermore, we also propose the feature fusion structure to generate dense feature maps by preforming element–wise sum operation on the upsampling features and corresponding encoder features. The simulation results illustrate that the average accuracy and mIOU on CamVid dataset can reach 90.7% and 63.1% respectively. It verifies the effectiveness and reliability of the proposed method.
An empirical analysis of source code metrics and smart contract resource consumption

Abstract A smart contract (SC) is a programme stored in the Ethereum blockchain by a contract‐creation transaction. SC developers deploy an instance of the SC and attempt to execute it in exchange for a fee, paid in Ethereum coins (Ether). If the computation needed for their execution turns out to be larger than the effort proposed by the developer (i.e., the gasLimit ), their client instantiation will not be completed successfully. In this paper, we examine SCs from 11 Ethereum blockchain‐oriented software projects hosted on GitHub.com , and we evaluate the resources needed for their deployment (i.e., the gasUsed ). For each of these contracts, we also extract a suite of object‐oriented metrics, to evaluate their structural characteristics. Our results show a statistically significant correlation between some of the object‐oriented (OO) metrics and the resources consumed on the Ethereum blockchain network when deploying SCs. This result has a direct impact on how Ethereum developers engage with a SC: evaluating its structural characteristics, they will be able to produce a better estimate of the resources needed to deploy it. Other results show specific source code metrics to be prioritised based on application domains when the projects are clustered based on common themes.
Heterogeneous Few-Shot Model Rectification With Semantic Mapping

There still involve lots of challenges when applying machine learning algorithms in unknown environments, especially those with limited training data. To handle the data insufficiency and make a further step towards robust learning, we adopt the learnware notion Z.-H. Zhou, "Learnware: On the future of machine learning," Front. Comput. Sci., vol. 10, no. 4 pp. 589-590, 2016 which equips a model with an essential reusable property-the model learned in a related task could be easily adapted to the current data-scarce environment without data sharing. To this end, we propose the REctiFy via heterOgeneous pRedictor Mapping (ReForm) framework enabling the current model to take advantage of a related model from two kinds of heterogeneous environment, i.e., either with different sets of features or labels. By Encoding Meta InformaTion (Emit) of features and labels as the model specification, we utilize an optimal transported semantic mapping to characterize and bridge the environment changes. After fine-tuning over a few labeled examples through a biased regularization objective, the transformed heterogeneous model adapts to the current task efficiently. We apply ReForm over both synthetic and real-world tasks such as few-shot image classification with either learned or pre-defined specifications. Experimental results validate the effectiveness and practical utility of the proposed ReForm framework.
Spatial-Content Image Search in Complex Scenes

Although the topic of image search has been heavily studied in the last two decades, many works have focused on either instance-level retrieval or semantic-level retrieval. In this work, we develop a novel visually similar spatial-semantic method, namely spatial-content image search, to search images that not only share the same spatial-semantics but also enjoy visual consistency as the query image in complex scenes. We achieve the goal by capturing spatial-semantic concepts as well as the visual representation of each concept contained in an image. Specifically, we first generate a set of bounding boxes and their category labels representing spatial-semantic constraints with YOLOV3, and then obtain visual content of each bounding box with deep features extracted from a convolutional neural network. After that, we customize a similarity computation method that evaluates the relevance between dataset images and input queries according to the developed image representations. Experimental results on two large-scale benchmark retrieval datasets with images consisting of multiple objects demonstrate that our method provides an effective way to query image databases. Our code is available at https://github.com/MaJinWakeUp/spatial-content.
Multimodal Image Outpainting with Regularized Normalized Diversification

In this paper, we study the problem of generating a set of realistic and diverse backgrounds when given only a small foreground region. We refer to this task as image outpainting. The technical challenge of this task is to synthesize not only plausible but also diverse image outputs. Traditional generative adversarial networks suffer from mode collapse. While recent approaches [32], [28] propose to maximize or preserve the pairwise distance between generated samples with respect to their latent distance, they do not explicitly prevent the diverse samples of different conditional inputs from collapsing. Therefore, we propose a new regularization method to encourage diverse sampling in conditional synthesis. In addition, we propose a feature pyramid discriminator to improve the image quality. Our experimental results show that our model can produce more diverse images without sacrificing visual quality compared to state-of-the-arts approaches in both the CelebA face dataset [29] and the Cityscape scene dataset [2]. Code is available at: https://github.com/owenzlz/DiverseOutpaint.
Cross-Conditioned Recurrent Networks for Long-Term Synthesis of Inter-Person Human Motion Interactions

