Reviewerly/exHarmony huggingface dataset:
Efficient Quantum Circuits for Accurate State Preparation of Smooth, Differentiable Functions

Effective quantum computation relies upon making good use of the exponential information capacity of a quantum machine. A large barrier to designing quantum algorithms for execution on real quantum machines is that, in general, it is intractably difficult to construct an arbitrary quantum state to high precision. Many quantum algorithms rely instead upon initializing the machine in a simple state, and evolving the state through an efficient (i.e. at most polynomial-depth) quantum algorithm. In this work, we show that there exist families of quantum states that can be prepared to high precision with circuits of linear size and depth. We focus on real-valued, smooth, differentiable functions with bounded derivatives on a domain of interest, exemplified by commonly used probability distributions. We further develop an algorithm that requires only linear classical computation time to generate accurate linear- depth circuits to prepare these states, and apply this to well-known and heavily-utilized functions including Gaussian and lognormal distributions. Our procedure rests upon the quantum state representation tool known as the matrix product state (MPS). By efficiently and scalably encoding an explicit amplitude function into an MPS, a high fidelity, linear-depth circuit can directly be generated. These results enable the execution of many quantum algorithms that, aside from initialization, are otherwise depth-efficient.
Least-Energy Path Planning With Building Accurate Power Consumption Model of Rotary Unmanned Aerial Vehicle

Rotary unmanned aerial vehicles (UAVs), also known as drones, have various advantages, yet their actual applications are limited owing to their flight range. However, increasing the flight range by enhancing the hardware is a challenging task. In this study, we introduce the first step of systematic drone low-power optimization based on the framework of electronic design automation (EDA). We attempt drone power management without in-depth knowledge of aerodynamics and control theory. Instead, we introduce a novel power model of drones using physical parameters that can affect power consumption, such as the three-axis velocity and acceleration, drone height, wind velocity, and the weight and volume of payloads. We detail the experimental setup, power modeling, accuracy verification, and optimization for minimum energy paths. We achieved over 90% accuracy in power modeling without depending on aerodynamics. The proposed approach shows the feasibility of energy-aware rotary UAV flight trajectory optimization considering the external forces affecting drones such as wind. The proposed method presents up to 24.01% energy saving through path changes considering external forces.
Spectrum Representation Based on STFT

A new spectrum representation method based on short time Fourier transform (STFT) is proposed. The formula of new spectrum representation is derived base on energy cumulant of short time Fourier transform (EC-STFT), which indicates that EC-STFT has the spectrum characteristics. Simulations on the linear frequency modulation (LFM) signal show that the EC-STFT spectrum is closer to the ideal spectrum curve than FT spectrum. During calculating EC-STFT, a time frequency domain iterative mean threshold (TFD-IMT) denoising method is presented to remove the addictive white Gaussian noise (AWGN), by which the EC-STFT spectrum has better anti-noise capacity than FT spectrum. Theoretical analyses and simulations verify the advantages of the EC-STFT over FT in conditions of low SNR.
Real-time Golf Ball Detection and Tracking Based on Convolutional Neural Networks

This paper focuses on the problem of real-time detection and tracking of a golf ball from video sequences. We propose an efficient and effective solution by integrating object detection and a discrete Kalman model. For ball detection, three classical convolutional neural network based detection models are implemented, including Faster R-CNN, YOLOv3, and YOLOv3 tiny. At the tracking stage, a discrete Kalman filter is employed to predict the location of the golf ball based on the previous observations. To increase the detection accuracy and speed, we propose to use image patches rather than the entire images for detection. In order to train the detection models and test the tracking algorithm, we collect and annotate a collection of golf ball dataset. Extensive experimental results are performed to demonstrate the effectiveness and superior performance of the proposed approach.
Deep learning powers cancer diagnosis in digital pathology

Technological innovation has accelerated the pathological diagnostic process for cancer, especially in digitizing histopathology slides and incorporating deep learning-based approaches to mine the subvisual morphometric phenotypes for improving pathology diagnosis. In this perspective paper, we provide an overview on major deep learning approaches for digital pathology and discuss challenges and opportunities of such approaches to aid cancer diagnosis in digital pathology. In particular, the emerging graph neural network may further improve the performance and interpretability of deep learning in digital pathology.
MaRS: A Modular and Robust Sensor-Fusion Framework

