Reviewerly/exHarmony huggingface dataset:
Enhancing transferability and discriminability simultaneously for unsupervised domain adaptation

Unsupervised domain adaptation aims to transfer knowledge from the labeled source domain to the unlabeled target domain. In recent studies, deep learning based unsupervised domain adaptation methods have achieved promising progress. However, most methods use a shared feature extractor in the source and target domains. Due to the large domain gap, the generated domain-invariant features may still contain some domain-specific information, which affects the transferability. In addition, the generated features change the geometric structure of the original feature space, causing the loss of discriminability. To this end, we propose an Enhancing Transferability and Discriminability Simultaneously (ETDS) method. Specifically, ETDS contains two domain-specific modules to explicitly capture domain-specific information, which contributes to preserving common characteristics in domain-invariant features, thereby enhancing transferability. Besides, ETDS enhances discriminability by forcing the features and their corresponding prototypes closer. In particular, we propose a new method for constructing prototypes without using ground-truth labels or pseudo-labels, and propose a balance strategy to control the relative contribution of source prototypes and target prototypes. Experiments are conducted on four widely used datasets, and the results show that our method outperforms recent domain adaptation methods, especially on DomainNet, the hardest domain adaptation dataset by far. And we employ spectral analysis to intuitively show the enhanced transferability and discriminability of generated features.
Blockchain and Other Distributed Ledger Technologies, an Advanced Primer

Although there are many versions of blockchain technology today, it was first introduced in 2008 as the technology supporting Bitcoin, the first successful virtual currency system. Yet in and of itself, blockchain technology is much more than the underpinning for Bitcoin (and other cryptocurrencies) and has found many applications beyond its initial purpose. The goal of this advanced primer is to review the current state of this technology and to discuss some of its advantages and drawbacks in settings beyond cryptocurrencies.
Automation of Citation Screening for Systematic Literature Reviews Using Neural Networks: A Replicability Study

In the process of Systematic Literature Review, citation screening is estimated to be one of the most time-consuming steps. Multiple approaches to automate it using various machine learning techniques have been proposed. The first research papers that apply deep neural networks to this problem were published in the last two years. In this work, we conduct a replicability study of the first two deep learning papers for citation screening [8, 16] and evaluate their performance on 23 publicly available datasets. While we succeeded in replicating the results of one of the papers, we were unable to replicate the results of the other. We summarise the challenges involved in the replication, including difficulties in obtaining the datasets to match the experimental setup of the original papers and problems with executing the original source code. Motivated by this experience, we subsequently present a simpler model based on averaging word embeddings that outperforms one of the models on 18 out of 23 datasets and is, on average, 72 times faster than the second replicated approach. Finally, we measure the training time and the invariance of the models when exposed to a variety of input features and random initialisations, demonstrating differences in the robustness of these approaches.
Extending CLIP for Category-to-Image Retrieval in E-Commerce

E-commerce provides rich multimodal data that is barely leveraged in practice. One aspect of this data is a category tree that is being used in search and recommendation. However, in practice, during a user’s session there is often a mismatch between a textual and a visual representation of a given category. Motivated by the problem, we introduce the task of category-to-image retrieval in e-commerce and propose a model for the task, CLIP-ITA. The model leverages information from multiple modalities (textual, visual, and attribute modality) to create product representations. We explore how adding information from multiple modalities (textual, visual, and attribute modality) impacts the model’s performance. In particular, we observe that CLIP-ITA significantly outperforms a comparable model that leverages only the visual modality and a comparable model that leverages the visual and attribute modality.
Asynchronous Information Fusion in Intelligent Driving Systems for Target Tracking Using Cameras and Radars

In this article, an asynchronous information fusion issue is investigated for a camera and a radar in an intelligent driving system. Local camera and radar estimators with missed detections are developed independently at synchronized state update time for target tracking. A Kuhn–Munkers algorithm is used to match local tracking results of camera and radar for fusion estimation of a same target. A fusion estimator is obtained by a matrix-weighted fusion algorithm with wide detection range and reliable fused estimates. Effectiveness of the proposed asynchronous fusion estimator is displayed by experimental results on road vehicle tracking.
Graph regularization multidimensional projection

