Reviewerly/exHarmony huggingface dataset:
JCS-Net: Joint Classification and Super-Resolution Network for Small-Scale Pedestrian Detection in Surveillance Images

While convolutional neural network (CNN)-based pedestrian detection methods have proven to be successful in various applications, detecting small-scale pedestrians from surveillance images is still challenging. The major reason is that the small-scale pedestrians lack much detailed information compared to the large-scale pedestrians. To solve this problem, we propose to utilize the relationship between the large-scale pedestrians and the corresponding small-scale pedestrians to help recover the detailed information of the small-scale pedestrians, thus improving the performance of detecting small-scale pedestrians. Specifically, a unified network (called JCS-Net) is proposed for small-scale pedestrian detection, which integrates the classification task and the super-resolution task in a unified framework. As a result, the super-resolution and classification are fully engaged, and the super-resolution sub-network can recover some useful detailed information for the subsequent classification. Based on HOG+LUV and JCS-Net, multi-layer channel features (MCF) are constructed to train the detector. The experimental results on the Caltech pedestrian dataset and the KITTI benchmark demonstrate the effectiveness of the proposed method. To further enhance the detection, multi-scale MCF based on JCS-Net for pedestrian detection is also proposed, which achieves the state-of-the-art performance.
PMS-Net: Robust Haze Removal Based on Patch Map for Single Images

In this paper, we proposed a novel haze removal algorithm based on a new feature called the patch map. Conventional patch-based haze removal algorithms (e.g. the Dark Channel prior) usually performs dehazing with a fixed patch size. However, it may produce several problems in recovered results such as oversaturation and color distortion. Therefore, in this paper, we designed an adaptive and automatic patch size selection model called the Patch Map Selection Network (PMS-Net) to select the patch size corresponding to each pixel. This network is designed based on the convolutional neural network (CNN), which can generate the patch map from the image to image. Experimental results on both synthesized and real-world hazy images show that, with the combination of the proposed PMS-Net, the performance in haze removal is much better than that of other state-of-the-art algorithms and we can address the problems caused by the fixed patch size.
ProGAN

Network embedding has attracted increasing attention in recent few years, which is to learn a low-dimensional representation for each node of a network to benefit downstream tasks, such as node classification, link prediction, and network visualization. Essentially, the task of network embedding can be decoupled into discovering the proximity in the original space and preserving it in the low dimensional space. Only with the well-discovered proximity can we preserve it in the low-dimensional space. Thus, it is critical to discover the proximity between different nodes to learn good node representations. To address this problem, in this paper, we propose a novel proximity generative adversarial network (ProGAN) which can generate proximities. As a result, the generated proximity can help to discover the complicated underlying proximity to benefit network embedding. To generate proximities, we design a novel neural network architecture to fulfill it. In particular, the generation of proximities is instantiated to the generation of triplets of nodes, which encodes the similarity relationship between different nodes. In this way, the proposed ProGAN can generate proximities successfully to benefit network embedding. At last, extensive experimental results have verified the effectiveness of ProGAN.
Applications of a deep learning method for anti-aliasing and super-resolution in MRI

Magnetic resonance (MR) images with both high resolutions and high signal-to-noise ratios (SNRs) are desired in many clinical and research applications. However, acquiring such images takes a long time, which is both costly and susceptible to motion artifacts. Acquiring MR images with good in-plane resolution and poor through-plane resolution is a common strategy that saves imaging time, preserves SNR, and provides one viewpoint with good resolution in two directions. Unfortunately, this strategy also creates orthogonal viewpoints that have poor resolution in one direction and, for 2D MR acquisition protocols, also creates aliasing artifacts. A deep learning approach called SMORE that carries out both anti-aliasing and super-resolution on these types of acquisitions using no external atlas or exemplars has been previously reported but not extensively validated. This paper reviews the SMORE algorithm and then demonstrates its performance in four applications with the goal to demonstrate its potential for use in both research and clinical scenarios. It is first shown to improve the visualization of brain white matter lesions in FLAIR images acquired from multiple sclerosis patients. Then it is shown to improve the visualization of scarring in cardiac left ventricular remodeling after myocardial infarction. Third, its performance on multi-view images of the tongue is demonstrated and finally it is shown to improve performance in parcellation of the brain ventricular system. Both visual and selected quantitative metrics of resolution enhancement are demonstrated.
UAV trajectory optimization for Minimum Time Search with communication constraints and collision avoidance