State-of-the-art recursive sensor filtering frameworks allow the fusion of multiple sensors tailored to a specific problem but do not allow a dynamic and efficient introduction of additional sensors during runtime - an important feature to enable long-term missions in dynamic environments. This letter presents a robust, modular sensor-fusion framework that enables the addition and removal of sensors at runtime.These sensors could not be a priori known to the system. The framework handles the complexity of system and sensor initialization, measurement updates, and switching of asynchronous multi-rate sensor information with sensor self-calibration in a truly modular and generic design. In addition, the framework can handle delayed measurements, out-of-sequence updates, and can monitor sensor health. The introduced true-modularity is based on covariance segmentation to allow the isolated (i.e., modular) processing of propagation and updates on a per-sensor basis. We show how crucial properties of the overall state covariance can be maintained as naive implementation of such a modularization would invalidate the covariance matrix. We evaluate our framework for a precision landing scenario switching between combinations of GNSS, barometer, and vision measurements. Tests are performed in simulation and in real-world scenarios to show the validity of the introduced method. The presented framework will be open-sourced and made available online to the community.
Recent advances in processing negation

Abstract Negation is a complex linguistic phenomenon present in all human languages. It can be seen as an operator that transforms an expression into another expression whose meaning is in some way opposed to the original expression. In this article, we survey previous work on negation with an emphasis on computational approaches. We start defining negation and two important concepts: scope and focus of negation. Then, we survey work in natural language processing that considers negation primarily as a means to improve the results in some task. We also provide information about corpora containing negation annotations in English and other languages, which usually include a combination of annotations of negation cues, scopes, foci, and negated events. We continue the survey with a description of automated approaches to process negation, ranging from early rule-based systems to systems built with traditional machine learning and neural networks. Finally, we conclude with some reflections on current progress and future directions.
The Open-Specimen Movement

Abstract The open-science movement seeks to increase transparency, reproducibility, and access to scientific data. As primary data, preserved biological specimens represent records of global biodiversity critical to research, conservation, national security, and public health. However, a recent decrease in specimen preservation in public biorepositories is a major barrier to open biological science. As such, there is an urgent need for a cultural shift in the life sciences that normalizes specimen deposition in museum collections. Museums embody an open-science ethos and provide long-term research infrastructure through curation, data management and security, and community-wide access to samples and data, thereby ensuring scientific reproducibility and extension. We propose that a paradigm shift from specimen ownership to specimen stewardship can be achieved through increased open-data requirements among scientific journals and institutional requirements for specimen deposition by funding and permitting agencies, and through explicit integration of specimens into existing data management plan guidelines and annual reporting.
RTChain

Blockchain technology, whose most successful application is Bitcoin, enables non-repudiation and non-tamperable online transactions without the participation of a trusted central party. As a global ledger, the blockchain achieves the consistency of replica stored on each node through a consensus mechanism. A well-designed consensus mechanism, on one hand, needs to be efficient to meet the high frequency of online transactions. For example, the existing electronic payment systems can handle over 50,000 transactions per second (TPS), while Bitcoin can only handle an average of about 3TPS. On the other hand, it needs to have good security and high fault tolerance; that is, in the case when some nodes are captured by adversaries, the network can still operate normally. In this article, we establish a reputation system, called RTChain, to be integrated into the e-commerce blockchain to achieve a distributed consensus and transaction incentives. The proposed scheme has the following advantages. First, an incentive mechanism is used to influence the consensus behavior of nodes and the transaction behavior of users, which in turn influence the reputation scores of both nodes and users. That is, when a node correctly processes a transaction, it will receive the corresponding reputation value as a reward, and the reputation value will be reduced as punishment not only when the node is dishonest and violates the consensus agreement but also the transaction is not completed as required. Just like electronic transactions in the real world, the higher the reputation of the user, the more likely it is to be selected as the transaction partner. A user with a low reputation will be gradually eliminated in our system because it is difficult to complete the transaction. Second, RTChain uses a verifiable random function to generate the leader in each round, which guarantees fairness for all participants and, unlike PoW, does not consume a large amount of computing resources. Then our consensus mechanism selects the nodes with high reputation scores to reduce the number of nodes participating in the consensus, thus improving the consensus efficiency, so that RTChain’s throughput can reach 4,000TPS. Third, we built a reputation chain to implement the distributed storage and management of reputation. Finally, our consensus mechanism is secure against existing attacks, such as flash attacks, selfish mining attacks, eclipse attacks, and double spending attacks, and allows nodes that participate in the consensus to fail, as long as the reputation of the failure node does not exceed one-third of the total reputation. We build a prototype of RTChain, and the experimental results show that RTChain is promising and deployable for e-commerce blockchains.
Feature concatenation for adversarial domain adaptation

