Reviewerly/exHarmony huggingface dataset:
Attention guided deep features for accurate body mass index estimation

• An end-to-end deep learning framework is proposed to obtain BMI from 2D body images. • Combine attention mechanism with the deep network to enhance the performance. • Extensive experiments prove the proposed network outperforms the SOAT approaches. Body Mass Index (BMI) has been widely used as an indicator to evaluate the health condition of individuals, classifying a person as underweight, normal weight, overweight, or obese. Recently, several methods have been proposed to obtain BMI values based on the visual information, e.g., face images or 3D body images. These methods by extrapolating anthropometric features from face images or 3D body images are advanced in BMI estimation accuracy, however, they suffer from the difficulties of obtaining the required data due to the privacy issue or the 3D camera limitations. Moreover, these methods are hard to achieve satisfactory performance when they are directly applied to 2D body images. To tackle these problems, we propose to estimate BMI results from 2D body images by an end-to-end Convolutional Neural Network (CNN) with attention guidance. The proposed method is evaluated on our collected dataset. Extensive experiments confirm that the proposed framework outperforms state-of-the-art approaches in most cases.
Frequent pattern mining from multivariate time series data

Discrete sequential data is the collection of an ordered series of discrete events or abstractions from a set of data points collected at different time periods. These processes are one of the most common and important process types encountered in various domains. It is customary to discover similarities and to detect the indicators of anomalies in multivariate form in a supervised setting. In this paper, we first use an effective data transformation technique that transforms multivariate time series into multivariate sequences and use a tree-based method to mine frequent patterns from multivariate time series. However, this problem is costly in terms of solution time and memory consumption. Specifically, this study aims to improve computational efficiency for memory with reasonable solution time. We demonstrate the efficiency of this algorithm on standard datasets and then apply the method to a real healthcare problem.
Measurement of diffuse and plane of array irradiance by a combination of a pyranometer and an all-sky imager

Accurate, robust and cost-efficient measurements of diffuse horizontal irradiance (DHI) and global tilted irradiance (GTI) are of great interest for solar energy applications. However, the available measurement techniques exhibit at least one of these shortcomings: restriction of GTI measurement to a single plane, intensive maintenance, high acquisition cost or increased deviations, especially at new measurement sites. To avoid these shortcomings, we suggest a comparably inexpensive and robust setup of a thermopile pyranometer and an all-sky imager (ASI) for measurement of DHI and GTI. The pyranometer measures global horizontal irradiance (GHI) and our method consecutively estimates diffuse sky radiance, DHI, direct normal irradiance (DNI) and GTI, by merging information from the combined setup. The system is developed and validated at two sites in Spain and Germany. Measurement of GTI is benchmarked for seven planes over GTI derived by transposition based on DHI and DNI from a tracker setup with a pyrheliometer and shaded thermopile pyranometer. Our results indicate that the measurement system can be applied at both sites. The proposed method avoids time-consuming radiometric calibrations of the camera by the combination of both sensors and a self-calibration. The measurement system is promising in particular for measurement of GTI. For 10-min average GTI, our approach yields an rRMSD of 1.6...4.8% for planes with tilts in the range of 20∘...61∘. Thus, at both sites and for all planes, it outperforms the tracker-based transposition yielding 2.3...6.5%. DHI is measured significantly more accurately than reported in previous works using an ASI alone.
Weakly Guided Hierarchical Encoder-Decoder Network for Brain CT Report Generation

Report-writing for Brain Computed Tomography (CT) imaging is a routine procedure for diagnosing cerebrovascular diseases, while it is time-consuming and tedious for radiologists especially in highly populated areas. Automatic report generation has the potential to alleviate radiologists’ workload and reduce the diagnose error. Currently, the development of image captioning and medical image processing has driven great achievements in medical report generation. However, there is no report generation study for the Brain CT imaging and this task faces the following challenges: First, Brain CT lesions are disperse in 3-D space, with more morphological instability. Second, the Brain CT reports are long paragraphs with similar medical term. These challenges increase the difficulty o f lesions recognition and report generation for Brain CT imaging. To cope with these challenges, we propose a weakly guided hierarchical encoder-decoder network for lesions learning and Brain CT report generation. Specifically, we propose a weakly guided attention model (WGAM) in encoder to capture the most important areas and scans gradually under the weak guidance of possible lesions areas. In addition, we propose a keywords-driven interactive recurrent network (KIRN) in decoder to generate paragraphs under the weak guidance of possible lesions keywords. Experiments on our Brain CT dataset demonstrate the effectiveness of the proposed method.
Differential Privacy and Noisy Confidentiality Concepts for European Population Statistics

