Reviewerly/exHarmony huggingface dataset:
Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor

Training a robust classifier and an accurate box regressor are difficult for occluded pedestrian detection. Traditionally adopted Intersection over Union (IoU) measurement does not consider the occluded region of the object and leads to improper training samples. To address such issue, a modification called visible IoU is proposed in this paper to explicitly incorporate the visible ratio in selecting samples. Then a newly designed box sign predictor is placed in parallel with box regressor to separately predict the moving direction of training samples. It leads to higher localization accuracy by introducing sign prediction loss during training and sign refining in testing. Following these novelties, we obtain state-of-the-art performance on CityPersons benchmark for occluded pedestrian detection.
A Large Curved Display System in Virtual Reality for Immersive Data Interaction

This work presents the design and implementation of a large curved display system in a virtual reality (VR) environment that supports visualization of 2D datasets (e.g., images, buttons and text). By using this system, users are allowed to interact with data in front of a wide field of view and gain a high level of perceived immersion. We exhibit two use cases of this system, including (1) a virtual image wall as the display component of a 3D user interface, and (2) an inventory interface for a VR-based educational game. The use cases demonstrate capability and flexibility of curved displays in supporting varied purposes of data interaction within virtual environments.
Multi-Label Zero-Shot Learning With Transfer-Aware Label Embedding Projection

Zero-Shot learning (ZSL) recently has drawn a lot of attention due to its ability to transfer knowledge from seen classes to novel unseen classes, which greatly reduces human labor of labelling data for building new classifiers. Much effort on ZSL however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has received limited attention. In this paper, we propose a transfer-aware embedding projection approach to tackle multi-label zero-shot learning. The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings. Auxiliary information can be conveniently incorporated to guide the label embedding projection to further improve label relation structures for zero-shot knowledge transfer. We conduct experiments for both standard zero-shot multi-label image classification and generalized zero-shot multi-label classification. The results demonstrate the efficacy of the proposed approach.
Multi-View Imaging System Using Paraboloidal Mirror Arrays for Efficient Acquisition of Dynamic Light Fields

Lens arrays enable us to easily achieve inexpensive light field acquisition in comparison with massive camera arrays. However, their micro-lenses often cause insufficient exposure to obtain high quality multi-view videos like camera arrays. In this paper, we propose a novel virtual camera array acquiring dynamic light fields as multi-view videos robustly and inexpensively, where mirror arrays consisting of tangent planes to a paraboloid are effectively combined with a single video camera. First, we demonstrate multi-view imaging of not an object but ordinary scenes with sufficient exposure by using a simple horizontal mirror array implemented as a prototype. Next, we theoretically reveal that there exists smooth extension of our mirror array to the vertical direction, that can be implemented easily by 3D printing, CNC machines, and so on. Finally, our design of light field acquisition is improved for effectively utilizing the whole of its mirror array by introducing an one-way mirror to prevent the camera itself from appearing on multi-view images.
Research on brush face payment system based on internet artificial intelligence

The facial information of a person has constant invariance, and at the same time, it is an ideal basis for identifying identity information through differences between different individuals. The use of face recognition in third-party payment platforms will greatly improve the efficiency and securit y of payment. Based on this, the paper studies and designs the third-party payment system for face recognition in the context of Internet artificial intelligence to improve the convenience and security of face payment.
Improving Big Data Application Performance in Edge-Cloud Systems

Data analysis is widely used in all domains of the economy. While the amount of data to process grows, the time criteria and the resource consumption constraints get stricter. These phenomena call for advanced resource orchestration for the big data applications. The challenge is actually even greater at the advent of edge computing: orchestration of big data resources in a hybrid edge-cloud infrastructure is challenging. The difficulty stems from the fact that wide-area networking and all its well-known issues come into play and affect the performance of the application. In this paper we present the steps we made towards network-aware big data application design over such distributed systems. We propose a HDFS block placement algorithm for the network reliability problem we identify in geographically distributed topologies. The heuristic algorithm we propose provides better big data application performance compared to the default block placement method. We implement our solution in our simulation environment and show the improved quality of big data applications.
A Novel Monocular Disparity Estimation Network with Domain Transformation and Ambiguity Learning