Minimum Time Search (MTS) algorithms help in search missions proposing search trajectories that minimize the target detection time considering the available information about the search scenario. This work proposes a MTS planner based on ant colony optimization that includes communication and collision avoidance constraints. This ensures that the Unmanned Aerial Vehicles (UAVs) are able to complete the optimized search trajectories without risk of collision or loss of communication with the ground control station. This approach is a great advantage nowadays, where UAVs flight regulation is quite strict, often requiring to monitor the state of the UAVs during the whole mission, impeding UAV deployments without continuous communication to the ground control station. The proposed algorithm is tested with several search scenarios and compared against two state of the art techniques based on Cross Entropy Optimization and Genetic Algorithms, which have been adapted to make them consider collision and communication constraints as well.
College Library Personalized Recommendation System Based on Hybrid Recommendation Algorithm

When the number of books provided by library is relatively large, it becomes difficult for user to select appropriate book from a lot of candidate books. In this case, this paper designs a personalized recommendation system for college libraries based on hybrid recommendation algorithm. First of all, paper studies the application of collaborative filtering and content-based recommendation algorithm in the recommendation of university books, which involves reader classification, the establishment of user-item scoring matrix, the construction of vector space model and the calculation of similarity among users. And considering the characteristics of books and readers in universities, the user - item scoring matrix is improved, and clustering is used to alleviate the data sparsity problem. Do comparative experiments using the hybrid algorithm in data sets of Library of Inner Mongolia University of Technology. The results demonstrate that the hybrid methods can provide more accurate recommendations than pure approaches. Finally, the Spark big data platform combined with the hybrid recommendation algorithm is used to achieve the personalized book recommendation system design.
An Efficient Attribute-Based Encryption Scheme With Policy Update and File Update in Cloud Computing

Recently, more and more users and enterprises have entrusted data storage and platform construction to proxy cloud service provider (PCSP) through cloud technology. Under this background, the attribute-based encryption (ABE) mechanism is an alternative to fill the drawbacks of the traditional encryption through flexible fine-grained access policy and collusion prevention. However, there exist some security issues when the access policy and file need to be updated in practical applications. And the ABE has the problems of excessive computation and storage costs. In this article, an efficient ciphertext-policy ABE scheme with policy update and file update is proposed in cloud computing. The ciphertext components generated by first encryption can be shared when the policy update and file update happens. It reduces the storage and communication costs of the client, and the computational cost of the PCSP. Moreover, the proposed scheme is proved to be secure under the assumption of decision q-parallel bilinear Diffie–Hellman exponent (BDHE). Finally, experimental simulation shows that the proposed scheme is highly efficient in terms of policy update and file update.
Stability and Generalization of Graph Convolutional Neural Networks

Inspired by convolutional neural networks on 1D and 2D data, graph convolutional neural networks (GCNNs) have been developed for various learning tasks on graph data, and have shown superior performance on real-world datasets. Despite their success, there is a dearth of theoretical explorations of GCNN models such as their generalization properties. In this paper, we take a first step towards developing a deeper theoretical understanding of GCNN models by analyzing the stability of single-layer GCNN models and deriving their generalization guarantees in a semi-supervised graph learning setting. In particular, we show that the algorithmic stability of a GCNN model depends upon the largest absolute eigenvalue of its graph convolution filter. Moreover, to ensure the uniform stability needed to provide strong generalization guarantees, the largest absolute eigenvalue must be independent of the graph size. Our results shed new insights on the design of new & improved graph convolution filters with guaranteed algorithmic stability. We evaluate the generalization gap and stability on various real-world graph datasets and show that the empirical results indeed support our theoretical findings. To the best of our knowledge, we are the first to study stability bounds on graph learning in a semi-supervised setting and derive generalization bounds for GCNN models.
Risk-Aware Motion Planning and Control Using CVaR-Constrained Optimization

We propose a risk-aware motion planning and decision-making method that systematically adjusts the safety and conservativeness in an environment with randomly moving obstacles. The key component of this method is the conditional value-at-risk (CVaR) used to measure the safety risk that a robot faces. Unlike chance constraints, CVaR constraints are coherent, convex, and distinguish between tail events. We propose a two-stage method for safe motion planning and control: A reference trajectory is generated by using RRT* in the first stage, and then a receding horizon controller is employed to limit the safety risk by using CVaR constraints in the second stage. However, the second stage problem is nontrivial to solve, as it is a triple-level stochastic program. We develop a computationally tractable approach through 1) a reformulation of the CVaR constraints; 2) a sample average approximation; and 3) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">linearly constrained</i> mixed integer convex program formulation. The performance and utility of this risk-aware method are demonstrated via simulation using a 12-dimensional model of quadrotors.
A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software