Modeling dynamics of human motion is one of the most challenging sequence modeling problem, with diverse applications in animation industry, human-robot interaction, motion-based surveillance, etc. Available attempts to use auto-regressive techniques for long-term single-person motion generation usually fails, resulting in stagnated motion or divergence to unrealistic pose patterns. In this paper, we propose a novel cross-conditioned recurrent framework targeting long-term synthesis of inter-person interactions beyond several minutes. We carefully integrate positive implications of both auto-regressive and encoder-decoder recurrent architecture, by interchangeably utilizing two separate fixed-length cross person motion prediction models for long-term generation in a novel hierarchical fashion. As opposed to prior approaches, we guarantee structural plausibility of 3D pose by training the recurrent model to regress latent representation of a separately trained generative pose embedding network. Different variants of the proposed frameworks are evaluated through extensive experiments on SBU-interaction, CMU-MoCAP and an inhouse collection of duet-dance dataset. Qualitative and quantitative evaluation on several tasks, such as Short-term motion prediction, Long-term motion synthesis and Interaction-based motion retrieval against prior state-of-the-art approaches clearly highlight superiority of the proposed framework.
Orthogonal neighborhood preserving discriminant analysis with patch embedding for face recognition

Intuitively, all facial images of a person are located on or near a manifold in the high-dimensional image space, and the process of face recognition can be regarded as the recovery process of multiple low-dimensional manifolds. To preserve the manifold structure information of intra-class samples after dimensionality reduction, we proposed a patch-based multi-manifold orthogonal neighborhood-preserving discriminant analysis algorithm, namely ONPDA. From the perspective of path alignment, we consider the intra-class compactness, intra-class structure and inter-class separability simultaneously. Moreover, we infuse intra-class structure information described by the sample reconstruction into intra-class compactness loss, considering the compactness of two reconstruction groups instead of sample pairs in the same class. By analyzing the relationship between the projection direction and the maximum inter-class margin, we select the samples that should participate in the inter-class separability on the patch. Meanwhile, a fast orthogonalization method is performed to obtain the orthogonal projection matrix. Besides, we perform ONPDA in reproducing kernel Hilbert space which gives rise to nonlinear maps, resulting in the kernel ONPDA (KONPDA). Experimental results compared with some state-of-the-art methods on a toy dataset and several benchmark face image databases demonstrate the effectiveness of ONPDA and KONPDA.
A Gray Level Indicator-Based Regularized Telegraph Diffusion Model: Application to Image Despeckling

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 22 August 2019Accepted: 13 February 2020Published online: 13 May 2020Keywordsspeckle noise, nonlinear diffusion, telegraph diffusion equation, gray level indicator, weak solution, Schauder fixed point theorem, despecklingAMS Subject Headings35L70, 65M06, 68U10Publication DataISSN (online): 1936-4954Publisher: Society for Industrial and Applied MathematicsCODEN: sjisbi
New label propagation algorithm with pairwise constraints

The label propagation algorithm is a well-known semi-supervised clustering method, which uses pre-given partial labels as constraints to predict the labels of unlabeled data. However, the algorithm has the following limitations: (1) it does not fully consider the misalignment between the pre-given labels and clustering labels, and (2) it only uses label information as clustering constraints. Real applications not only contain partial label information but pairwise constraints on a dataset. To overcome these deficiencies, a new version of the label propagation algorithm is proposed, which makes use of pairwise relations of labels as constraints to construct an optimization model for spreading labels. Experimental analysis was used to compare the proposed algorithm with 8 other semi-supervised clustering algorithms on 11 benchmark datasets. The experimental results demonstrated that the proposed algorithm is more effective than other algorithms.
Mechanisms of Moral Responsibility: Rethinking Technologies for Domestic Violence Prevention Work

