Reviewerly/exHarmony huggingface dataset:
The Security of Medical Data on Internet Based on Differential Privacy Technology

The study aims at discussing the security of medical data in the Internet era. By using k-anonymity (K-A) and differential privacy (DP), an algorithm model combining K-A and DP was proposed, which was simulated through the experiments. In the Magic and EIA datasets, the algorithm constructed was compared with K-A and the L-diversity model to verify the performance of the model. The model constructed based on DP had the lowest privacy-leakage risks, which increased with the number of identifiers in the Magic and EIA datasets, and the information disclosure was the least. In addition, in its usability analysis, it was found that its value was the most obviously improved and its operation efficiency was the highest. The K-A-DP algorithm can effectively reduce the risk of privacy leakage and information loss, and has achieved excellent results. Despite the deficiencies in the process of the experiment, the study still provides a reference for solving the problem of medical data security.
Semi-supervised Medical Image Classification with Global Latent Mixing

Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled data. One effective SSL approach is to regularize the local smoothness of neural functions via perturbations around single data points. In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL. We present a novel SSL approach that trains the neural network on linear mixing of labeled and unlabeled data, at both the input and latent space in order to regularize different portions of the network. We evaluated the presented model on two distinct medical image data sets for semi-supervised classification of thoracic disease and skin lesion, demonstrating its improved performance over SSL with local perturbations and SSL with global mixing but at the input space only. Our code is available at https://github.com/Prasanna1991/LatentMixing.
Using BI-RADS Stratifications as Auxiliary Information for Breast Masses Classification in Ultrasound Images

Breast Ultrasound (BUS) imaging has been recognized as an essential imaging modality for breast masses classification in China. Current deep learning (DL) based solutions for BUS classification seek to feed ultrasound (US) images into deep convolutional neural networks (CNNs), to learn a hierarchical combination of features for discriminating malignant and benign masses. One existing problem in current DL-based BUS classification was the lack of spatial and channel-wise features weighting, which inevitably allow interference from redundant features and low sensitivity. In this study, we aim to incorporate the instructive information provided by breast imaging reporting and data system (BI-RADS) within DL-based classification. A novel DL-based BI-RADS Vector-Attention Network (BVA Net) that trains with both texture information and decoded information from BI-RADS stratifications was proposed for the task. Three baseline models, pre-trained DenseNet-121, ResNet-50 and Residual-Attention Network (RA Net) were included for comparison. Experiments were conducted on a large scale private main dataset and two public datasets, UDIAT and BUSI. On the main dataset, BVA Net outperformed other models, in terms of AUC (area under the receiver operating curve, 0.908), ACC (accuracy, 0.865), sensitivity (0.812) and precision (0.795). BVA Net also achieved the high AUC (0.87 and 0.882) and ACC (0.859 and 0.843), on UDIAT and BUSI. Moreover, we proposed a method that integrates both BVA Net binary classification and BI-RADS stratification estimation, called integrated classification. The introduction of integrated classification helped improving the overall sensitivity while maintaining a high specificity.
Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control

Electric motors are used in many applications, and their efficiency is strongly dependent on their control. Among others, linear feedback approaches or model predictive control methods are well known in the scientific literature and industrial practice. A novel approach is to use reinforcement learning (RL) to have an agent learn electric drive control from scratch merely by interacting with a suitable control environment. RL achieved remarkable results with superhuman performance in many games (e.g., Atari classics or Go) and also becomes more popular in control tasks, such as cart-pole or swinging pendulum benchmarks. In this work, the open-source Python package gym-electric-motor (GEM) is developed for ease of training of RL-agents for electric motor control. Furthermore, this package can be used to compare the trained agents with other state-of-the-art control approaches. It is based on the OpenAI Gym framework that provides a widely used interface for the evaluation of RL-agents. The package covers different dc and three-phase motor variants, as well as different power electronic converters and mechanical load models. Due to the modular setup of the proposed toolbox, additional motor, load, and power electronic devices can be easily extended in the future. Furthermore, different secondary effects, such as converter interlocking time or noise, are considered. An intelligent controller example based on the deep deterministic policy gradient algorithm that controls a series dc motor is presented and compared to a cascaded proportional–integral controller as a baseline for future research. Here, safety requirements are particularly highlighted as an important constraint for data-driven control algorithms applied to electric energy systems. Fellow researchers are encouraged to use the GEM framework in their RL investigations or contribute to the functional scope (e.g., further motor types) of the package.
Monocular 3D Object Detection via Feature Domain Adaptation

