Reviewerly/exHarmony huggingface dataset:
Differential Privacy in the 2020 Census Will Distort COVID-19 Rates

Scholars rely on accurate population and mortality data to inform efforts regarding the coronavirus disease 2019 (COVID-19) pandemic, with age-specific mortality rates of high importance because of the concentration of COVID-19 deaths at older ages. Population counts, the principal denominators for calculating age-specific mortality rates, will be subject to noise infusion in the United States with the 2020 census through a disclosure avoidance system based on differential privacy. Using empirical COVID-19 mortality curves, the authors show that differential privacy will introduce substantial distortion in COVID-19 mortality rates, sometimes causing mortality rates to exceed 100 percent, hindering our ability to understand the pandemic. This distortion is particularly large for population groupings with fewer than 1,000 persons: 40 percent of all county-level age-sex groupings and 60 percent of race groupings. The U.S. Census Bureau should consider a larger privacy budget, and data users should consider pooling data to minimize differential privacy’s distortion.
Cloud Versus Edge Deployment Strategies of Real-Time Face Recognition Inference

Choosing the appropriate deployment strategy for any Deep Learning (DL) project in a production environment has always been the most challenging problem for industrial practitioners. There are several conflicting constraints and controversial approaches when it comes to deployment. Among these problems, the deployment on cloud versus the deployment on edge represents a common dilemma. In a nutshell, each approach provides benefits where the other would have limitations. This paper presents a real-world case study on deploying a face recognition application using MTCNN detector and FaceNet recognizer. We report the challenges faced to decide on the best deployment strategy. We propose three inference architectures for the deployment, including cloud-based, edge-based, and hybrid. Furthermore, we evaluate the performance of face recognition inference on different cloud-based and edge-based GPU platforms. We consider different models of Jetson boards for the edge (Nano, TX2, Xavier NX, Xavier AGX) and various GPUs for the cloud (GTX 1080, RTX 2080Ti, RTX 2070, and RTX 8000). We also investigate the effect of deep learning model optimization using TensorRT and TFLite compared to a standard Tensorflow GPU model, and the effect of input resolution. We provide a benchmarking study for all these devices in terms of frames per second, execution times, energy and memory usages. After conducting a total of 294 experiments, the results demonstrate that the TensorRT optimization provides the fastest execution on all cloud and edge devices, at the expense of significantly larger energy consumption (up to +40% and +35% for edge and cloud devices, respectively, compared to Tensorflow). Whereas TFLite is the most efficient framework in terms of memory and power consumption, while providing significantly less (-4% to -62%) processing acceleration than TensorRT. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Practitioners Note:</i> The study reported in this paper presents the real-challenges that we faced during our development and deployment of a face-recognition application both on the edge and on the cloud, and the solutions we have developed to solve these problems. The code, results, and interactive analytic dashboards of this paper will be put public upon publication.
New Insights into the Origin of the World-Class Jinding Sediment-Hosted Zn-Pb Deposit, Southwestern China: Evidence from LA-ICP-MS Analysis of Individual Fluid Inclusions

Abstract The Jinding deposit in the Lanping basin, southwest China, is the largest sandstone-hosted Zn deposit in the world and the second largest Zn-Pb deposit in China. However, questions related to the metal compositions and origin of the ore fluids remain. In this study, microthermometry and laser ablation-inductively coupled plasmamass spectrometry (LA-ICP-MS) were employed to determine the properties and compositions of individual fluid inclusions trapped in sphalerite and calcite. The results show that the fluid inclusions trapped in sphalerite and calcite have similar homogenization temperatures (79°–173°C with the majority 100°–130°C), salinities (10.3–29.1 wt % NaCl + CaCl2 equiv with the majority 24.5–27.4 wt % NaCl + CaCl2 equiv), and concentrations of alkali and alkali earth elements (e.g., Na, Ca, Mg, K, Sr, Ba, Li, Rb, and Cs). However, the concentrations of ore and associated metals (e.g., Pb, Sb, Ag, and Tl) in the fluid inclusions hosted by sphalerite are significantly higher than those hosted by calcite. Based on these observations, we propose that the sulfides including sphalerite were precipitated from a low-temperature, high-salinity, Ca-rich, metal-rich fluid, while the gangue minerals such as calcite crystallized subsequently from fluids depleted in metals due to prior precipitation of sulfides, and that the high salinities of the fluid inclusions are likely due to a combination of seawater evaporation and subsequent dissolution of evaporitic sequences during fluid percolation. The LA-ICP-MS analyses reveal that the fluid inclusions have K/Na, Rb/Na, and Cs/Na ratios within the range of modern basinal brines, and Li/Na, Ba/Na, and Ca/Na ratios share similar compositions with the ore fluids of basement interacted deposits in the world. The Jinding ore fluids contain ~200 to 650 ppm Pb, based on the data of fluid inclusions trapped in sphalerite. The estimated concentrations of Zn in the ore fluids are also very high at ~200 to 6,500 ppm. Our results reveal that anomalously metal rich fluids played a critical role in the formation of the giant Jinding sediment-hosted Pb-Zn deposit. We concur with the previous suggestion that sulfide precipitation at Jinding occurred when ascending metal-rich brines encountered an H2S-rich, Ca-rich fluid, which was produced by interaction of hydrocarbons with evaporites, in the cap of the Jinding dome.
Investigating the Bilateral Connections in Generative Zero-Shot Learning

