Reviewerly/exHarmony huggingface dataset:
Secure medical data transmission using a fusion of bit mask oriented genetic algorithm, encryption and steganography

This paper presents a bit mask oriented genetic algorithm based secure medical data transmission mechanism. A bit mask oriented genetic algorithm (BMOGA) is utilized to reduce the replication of medical tests data which are transferred across organizations. Medical data is considered very sensitive, therefore secure medical data transmission is must. BMOGA is a variant of the traditional genetic algorithm. Literature reveals that it can avoid premature convergence – a situation when optimization algorithms get stuck at local optimum. BOMGA utilizes Boolean based mask-fill operators and performs reproduction operations in two different phases that helps to avoid premature convergence. Cryptographic features are integrated with the BMOGA for secure data transmission. The encrypted data is embedded into the medical images through 1-level and 2-level Discrete Wavelet Transform (DWT). The reverse process of the BMOGA is implemented for the extraction of secret message from the encrypted one. Numerical experiments are conducted to determine the performance of the proposed algorithm. Results reveals that the proposed algorithm is capable of secure data transmission. Performance comparison is done with the state-of-the-art algorithm with respect to the datasets. Comparative results indicated the superiority of the proposed algorithm in terms of various statistical measures such as peak signal to noise ratio (PSNR), correlation, structural content (SC), structure similarity (SSIM) and mean square error (MSE) to report the results.
Simplified unsupervised image translation for semantic segmentation adaptation

Image to image translation achieves superior performance with the advent of generative adversarial networks. In this paper, we propose a Simplified Unsupervised Image Translation (SUIT) model for domain adaptation on semantic segmentation. We adopt adversarial training for superior image generation, and design a novel semantic-content loss to enhance visual appearance preservation. Thus, the high-fidelity generated images with target-style can help the model generalize to the target domain. Besides, the semantic-content loss contains two components, which focus on label- and content-consistency, respectively. Both of them can be derived from existing modules of SUIT, which makes it simple yet suitable for domain adaptation on semantic segmentation tasks. Meanwhile, since the transformation network (generator) is decoupled from the segmentation network, the former can be easily transplanted to other semantic segmentation models. Extensive experimental results demonstrate that these translated images within SUIT can significantly improve performance of the model on the target domain, and our model with FCN8s-VGG16 architecture achieves around 13 percentage points improvement in terms of mIoU on multiple semantic segmentation adaptation benchmarks.
Deep Neural Network Ensembles Against Deception: Ensemble Diversity, Accuracy and Robustness

Ensemble learning is a methodology that integrates multiple DNN learners for improving prediction performance of individual learners. Diversity is greater when the errors of the ensemble prediction is more uniformly distributed. Greater diversity is highly correlated with the increase in ensemble accuracy. Another attractive property of diversity optimized ensemble learning is its robustness against deception: an adversarial perturbation attack can mislead one DNN model to misclassify but may not fool other ensemble DNN members consistently. In this paper we first give an overview of the concept of ensemble diversity and examine the three types of ensemble diversity in the context of DNN classifiers. We then describe a set of ensemble diversity measures, a suite of algorithms for creating diversity ensembles and for performing ensemble consensus (voted or learned) for generating high accuracy ensemble output by strategically combining outputs of individual members. This paper concludes with a discussion on a set of open issues in quantifying ensemble diversity for robust deep learning.
A Comparative Analysis of Cyber-Threat Intelligence Sources, Formats and Languages

The sharing of cyber-threat intelligence is an essential part of multi-layered tools used to protect systems and organisations from various threats. Structured standards, such as STIX, TAXII and CybOX, were introduced to provide a common means of sharing cyber-threat intelligence and have been subsequently much-heralded as the de facto industry standards. In this paper, we investigate the landscape of the available formats and languages, along with the publicly available sources of threat feeds, how these are implemented and their suitability for providing rich cyber-threat intelligence. We also analyse at a sample of cyber-threat intelligence feeds, the type of data they provide and the issues found in aggregating and sharing the data. Moreover, the type of data supported by various formats and languages is correlated with the data needs for several use cases related to typical security operations. The main conclusions drawn by our analysis suggest that many of the standards have a poor level of adoption and implementation, with providers opting for custom or traditional simple formats.
Quantum illumination with a generic Gaussian source