Monocular 3D object detection is a challenging task due to unreliable depth, resulting in a distinct performance gap between monocular and LiDAR-based approaches. In this paper, we propose a novel domain adaptation based monocular 3D object detection framework named DA-3Ddet, which adapts the feature from unsound image-based pseudo-LiDAR domain to the accurate real LiDAR domain for performance boosting. In order to solve the overlooked problem of inconsistency between the foreground mask of pseudo and real LiDAR caused by inaccurately estimated depth, we also introduce a context-aware foreground segmentation module which helps to involve relevant points for foreground masking. Extensive experiments on KITTI dataset demonstrate that our simple yet effective framework outperforms other state-of-the-arts by a large margin.
Survey of Neural Text Representation Models

In natural language processing, text needs to be transformed into a machine-readable representation before any processing. The quality of further natural language processing tasks greatly depends on the quality of those representations. In this survey, we systematize and analyze 50 neural models from the last decade. The models described are grouped by the architecture of neural networks as shallow, recurrent, recursive, convolutional, and attention models. Furthermore, we categorize these models by representation level, input level, model type, and model supervision. We focus on task-independent representation models, discuss their advantages and drawbacks, and subsequently identify the promising directions for future neural text representation models. We describe the evaluation datasets and tasks used in the papers that introduced the models and compare the models based on relevant evaluations. The quality of a representation model can be evaluated as its capability to generalize to multiple unrelated tasks. Benchmark standardization is visible amongst recent models and the number of different tasks models are evaluated on is increasing.
From Capturing to Rendering: Volumetric Media Delivery with Six Degrees of Freedom

Technological improvements are rapidly advancing holographic-type content distribution. Significant research efforts have been made to meet the low latency and high bandwidth requirements set forward by interactive applications such as remote surgery and virtual reality. Recent research made six degrees of freedom (6DoF) for immersive media possible, where users may both move their head and change their position within a scene. In this article, we present the status and challenges of 6DoF applications based on volumetric media, focusing on the key aspects required to deliver such services. Furthermore, we present results from a subjective study to highlight relevant directions for future research.
The Effects of Self- and External Perception of Avatars on Cognitive Task Performance in Virtual Reality

Virtual reality (VR) allows embodying any possible avatar. Known as the Proteus effect, avatars can change users’ behavior and attitudes. Previous work found that embodying Albert Einstein can increase cognitive task performance. The behavioral confirmation paradigm, however, predicts that our behavior is also affected by others’ perception of us. Therefore, we investigated the cognitive performance in collaborative VR when self-perception and external perception of the own avatar differ. 32 male participants performed a Tower of London task in pairs. One participant embodied Einstein or a young adult while the other perceived the participant as Einstein or a young adult. We show that the perception by others affects cognitive performance. The Einstein avatar also decreased the perceived workload. Results imply that avatars’ appearance to both, the user and the others must be considered when designing for cognitively demanding tasks.
A Hyper-ledger Fabric Framework as a Service for Improved Quality E-voting System

Blockchain is often referred to as a disruptive technology but it has emerged from relative obscurity to become a relatively popular conversation topic across various industries. Many sectors, like finance, medicine, manufacturing, and education, use blockchain applications to profit from the unique bundle of characteristics of this technology. Blockchain technology (BT) promises benefits in trustability, collaboration, organization, identification, credibility, and transparency. As we know, Elections are a symbol of hope and freedom, and the right to vote is an expression of belonging and of having a voice. In this paper, we'll describe about blockchain on E-voting which offers to the electoral process is a combination of accuracy, transparency, and immutability. The applications are virtually limitless, and government is certainly not the only realm within which important elections take place, where security is paramount, and where blockchain can make a difference. No matter the arena, voters deserve trust. They demand it, they deserve it. And in this paper, we proposed the most secured way of voting with the help of Blockchain using Hyper-ledger Fabric based Framework as a Service (FaaS) which can used to implement a highly maintainable, large-scale, cost effective E-voting solution within personalized private blockchain.
LowCon: A Design-based Subsampling Approach in a Misspecified Linear Model

We consider a measurement constrained supervised learning problem, that is, (i) full sample of the predictors are given; (ii) the response observations are unavailable and expensive to measure. Thus, it is ideal to select a subsample of predictor observations, measure the corresponding responses, and then fit the supervised learning model on the subsample of the predictors and responses. However, model fitting is a trial and error process, and a postulated model for the data could be misspecified. Our empirical studies demonstrate that most of the existing subsampling methods have unsatisfactory performances when the models are misspecified. In this paper, we develop a novel subsampling method, called “LowCon,” which outperforms the competing methods when the working linear model is misspecified. Our method uses orthogonal Latin hypercube designs to achieve a robust estimation. We show that the proposed design-based estimator approximately minimizes the so-called worst-case bias with respect to many possible misspecification terms. Both the simulated and real-data analyses demonstrate the proposed estimator is more robust than several subsample least-squares estimators obtained by state-of-the-art subsampling methods. Supplementary materials for this article are available online.
From Needs to Strengths