This paper provides a critical examination of how digital systems within a charitable organisation in the North of England are being used to both support and challenge male perpetrators of domestic violence. While there exists a range of digital tools to support the victim-survivors of domestic violence, no tools are available to challenge the abusive and harmful behaviours of perpetrators. Through this work, we uncovered the compelling moral responsibilities intrinsic within interactions with technological systems between perpetrators and support workers. As such, we highlight four spaces of negotiation concerning a person's responsibility in changing their abusive behaviour, which we have coined as mechanisms to represent their fundamental and interconnected nature. These mechanisms include self-awareness, acknowledging the extent of harms, providing peer support and respecting authorities. These insights are the basis for offering some practical considerations for HCI scholars, policymakers and intervention designers in their work with perpetrators of violence.
TraceGen: User activity emulation for digital forensic test image generation

Digital forensic test images are commonly used across a variety of digital forensic use cases including education and training, tool testing and validation, proficiency testing, malware analysis, and research and development. Using real digital evidence for these purposes is often not viable or permissible, especially when factoring in the ethical and in some cases legal considerations of working with individuals' personal data. Furthermore, when using real data it is not usually known what actions were performed when, i.e., what was the 'ground truth'. The creation of synthetic digital forensic test images typically involves an arduous, time-consuming process of manually performing a list of actions, or following a 'story' to generate artefacts in a subsequently imaged disk. Besides the manual effort and time needed in executing the relevant actions in the scenario, there is often little room to build a realistic volume of non-pertinent wear-and-tear or 'background noise' on the suspect device, meaning the resulting disk images are inherently limited and to a certain extent simplistic. This work presents the TraceGen framework, an automated system focused on the emulation of user actions to create realistic and comprehensive artefacts in an auditable and reproducible manner. The framework consists of a series of actions contained within scripts that are executed both externally and internally to a target virtual machine. These actions use existing automation APIs to emulate a real user's behaviour on a Windows system to generate realistic and comprehensive artefacts. These actions can be quickly scripted together to form complex stories or to emulate wear-and-tear on the test image. In addition to the development of the framework, evaluation is also performed in terms of the ability to produce background artefacts at scale, and also the realism of the artefacts compared with their human-generated counterparts.
Multi-Layer Transformer Aggregation Encoder for Answer Generation

Answer generation is one of the most important tasks in natural language processing, and deep learning-based methods have shown their strength over traditional machine learning based methods. However, most previous deep learning-based answer generation models were built on traditional recurrent neural networks or convolutional neural networks. The former model cannot well exploit contextual correlation preserved in paragraphs due to their inherent computation complexity. For the latter, since the size of the convolutional kernel is fixed, the model cannot extract complete semantic information features. In order to alleviate this problem, based on multi-layer Transformer aggregation coder, we propose an end-to-end answer generation model (AG-MTA). AG-MTA consists of a multi-layer attention Transformer unit and a multi-layer attention Transformer aggregation encoder (MTA). It can focus on information representation at different positions and aggregate nodes at same layer to combine the context information. Thereby, it fuses semantic information from base layer to top layer, enhancing the information representation of the encoder. Furthermore, based on trigonometric function, a novel position encoding method is also proposed. Experiments are conducted on public datasets SQuAD. AG-MTA reaches the state-of-the-art performance, EM score achieves 71.1 and F1 score achieves 80.3.
Distributed multiple model joint probabilistic data association with Gibbs sampling-aided implementation

This paper proposes a new distributed multiple model multiple manoeuvring target tracking algorithm. The proposed tracker is derived by combining joint probabilistic data association (JPDA) with consensus-based distributed filtering. Exact implementation of the JPDA involves enumerating all possible joint association events and thus often becomes computationally intractable in practice. We propose a computationally tractable approximation of calculating the marginal association probabilities for measurement-target mappings based on stochastic Gibbs sampling. In order to achieve scalability for a large number of sensors and high tolerance to sensor failure, a simple average consensus algorithm-based information JPDA filter is proposed for distributed tracking of multiple manoeuvring targets. In the proposed framework, the state of each target is updated using consensus-based information fusion while the manoeuvre mode probability of each target is corrected with measurement probability fusion. Simulations clearly demonstrate the effectiveness and characteristics of the proposed algorithm. The results reveal that the proposed formulation is scalable and much more efficient than classical JPDA without sacrificing tracking accuracy.
Real-Time Energy Efficient Hand Pose Estimation: A Case Study