Convolutional neural networks (CNN) have shown state-of-the-art results for low-level computer vision problems such as stereo and monocular disparity estimations, but still, have much room to further improve their performance in terms of accuracy, numbers of parameters, etc. Recent works have uncovered the advantages of using an unsupervised scheme to train CNN's to estimate monocular disparity, where only the relatively-easy-to-obtain stereo images are needed for training. We propose a novel encoder-decoder architecture that outperforms previous unsupervised monocular depth estimation networks by (i) taking into account ambiguities, (ii) efficient fusion between encoder and decoder features with rectangular convolutions and (iii) domain transformations between encoder and decoder. Our architecture outperforms the Monodepth baseline in all metrics, even with a considerable reduction of parameters. Furthermore, our architecture is capable of estimating a full disparity map in a single forward pass, whereas the baseline requires two passes. We perform extensive experiments to verify the effectiveness of our method on the KITTI dataset.
Looking-Ahead: Neural Future Video Frame Prediction

We have developed a Looking-Ahead system to facilitate the future video frame prediction via deep learning, which is of practical value in the domain like autonomous driving etc. The overall problem is decomposed into cascaded optical flow prediction and subsequent predictive frame post-processing for quality refinement. A pyramid flow calculation across existing frames is used to efficiently infer the motion of target frame; while a universal inpainting network is applied to restore those motion-induced occluded pixels. Compared with those published methods, our Looking-Ahead offers the state-of-the-art performance measured objectively with better Peak-Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM), and more appealing reconstructions.
Image Anonymization Detection with Deep Handcrafted Features

In recent years, the number of images shared online has continuously grown. The forensics community has kept the pace by developing techniques to both reliably extract information from these images, but also to remove it. In particular, the latest developments in image anonymization methods exposes an attack vector when used by skilled ill-intentioned image producers that may want to elude prosecution. We present an approach to detect whether or not an image has undergone a laundering process, i.e., it has been tampered with so that its unique characterizing features have been changed to avoid detection. We focus on the photo response non uniformity (PRNU) noise unique to every imaging sensor, and we consider that an image has been "laundered" when we detect the absence of PRNU from an image. We propose a per image preprocessing pipeline that generates information-rich features later used as input of fine-tuned convolutional neural networks (CNNs). We study the performance of the proposed approach using various CNN architectures and blind anonymization techniques and show its effectiveness under several training and testing scenarios. Our results also show that CNN models trained with the proposed feature are capable of generalizing over unseen devices and are robust against non-geometric transformations.
An Approach to Cross-Lingual Sentiment Lexicon Construction

Lexicon-based sentiment analysis is a popular and practical approach for sentiment analysis. However, sentiment lexicons, which may be abundant in some language such as English, are scarce in many other languages. The cross-lingual lexicon learning aims to extend lexicons for the language with less resources from those lexicons available in other languages. In this paper, we propose an approach that builds a skip-gram variant to map word spaces across languages so as to construct lexicons for the language with less resources. We show in our preliminary experiment that our approach can generate lexicons that are similar to those crafted by human experts.
Exploiting CNN Layer Activations to Improve Adversarial Image Classification

Neural networks are now used in many sectors of our daily life thanks to efficient solutions such instruments provide for diverse tasks. Leaving to artificial intelligence the chance to make choices on behalf of humans inevitably exposes these tools to be fraudulently attacked. In fact, adversarial examples, intentionally crafted to fool a neural network, can dangerously induce a misclassification though appearing innocuous for a human observer. On such a basis, this paper focuses on the problem of image classification and proposes an analysis to better insight what happens inside a convolutional neural network (CNN) when it evaluates an adversarial example. In particular, the activations of the internal network layers have been analyzed and exploited to design possible countermeasures to reduce CNN vulnerability. Experimental results confirm that layer activations can be adopted to detect adversarial inputs.
Extrinsic Calibration of a Monocular Camera and a Single Line Scanning Lidar

This paper proposes a calibration method for monocular cameras and 2D Lidar external parameters based on automatic detection of features. The triangle calibration target is used, and the point and line features are automatically extracted. The point line constraint and the proposed European transformation invariance constraint are applied simultaneously in the calibration process to obtain more accurate and effective external parameters. First, a line feature extraction method is proposed. The triangle region in the image is extracted by Hough transform and K-means clustering, and the 3D pose of the triangle calibration target is calculated. Second, we analyze the effects of image feature extraction and Lidar measurement range errors on rotating and translational external parameters, respectively, through simulation experiments. The error characteristics of camera-lidar external parameters are analyzed and compared with those of Zhang and Pless. Finally, real-data experiments show that our method can obtain accurate external calibration.
Autonomous Choice of Deep Neural Network Parameters by a Modified Generative Adversarial Network