With the aim to loosen the entanglement requirements of quantum illumination, we study the performance of a family of Gaussian states at the transmitter, combined with an optimal and joint quantum measurement at the receiver. We find that maximal entanglement is not strictly necessary to achieve quantum advantage over the classical benchmark of a coherent-state transmitter, in both settings of symmetric and asymmetric hypothesis testing. While performing this quantum-classical comparison, we also investigate a suitable regime of parameters for potential short-range radar (or scanner) applications.
Deep Learning for RF-Based Drone Detection and Identification: A Multi-Channel 1-D Convolutional Neural Networks Approach

Commercial unmanned aerial vehicles, or drones, are getting increasingly popular in the last few years. The fact that these drones are highly accessible to public may bring a range of security and technical issues to sensitive areas such as airfields and military bases. Consequently, drone detection and state identification are becoming very crucial and essential for governments and security agencies. This paper proposes a deep learning based approach for drone detection, type identification and state identification using a multi-channel 1-dimensional convolutional neural network. The deep learning model is trained utilizing a publicly published database for drone's radio frequency signals. The proposed model can be used to produce new features that can represent the whole dataset in a more compact form which enables the use of classical machine learning algorithms for classification.
Efficient Low-Resolution Face Recognition via Bridge Distillation

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, 934 and 763 faces per second on GPU, CPU and mobile phone, respectively.
Multi-objective scheduling strategy for scientific workflows in cloud environment: A Firefly-based approach

Cloud computing is a distributed computing paradigm, that provides infrastructure and services to the users using the pay-as-you-use billing model. With the increasing demands and diversity of the scientific workflows, the cloud providers face a fundamental issue of resource provisioning and load balancing. Although, the workflow scheduling in the cloud environment is extensively studied, however, most of the strategies ignore to consider the multiple conflicting objectives of the workflows for scheduling and resource provisioning. To address the above-mentioned issues, in the paper, we introduce a new workflow scheduling strategy using the Firefly algorithm (FA) by considering multiple conflicting objectives including workload of cloud servers, makespan, resource utilization, and reliability. The main purpose of the FA is to find a suitable cloud server for each workflow that can meet its requirements while balancing the loads and resource utilization of the cloud servers. In addition, a rule-based approach is designed to assign the tasks on the suitable VM instances for minimizing the makespan of the workflow while meeting the deadline. The proposed scheduling strategy is evaluated over Google cluster traces using various simulation runs. The control parameters of the FA are also thoroughly investigated for better performance. Through the experimental analysis, we prove that the proposed strategy performs better than the state-of-the-art-algorithms in terms of different Quality-of-Service (QoS) parameters including makespan, reliability, resource utilization and loads of the cloud servers.
Differentially Private Online Task Assignment in Spatial Crowdsourcing: A Tree-based Approach

With spatial crowdsourcing applications such as Uber and Waze deeply penetrated into everyday life, there is a growing concern to protect user privacy in spatial crowdsourcing. Particularly, locations of workers and tasks should be properly processed via certain privacy mechanism before reporting to the untrusted spatial crowdsourcing server for task assignment. Privacy mechanisms typically permute the location information, which tends to make task assignment ineffective. Prior studies only provide guarantees on privacy protection without assuring the effectiveness of task assignment. In this paper, we investigate privacy protection for online task assignment with the objective of minimizing the total distance, an important task assignment formulation in spatial crowdsourcing. We design a novel privacy mechanism based on Hierarchically Well-Separated Trees (HSTs). We prove that the mechanism is ε-Geo-Indistinguishable and show that there is a task assignment algorithm with a competitive ratio of O( <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ε</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/4</sup> log N log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> k), where ε is the privacy budget, N is the number of predefined points on the HST, and k is the matching size. Extensive experiments on synthetic and real datasets show that online task assignment under our privacy mechanism is notably more effective in terms of total distance than under prior differentially private mechanisms.
DeepRacer: Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning

DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems. Using the platform, we demonstrate how a 1/18th scale car can learn to drive autonomously using RL with a monocular camera. It is trained in simulation with no additional tuning in the physical world and demonstrates: 1) formulation and solution of a robust reinforcement learning algorithm, 2) narrowing the reality gap through joint perception and dynamics, 3) distributed on-demand compute architecture for training optimal policies, and 4) a robust evaluation method to identify when to stop training. It is the first successful large-scale deployment of deep reinforcement learning on a robotic control agent that uses only raw camera images as observations and a model-free learning method to perform robust path planning. We open source our code and video demo on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Blockchain Performance Analysis for Supporting Cross-Border E-Government Services