Domain adaptation aims to mitigate the domain gap between the source and target domains so that knowledge can be transferred between domains. There are two key factors that determine the adaptation performance: transferability and discriminability. Transferability depends on the similarity of two domains. With transferability, the model learnt on the source domain can be used in the target domain. Discriminability indicates the separability of different classes. With discriminability, the adapted target features can be classified more accurately. Adversarial domain adaptation methods learn domain-invariant feature representations through adversarial learning. The domain-invariant feature representation guarantees the transferability. However, to obtain domain-invariant features, certain domain-specific information is suppressed, which may cause the loss of discriminability. To this end, we aim to enhance the discriminability by enriching the information contained in the domain-invariant features. We propose a Feature Concatenation for adversarial Domain Adaptation (FCDA) method. FCDA learns two feature extractors that can generate two different feature views for a sample. The concatenation of these two views is used as the feature representation of a sample, which we call the concatenation feature. Distribution alignment is performed on the concatenation features. We find that when the distributions of the concatenation features are aligned, the two feature views involved in a concatenation feature have different distributions. Thus, the concatenation feature contains more discriminative information, thereby enhancing the discriminative ability of the domain-invariant features. Experiments are carried out on four widely used datasets and FCDA exceeds some recent domain adaptation methods.
A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data

Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and others. However, human behavior analysis using the accelerometer and gyroscope data are typically grounded on supervised classification techniques, where models are showing sub-optimal performance for qualitative and quantitative features. Considering this factor, this paper proposes an efficient and reduce dimension feature extraction model for human activity recognition. In this feature extraction technique, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal using frequency domain analysis which is more robust and noise insensitive. The Linear Discriminant Analysis (LDA) is used as dimensionality reduction procedure to extract the minimum number of discriminant features from envelop spectrum for human activity recognition (HAR). The extracted features are used for human activity recognition using Multi-class Support Vector Machine (MCSVM). The proposed model was evaluated by using two benchmark datasets, i.e., the UCI-HAR and DU-MD datasets. This model is compared with other state-of-the-art methods and the model is outperformed.
<i>e-TLD</i>: Event-Based Framework for Dynamic Object Tracking

This paper presents a long-term object tracking framework with a moving event camera under general tracking conditions. A first of its kind for these revolutionary cameras, the tracking framework uses a discriminative representation for the object with online learning, and detects and re-tracks the object when it comes back into the field-of-view. One of the key novelties is the use of an event-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local sliding window</i> technique that tracks reliably in scenes with cluttered and textured background. In addition, Bayesian bootstrapping is used to assist real-time processing and boost the discriminative power of the object representation. On the other hand, when the object re-enters the field-of-view of the camera, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-driven, global sliding window</i> detector locates the object for subsequent tracking. Extensive experiments demonstrate the ability of the proposed framework to track and detect arbitrary objects of various shapes and sizes, including dynamic objects such as a human. This is a significant improvement compared to earlier works that simply track objects as long as they are visible under simpler background settings. Using the ground truth locations for five different objects under three motion settings, namely translation, rotation and 6-DOF, quantitative measurement is reported for the event-based tracking framework with critical insights on various performance issues. Finally, real-time implementation in C++ highlights tracking ability under scale, rotation, view-point and occlusion scenarios in a lab setting.
Effect Digital Learning on Student Motivation during Covid-19

This study aims to measure the level of student motivation in using digital learning during Covid-19.In conducting this research, the structural equation modeling method is used by using the Partial Least Square (PLS) theory, the software used is SmartPLS 2.0.with the number of responding 190 students.From the proposed hypothesis, there is a significant value, namely digital learning has a positive impact on student learning motivation.The novelty of this study explores the impact of digital learning on student learning motivation during the Covid-19 pandemic, in which few researchers conducted this research.
Target attack on biomedical image segmentation model based on multi-scale gradients