Abstract The article discusses various approaches to statistical disclosure control based on random noise that are currently being discussed for official population statistics and censuses. A particular focus is on a stringent delineation between different concepts influencing the discussion: we separate clearly between risk measures, noise distributions, and output mechanisms—putting these concepts into scope and into relation with each other. The article also remarks on utility and risk aspects of some specific output mechanisms and parameter setups, with special attention on static outputs that are rather typical in official population statistics. In particular, it is argued that unbounded noise distributions, such as plain Laplace, may jeopardize key unique census features without a clear need from a risk perspective. On the other hand, bounded noise distributions, such as the truncated Laplace or the cell key method, can contribute effectively to safeguarding these unique census features while controlling disclosure risks in census-like outputs. Finally, the article analyses some typical attack scenarios to constrain generic noise parameter ranges that suggest a good risk/utility compromise for the 2021 EU census output scenario. The analysis also shows that strictly differentially private mechanisms would be severely constrained in this scenario.
Towards Practical Auditing of Dynamic Data in Decentralized Storage

Decentralized storage (DS) projects such as Filecoin are gaining traction. Their openness mandates effective auditing mechanisms to assure users that their data remains intact. A blockchain is typically employed here as an unbiased public auditor. While the case for static data is relatively easy to handle, on-chain auditing of dynamic data with practical performance guarantees is still an open problem. Dynamic Proof-of-Storage (PoS) schemes developed for conventional cloud storage are not applicable to DS, since they require large storage proofs and/or large auditor states that are unmanageable by a resource-constrained blockchain. To fill the gap, we propose a family of dynamic on-chain auditing protocols that can produce concretely small auditor states while retaining the compact proofs promised by static PoS schemes. Our design revolves around a set of succinct data structures and optimization techniques for index information management. With proper instantiation and realistic parameters, our protocols can achieve 0.25MB on-chain state and 1.2KB storage proof for the auditing of 1TB data, outperforming previous dynamic PoS schemes that are adaptable for DS by orders of magnitude. As another practical contribution, we introduce a data abstraction layer that allows one to deploy the auditing protocols on arbitrary storage systems hosting dynamic data.
Repurposing existing deep networks for caption and aesthetic-guided image cropping

We propose a novel optimization framework that crops a given image based on user description and aesthetics. Unlike existing image cropping methods, where one typically trains a deep network to regress to crop parameters or cropping actions, we propose to directly optimize for the cropping parameters by repurposing pre-trained networks on image captioning and aesthetic tasks, without any fine-tuning, thereby avoiding training a separate network. Specifically, we search for the best crop parameters that minimize a combined loss of the initial objectives of these networks. To make the optimization stable, we propose three strategies: (i) multi-scale bilinear sampling, (ii) annealing the scale of the crop region, therefore effectively reducing the parameter space, (iii) aggregation of multiple optimization results. Through various quantitative and qualitative evaluations, we show that our framework can produce crops that are well-aligned to intended user descriptions and aesthetically pleasing.
Blind Image Restoration Based on Cycle-Consistent Network

This paper studies the blind image restoration where the ground truth is unavailable and the downsampling process is unknown. This complicated setting makes supervised learning and accurate kernel estimation impossible. Inspired by the recent success of image-to-image translation, this paper resorts to the unsupervised Cycle-consistent based framework to tackle this challenging problem. Different from the image-to-image task, the fidelity of reconstructed image is important for image restoration. Therefore, to improve the reconstruction ability of the Cycle-consistent network, we make explorations from the following aspects. First, we constrain low-frequency content in data to preserve the content of output from LR input. Second, we impose constraint on the content of training data to provide better supervision for discriminator, helping to suppress high-frequency artifacts or fake textures. Third, we average model parameters to further improve the generated image quality and help with model selection for GAN-based methods. Since GAN-based methods tend to produce various artifacts with different models, model average could realize a smoother control of balancing artifacts and fidelity. We have conducted extensive experiments on real noise and super resolution datasets to validate the effectiveness of the above techniques. The proposed ECycleGAN also demonstrates superior performance to SOTA methods in two applications – blind SR and blind denoising.
A two-stage deep generative adversarial quality enhancement network for real-world 3D CT images