Zero-shot learning (ZSL) is a pretty intriguing topic in the computer vision community since it handles novel instances and unseen categories. In a typical ZSL setting, there is a main visual space and an auxiliary semantic space. Most existing ZSL methods handle the problem by learning either a visual-to-semantic mapping or a semantic-to-visual mapping. In other words, they investigate a unilateral connection from one end to the other. However, the connection between the visual space and the semantic space are bilateral in reality, that is, the visual space depicts the semantic space; the semantic space, on the other hand, describes the visual space. In this article, therefore, we investigate the bilateral connections in ZSL and present a novel model, called Boomerang-GAN, by taking advantage of conditional generative adversarial networks (GANs). Specifically, we generate unseen visual samples from their category semantic embeddings by a conditional GAN. Different from the existing generative ZSL methods that only consider generating visual features from class descriptions, our method also considers that the generated visual features can be translated back to their corresponding semantic embeddings by introducing a multimodal cycle-consistent loss. Extensive experiments of both ZSL and generalized ZSL on five widely used datasets verify that our method is able to outperform previous state-of-the-art approaches in both recognition and segmentation tasks.
Automatic Modulation Recognition Based on a DCN-BiLSTM Network

Automatic modulation recognition (AMR) is a significant technology in noncooperative wireless communication systems. This paper proposes a deep complex network that cascades the bidirectional long short-term memory network (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of the traditional convolutional neural network (CNN) loses the partial phase information of the modulated signal, resulting in low recognition accuracy, we first apply a deep complex network (DCN) to extract the features of the modulated signal containing phase and amplitude information. Then, we cascade bidirectional long short-term memory (BiLSTM) layers to build a bidirectional long short-term memory model according to the extracted features. The BiLSTM layers can extract the contextual information of signals well and address the long-term dependence problems. Next, we feed the features into a fully connected layer. Finally, a softmax classifier is used to perform classification. Simulation experiments show that the performance of our proposed algorithm is better than that of other neural network recognition algorithms. When the signal-to-noise ratio (SNR) exceeds 4 dB, our model’s recognition rate for the 11 modulation signals can reach 90%.
A quantum router architecture for high-fidelity entanglement flows in quantum networks

The past decade has seen tremendous progress in experimentally realizing the building blocks of quantum repeaters. Repeater architectures with multiplexed quantum memories have been proposed to increase entanglement distribution rates, but an open challenge is to maintain entanglement fidelity over long-distance links. Here, we address this with a quantum router architecture comprising many quantum memories connected in a photonic switchboard to broker entanglement flows across quantum networks. We compute the rate and fidelity of entanglement distribution under this architecture using an event-based simulator, finding that the router improves the entanglement fidelity as multiplexing depth increases without a significant drop in the entanglement distribution rate. Specifically, the router permits channel-loss-invariant fidelity, i.e. the same fidelity achievable with lossless links. Furthermore, this scheme automatically prioritizes entanglement flows across the full network without requiring global network information. The proposed architecture uses present-day photonic technology, opening a path to near-term deployable multi-node quantum networks.
Research on Supply Chain Financial Risk Assessment Based on Blockchain and Fuzzy Neural Networks

With the development of supply chain finance, the credit risk of small- and medium-sized financing enterprises from the perspective of supply chain finance has arisen. Risk management is one of the key tasks of the credit business of banks and other financial institutions, which runs through all aspects of the credit business before, during, and after the loan. This article combines blockchain and fuzzy neural network algorithms to study the credit risk of SME financing from the perspective of supply chain finance. This article builds a supply chain financial system through blockchain technology and integrates supply chain financial information into blocks. The fuzzy neural network algorithm is used for financial data processing and risk assessment, effectively solving and improving the risk processing level of the supply chain. Through further simulation, the application effect of blockchain and machine learning algorithms in the supply chain financial system was verified.
Exploring Text-based Emotions Recognition Machine Learning Techniques on Social Media Conversation

