Reviewerly/exHarmony huggingface dataset:
Progressive Feature Fusion Network for Realistic Image Dehazing

Single image dehazing is a challenging ill-posed restoration problem. Various prior-based and learning-based methods have been proposed. Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium. The formulation of haze in realistic environment is more complicated. In this paper, we propose to take its essential mechanism as "black box", and focus on learning an input-adaptive trainable end-to-end dehazing model. An U-Net like encoder-decoder deep network via progressive feature fusions has been proposed to directly learn highly nonlinear transformation function from observed hazy image to haze-free ground-truth. The proposed network is evaluated on two public image dehazing benchmarks. The experiments demonstrate that it can achieve superior performance when compared with popular state-of-the-art methods. With efficient GPU memory usage, it can satisfactorily recover ultra high definition hazed image up to 4K resolution, which is unaffordable by many deep learning based dehazing algorithms.
An Efficient Forensics Architecture in Software-Defined Networking-IoT Using Blockchain Technology

A Potential solution for solving forensic is the use of blockchain in software-defined networking (SDN). The blockchain is a distributed peer-to-peer network that can be utilized on SDN-based Internet of Things (IoT) environments for security provisioning. Hence, to meet some challenges in digital forensics such as data integrity, evidence deletion or alteration, blockchain is used. However, some problems such as poor attack detection and slow processing existed in previous works. To address these issues, an efficient forensics architecture is proposed in SDN-IoT that establishes the Chain of Custody (CoC) in blockchain technology. The proposed SDN-based IoT architecture is initiated with flow table rules on switches for the three different traffics Voice over Internet Protocol (VoIP), File Transfer Protocol (FTP), and Hyper Text Transfer Protocol (HTTP). In this work, overloaded switches migrate the packets to nearby switches to balance the packet flow. The packets disobeying flow rules will be discarded by switches. The blockchain-based distributed controller in this forensic architecture is designed to use the Linear Homomorphic Signature (LHS) algorithm for validating users. Each controller is fed with a classifier that uses the Neuro Multi-fuzzy to classify malicious packets based on packet features. The logs of events are used and stored on the blockchain in the proposed SDN-IoT architecture. We evaluated the performance of our forensic architecture and compared it to the existing model using various performance measures. Our evaluation results demonstrate performance improvement by reducing delay, response time and processing time, increasing throughput, accuracy, and security parameters.
Assessment of Deep Learning techniques for Prognosis of solar thermal systems

Solar Hot Water (SHW) systems are a sustainable and renewable alternative for domestic and low-temperature industrial applications. As solar energy is a variable resource, performance prediction methods are useful tools to increase the overall availability and effective use of these systems. Recently, data-driven techniques have been successfully used for Prognosis and Health Management applications. In the present work, Deep Learning models are trained to predict the performance of an SHW system under different meteorological conditions. Techniques such as artificial neural networks (ANN) recurrent neural networks (RNN) and long short-term memory (LSTM) are explored. A physical simulation model is developed in TRNSYS software to generate large quantities of synthetic operational data in nominal conditions. Although similar results are achieved with the tested architectures, both RNN and LSTM outperform ANN when replicating the data's temporal behavior; all of which outperform naïve predictors and other regression models such as Bayesian Ridge, Gaussian Process and Linear Regression. LSTM models achieved a low Mean Absolute Error of 0.55 °C and the lowest Root Mean Square Error scores (1.27 °C) for temperature sequence predictions, as well as the lowest variance (0.520 °C2) and relative prediction errors (3.45%) for single value predictions, indicating a more reliable prediction performance.
Silencing Women? Gender and Online Harassment

While gendered online harassment has received increased attention in academic and public debates, there is a lack of empirical studies examining gender differences in experiences with online harassment. Relying on two independent large-scale population surveys carried out in Norway, this article examines whether women experience more—and different—online harassment than men, to what extent different types of online harassment silence its targets, and whether there are gendered patterns in how online harassment works as a silencing mechanism. Analytically, we distinguish between different levels of severity of online harassment and what the harassment is directed toward. Contrary to popular expectations, we find that more men than women have experienced online harassment. The main reason is that men receive more comments directed at their opinions and attitudes; women and men are equally exposed to harassment directed toward group characteristics. However, targeted women are more likely than targeted men to become more cautious in expressing their opinions publicly. Furthermore, the gender differences increase as the harassment becomes more aggressive and directed toward group characteristics.
A single shot coherent Ising machine based on a network of injection-locked multicore fiber lasers

