Reviewerly/exHarmony huggingface dataset:
DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network

This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D medical image reconstruction requires near hundreds of projections, which cost insufferable computational time and deliver undesirable high imaging / radiation dose to human subjects. Moreover, it always needs further notorious processes to segment or extract the accurate 3D organ models subsequently. The computational time and imaging dose can be reduced by decreasing the number of projections, but the reconstructed image quality is degraded accordingly. To our knowledge, there is no method directly and explicitly reconstructing multiple 3D organ meshes from a single 2D medical grayscale image on the fly. Given single-view 2D medical images, e.g., 3D / 4D-CT projections or X-ray images, our end-to-end DeepOrganNet framework can efficiently and effectively reconstruct 3D / 4D lung models with a variety of geometric shapes by learning the smooth deformation fields from multiple templates based on a trivariate tensor-product deformation technique, leveraging an informative latent descriptor extracted from input 2D images. The proposed method can guarantee to generate high-quality and high-fidelity manifold meshes for 3D / 4D lung models; while, all current deep learning based approaches on the shape reconstruction from a single image cannot. The major contributions of this work are to accurately reconstruct the 3D organ shapes from 2D single-view projection, significantly improve the procedure time to allow on-the-fly visualization, and dramatically reduce the imaging dose for human subjects. Experimental results are evaluated and compared with the traditional reconstruction method and the state-of-the-art in deep learning, by using extensive 3D and 4D examples, including both synthetic phantom and real patient datasets. The efficiency of the proposed method shows that it only needs several milliseconds to generate organ meshes with 10K vertices, which has great potential to be used in real-time image guided radiation therapy (IGRT).
Analyzing the Noise Robustness of Deep Neural Networks

Adversarial examples, generated by adding small but intentionally imperceptible perturbations to normal examples, can mislead deep neural networks (DNNs) to make incorrect predictions. Although much work has been done on both adversarial attack and defense, a fine-grained understanding of adversarial examples is still lacking. To address this issue, we present a visual analysis method to explain why adversarial examples are misclassified. The key is to compare and analyze the datapaths of both the adversarial and normal examples. A datapath is a group of critical neurons along with their connections. We formulate the datapath extraction as a subset selection problem and solve it by constructing and training a neural network. A multi-level visualization consisting of a network-level visualization of data flows, a layer-level visualization of feature maps, and a neuron-level visualization of learned features, has been designed to help investigate how datapaths of adversarial and normal examples diverge and merge in the prediction process. A quantitative evaluation and a case study were conducted to demonstrate the promise of our method to explain the misclassification of adversarial examples.
Safe but not Overcautious Motion Planning under Occlusions and Limited Sensor Range

For a successful introduction of fully automated vehicles, they must behave both provably safe but also convenient, i.e. comfortable and not overcautious. Given the limited sensing capabilities, especially in urban scenarios where buildings and parking vehicles impose occlusions, this is a challenging task. While recent approaches gave first ideas for boundary conditions of safe behavior, an approach for convenient motion planning that fulfills these constraints is still an open issue. Therefore, we utilize and enhance safety approaches for occlusion handling in order to facilitate comfortable and safe motion planning. We consider worst case assumptions, arising from potential objects at critical sensing field edges, along with their probability. With this information, we can ensure to not act overcautiously while still moving provably safe. The potential of our approach is shown in a modified CommonROAD scenario.
Multiview High Dynamic Range Image Synthesis Using Fuzzy Broad Learning System

Compared with the normal low dynamic range (LDR) images, the high dynamic range (HDR) images provide more dynamic range and image details. Although the existing techniques for generating the HDR images have a good effect for static scenes, they usually produce artifacts on the HDR images for dynamic scenes. In recent years, some learning-based approaches are used to synthesize the HDR images and obtain good results. However, there are also many problems, including the deficiency of explaining and the time-consuming training process. In this article, we propose a novel approach to synthesize multiview HDR images through fuzzy broad learning system (FBLS). We use a set of multiview LDR images with different exposure as input and transfer corresponding Takagi-Sugeno (TS) fuzzy subsystems; then, the structure is expanded in a wide sense in the “enhancement groups” which transfer from the TS fuzzy rules with nonlinear transformation. After integrating fuzzy subsystems and enhancement groups with the trained-well weight, the HDR image is generated. In FBLS, applying the incremental learning algorithm and the pseudoinverse method to compute the weights can greatly reduce the training time. In addition, the fuzzy system has better interpretability. In the learning process, IF-THEN fuzzy rules can effectively help the model to detect the artifacts and reject them in the final HDR result. These advantages solve the problem of existing deep-learning methods. Furthermore, we set up a new dataset of multiview LDR images with corresponding HDR ground truth to train our system. Our experimental results show that our system can synthesize high-quality multiview HDR images, which has a higher training speed than other learning methods.
15 years of collostructions