High-quality (HQ) three-dimensional (3D) images are the premise of analyzing the properties of porous media such as rocks. X-ray computed tomography (CT) is one of the most widely used imaging tools to capture the 3D images of rock samples. Nevertheless, the quality (e.g., resolution, sharpness, and the signal-to-noise ratio) of the collected rock CT images may not meet the needs of practical applications in some cases due to the limitations of imaging systems, leading to inaccurate results of property analysis. In this paper, aiming at improving the quality of rock CT images as well as the accuracy of property analysis, we develop a two-stage deep generative adversarial quality enhancement network for real-world 3D CT images, namely the CTQENet. More specifically, the proposed CTQENet consists of a two-dimensional (2D) reconstruction module (2DRM) and a 3D fusion module (3DFM), which enhance the quality of 3D CT images from the perspective of 2D slices and 3D volumes, respectively. In order to remove artifacts and enhance the resolution of real-world CT images, the 2DRM takes the cycle-consistent generative adversarial network as the backbone to learn the mapping from low-quality (LQ) 2D slices to HQ ones without one-to-one paired training data. Then, the 3D CT volumes stacked by the reconstructed HQ slices along the x/y/z-axis are adaptively fused in the generative adversarial network-based 3DFM, to achieve more reliable 3D morphological structures. Qualitative and quantitative comparisons show the effectiveness of the proposed CTQENet for real-world 3D CT images of rock samples. In particular, the reconstructed HQ 3D CT images by CTQENet show similar morphological characteristics and statistical properties with HQ targets. This study makes it possible to obtain higher quality 3D CT images that partly exceed the limitations of CT imaging systems for better visual experience and more accurate property analysis.
Does Augmented Reality Also Augment Knowledge Acquisition? Augmented Reality Compared to Reading in Learning About the Human Digestive System?

Augmented Reality (AR) has become an emerging educational technology in classroom practice and science education. While most research and contemporary meta-analyses reveal benefits with regard to knowledge acquisition and motivation of Augmented Reality–based learning environments, most of the studies lack a clear and fair control condition. In this research, we compared an AR-based learning environment with a paper-based learning environment for self-directed learning about the human digestive system. In an experiment, we examined the effects of these two conditions for knowledge acquisition, cognitive load, and experience of flow and immersion. Results reveal significantly higher outcomes in favor of the text-based instruction with regard to conceptual domain knowledge and mental representation of the human digestive system and its involved organs. Positive effects of the AR learning environment are limited to a higher experience of immersion.
AWD3: Dynamic Reduction of the Estimation Bias

Value-based deep Reinforcement Learning (RL) algorithms suffer from the estimation bias primarily caused by function approximation and temporal difference (TD) learning. This problem induces faulty state-action value estimates and therefore harms the performance and robustness of the learning algorithms. Although several techniques were proposed to tackle, learning algorithms still suffer from this bias. Here, we introduce a technique that eliminates the estimation bias in off-policy continuous control algorithms using the experience replay mechanism. We adaptively learn the weighting hyper-parameter beta in the Weighted Twin Delayed Deep Deterministic Policy Gradient algorithm. Our method is named Adaptive-WD3 (AWD3). We show through continuous control environments of OpenAI gym that our algorithm matches or outperforms the state-of-the-art off-policy policy gradient learning algorithms.
Application of field-portable geophysical and geochemical methods for tracing the Mesozoic-Cenozoic vein-type fluorite deposits in shallow overburden areas: A case from the Wuliji’Oboo deposit, Inner Mongolia, NE China

Fluorite deposits are widespread throughout eastern China and commonly occur in vein systems. These vein-type fluorite deposits are the primary source of fluorine worldwide and were predominantly formed during the Mesozoic or Cenozoic periods. In the absence of ore outcrops, fluorite exploration is necessarily concentrated within the shallow overburden area. The Wuliji'Oboo fluorite deposit is an example of a medium–low temperature vein-type subsurface orebody in eastern Inner Mongolia. Five portable technology methods have been implemented within the study area, including multispectral remote sensing (RS) technology, very low-frequency electromagnetic measurement (VLF-EM), portable X-ray fluorescence analyses (PXRF), portable gamma-ray spectroscopy, and partial extraction geochemistry. This paper describes the application principles, influencing factors, and technical solutions for each method. By investigating various examples, the applications of portable technology methods and their associated limitations were evaluated. Combining geological research with multi-spectral RS techniques is essential for optimum fluorite ore body prediction, thus narrowing prospecting targets. First, the target area was scanned using VLF-EM to identify the distribution of potential anomalies. Second, PXRF was used to distinguish the mineralised anomaly. Finally, trenching and drilling were performed to verify the effectiveness of the combination of methods. This study establishes a geology-geophysics-geochemistry-remote sensing integrated exploration model that may provide theoretical and technical support for the prediction of fluorite ore bodies in large Quaternary shallow overburden areas.
A Survey on Data-Driven Learning for Intelligent Network Intrusion Detection Systems