Abstract Combinatorial optimization problems over large and complex systems have many applications in social networks, image processing, artificial intelligence, computational biology and a variety of other areas. Finding the optimized solution for such problems in general are usually in non-deterministic polynomial time (NP)-hard complexity class. Some NP-hard problems can be easily mapped to minimizing an Ising energy function. Here, we present an analog all-optical implementation of a coherent Ising machine (CIM) based on a network of injection-locked multicore fiber (MCF) lasers. The Zeeman terms and the mutual couplings appearing in the Ising Hamiltonians are implemented using spatial light modulators (SLMs). As a proof-of-principle, we demonstrate the use of optics to solve several Ising Hamiltonians for up to thirteen nodes. Overall, the average accuracy of the CIM to find the ground state energy was ~90% for 120 trials. The fundamental bottlenecks for the scalability and programmability of the presented CIM are discussed as well.
Content-Aware Multi-Level Guidance for Interactive Instance Segmentation

In interactive instance segmentation, users give feedback to iteratively refine segmentation masks. The user-provided clicks are transformed into guidance maps which provide the network with necessary cues on the whereabouts of the object of interest. Guidance maps used in current systems are purely distance-based and are either too localized or non-informative. We propose a novel transformation of user clicks to generate content-aware guidance maps that leverage the hierarchical structural information present in an image. Using our guidance maps, even the most basic FCNs are able to outperform existing approaches that require state-of-the-art segmentation networks pre-trained on large scale segmentation datasets. We demonstrate the effectiveness of our proposed transformation strategy through comprehensive experimentation in which we significantly raise state-of-the-art on four standard interactive segmentation benchmarks.
Progressivity for voice interface design

Drawing from Conversation Analysis (CA), we examine how the orientation towards progressivity in talk-keeping things moving-might help us better understand and design for voice interactions. We introduce progressivity by surveying its explication in CA, and then look at how a strong preference for progressivity in conversation works out practically in sequences of voice interaction recorded in people's homes. Following Stivers and Robinson's work on progressivity, we find our data shows: how non-answer responses impede progress; how accounts offered for non-answer responses can lead to recovery; how participants work to receive answers; and how, ultimately, moving the interaction forwards does not necessarily involve a fitted answer, but other kinds of responses as well. We discuss the wider potential of applying progressivity to evaluate and understand voice interactions, and consider what designers of voice experiences might do to design for progressivity. Our contribution is a demonstration of the progressivity principle and its interactional features, which also points towards the need for specific kinds of future developments in speech technology.
Fast Radar Motion Estimation with a Learnt Focus of Attention using Weak Supervision

This paper is about fast motion estimation with scanning radar. We use weak supervision to train a focus of attention policy which actively down-samples the measurement stream before data association steps are undertaken. At training, we avoid laborious manual labelling by exploiting short-term sensor coherence from multiple poses in the presence of an external ego-motion estimator (for example, wheel odometry). In this way, we generate copious annotated measurements which can be used for training a learning algorithm in a weakly-supervised fashion. We demonstrate the validity of the approach in the context of a Radar Odometry (RO) task, pre-filtering raw data with a popular image segmentation network trained as presented. We evaluate our system against 26 km of data collected in Central Oxford and show consistent motion estimation with greatly reduced radar processing times (by a factor of 2.36).
Alloy

Exploiting a simple, expressive logic based on relations to describe designs and automate their analysis.
Data Transparency with Blockchain and AI Ethics

Providing a 360° view of a given data item especially for sensitive data is essential toward not only protecting the data and associated privacy but also assuring trust, compliance, and ethics of the systems that use or manage such data. With the advent of General Data Protection Regulation, California Data Privacy Law, and other such regulatory requirements, it is essential to support data transparency in all such dimensions. Moreover, data transparency should not violate privacy and security requirements. In this article, we put forward a vision for how data transparency would be achieved in a de-centralized fashion using blockchain technology.
Social-Enhanced Attentive Group Recommendation