The estimation of human hand pose has become the basis for many vital applications where the user depends mainly on the hand pose as a system input. Virtual reality (VR) headset, shadow dexterous hand and in-air signature verification are a few examples of applications that require to track the hand movements in real-time. The state-of-the-art 3D hand pose estimation methods are based on the Convolutional Neural Network (CNN). These methods are implemented on Graphics Processing Units (GPUs) mainly due to their extensive computational requirements. However, GPUs are not suitable for the practical application scenarios, where the low power consumption is crucial. Furthermore, the difficulty of embedding a bulky GPU into a small device prevents the portability of such applications on mobile devices. The goal of this work is to provide an energy efficient solution for an existing depth camera based hand pose estimation algorithm. First, we compress the deep neural network model by applying the dynamic quantization techniques on different layers to achieve maximum compression without compromising accuracy. Afterwards, we design a custom hardware architecture. For our device we selected the FPGA as a target platform because FPGAs provide high energy efficiency and can be integrated in portable devices. Our solution implemented on Xilinx UltraScale+ MPSoC FPGA is 4.2× faster and 577.3× more energy efficient than the original implementation of the hand pose estimation algorithm on NVIDIA GeForce GTX 1070.
A Hybrid Classification Method via Character Embedding in Chinese Short Text With Few Words

Last decades have witnessed the significance development of research in short text classification.However, most existing methods only focus on the text which contained dozens of words like Twitter or MicroBlog, but not take the short text with few words like news headline or invoice name into consideration.Meanwhile, contemporary short text classification methods either to expand feature of short text with external corpus or to learn the feature representation from all the texts, which have not take the difference between words of short text into full consideration.Notably, the classification of short text with few words are usually determined by a few specific key words contrary to documents classification or traditional short text classification.To address these problems, this paper propose a hybrid classification method of Attention mechanism and Feature selection via Character embedding in Chinese short text with few words, called AFC.More specifically, firstly, the character embedding is computed to represent Chinese short texts with few words, which takes full advantage of short text information without external corpus.Secondly, attention-based LSTM is introduced in our method to project the data into feature representation space with weighting, which make the keywords in classification have more subtle value.Furthermore, the semantic similarity between content and class label information is calculated for feature selection, which reduces the possible negative influence of some redundant information on classification.Experiments on real-world datasets demonstrate the effectiveness of our method compared to other competing methods.
Active deep learning to detect demographic traits in free-form clinical notes

The free-form portions of clinical notes are a significant source of information for research, but before they can be used, they must be de-identified to protect patients' privacy. De-identification efforts have focused on known identifier types (names, ages, dates, addresses, ID's, etc.). However, a note can contain residual "Demographic Traits" (DTs), unique enough to re-identify the patient when combined with other such facts. Here we examine whether any residual risks remain after removing these identifiers. After manually annotating over 140,000 words worth of medical notes, we found no remaining directly identifying information, and a low prevalence of demographic traits, such as marital status or housing type. We developed an annotation guide to the discovered Demographic Traits (DTs) and used it to label MIMIC-III and i2b2-2006 clinical notes as test sets. We then designed a "bootstrapped" active learning iterative process for identifying DTs: we tentatively labeled as positive all sentences in the DT-rich note sections, used these to train a binary classifier, manually corrected acute errors, and retrained the classifier. This train-and-correct process may be iterated. Our active learning process significantly improved the classifier's accuracy. Moreover, our BERT-based model outperformed non-neural models when trained on both tentatively labeled data and manually relabeled examples. To facilitate future research and benchmarking, we also produced and made publicly available our human annotated DT-tagged datasets. We conclude that directly identifying information is virtually non-existent in the multiple medical note types we investigated. Demographic traits are present in medical notes, but can be detected with high accuracy using a cost-effective human-in-the-loop active learning process, and redacted if desired.2