Emotions hold a paramount role in the conversation, as it expresses context to the conversation. Text/word in conversation consists of lexical and contextual meanings. Extracting emotions from text has been an interesting work recent thees years. With the advancement of machine learning techniques and hardware to support the machine learning process, recognising emotions from a text with machine learning provides promising and significant results. This research aims to explore several popular machine learning to recognise emotions from a conversation in social media. The algorithms proposed in this research are ranged from traditional machine learning to deep learning techniques. The dataset used in this paper is provided by AffectiveTweets, with a baseline of F1Score of 0.71 with word N-grams and SentiStrength. The research contributes extensive explorations in a number of machine learning algorithms, resulting in a total of 2302 features sets were explored, where each features sets has 100-1000 features extracted from the text. The results demonstrate Generalised Linear Model provides the best Accuracy score (0.92), Recall (0.902), Precision (0.902), F1 score (0.901) with standard deviation of accuracy of ±1,2%.
Confidence Guided Stereo 3D Object Detection with Split Depth Estimation

Accurate and reliable 3D object detection is vital to safe autonomous driving. Despite recent developments, the performance gap between stereo-based methods and LiDAR-based methods is still considerable. Accurate depth estimation is crucial to the performance of stereo-based 3D object detection methods, particularly for those pixels associated with objects in the foreground. Moreover, stereo-based methods suffer from high variance in the depth estimation accuracy, which is often not considered in the object detection pipeline. To tackle these two issues, we propose CG-Stereo, a confidence-guided stereo 3D object detection pipeline that uses separate decoders for foreground and background pixels during depth estimation, and leverages the confidence estimation from the depth estimation network as a soft attention mechanism in the 3D object detector. Our approach outperforms all state-of-the-art stereo-based 3D detectors on the KITTI benchmark.
Going deeper: magnification‐invariant approach for breast cancer classification using histopathological images

Breast cancer has the highest fatality for women compared with other types of cancer. Generally, early diagnosis of cancer is crucial to increase the chances of successful treatment. Early diagnosis is possible through physical examination, screening, and obtaining a biopsy of the dubious area. In essence, utilizing histopathology slides of biopsies is more efficient than using typical screening methods. Nevertheless, the diagnosing process is still tiresome and is prone to human error during slide preparation, such as when dyeing and imaging. Therefore, a novel method is proposed for diagnosing breast cancer into benign or malignant in a magnification-specific binary (MSB) classification. Besides, the introduced method classifies each type into four subclasses in a magnification-specific multi-category (MSM) fashion. The proposed method involves normalizing the hematoxylin and eosin stains to enhance colour separation and contrast. Then, two types of novel features—deep and shallow features—are extracted using two deep structure networks based on DenseNet and Xception. Finally, a multi-classifier method based on the maximum value is utilized to achieve the best performance. The proposed method is evaluated using the BreakHis histopathology data set, and the results in terms of diagnostic accuracy are promising, achieving 99% and 92% in terms of MSB and MSM, respectively, compared with recent state-of-the-art methods reported in the survey conducted by Benhammou on the BreakHis data set using deep learning and texture-based models.
Estimation of remaining fatigue life under two-step loading based on kernel-extreme learning machine

Remaining fatigue life estimations are not trivial problems for most engineering applications. They could even become quite challenging for general multistep load spectrums, particularly when elastoplastic stresses and strains are involved. In such case, the remaining life predictions could be very sensitive to the chosen damage indicators, leading to complex and non-uniform processes to model non-linear behavior of the materials which exhibit different characteristics for fatigue damage accumulation. To overcome this problem, a data-driven model based upon the kernel-extreme learning machine is proposed to estimate the remaining life of materials under two-step loading. Different from conventional empirical damage models, the proposed model can automatically acquire the optimal mapping relationship from the training samples, which is a quite versatile and flexible method to mathematically describe the indications of fatigue damage mechanism. Moreover, to maintain the basic physical rationality and good measurability, the input variables can also be referenced from conventional damage models. Extensive experimental results of nine materials under two-step loading are collected from the open literature and are used to validate the proposed model. It is shown that the proposed model can provide a much better estimation of the remaining life against the other three conventional models and the standard extreme learning machine (ELM)-based model.
Augmented Memory for Correlation Filters in Real-Time UAV Tracking

The outstanding computational efficiency of discriminative correlation filter (DCF) fades away with various complicated improvements. Previous appearances are also gradually forgotten due to the exponential decay of historical views in traditional appearance updating scheme of DCF framework, reducing the model's robustness. In this work, a novel tracker based on DCF framework is proposed to augment memory of previously appeared views while running at real-time speed. Several historical views and the current view are simultaneously introduced in training to allow the tracker to adapt to new appearances as well as memorize previous ones. A novel rapid compressed context learning is proposed to increase the discriminative ability of the filter efficiently. Substantial experiments on UAVDT and UAV123 datasets have validated that the proposed tracker performs competitively against other 26 top DCF and deep-based trackers with over 40 FPS on CPU.
Blockchain technology as an enabler of consumer trust: A text mining literature analysis