With the proliferation of social networks, group activities have become an essential ingredient of our daily life. A growing number of users share their group activities online and invite their friends to join in. This imposes the need of an in-depth study on the group recommendation task, i.e., recommending items to a group of users. Despite its value and significance, group recommendation remains an unsolved problem due to 1) the weights of group members are crucial to the recommendation performance but are rarely learnt from data; 2) social followee information is beneficial to understand users' preferences but is rarely considered; and 3) user-item interactions are helpful to reinforce the performance of group recommendation but are seldom investigated. Toward this end, we devise neural network-based solutions by utilizing the recent developments of attention network and neural collaborative filtering (NCF). First of all, we adopt an attention network to form the representation of a group by aggregating the group members' embeddings, which allows the attention weights of group members to be dynamically learnt from data. Second, the social followee information is incorporated via another attention network to enhance the representation of individual user, which is helpful to capture users' personal preferences. Third, considering that many online group systems also have abundant interactions of individual users on items, we further integrate the modeling of user-item interactions into our method. Through this way, the recommendation for groups and users can be mutually reinforced. Extensive experiments on the scope of both macro-level performance comparison and micro-level analyses justify the effectiveness and rationality of our proposed approaches.
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic LiDAR data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. Our work creates a precise LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with our framework is entirely automatic and requires no human annotation of any kind. We demonstrate the effectiveness of our dataset by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with our data. The data and code are available at https://tinyurl.com/y3tb9sxy.
Structured Pruning of Neural Networks With Budget-Aware Regularization

Pruning methods have shown to be effective at reducing the size of deep neural networks while keeping accuracy almost intact. Among the most effective methods are those that prune a network while training it with a sparsity prior loss and learnable dropout parameters. A shortcoming of these approaches however is that neither the size nor the inference speed of the pruned network can be controlled directly; yet this is a key feature for targeting deployment of CNNs on low-power hardware. To overcome this, we introduce a budgeted regularized pruning framework for deep CNNs. Our approach naturally fits into traditional neural network training as it consists of a learnable masking layer, a novel budget-aware objective function, and the use of knowledge distillation. We also provide insights on how to prune a residual network and how this can lead to new architectures. Experimental results reveal that CNNs pruned with our method are more accurate and less compute-hungry than state-of-the-art methods. Also, our approach is more effective at preventing accuracy collapse in case of severe pruning; this allows pruning factors of up to 16× without significant accuracy drop.
Named entity recognition in electronic health records using transfer learning bootstrapped Neural Networks

Neural networks (NNs) have become the state of the art in many machine learning applications, such as image, sound (LeCun et al., 2015) and natural language processing (Young et al., 2017; Linggard et al., 2012). However, the success of NNs remains dependent on the availability of large labelled datasets, such as in the case of electronic health records (EHRs). With scarce data, NNs are unlikely to be able to extract this hidden information with practical accuracy. In this study, we develop an approach that solves these problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009 Medical Extraction Challenge (Uzuner et al., 2010), 4.3 above the architecture that won the competition. To achieve this, we bootstrap our NN models through transfer learning by pretraining word embeddings on a secondary task performed on a large pool of unannotated EHRs and using the output embeddings as a foundation of a range of NN architectures. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on extracting relationships between medical terms using attention-based seq2seq models bootstrapped in the same manner.
Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN

Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compared to traditional optical microscopic imaging, hyperspectral imagery, combined with both spatial and spectral information, provides more wealthy information for recognizing cells. In this paper, a novel blood cell classification framework, which combines a modulated Gabor wavelet and deep convolutional neural network (CNN) kernels, named as MGCNN, is proposed based on medical hyperspectral imaging. For each convolutional layer, multi-scale and orientation Gabor operators are taken dot product with initial CNN kernels. The essence is to transform the convolutional kernels into the frequency domain to learn features. By combining characteristics of Gabor wavelets, the features learned by modulated kernels at different frequencies and orientations are more representative and discriminative. Experimental results demonstrate that the proposed model can achieve better classification performance than traditional CNNs and widely used support vector machine approaches, especially as training small-sample-size situations.
Vanishing Gradient Mitigation with Deep Learning Neural Network Optimization

Deep structured learning has emerged as a new area of machine learning research with its wide range of applications in signal and information processing. There are two general aspects often discussed in various high level descriptions of deep learning: i.e. (a) its structural models consisting of multiple layers or stages intended for non-linear information processing, (b) optimization techniques specifically for supervised or unsupervised learning of feature representation at sequentially higher abstract layers. However, non-linear higher level abstraction are prone to vanishing gradient problem with saturated activation functions. In this paper, the experiment is setup to examine the plausibility of applying Approximate Greatest Descent in deep neural networks. The experiments tested both saturated and unsaturated activation function with up to five hidden layers. As a result, stochastic diagonal approximate greatest descent (SDAGD) demonstrated no sign of vanishing gradient when training with both saturated and unsaturated activation functions. When hidden layers are added, SDAGD could obtain reasonable results as compared to Stochastic Gradient Descent (SGD).
An integrated VR/AR framework for user-centric interactive experience of cultural heritage: The ArkaeVision project