Guided by a human-centered design focus on users' needs, Computer-Supported Collaborative Work (CSCW) research and practice have increasingly explored how to address the multiple inequities affecting historically marginalized groups. A growing body of CSCW and Human-Computer Interaction (HCI) research, building upon education and community development literature, argues that centering on needs dismisses marginalized users' capacity for driving change. Needs-based views often lead to designs for the "here and now,'' further marginalizing populations and perpetuating stereotypes. In contrast, an assets-based approach that puts users' knowledge, strengths, and capacities---\textitassets ---at the core of design can better promote sustained impact. Translating assets into meaningful designs that interact with intersecting systems of oppression, however, raises critical questions:What are assets? Whose assets are privileged? What ethical considerations surface when facilitating assets-based reflections? How can an assets-based design tackle systems-level problems? In this workshop, we will bring together researchers and industry actors to explore the implications of assets-based perspectives across domains, including Education, Information and Communication Technologies and Development (ICTD), and Participatory Design (PD). Specifically, we will work to develop guidelines and methodologies for CSCW researchers and designers to identify when and how to pursue an assets-based approach, navigating issues of power to translate assets into design effectively.
RD-GAN: Few/Zero-Shot Chinese Character Style Transfer via Radical Decomposition and Rendering

Style transfer has attracted much interest owing to its various applications. Compared with English character or general artistic style transfer, Chinese character style transfer remains a challenge owing to the large size of the vocabulary (70224 characters in GB18010-2005) and the complexity of the structure. Recently some GAN-based methods were proposed for style transfer; however, they treated Chinese characters as a whole, ignoring the structures and radicals that compose characters. In this paper, a novel radical decomposition-and-rendering-based GAN (RD-GAN) is proposed to utilize the radical-level compositions of Chinese characters and achieves few-shot/zero-shot Chinese character style transfer. The RD-GAN consists of three components: a radical extraction module (REM), radical rendering module (RRM), and multi-level discriminator (MLD). Experiments demonstrate that our method has a powerful few-shot/zero-shot generalization ability by using the radical-level compositions of Chinese characters.
Fairness by Learning Orthogonal Disentangled Representations

Learning discriminative powerful representations is a crucial step for machine learning systems. Introducing invariance against arbitrary nuisance or sensitive attributes while performing well on specific tasks is an important problem in representation learning. This is mostly approached by purging the sensitive information from learned representations. In this paper, we propose a novel disentanglement approach to invariant representation problem. We disentangle the meaningful and sensitive representations by enforcing orthogonality constraints as a proxy for independence. We explicitly enforce the meaningful representation to be agnostic to sensitive information by entropy maximization. The proposed approach is evaluated on five publicly available datasets and compared with state of the art methods for learning fairness and invariance achieving the state of the art performance on three datasets and comparable performance on the rest. Further, we perform an ablative study to evaluate the effect of each component.
Cyber Threat Dictionary Using MITRE ATT&amp;CK Matrix and NIST Cybersecurity Framework Mapping

Cyber-attack and defense frameworks offer numerous ways to protect systems and networks from threats. However, only a few of these numerous attack and defense frameworks provide countermeasures by linking multiple frameworks. Due to the lack of attack-defense mapped frameworks, a number of cyber security practitioners are often puzzled how to cope with cyber-attacks when it occurs. The objective of this paper is to present a tool called the "Cyber Threat Dictionary" to solve the problem. Cyber Threat Dictionary offers approaches and practical solutions to the threats by mapping MITRE ATT&CK Matrix to the NIST Cybersecurity Framework. By providing immediate solutions to cyber security practitioners, Cyber Threat Dictionary enables effective responses against cyber-attacks.
Pathology Segmentation Using Distributional Differences to Images of Healthy Origin