Blockchain (BC) technology is gaining momentum in a variety of application domains, beyond finance. We present a text mining literature analysis of a body of published articles queried in the Scopus database, regarding BC technology and consumer trust. We applied a semiautomated text mining and topic modelling approach: we mix top-down and bottom-up procedures to align the existing literature on BC taxonomies with the gathered articles' list of keywords; we then feed automatically the latent Dirichlet allocation (LDA) algorithm to uncover relevant topics enabling to analyse the existing body of knowledge. Our analysis highlights the multidisciplinary nature of BC research within consumer trust. Among others, findings show pertinent aspects to consumer trust, such as traceability and privacy, are receiving only marginal attention from scholars. Our analysis also reveals the marketing, social and economic sciences’ researchers should devote efforts to the application of BC and its impact to consumer trust. We provide future research trends we deem crucial to be addressed regarding sustainable blockchain trustability.
Modified Magnitude-Phase Spectrum Information for Spoofing Detection

Most of the existing feature representations for spoofing countermeasures consider information either from the magnitude or phase spectrum. We hypothesize that both magnitude and phase spectra can be beneficial for spoofing detection (SD) when collectively used to capture the signal artifacts. In this work, we propose a novel feature referred to as modified magnitude-phase spectrum (MMPS) to capture both magnitude and phase information from the speech signal. The constant-Q transform is used to obtain the magnitude and phase information in terms of MMPS, which can be denoted as CQT-MMPS. We then use this information for the proposal of a handcrafted feature, namely, constant-Q modified octave coefficients (CQMOC). To evaluate the proposed CQT-MMPS and CQMOC features, three classic anti-spoofing models are adopted, including the Gaussian mixture model (GMM), the light CNN (LCNN) and the ResNet. Additionally, since there is usually no prior knowledge about the spoofing kind in real-world applications, two novel methods referred to as three-class classifiers with maximum spoofing-score (TCMS) and multi-task learning (MTL) are designed for unknown-kind SD (UKSD). The experimental results on ASVspoof 2019 corpus show that CQMOC outperforms most of the commonly-used handcrafted features, and the CQT-based MMPS performs better than the magnitude-phase spectrum and the commonly-used log power spectrum. Further, the MMPS-based systems can achieve comparable or even better performance when compared with the state-of-the-art systems. We find that the newly-designed TCMS and MTL methods outperform the combination-based method for UKSD and meanwhile, generalize much better than the respective-kind-based methods in cross-spoofing-kind evaluation scenarios.
Edge-Network-Assisted Real-Time Object Detection Framework for Autonomous Driving

Computer vision tasks such as object detection are crucial for the operations of autonomous vehicles (AVs). Results of many tasks, even those requiring high computational power, can be obtained within a short delay by offloading them to edge clouds. However, although edge clouds are exploited, real-time object detection cannot always be guaranteed due to dynamic channel quality. To mitigate this problem, we propose an edge-network-assisted real-time object detection framework (EODF). In an EODF, AVs extract the region of interest (Rols) of the captured image when the channel quality is not sufficiently good for supporting real-time object detection. Then AVs compress the image data on the basis of the Rols and transmit the compressed one to the edge cloud. In so doing, real-time object detection can be achieved due to the reduced transmission latency. To verify the feasibility of our framework, we evaluate the probability that the results of object detection are not received within the inter-frame duration (i.e., outage probability) and their accuracy. From the evaluation, we demonstrate that the proposed EODF provides the results to AVs in real time and achieves satisfactory accuracy.
Multimodal Sensor Fusion with Differentiable Filters

Leveraging multimodal information with recursive Bayesian filters improves performance and robustness of state estimation, as recursive filters can combine different modalities according to their uncertainties. Prior work has studied how to optimally fuse different sensor modalities with analytical state estimation algorithms. However, deriving the dynamics and measurement models along with their noise profile can be difficult or lead to intractable models. Differentiable filters provide a way to learn these models end-to-end while retaining the algorithmic structure of recursive filters. This can be especially helpful when working with sensor modalities that are high dimensional and have very different characteristics. In contact-rich manipulation, we want to combine visual sensing (which gives us global information) with tactile sensing (which gives us local information). In this paper, we study new differentiable filtering architectures to fuse heterogeneous sensor information. As case studies, we evaluate three tasks: two in planar pushing (simulated and real) and one in manipulating a kinematically constrained door (simulated). In extensive evaluations, we find that differentiable filters that leverage crossmodal sensor information reach comparable accuracies to unstructured LSTM models, while presenting interpretability benefits that may be important for safety-critical systems. We also release an open-source library for creating and training differentiable Bayesian filters in PyTorch, which can be found on our project website: https://sites.google.com/view/multimodalfilter.