The ArkaeVision project is aimed at enabling a new way of enjoying Cultural Heritage through a more engaging and culturally-qualified user experience. The main goal is the creation of a technological infrastructure for the permanent enhancement of cultural resources. Hence, ArkaeVision represents a user-centric integrated system able to offer different modalities of exploitation of Cultural Heritage assets, including virtual representations of monuments, works of art and objects, as well as the stories associated to them. ArkaeVision therefore introduces a new communication paradigm, made of game-alike exploration of a 3D environment, virtually reconstructed, with elements of digital fiction and an engaging stortytelling, applied to two case studies: the exploration of the Hera II Temple of Paestum with Virtual Reality (VR) technology, and the exploration of the slab of the Swimmer Tomb with Augmented Reality (AR). The emotional component is fundamental in ArkaeVision, because it generates the motivation and leads people to use immersive viewers, as the HTC Vive, which allow the learning process to be activated more quickly, as many cognitivists and psychologists reported in the latest researches. Also the involvement of users through gamification is well expressed in ArkaeVision. This model of action (and interaction) designed for the public allows to emphasize the role of users through a system that is “guided” by their choices and times, in order to increase users’ engagement within the virtual exploration, and favour the content understanding by direct experience. Evaluations conducted on a preliminary prototype suggested that the communicative approach is very promising for education and engagement into cultural heritage experiences.
Evolutionary-based image encryption using RNA codons truth table

Symmetric image cryptography is a mechanism in which image pixels are encrypted into some meaningless format called cipher image. Only authorized users have access to the secret code for symmetric key encryption. In this paper a new symmetric image encryption method has been proposed using the concepts of ribonucleic acid (RNA) sequence and genetic algorithm (GA), called RNA-GA. The proposed method starts by generating specified number of initial cipher images using logistic map function. Then, the initial cipher images are converted to the corresponding one-dimensional binary sequences and relevant codons array using codons truth table. The codons array are then updated using encryption key and encryption RNA tables to form the initial population of genetic algorithm. Next, genetic algorithm optimizes the population using selection, crossover, and mutation operators. The results approve high resistance of the proposed method against well-known attacks and entropy of 7.9987 regarding standard image of size 256×256 after 30 repetitions.
GoogLeNet-Based Ensemble FCNet Classifier for Focal Liver Lesion Diagnosis

Transfer learning techniques are recently preferred for the computer aided diagnosis (CAD) of variety of diseases, as it makes the classification feasible from limited training dataset. In this work, an ensemble FCNet classifier is proposed to classify hepatic lesions from the deep features extracted using GoogleNet-LReLU transfer learning approachs. In the existing GoogLeNet architecture three modifications are done: ReLU activation functions in the inception modules are replaced by leaky ReLU activation function; a stack of three fully connected layers are included before the classification layer; and deep features of different level of abstraction extracted from the output of every inception layer given as classifier input in order to significantly enhance the classifier performance. The performance of the proposed classifier by the virtue of the above mentioned modifications is tested on six classes of liver CT images namely normal, hepatocellular carcinoma, hemangioma, cyst, abscess and liver metastasis. The results presented in this work demonstrate the efficacy of the proposed classifier design in achieving better classification accuracy.
Automated liver and tumor segmentation based on concave and convex points using fuzzy c-means and mean shift clustering

Identifying liver and tumour regions from medical images using fully automated computer-aided software is a challenging task for the diagnosis of liver disease. In this paper, a novel method is presented to overcome the problems of liver and tumour segmentation in CT images such as weak boundaries, touching organs, and heterogeneity of the liver. Organ edges were extracted using the Kirsch filter, and the concave and convex points were subsequently calculated. The mean-shift algorithm was employed to make the images uniform along the organ borders. Finally, the FCM approach was used to segment the liver and tumours. The results demonstrated that our complex algorithm achieved an average surface distance (ASD) of 1.1±0.39mm and volume overlap error (VOE) of 1.8±0.34% while segmenting the liver. An ASD of 1.5±0.55mm and VOE of 9.8±3.9% were obtained with respect to tumour segmentation.