Abstract This paper discusses a variety of potential shortcomings of most of the most widely-used association measures as used in collocation research and collostructional analyses. To address these shortcomings, I then discuss a research program called tupleization , an approach that does away with the usual kinds of information conflation by keeping relevant corpus-linguistic dimensions of information – e.g. frequency, association/contingency, dispersion, entropy, etc. – separate and analyzing them in a multidimensional way; I conclude with pointers towards how these dimensions could, if deemed absolutely necessary, be conflated for the simplest kinds of of rankings as well as strategies for future research.
Unsupervised Person Re-Identification With Iterative Self-Supervised Domain Adaptation

In real applications, person re-identification (re-id) is an inherently domain adaptive computer vision task which often requires the model trained on a group of people to perform well on an unlabeled dataset consisting of another group of pedestrians without supervised fine-tuning. Furthermore, there are typically a large number of classes (people) with small number of samples belonging to each class. Based on the characteristics of person re-id and general assumptions related to domain adaptation, we put forward a novel algorithm for cross-dataset person re-id. Our idea is simple yet effective: first, we preprocess the source dataset with style transfer GAN and train a baseline on it in a supervised learning manner, then we assign pseudo labels to unlabeled samples in target dataset based on the model trained on labeled source dataset; finally, we train on the target dataset with pseudo labels in traditional supervised learning manner. We adopt the idea of co-training in the training process to make the pseudo labels more reliable. We show the superiority of our model over all state-of-the-art methods through extensive experiments.
Deep Learning for Channel Coding via Neural Mutual Information Estimation

End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which comes close to well-developed classical encoder-decoder designs. However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks. In real-world scenarios, such a channel model is hardly available and often the channel density is not even known at all. Some works, therefore, focus on a generative approach, i.e., generating the channel from samples, or rely on reinforcement learning to circumvent this problem. We present a novel approach which utilizes a recently proposed neural estimator of mutual information. We use this estimator to optimize the encoder for a maximized mutual information, only relying on channel samples. Moreover, we show that our approach achieves the same performance as state-of-the-art end-to-end learning with perfect channel model knowledge.
Belif: Blind Quality Evaluator Of Light Field Image With Tensor Structure Variation Index

With the development of immersive media, Light Field Image (LFI) quality assessment is becoming more and more important, which helps to better guide light field acquisition, processing and application. However, almost all existing LFI quality assessment schemes utilize the 2D or 3D quality assessment methods while ignoring the intrinsic high dimensional characteristics of LFI. Therefore, we adopt the tensor theory to explore the LF 4D structure characteristics and propose the first Blind quality Evaluator of LIght Field image (BELIF). We generate cyclopean images tensor from the original LFI and then the features are extracted by the tucker decomposition. Specifically, Tensor Spatial Characteristic Features (TSCF) for spatial quality and Tensor Structure Variation Index (TSVI) for angular consistency are designed to fully assess the LFI quality. Extensive experimental results on the public LFI databases demonstrate that BELIF signifi-cantly outperforms the existing image quality assessment algorithms.
Blind Image Quality Assessment by Learning from Multiple Annotators

Models for image quality assessment (IQA) are generally optimized and tested by comparing to human ratings, which are expensive to obtain. Here, we develop a blind IQA (BIQA) model, and a method of training it without human ratings. We first generate a large number of corrupted image pairs, and use a set of existing IQA models to identify which image of each pair has higher quality. We then train a convolutional neural network to estimate perceived image quality along with the uncertainty, optimizing for consistency with the binary labels. The reliability of each IQA annotator is also estimated during training. Experiments demonstrate that our model outperforms state-of-the-art BIQA models in terms of correlation with human ratings in existing databases, as well in group maximum differentiation (gMAD) competition.
Accelerating Convolutional Neural Networks via Activation Map Compression