The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks. However, this task still heavily depends on trial and error, and empirical results. Considering that there are many design and parameter choices, it is very hard to cover every configuration, and find the optimal structure. In this paper, we propose a novel method that autonomously and simultaneously optimizes multiple parameters of any given deep neural network by using a modified generative adversarial network (GAN). In our approach, two different models compete and improve each other progressively. Without loss of generality, the proposed method has been tested with three different neural network architectures, and three very different datasets and applications. The results show that the presented approach can simultaneously and successfully optimize multiple neural network parameters, and achieve increased accuracy in all three scenarios.
Multilevel Image-Enhanced Sentence Representation Net for Natural Language Inference

Natural language inference (NLI) task requires an agent to determine the semantic relation between a premise sentence ( p) and a hypothesis sentence ( h), which demands sufficient understanding about sentences semantic. Due to the issues, such as polysemy, ambiguity, as well as fuzziness of sentences, intense sentence understanding is very challenging. To this end, in this article, we introduce the corresponding image of sentences as reference information for enhancing sentence semantic understanding and representing. Specifically, we first propose an image-enhanced multilevel sentence representation net (IEMLRN), that utilizes the image features from pretrained models for enhancing the sentence semantic understanding at different scales, i.e., lexical-level, phrase-level, and sentence-level. The proposed model advances the performance on NLI tasks by leveraging the pretrained global features of images. However, as these pretrained image features are optimized on specific image classification datasets, they may not show the best performance on NLI tasks. Therefore, we further propose to design an adaptive image feature generator that extracts fine-grained image labels from the corresponding sentences. After that, we extend the IEMLRN to multilevel image-enhanced sentence representation net (MIESR) by utilizing not only the coarse-grained pretrained features of images, but also the fine-grained adaptive features of images. Therefore, sentence semantic can be evaluated and enhanced more comprehensively and precisely. Extensive experiments on two benchmark datasets (SNLI, SICK) clearly show our proposed IEMLRN significantly outperform the state-of-the-art baselines, and our proposed MIESR model achieves the best performance by considering not only the text but also images in an adaptive multigranularities way.
Wildcat: In-The-Wild Color-And-Thermal Patch Comparison with Deep Residual Pseudo-Siamese Networks

Multi-modal color-thermal matching is a difficult task due to innate appearance differences. Previous methods either use hand crafted features such as Histogram of Oriented Gradients (HOG), or use deep learning within a limited domain (faces, pedestrians). We improve on the state-of-the-art methods by creating a novel deep residual pseudo-siamese architecture trained on in-the-wild data. The siamese branches of the architecture learn to process each modality in a modular fashion, and the residual blocks allow for a deeper network without reducing branch dimensionality too quickly or making training infeasible. We compare our proposed network against other state-of-the-art networks, trained and tested on the CATS and KAIST dataset, respectively. Our experiments show an increase in performance when using the proposed network. The WILDCAT model, along with training and testing code, will be made available upon publication.
Bilateral Histogram Equalization for X-Ray Image Tone Mapping

This paper introduces a novel tone mapping operator, designed to offer a good rendering of the local structures. The new operator fusions the multiple versions of a single HDR input obtained by clipping and normalizing its intensity based on a complete set of disjoint intervals. Defining the weight map associated to each version to be its clipping interval indicator function promotes contrast enhancement, but induces artifacts when neighboring pixels belong to distinct intervals. We thus propose to smooth out the indicators across neighboring pixels with similar intensity, using a standard cross-bilateral filter. With such weight maps, the fusion operator becomes equivalent to applying histogram equalization on the image regions on which the cross-bilateral filter diffuses the indicators, and is therefore referred to as Bilateral Histogram Equalization (BHE) operator. It compares favorably to previous tone mapping algorithms.
A Comparison of Stemming Techniques in Tracing

We examine the effects of stemming on the tracing of software engineering artifacts. We compare two common stemming algorithms to each other as well as to a baseline of no stemming. We evaluate the algorithms on eight tracing datasets. We run the experiment using the TraceLab experimental framework to allow for ease of repeatability and knowledge sharing among the tracing community. We compare the algorithms on precision at recall levels of [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], as well as on mean average precision values. The experiment indicated that neither the Porter stemmer nor the Krovetz stemmer outperformed the other on all datasets tested.
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks

We investigate a sequential optimization procedure to minimize the empirical risk functional $f_{\hat\theta}(x) = \frac{1}{2}\|G_{\hat\theta}(x) - y\|^2$ for certain families of deep networks $G_{\theta}(x)$. The approach is to optimize a sequence of objective functions that use network parameters obtained during different stages of the training process. When initialized with random parameters $\theta_0$, we show that the objective $f_{\theta_0}(x)$ is ``nice'' and easy to optimize with gradient descent. As learning is carried out, we obtain a sequence of generative networks $x \mapsto G_{\theta_t}(x)$ and associated risk functions $f_{\theta_t}(x)$, where $t$ indicates a stage of stochastic gradient descent during training. Since the parameters of the network do not change by very much in each step, the surface evolves slowly and can be incrementally optimized. The algorithm is formalized and analyzed for a family of expansive networks. We call the procedure {\it surfing} since it rides along the peak of the evolving (negative) empirical risk function, starting from a smooth surface at the beginning of learning and ending with a wavy nonconvex surface after learning is complete. Experiments show how surfing can be used to find the global optimum and for compressed sensing even when direct gradient descent on the final learned network fails.
Recurrent Neural Network Architectures for Vulnerable Road User Trajectory Prediction

We present an experimental study comparing various Recurrent Neural Network architectures for the task of Vulnerable Road User (VRU) motion trajectory prediction in the intelligent vehicle domain. Making use of temporal motion cues and visual appearance features, we design multi-cue RNN-based architectures with dedicated optimization process to predict future moving trajectories from historical consecutive frames. Experiments are performed on image sequences recorded from on-board a moving vehicle and public tracking datasets. In particular, the Tsinghua-Daimler Cyclist Benchmark (TDCB) has been augmented with additional annotations (vari-ous VRU types) to support the evaluation of object tracking approaches and trajectory prediction methods. This newly introduced dataset is termed TDCB-Track. We demonstrate the effectiveness of the proposed RNN architectures on the public MOT16 dataset and the TDCB-Track dataset. We show that the proposed approaches outperform simpler baseline methods and stay ahead with the state-of-the-art.
Accelerating Redundant DCT Filtering for Deblurring and Denoising

In this paper, we propose an acceleration method of redundant DCT filtering for deblurring and denoising. Current CCD cameras have high-resolution images, and the resolution has been increasing. Even if pixels are in focus, the pixels have slight blurring due to diffraction and Bayer interpolation. Therefore, we focus deblurring for slight blurring on real-time performance. Traditional approaches have a fast computational performance for this purpose, but these methods do not contain denoising architecture. In this paper, we simultaneously perform deblurring and denoising on the redundant DCT domain for accelerating the process. Also, we show that a post-scaling DCT can accelerate the proposed filtering. Experimental results show that the proposed method is the fastest method and the accuracy is also high among the fast approaches.
Changes Over Time in a Planned Inter-team Coordination Routine

The benefits of agile ways of working in small teams have inspired larger organizations to implement large-scale agile frameworks. To manage dependencies between teams, there is a need for routines to plan and divide work between teams as well as routines to manage emerging dependency issues. These routines are often changed over time, but how tailoring is performed is not much studied. This study aims to fill that gap by presenting the tailoring of a planned coordination routine in three organizations over a period of one and a half year. By visiting planning sessions, 379 h of observation data were collected. Investigating details of this routine gives a much more dynamic view, compared to the static description presented in the framework. Different logics for tailoring could be seen in the three cases. For deciding on a cadence for the planning period, three diverse logics were used as the basis for the decisions: knowledge, time, and resources.
A Survey of Methods Guaranteeing User Privacy based on Blockchain in Internet-of -Things

The emerging technology that is the internet of things (IOT) has attracted both the industrial sector and the academic sector. In the industrial sector like transportation, healthcare, smart cities and energy systems, this IOT technology will be important. However, there are many problems to be solved. One of the problems is that security must be guaranteed. For safe security, we are considering two privacy issues which are mostly encountered in the field of IOT that is device authentication and decentralized identifiers. To provide device authentication and decentralized identifiers, the Blockchain technology can be applied to. This paper survey the related work for device authentication and decentralized identifiers in the IOT.
An Online and Vision-Based Method for Fixtured Pose Measurement of Nondatum Complex Component

The nondatum complex component is a complex component and does not have reliable positioning/measurement datum during the modification process. With the rapid development of the aerospace industry, the manual manner of shape trimming for the nondatum complex component needs to be replaced by laser automatic shape trimming. This study develops a vision-based, online, and high-precision method for measuring the fixtured pose error of the component. Limited by burrs, deformation, and complex structure, the feature-based method is not applicable to this component. The present method is based on iterative optimization and not required feature extraction. To improve pose measurement efficiency and accuracy, the optimization of the reference model can effectively reduce the error caused by deformation. The experiment results showed that the proposed method is fast and accurate, and satisfied the measurement requirement of the fixtured component's pose in six degrees-of-freedom.