Research shows that deep neural networks are vulnerable to adversarial examples due to the highly linear nature of deep neural networks (DNNs). Therefore, adversarial examples involve security of deep learning. However, there is a lack of research on the impact of adversarial examples on the biomedical segmentation model. Since a large part of medical image problems are segmentation problems, this paper analyzes the impact of adversarial examples on image segmentation models based on deep learning. We propose to fool the biomedical segmentation model and generate target segmentation masks with feature space perturbations and cross-entropy loss function. Different from the traditional gradient-based attack methods, which usually use the gradient of the final loss function, this paper adopts a Multi-scale Attack (MSA) method based on multi-scale gradients. Extensive experiments to attack U-Net on the ISIC skin lesion segmentation challenge dataset and the glaucoma optic disc segmentation dataset have proved that the predicted mask generated by this method has a high intersection over union(IoU) and pixel accuracy with the target mask. Besides, the L2 and L∞ distances between the adversarial and clean examples are reduced compared with the state-of-the-art method.
An Optimized Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks

Abstract Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation through time (BPTT) which is prohibitively expensive, especially when the length of the time dependencies and the number of hidden neurons increase. To reduce the training time, extreme learning machines (ELMs) have been recently applied to RNN training, reaching a 99% speedup on some applications. Due to its non-iterative nature, ELM training, when parallelized, has the potential to reach higher speedups than BPTT. In this work, we present Opt-PR-ELM, an optimized parallel RNN training algorithm based on ELM that takes advantage of the GPU shared memory and of parallel QR factorization algorithms to efficiently reach optimal solutions. The theoretical analysis of the proposed algorithm is presented on six RNN architectures, including LSTM and GRU, and its performance is empirically tested on ten time-series prediction applications. Opt-PR-ELM is shown to reach up to 461 times speedup over its sequential counterpart and to require up to 20x less time to train than parallel BPTT. Such high speedups over new generation CPUs are extremely crucial in real-time applications and IoT environments.
A Blockchain Solution for Enhancing Cybersecurity Defence of IoT

The growth of IoT devices during the last decade has led to the development of smart ecosystems, such as smart homes, prone to cyberattacks. Traditional security methodologies support to some extend the requirement for preserving privacy and security of such deployments, but their centralized nature in conjunction with low computational capabilities of smart home gateways make such approaches not efficient. Last achievements on blockchain technologies allowed the use of such decentralized architectures to support cybersecurity defence mechanisms. In this work, a blockchain framework is presented to support the cybersecurity mechanisms of smart homes installations, focusing on the immutability of users and devices that constitute such environments. The proposed methodology provides also the appropriate smart contracts support for ensuring the integrity of the smart home gateway and IoT devices, as well as the dynamic and immutable management of blocked malicious IPs. The framework has been deployed on a real smart home environment demonstrating its applicability and efficiency.
A Novel ( <i>t</i> , <i>s</i> , <i>k</i> , <i>n</i> )-Threshold Visual Secret Sharing Scheme Based on Access Structure Partition

Visual secret sharing (VSS) is a new technique for sharing a binary image into multiple shadows. For VSS, the original image can be reconstructed from the shadows in any qualified set, but cannot be reconstructed from those in any forbidden set. In most traditional VSS schemes, the shadows held by participants have the same importance. However, in practice, a certain number of shadows are given a higher importance due to the privileges of their owners. In this article, a novel ( t , s , k , n )-threshold VSS scheme is proposed based on access structure partition. First, we construct the basis matrix of the proposed ( t , s , k , n )-threshold VSS scheme by utilizing a new access structure partition method and sub-access structure merging method. Then, the secret image is shared by the basis matrix as n shadows, which are divided into s essential shadows and n - s non-essential shadows. To reconstruct the secret image, k or more shadows should be collected, which include at least t essential shadows; otherwise, no information about the secret image can be obtained. Compared with related schemes, our scheme achieves a smaller shadow size and a higher visual quality of the reconstructed image. Theoretical analysis and experiments indicate the effectiveness of the proposed scheme.