The deep learning revolution brought us an extensive array of neural network architectures that achieve state-of-the-art performance in a wide variety of Computer Vision tasks including among others, classification, detection and segmentation. In parallel, we have also been observing an unprecedented demand in computational and memory requirements, rendering the efficient use of neural networks in low-powered devices virtually unattainable. Towards this end, we propose a three-stage compression and acceleration pipeline that sparsifies, quantizes and entropy encodes activation maps of Convolutional Neural Networks. Sparsification increases the representational power of activation maps leading to both acceleration of inference and higher model accuracy. Inception-V3 and MobileNet-V1 can be accelerated by as much as 1.6x with an increase in accuracy of 0.38% and 0.54% on the ImageNet and CIFAR-10 datasets respectively. Quantizing and entropy coding the sparser activation maps lead to higher compression over the baseline, reducing the memory cost of the network execution. Inception-V3 and MobileNet-V1 activation maps, quantized to 16 bits, are compressed by as much as 6x with an increase in accuracy of 0.36% and 0.55% respectively.
Deep Learning based Vehicle Position and Orientation Estimation via Inverse Perspective Mapping Image

In this paper, we present a method for estimating a position, size, and orientation using a single monocular image. The proposed method makes use of an inverse perspective mapping to effectively estimate the distance from the image. The proposed method consists of two stages: 1) cancel the pitch and roll motion of the camera using inertial measurement unit and project the corrected front view image onto the bird's eye view using inverse perspective mapping. 2) detect the position, size, and orientation of the vehicle using a convolutional neural network. The camera motion cancellation process makes vanishing point to be located at the same point regardless of the ego vehicle attitude change. Through this process, the projected bird's eye view image can be parallel and linear to the x-y plane of the vehicle coordinate system. The convolutional neural network predicts not only the position and size but also the orientation of the vehicle for the 3D localization. The predicted oriented bounding box from the bird's eye view image is converted in the meter unit by the inverse projection matrix. The proposed method was evaluated on the KITTI raw dataset on the metric of the root mean square error, mean average percentage error, and average precision. Despite the conceptually simple architecture, the proposed method achieves promising performance compared to other image based approaches. The video demonstration is available online [1].
A framework for competence development and assessment in hybrid cybersecurity exercises

Rising numbers and sophistication of security threats in the digital domain cause an increase in the demand for skilled cybersecurity professionals. In response, cybersecurity exercises, and in particular—cyber defence exercises (CDX) are becoming ever more popular. They provide a training platform to simulate real-life situations. CDX are significant events involving months of preparation, and previous studies show a lack of objective evidence of their relevance regarding the learning impact. Skills of exercise participants are usually different and vary from tech-savvy to beginners. Also, trainees are diverse when considering their background, current work profile (position and institution), and experience. Assessment of their competencies is essential to ensure quality in training. The complexity and multi-dimensionality of the usual CDX make it challenging. Additionally, the costly event usually focuses on just a subset of participants, and non-technical members of an organisation are not included. The goal of our research is to provide a proper methodology to optimise the exercises so that every team and each participant, including a non-technical trainee, are adequately evaluated and trained using the allocated resources most effectively. This paper presents a framework to aid in the development and assessment of cybersecurity competences of all teams during hybrid CDX. The framework aims towards raised cybersecurity awareness—a state when every user of digital technologies understands the associated risks. The framework consists of a sequence of steps including stages of formative assessment, team construction, determination of objectives for different types of teams, and the exercise flow. It complements standard methodologies for cybersecurity training programs. The framework was developed based on data collected using questionnaires, interviews, and direct observation in a case study carried out during international cybersecurity exercises. The framework would help organise hybrid exercises for a diverse community of trainees, including non-technical members of an organisation.
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism

Scaling CNN training is necessary to keep up with growing datasets and reduce training time. We also see an emerging need to handle datasets with very large samples, where memory requirements for training are large. Existing training frameworks use a data-parallel approach that partitions samples within a mini-batch, but limits to scaling the minibatch size and memory consumption makes this untenable for large samples. We describe and implement new approaches to convolution, which parallelize using spatial decomposition or a combination of sample and spatial decomposition. This introduces many performance knobs for a network, so we develop a performance model for CNNs and present a method for using it to automatically determine efficient parallelization strategies. We evaluate our algorithms with microbenchmarks and image classification with ResNet-50. Our algorithms allow us to prototype a model for a mesh-tangling dataset, where sample sizes are very large. We show that our parallelization achieves excellent strong and weak scaling and enables training for previously unreachable datasets.
The pedagogic value of learning design with virtual reality