This paper introduces a novel multidimensional projection method of datasets. Our method called Graph Regularization Multidimensional Projection (GRMP) is based on a technique from the graph signal processing theory, the graph regularization. Initially, a similarity graph is built on the high-dimensional space where the dataset lies. A two-dimensional distribution of points is then created in the visual space using a phyllotactic distribution. The similarity graph is copied properly over the phyllotactic distribution and the graph regularization is applied to their coordinates, which are interpreted as graph signals. The graph regularization reorganizes the phyllotactic distribution by bringing together points that represent similar data in the high-dimensional space. We employ synthetic and real datasets to demonstrate the effectiveness of our method. Furthermore, since the solution of the graph regularization can still be approximated using a fast approximation mechanism based on the Chebyshev polynomials, our method is computationally efficient even for large graphs.
A machine-learning-based method of detecting and picking the first <i>P</i>-wave arrivals of acoustic emission events in laboratory experiments

SUMMARY Detecting and picking the first P-wave arrivals of seismic events in seismograms is fundamental in observational seismology. Recently, several machine-learning-based algorithms have been developed to incorporate human expertise for picking P-wave arrival times automatically. One shortcoming of these models is that they pick arrival times at individual seismic stations separately, which need to be sorted and associated to identify the seismic event. Also, most of them rely on existence of P-wave arrivals in the seismograms to be picked. Here, we developed a machine-learning-based seismic event detection and P-wave arrival time picking method called MultiNet and applied it to acoustic emission (AE) waveform data recorded in laboratory experiments. The MultiNet uses 2-D waveform images from multichannel AE recordings as the input to a convolutional neural network (CNN) to detect whether there is an AE event in an image and, if so, uses a fully convolutional neural network (FCN) to pick the P-wave arrival time at each channel in the image. We tested the MultiNet using 550 known AE events recorded during syn-deformational phase transformation from olivine to spinel in Mg2GeO4 (an analogue to Mg2SiO4) in a high-pressure experiment. Waveform data of 50 events were used to train the neural networks and the rest of data were used to validate the method. At the optimal image length and detection threshold, the CNN was able to detect all 500 known events plus 48 more events missed previously. Overall, 98.7 per cent of P-wave arrival times picked by the FCN were within 0.5 $\mu$s from the manually picked times. The average picking errors at different channels range from 0.01 ± 0.05 to −0.06 ± 0.22 $\mu$s. Our method greatly reduces the amount of human labour in picking P-wave arrival times for event location and source moment tensor inversion. It can be easily adapted to process continuous waveform data of a seismic network for earthquake detection and location in real time.
Grateful Med: Direct Access to MEDLINE for Health Professionals with Personal Computers

Donald A.B. Lindberg M.D. arrived as Director, U.S. National Library of Medicine (NLM) in late 1984 with the intention of implementing a physician-friendly interface to MEDLINE, a prime example of his interest in making NLM information services more directly useful in medical care. By early 1986, NLM's Grateful Med, an inexpensive PC search interface to MEDLINE useful for health professionals, had joined the group of end-user systems for searching MEDLINE that emerged in the 1980s. This chapter recounts Grateful Med's rapid iterative development and the subsequent campaign to bring it to attention of health professionals. It emphasizes Lindberg's role, the challenges faced by those introducing and using the interface in a pre-Internet world, and some longer-term effects of the effort to expand health professionals' use of MEDLINE during the decade from 1986 to 1996.
Orientation Keypoints for 6D Human Pose Estimation

Most realtime human pose estimation approaches are based on detecting joint positions. Using the detected joint positions, the yaw and pitch of the limbs can be computed. However, the roll along the limb, which is critical for application such as sports analysis and computer animation, cannot be computed as this axis of rotation remains unobserved. In this paper we therefore introduce orientation keypoints, a novel approach for estimating the full position and rotation of skeletal joints, using only single-frame RGB images. Inspired by how motion-capture systems use a set of point markers to estimate full bone rotations, our method uses virtual markers to generate sufficient information to accurately infer rotations with simple post processing. The rotation predictions improve upon the best reported mean error for joint angles by 48% and achieves 93% accuracy across 15 bone rotations. The method also improves the current state-of-the-art results for joint positions by 14% as measured by MPJPE on the principle dataset, and generalizes well to in-the-wild datasets.
Full polarization random drift compensation method for quantum communication