An effective anomaly-based intelligent IDS (AN-Intel-IDS) must detect both known and unknown attacks. Hence, there is a need to train AN-Intel-IDS using dynamically generated, real-time data in an adversarial setting. Unfortunately, the public datasets available to train AN-Intel-IDS are ineluctably static, unrealistic, and prone to obsolescence. Further, the need to protect private data and conceal sensitive data features has limited data sharing, thus encouraging the use of synthetic data for training predictive and intrusion detection models. However, synthetic data can be unrealistic and potentially bias. On the other hand, real-time data are realistic and current; however, it is inherently imbalanced due to the uneven distribution of anomalous and non-anomalous examples. In general, non-anomalous or normal examples are more frequent than anomalous or attack examples, thus leading to skewed distribution. While imbalanced data are commonly predominant in intrusion detection applications, it can lead to inaccurate predictions and degraded performance. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. This paper investigates imbalanced learning and adversarial learning for training AN-Intel-IDS using a qualitative study. It surveys and synthesizes generative-based data augmentation techniques for addressing the uneven data distribution and generative-based adversarial techniques for generating synthetic yet realistic data in an adversarial setting using rapid review, structured reporting, and subgroup analysis.
Proximal Policy Optimization with Elo-based Opponent Selection and Combination with Enhanced Rolling Horizon Evolution Algorithm

Two-player zero-sum video game is a basic and important problem in game artificial intelligence. In 2020, enhanced rolling horizon evolution algorithm with policy gradient (ERHEAPI) beat heuristics, Monte-Carlo tree search and other methods to win the championship of Fighting Game Artificial Intelligence Competition (FTGAIC). However, the performance of ERHEAPI in the first round was not good. In this paper, we present an effective method noted as ERHEAPPO that combines proximal policy optimization (PPO) and enhanced rolling horizon evolution algorithm (ERHEA) with opponent model learning to further improve performance. We train the PPO agent and find that the Elo-based opponent selection can improve the sample efficiency. We compare the performance of the proposed ERHEAPPO with ERHEAPI. The experimental results demonstrate the effectiveness of ERHEAPPO.
Behavior Privacy Preserving in RF Sensing

Recent years have witnessed the booming development of RF sensing, which supports both identity authentication and behavior recognition by analysing the signal distortion caused by human body. In particular, RF-based identity authentication is more attractive to researchers, because it can capture the unique biological characteristics of users. However, the openness of wireless transmission raises privacy concerns since human behaviors could expose massive private information of users, which impedes the real-world implementation of RF-based user authentication applications. It is difficult to filter out the behavior information from the collected RF signals. In this article, we propose a privacy-preserving deep neural network named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BPCloak</i> to erase the behavior information in RF signals while retaining the ability of user authentication. We conduct extensive experiments over mainstream RF signals collected from three real wireless systems, including the WiFi, radio frequency identification (RFID), and millimeter-wave (mmWave) systems. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BPCloak</i> significantly reduces the behavior recognition accuracy, i.e., 85%+, 75%+, and 65%+ reduction for WiFi, RFID, and mmWave systems respectively, merely with a slight penalty of accuracy decrease when using these three systems for user authentication, i.e., 1%-, 3%-, and 5%-, respectively.
Blender: A Container Placement Strategy by Leveraging Zipf-Like Distribution Within Containerized Data Centers

Instantiated containers of an application are distributed across multiple Physical Machines (PMs) to achieve high parallel performance. Container placement plays a vital role in network traffic and the performance of containerized data centers. Existing container placement techniques are inadequate due to the ignorance of container traffic patterns. To solve this issue, we first investigate the network traffic between containers and observe that it exhibits a Zipf-like distribution. Motivated by this finding, we propose a novel container placement approach- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Blender</i> -by taking into account the Zipf-like distribution. Blender employs two algorithms called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefineAlg</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SplitAlg</i> to divide containers of applications into blocks, and place these blocks across Virtual Machines (VMs). Blender exhibits two salient features: (i) it minimizes inter-block traffic by arranging the containers that communicate frequently in the same block. (ii) it achieves good load balancing by combining complementary blocks that request different resource types (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPU-intensive</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">memory-intensive</i> blocks) and distributing these blocks across multiple VMs. The experimental results show that Blender significantly reduces communication traffic and network latency. In particular, Blender reduces the traffic of SBP and CA-WFD by 22% and 32%, respectively. Blender decreases network latency by 16% and 26% compared to SBP and CA-WFD. Furthermore, with Blender in place, the physical resources of hosting PMs are well balanced and utilized.