With the continuous development of distributed ledger and blockchain technologies, new use cases apart from cryptocurrencies have come into the spotlight. In this article, we evaluate whether an e-government service could be a suitable candidate for a blockchain transformation. We selected as a reference test system an existing cross-border e-government service that is used for supporting goods exchanges across the European Union. We show how such an indicative paradigm can be transformed into a blockchain system. In order to do so, we deployed it in an emulated architecture for evaluating its performance under various realistic conditions. Our results show that the deployed system is able to meet the requirements, both in terms of throughput and transaction speed. Moreover, it shows clear advantages in terms of usability and synchronization between all entities.
PinchType: Text Entry for Virtual and Augmented Reality Using Comfortable Thumb to Fingertip Pinches

Text entry is an integral component to many use cases in virtual and augmented reality. We present PinchType: A new method of virtual text entry that combines users' existing knowledge of the QWERTY keyboard layout with simple thumb and finger interactions. Users pinch with the thumb and fingertip to select from the same group of letters the finger would press on a QWERTY keyboard; a language model disambiguates. In a preliminary study with 14 participants, we investigated PinchType's speed and accuracy on initial use, as well as its physical comfort relative to a mid-air keyboard. After entering 40 phrases, most people reported that PinchType was more comfortable than the mid-air keyboard. Most participants reached a mean speed of 12.54 WPM, or 20.07 WPM without the time spent correcting errors. This compares favorably to other thumb-to-finger virtual text entry methods.
Evaluation of Robust Spatial Pyramid Pooling Based on Convolutional Neural Network for Traffic Sign Recognition System

Traffic sign recognition (TSR) is a noteworthy issue for real-world applications such as systems for autonomous driving as it has the main role in guiding the driver. This paper focuses on Taiwan’s prohibitory sign due to the lack of a database or research system for Taiwan’s traffic sign recognition. This paper investigates the state-of-the-art of various object detection systems (Yolo V3, Resnet 50, Densenet, and Tiny Yolo V3) combined with spatial pyramid pooling (SPP). We adopt the concept of SPP to improve the backbone network of Yolo V3, Resnet 50, Densenet, and Tiny Yolo V3 for building feature extraction. Furthermore, we use a spatial pyramid pooling to study multi-scale object features thoroughly. The observation and evaluation of certain models include vital metrics measurements, such as the mean average precision (mAP), workspace size, detection time, intersection over union (IoU), and the number of billion floating-point operations (BFLOPS). Our findings show that Yolo V3 SPP strikes the best total BFLOPS (65.69), and mAP (98.88%). Besides, the highest average accuracy is Yolo V3 SPP at 99%, followed by Densenet SPP at 87%, Resnet 50 SPP at 70%, and Tiny Yolo V3 SPP at 50%. Hence, SPP can improve the performance of all models in the experiment.
Estimating Numerical Distributions under Local Differential Privacy

When collecting information, local differential privacy (LDP) relieves the concern of privacy leakage from users' perspective, as user's private information is randomized before sent to the aggregator. We study the problem of recovering the distribution over a numerical domain while satisfying LDP. While one can discretize a numerical domain and then apply the protocols developed for categorical domains, we show that taking advantage of the numerical nature of the domain results in better trade-off of privacy and utility. We introduce a new reporting mechanism, called the square wave (SW) mechanism, which exploits the numerical nature in reporting. We also develop an Expectation Maximization with Smoothing (EMS) algorithm, which is applied to aggregated histograms from the SW mechanism to estimate the original distributions. Extensive experiments demonstrate that our proposed approach, SW with EMS, consistently outperforms other methods in a variety of utility metrics.
Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks

Automated brain segmentation is an active research domain due to the association of various neurological disorders with different regions of the brain, to help medical professionals in prognostics and diagnostics. Traditional techniques like atlas-based and pattern recognition-based methods led to the development of various tools for automated brain segmentation. Recently, deep learning techniques are outperforming classical state-of-the-art methods and gradually becoming more mature. Consequently, deep learning has been extensively employed as a tool for precise segmentation of brain regions because of its capability to learn the intricate features of the high-dimensional data. In this work, a network for the segmentation of multiple brain regions has been proposed that is based on 3D convolutional neural networks and utilizes residual learning and dilated convolution operations to efficiently learn the end-to-end mapping from MRI volumes to the voxel-level brain segments. This research is focused on the segmentation of up to nine brain regions including cerebrospinal fluid, white matter and gray matter as well as their sub-regions. Mean dice scores of 0.879 and 0.914 have been achieved for three and nine brain regions, respectively by using the data from three different sources. Comparative analysis shows that our network gives better dice scores for most of the brain regions than state-of-the-artwork. Moreover, the mean dice score of 0.903, obtained for eight brain regions segmentation with MRBrains18 dataset, is better than 0.876 which was achieved in the previous work.
Blended, precise semantic program embeddings

Learning neural program embeddings is key to utilizing deep neural networks in program languages research --- precise and efficient program representations enable the application of deep models to a wide range of program analysis tasks. Existing approaches predominately learn to embed programs from their source code, and, as a result, they do not capture deep, precise program semantics. On the other hand, models learned from runtime information critically depend on the quality of program executions, thus leading to trained models with highly variant quality. This paper tackles these inherent weaknesses of prior approaches by introducing a new deep neural network, Liger, which learns program representations from a mixture of symbolic and concrete execution traces. We have evaluated Liger on two tasks: method name prediction and semantics classification. Results show that Liger is significantly more accurate than the state-of-the-art static model code2seq in predicting method names, and requires on average around 10x fewer executions covering nearly 4x fewer paths than the state-of-the-art dynamic model DYPRO in both tasks. Liger offers a new, interesting design point in the space of neural program embeddings and opens up this new direction for exploration.
Infrared facial expression recognition via Gaussian-based label distribution learning in the dark illumination environment for human emotion detection

Facial expression recognition task as a crucial step for emotion recognition remains an open challenge that due to individual expression correlation/ambiguity. In this paper, to tackle these challenges, a novel model with the correlation emotion label distribution learning is proposed for near-infrared (NIR) facial expression recognition which associates multiple emotions with each expression depend on the similarity of expressions. Firstly, the similarities of the seven basic expressions are calculated, and then guide the correlation emotion label distribution by predicting the latent label probability distribution of the expression. Furthermore, the proposed model can be learned in an end-to-end manner via a constructed convolutional neural network to classify the six basic facial expressions. Experimental results on Oulu_CASIA database demonstrate that the proposed method has achieved the superior performance on NIR expression recognition.
Iterative Context-Aware Graph Inference for Visual Dialog

Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relation inference in a graphical model with sparse contexts and unknown graph structure (relation descriptor), and how to model the underlying context-aware relation inference is critical. To this end, we propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations. The graph structure (relations in dialog) is iteratively updated using an adaptive top-K message passing mechanism. Specifically, in every message passing step, each node selects the most K relevant nodes, and only receives messages from them. Then, after the update, we impose graph attention on all the nodes to get the final graph embedding and infer the answer. In CAG, each node has dynamic relations in the graph (different related K neighbor nodes), and only the most relevant nodes are attributive to the context-aware relational graph inference. Experimental results on VisDial v0.9 and v1.0 datasets show that CAG outperforms comparative methods. Visualization results further validate the interpretability of our method.
Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging

High-dynamic range (HDR) imaging is an essential imaging modality for a wide range of applications in uncontrolled environments, including autonomous driving, robotics, and mobile phone cameras. However, existing HDR techniques in commodity devices struggle with dynamic scenes due to multi-shot acquisition and post-processing time, e.g. mobile phone burst photography, making such approaches unsuitable for real-time applications. In this work, we propose a method for snapshot HDR imaging by learning an optical HDR encoding in a single image which maps saturated highlights into neighboring unsaturated areas using a diffractive optical element (DOE). We propose a novel rank-1 parameterization of the proposed DOE which avoids vast trainable parameters and keeps high frequencies' encoding compared with conventional end-to-end design methods. We further propose a reconstruction network tailored to this rank-1 parametrization for recovery of clipped information from the encoded measurements. The proposed end-to-end framework is validated through simulation and real-world experiments and improves the PSNR by more than 7 dB over state-of-the-art end-to-end designs.