Long-term quantum key distribution (QKD) using polarization encoding requires a random drift compensation method. We propose a method to compensate any state of polarization based on the quantum bit error rate (QBER) of two states from two non-orthogonal mutually unbiased bases. The proposed method does not require dedicated equipment, and through a simple but highly efficient feedback loop it compensates the polarization random drift suffered by photons while transmitted over the optical fiber quantum channel. A QBER lower than 2% was observed even considering imperfect single photon detectors. Besides, we verify a 82% secret key rate generation improvement in a finite-key size BB84 implementation for a 40 km fiber-optics quantum channel.
Application of Realtime Robotics platform to execute unstructured industrial tasks involving industrial robots, cobots, and human operators

Small and medium-sized craft enterprises are still characterized by production process involving human labour to a large extent. The possibility of a safe interaction of the operator in the collaborative working area of the machine, and the development of an easy-to-program robotic cell, would pave the way to the introduction of automation in such an environment. This paper proposes an application of a commercial platform for collision avoidance, based on Dynamic Road-map algorithms, to execute unstructured industrial tasks involving industrial robots, cobots, and human operators at the same time. An example is presented in a pick-and-place and assembling application, in which the components are feed by a conveyor belt with random order and random flow rate. The adopted platform enhances the possibility to exploit in the same robotic cell both industrial robots and human labour, thus allowing a reduction of costs and more precise applications. Moreover, the possibility to calculate at run-time alternative trajectories to avoid obstacles (both human being and other robot in the cell), avoiding to make the robot temporarily inactive, allows to improve the system productivity.
The Group Loss++: A Deeper Look Into Group Loss for Deep Metric Learning

Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes. Much research has been devoted to the design of smart loss functions or data mining strategies for training such networks. Most methods consider only pairs or triplets of samples within a mini-batch to compute the loss function, which is commonly based on the distance between embeddings. We propose Group Loss, a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting, at the same time, low-density regions amongst data points belonging to different groups. Guided by the smoothness assumption that "similar objects should belong to the same group", the proposed loss trains the neural network for a classification task, enforcing a consistent labelling amongst samples within a class. We design a set of inference strategies tailored towards our algorithm, named Group Loss++ that further improve the results of our model. We show state-of-the-art results on clustering and image retrieval on four retrieval datasets, and present competitive results on two person re-identification datasets, providing a unified framework for retrieval and re-identification.
A Hybrid Training-Time and Run-Time Defense Against Adversarial Attacks in Modulation Classification

Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks.However, several recent works have pointed out that imperceptible and carefully designed adversarial examples (attacks) can significantly deteriorate the classification accuracy.In this paper, we investigate a defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks.The training-time defense consists of adversarial training and label smoothing, while the run-time defense employs a support vector machine-based neural rejection (NR).Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies.
Exploratory Analysis of Different Metaheuristic Optimization Methods for Medical Image Enhancement

Metaheuristic optimization algorithms (MOAs) are popularly deployed for medical image enhancement (MIE) purposes.However, with an ever-increasing rate of newer MOAs being proposed in the literature, the question arises as to whether there exist any significant advantage(s) among these different MOAs, particularly as it pertains to MIE.In this paper, we explore this question by analyzing nine well-known MOAs for MIE, namely the artificial bee colony, cuckoo search, differential evolution, firefly, genetic algorithm, particle swarm optimization (PSO), covariance matrix adaptive evolutionary strategy (CMAES), whale optimization algorithm (WOA), and the grey wolf optimization (GWO) algorithms.First, instead of measuring an MOA's performance based on the number of generations, we adopted the fitness computation rate (FCR), which enables MOAs to be compared in a fairer sense.Secondly, we used a combination of a well-known transformation function and a robust evaluation function as our objective function in the MOAs considered in our study.Then, medical images were obtained from the Medpix database with representative samples selected from across the different parts of the body for MIE evaluation purposes.Within the constraints of the datasets used, the results indicate that, while the GWO and WOA algorithms performed slightly better empirically than the other methods over an average of 1000 Monte Carlo trials, there was little/no statistical significant difference between the other methods.The timing performance also demonstrates that there was no significant difference in the real-time processing speeds of the various MOAs, particularly when evaluated under the same FCR.As a consequence, preliminary findings from our study suggest that employing a range of past and current MOAs or proposing newer MOAs for MIE may not necessarily guarantee substantial comparative enhancement benefits.This might suggest that under high FCR levels, any MOA can be utilized for MIE.
Eavesdropping Detection in BB84 Quantum Key Distribution Protocols