Advancing our understanding of software vulnerabilities, automating their identification, the analysis of their impact, and ultimately their mitigation is necessary to enable the development of software that is more secure. While operating a vulnerability assessment tool, which we developed, and that is currently used by hundreds of development units at SAP, we manually collected and curated a dataset of vulnerabilities of open-source software, and the commits fixing them. The data were obtained both from the National Vulnerability Database (NVD), and from project-specific web resources, which we monitor on a continuous basis. From that data, we extracted a dataset that maps 624 publicly disclosed vulnerabilities affecting 205 distinct opensource Java projects, used in SAP products or internal tools, onto the 1282 commits that fix them. Out of 624 vulnerabilities, 29 do not have a CVE (Common Vulnerability and Exposure) identifier at all, and 46, which do have such identifier assigned by a numbering authority, are not available in the NVD yet. The dataset is released under an open-source license, together with supporting scripts that allow researchers to automatically retrieve the actual content of the commits from the corresponding repositories, and to augment the attributes available for each instance. Moreover, these scripts allow to complement the dataset with additional instances that are not security fixes (which is useful, for example, in machine learning applications). Our dataset has been successfully used to train classifiers that could automatically identify security-relevant commits in code repositories. The release of this dataset and the supporting code as open-source will allow future research to be based on data of industrial relevance; it also represents a concrete step towards making the maintenance of this dataset a shared effort involving open-source communities, academia, and the industry.
BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network

The recent development in IoT and 5G translates into a significant growth of Big data in 5G—envisioned industrial automation. To support big data analysis, Deep Learning (DL) has been considered the most promising approach in recent years. Note, however, that designing an effective DL paradigm for IoT has certain challenges such as single point of failure, privacy leak of IoT devices, lack of valuable data for DL, and data poisoning attacks. To this end, we present BlockDeepNet, a Blockchain-based secure DL that combines DL and blockchain to support secure collaborative DL in IoT. In BlockDeepNet, collaborative DL is performed at the device level to overcome privacy leak and obtain enough data for DL, whereas blockchain is employed to ensure the confidentiality and integrity of collaborative DL in IoT. The experimental evaluation shows that BlockDeepNet can achieve higher accuracy for DL with acceptable latency and computational overhead of blockchain operation.
Solcast: Validation of a satellite-derived solar irradiance dataset

Solcast (https://solcast.com/) is a global solar forecasting and historical solar irradiance data company. Their datasets are important to the scientific community as substantial amounts of historical data is made freely available to researchers. Solcast's hourly averages and sub-hourly instantaneous (10/15-min) global horizontal irradiance (GHI) estimates are validated at 48 BSRN ground stations that contain 1,014,792 valid hourly average measurements and 2,932,976 valid instantaneous measurements. The data are validated globally and across four climates under clear-sky, clouded-sky and all-sky periods, repeated for 'all zenith angles' (θ_z<85°) and high-sun zenith angles (θ_z<50°). A site-by-site validation is also performed for both hourly and instantaneous datasets. All validations and recommendations are impartial and unbiased. The hourly dataset validated similarly for both zenith angle scenarios, which is remarkable due to the added complexities at low-sun angles. In terms of rRMSE, the global performances of hourly GHI under clear-sky, clouded-sky and all-sky were 3.4%, 25.6% and 16.9%, respectively. With each ground station equally weighted, the overall bias was 0.33%, and standard deviation of biases at 1.7%. Solcast's instantaneous GHI product validations were at times similar to the hourly validations though is naturally harder with increasing temporal resolution. The historical datasets from Solcast are found to be of good quality, thus, they are recommended for use by researchers. Identified areas for improvement herein will guide future developments of Solcast's methodology.
Influence of Students’ Learning Style, Sense of Presence, and Cognitive Load on Learning Outcomes in an Immersive Virtual Reality Learning Environment