Fully supervised segmentation methods require a large training cohort of already segmented images, providing information at the pixel level of each image. We present a method to automatically segment and model pathologies in medical images, trained solely on data labelled on the image level as either healthy or containing a visual defect. We base our method on CycleGAN, an image-to-image translation technique, to translate images between the domains of healthy and pathological images. We extend the core idea with two key contributions. Implementing the generators as residual generators allows us to explicitly model the segmentation of the pathology. Realizing the translation from the healthy to the pathological domain using a variational autoencoder allows us to specify one representation of the pathology, as this transformation is otherwise not unique. Our model hence not only allows us to create pixelwise semantic segmentations, it is also able to create inpaintings for the segmentations to render the pathological image healthy. Furthermore, we can draw new unseen pathology samples from this model based on the distribution in the data. We show quantitatively, that our method is able to segment pathologies with a surprising accuracy being only slightly inferior to a state-of-the-art fully supervised method, although the latter has per-pixel rather than per-image training information. Moreover, we show qualitative results of both the segmentations and inpaintings. Our findings motivate further research into weakly-supervised segmentation using image level annotations, allowing for faster and cheaper acquisition of training data without a large sacrifice in segmentation accuracy.
Re-examining the causal relationships among FDI, economic growth and financial sector development in Africa

Studies on the causal relationships among foreign direct investment (FDI), economic growth and financial sector development are far from being conclusive, a reflection of the different methodologies and country-settings employed. In this study, we depart from the use of single country cases to relying on a panel data comprising 45 African countries over the period 1980 to 2016 in examining such causal linkages. By applying a panel Granger Non-Causality test, we find that the causal nexus between FDI and economic growth is conditioned on the indicator of economic growth while we observe feedback causality between (i) FDI and financial sector development, and (ii) financial sector development and economic growth. These findings hold irrespective of the proxy of finance and economic growth. The study concludes by discussing key implications for policy.
Tunable Microwave Single-Photon Source Based on Transmon Qubit with High Efficiency

Single-photon sources are of great interest because they are key elements in different promising applications of quantum technologies. Here we demonstrate a highly efficient tunable on-demand microwave single-photon source based on a transmon qubit with an intrinsic emission efficiency above $98\mathrm{%}$. The high efficiency ensures a negligible pure dephasing rate and the necessary conditions for generation of indistinguishable photons. We provide an extended discussion and analysis of the efficiency of the photon generation. To further experimentally confirm the single-photon property of the source, correlation functions of the emission field are also measured using linear detectors with a GPU-enhanced signal-processing technique. Our results experimentally demonstrate that frequency tunability and negligible pure dephasing rate can be achieved simultaneously and show that such a tunable single-photon source can be good for various practical applications in quantum communication, simulations and information processing in the microwave regime.
Supervised Scene Illumination Control in Stereo Arthroscopes for Robot Assisted Minimally Invasive Surgery

Minimally invasive surgery (MIS) offers many advantages to patients but it also imposes limitations on surgeons ability, as no tactile or haptic feedback is available. From medical robotics perspective, visualizations issues specific to MIS such as limited field of view and the lack of automatic exposure control of the surgical area make it challenging when it comes to tracking tissue, tools and camera pose as well as in perceiving depth. Lighting plays an important role in 3D reconstruction and variations due to internal illumination conditions are known to degrade vital visual information. In this work, we describe a supervised adaptive light control system to solve some of the image visualization problems of MIS. Our proposed method is able to classify underexposed and over-exposed frames and adjust lighting condition automatically to enrich image quality. Our method uses support vector machines to classify different illumination conditions. Visual feedback is provided by gradient information to assess image quality and justify classifier decision. The output of this system has been tested against two cadaver knee experiment data with an overall accuracy of 97.75% for under-exposed and 89.11% for over-exposed classes. Hardware implementation of this classifier is expected to result in adaptive lighting for robot assisted surgery as well as in providing support to surgeons by freeing them from manual adjustments to lighting controls.
Optimal Frontier-Based Autonomous Exploration in Unconstructed Environment Using RGB-D Sensor

Aerial robots are widely used in search and rescue applications because of their small size and high maneuvering. However, designing an autonomous exploration algorithm is still a challenging and open task, because of the limited payload and computing resources on board UAVs. This paper presents an autonomous exploration algorithm for the aerial robots that shows several improvements for being used in the search and rescue tasks. First of all, an RGB-D sensor is used to receive information from the environment and the OctoMap divides the environment into obstacles, free and unknown spaces. Then, a clustering algorithm is used to filter the frontiers extracted from the OctoMap, and an information gain based cost function is applied to choose the optimal frontier. At last, the feasible path is given by A* path planner and a safe corridor generation algorithm. The proposed algorithm has been tested and compared with baseline algorithms in three different environments with the map resolutions of 0.2 m, and 0.3 m. The experimental results show that the proposed algorithm has a shorter exploration path and can save more exploration time when compared with the state of the art. The algorithm has also been validated in the real flight experiments.