Spatial design as a discipline relies on the psychological construct of space. In the light of shortcomings and challenges faced by traditional approaches to spatial design learning, this paper investigates the role and value of Virtual Reality (VR) as a pedagogic vehicle. Based on understandings informed by action-research using VR with learners in two modules, and underpinning interdisciplinary research on cognition, perception of space, and learning pedagogies; the paper works through defined research questions on both impact of learning with VR, development of spatial cognition with VR, and the differences between traditional approaches to spatial design curriculum and VR enabled approach and whether or not these differences are associated with differential outcomes. We propose VR enhanced design education pedagogical framework and argues that such an approach is needed to shape futuristic thinking in spatial design teaching and learning.
Fractional chaos based-cryptosystem for generating encryption keys in Ad Hoc networks

Ad Hoc networks are considered essential elements of the Internet of Things (IoT), which is a technological trend for innovative future applications. Since in IoT systems the devices are mostly connected among them, the data exchange is a vital activity in the network. In this kind of networks, not only non-sensitive data exchange is performed, but also sensitive data (digital signatures, login credentials, account numbers, personal images, and so on) are shared. Therefore, new appropriate approaches to encrypt the secret information are highly required. This paper presents an experimental setup of a fractional-chaos based-cryptosystem for ad hoc networks under the IEEE 802.15.4 standard. The proposed cryptosystem is composed of a novel chaotic pairing approach between the coordinator node and end-devices nodes to sending data into the network. More specifically, the RF-data of the IEEE 802.15.4 frame is encrypted using a fractional-order encryption key and four iterative steps. It is worth to note that the chaotic key is based on a fractional-order chaotic system, which is derived from the fractional calculus theory. Fractional-order chaotic dynamics are demonstrated using the Lyapunov method and equilibria stability. Next, the experimental validation of the cryptosystem is shown sending an encrypted image between ARM digital platforms and wireless Xbee modules. Several security analyses indicate the proposed encryption scheme has suitable statistical properties, sizeable keyspace, and high sensitivity providing secure communications.
Optimal data-based binning for histograms and histogram-based probability density models

Histograms are convenient non-parametric density estimators, which continue to be used ubiquitously. Summary quantities estimated from histogram-based probability density models depend on the choice of the number of bins. We introduce a straightforward data-based method of determining the optimal number of bins in a uniform bin-width histogram. By assigning a multinomial likelihood and a non-informative prior, we derive the posterior probability for the number of bins in a piecewise-constant density model given the data. In addition, we estimate the mean and standard deviations of the resulting bin heights, examine the effects of small sample sizes and digitized data, and demonstrate the application to multi-dimensional histograms.
Blockchain - based IoT: A Survey

Blockchain and Internet of Things (IoT), two of the top disruptive technologies, are already on their way of reshaping our future of the digital world, characterized by a drastic change in the current network architecture. Incorporation of IoT has brought the objects around us to life, making them ‘smart’ and capable of communicating with each other, thereby amassing massive data by constantly capturing the physical world, for analyzing and performing some intelligent action based on the same. It has made possible our dream of seamless integration of the digital and physical worlds, changing the very essence of our perception of the physical world. But, the problem with current IoT solutions is its need for a centralized party (like a cloud server), for connecting and communicating via the Internet, which poses a great threat to the privacy and security of the vast sensitive data being generated, whereas the original architecture design demands for a decentralized one like distributed or peer-to- peer (P2P) system. So, blockchain comes into play, providing a secure and trustworthy way of sharing information using a distributed/P2P model, to achieve transparency, security, privacy, auditability, resilience, access authentication, data immutability, etc. In this paper, we will look into how to combine both the technologies to overcome their shortcomings and obtain a greater gain from their benefits. We have presented a comprehensive survey on the basics of both the technologies, and the blockchain-based IoT (BIoT) architecture, protocols and functioning, and few examples BIoT applications that can be built on top of it, and comparison.
Sentiment Analysis on Twitter Data

Twitter is the popular micro blogging site where thousands of people exchange their thoughts daily in the form of tweets. The characteristics of tweet is to be short and simple way of expressions. Though this paper will focus on sentiment analysis of twitter data. The research area of sentiment analysis are text data mining and NLP. In different form we can perform the sentiment analysis on twitter data. This research paper will focus on techniques of sentiment analysis where we will perform how to extract tweets from twitter. Eventually we will compare different sentiment analysis techniques and also the approaches containing twitter dataset.