The purpose of this study was to investigate the relationship among Taiwanese high school students’ learning style, sense of presence, cognitive load, and affective and cognitive learning outcomes in an immersive virtual reality-based learning environment. This study used a teaching experiment intervention method. Seventy-seven students participated in the virtual reality-based learning environment and completed related scales and a test. This study found that although the students’ learning style does not influence learning outcomes, it may influence the subjective sense of presence and cognitive load in the learning process. Regarding the affective learning outcome, involvement/immersion, sensory fidelity, and mental effort are positive predictors. In addition, involvement/immersion, interface quality, mental load, and mental effort are negative predictors of cognitive learning outcomes. The conclusion from this study is that students with some learning style preferences must bear a greater cognitive load to achieve the same learning outcomes as other students. This study also points out that the components of sense of presence and cognitive load generate inconsistent predictive effects on affective and cognitive learning outcomes, respectively. Therefore, it is important to deeply explore the influence of sense of presence and cognitive load structure on learning in virtual environments.
P-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization

This paper proposes an end-to-end fine-grained visual categorization system, termed Part-based Convolutional Neural Network (P-CNN), which consists of three modules. The first module is a Squeeze-and-Excitation (SE) block, which learns to recalibrate channel-wise feature responses by emphasizing informative channels and suppressing less useful ones. The second module is a Part Localization Network (PLN) used to locate distinctive object parts, through which a bank of convolutional filters are learned as discriminative part detectors. Thus, a group of informative parts can be discovered by convolving the feature maps with each part detector. The third module is a Part Classification Network (PCN) that has two streams. The first stream classifies each individual object part into image-level categories. The second stream concatenates part features and global feature into a joint feature for the final classification. In order to learn powerful part features and boost the joint feature capability, we propose a Duplex Focal Loss used for metric learning and part classification, which focuses on training hard examples. We further merge PLN and PCN into a unified network for an end-to-end training process via a simple training technique. Comprehensive experiments and comparisons with state-of-the-art methods on three benchmark datasets demonstrate the effectiveness of our proposed method.
Borrow from rich cousin: transfer learning for emotion detection using cross lingual embedding

Performance of any natural language processing (NLP) system greatly depends on the amount of resources and tools available in a particular language or domain. Therefore, while solving any problem in low-resource setting, it is important to investigate techniques to leverage the resources and tools available in resource-rich languages. In this paper we propose an efficient technique to mitigate the problem of resource scarcity for emotion detection in Hindi by leveraging information from a resource-rich language like English. Our method follows a deep transfer learning framework which efficiently captures relevant information through the shared space of two languages, showing significantly better performance compared to the monolingual scenario that learns in the vector space of only one language. As base learning models, we use Convolution Neural Network (CNN) and Bi-Directional Long Short Term Memory (Bi-LSTM). As there are no available emotion labeled dataset for Hindi, we create a new dataset for emotion detection in disaster domain by annotating sentences of news documents with nine different classes based on Plutchikâ;;s wheel of emotions. To improve the performance of emotion classification in Hindi, we employ transfer learning to exploit the resources available in the related domains. The core of our approach lies in generating a cross-lingual word embedding representation of words in the shared embedding space. The neural networks are trained on the existing datasets, and then weights are fine-tuned following the four different transfer learning strategies for emotion classification in Hindi. We obtain a significant performance gain in our our proposed transfer learning techniques, achieving an F1-score of 0.53 (compared to 0.47)-thereby implying that knowledge from a resource-rich language can be transferred across language and domains.1
ViDeNN: Deep Blind Video Denoising

We propose ViDeNN: a CNN for Video Denoising without prior knowledge on the noise distribution (blind denoising). The CNN architecture uses a combination of spatial and temporal filtering, learning to spatially denoise the frames first and at the same time how to combine their temporal information, handling objects motion, brightness changes, low-light conditions and temporal inconsistencies. We demonstrate the importance of the data used for CNNs training, creating for this purpose a specific dataset for low-light conditions. We test ViDeNN on common benchmarks and on self-collected data, achieving good results comparable with the state-of-the-art.
Model-Blind Video Denoising via Frame-To-Frame Training

Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available. This makes model based video processing a still more complex task. In this paper we propose a fully blind video denoising method, with two versions off-line and on-line. This is achieved by fine-tuning a pre-trained AWGN denoising network to the video with a novel frame-to-frame training strategy. Our denoiser can be used without knowledge of the origin of the video or burst and the post-processing steps applied from the camera sensor. The on-line process only requires a couple of frames before achieving visually pleasing results for a wide range of perturbations. It nonetheless reaches state-of-the-art performance for standard Gaussian noise, and can be used off-line with still better performance.