The nature of quantum mechanics provides us with an opportunity to statistically detect eavesdropping in quantum key distribution (QKD) protocols, which is unimaginable in classical digital communications. By utilizing Hoeffding’s inequality, this study analyzes the upper bounds of the false-positive ratio (FPR) and false-negative ratio (FNR) of eavesdropping detection in the Bennett–Brassard-84 (BB84) QKD protocol, where eavesdropping is detected if the measured quantum bit error rate (QBER) is equal to or higher than a threshold. The analysis clarifies the trade-off between the accuracy of eavesdropping detection and the economy of quantum resources in the BB84 protocol. Owing to the central limit theorem, the QBER measured by 300 quantum bits (qubits) is sufficient to guarantee lower than 0.009% of the FPR and FNR of eavesdropping detection. To deal with rapidly varying quantum channel conditions, this study further introduces grouped BB84 protocol and combinatory eavesdropping detection algorithms. A polarization basis is changeable for a group of qubits, and eavesdropping is judged by a combination of criteria between QBER and group-QBER in the proposed protocol and algorithms. In our extensive simulation study, the grouped BB84 protocol with 300 qubits comparison guarantees at least 99.92% accuracy in eavesdropping detection under rapidly varying quantum channel conditions.
Performance analysis of continuous-variable quantum key distribution using non-Gaussian states

In this study, we analyze the efficiency of a protocol with discrete modulation of continuous variable non-Gaussian states, the coherent states having one photon added and then one photon subtracted (PASCS). We calculate the secure key generation rate against collective attacks using the fact that Eve's information can be bounded based on the protocol with Gaussian modulation, which in turn is unconditionally secure. Our results for a four-state protocol show that the PASCS always outperforms the equivalent coherent states protocol under the same environmental conditions. Interestingly, we find that for the protocol using discrete-modulated PASCS, the noisier the line, the better will be its performance compared to the protocol using coherent states. Thus, our proposal proves to be advantageous for performing quantum key distribution in non-ideal situations.
Hybrid system for optimizing the robot mobile navigation using ANFIS and PSO

The important thing about mobile robotics is that its use satisfies rapid movement without collisions. Several methods have been designed for this purpose, but not all of them give the results expected by the user. Our contribution in this article is to allow the mobile robot to take advantage of two methods by the hybridization effect. These methods, implemented are ANFIS “Adaptive Neuro-Flou Inference System” and the PSO “Particle Swarm Optimization”. Learning by the connectionist approach of the ANFIS controller is sufficient to allow the robot to reach its target but remains unsatisfactory. The contribution of the PSO algorithm allows the hybrid controller to optimize, the speed and therefore the positions of robot. The controller thus constituted gives better simulation results of the distances measured in pixels traveled by the hybrid method.
Identity-Based Privacy Preserving Remote Data Integrity Checking With a Designated Verifier

The remote data possession checking mechanism can effectively verify the integrity of outsourced data, which can usually be divided into public verification and private verification. The verifier of public verification can be any cloud user, while private verification can only be the data owner. However, in most practical situations, the data owner expects that only a specific verifier can perform integrity checking tasks and that verifier cannot gain any knowledge about the data. Yan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed a remote data possession checking scheme with the designated verifier, which can guarantee that only the designated verifier can check data integrity, whereas others cannot do it. However, this scheme relies on public key infrastructure technology and does not consider data privacy protection issues. To overcome these shortcomings, we propose an identity-based remote data possession checking scheme that satisfies the data owner’s requirement to specify a unique verifier. Moreover, in this scheme, we use a random integer to blind data integrity proof to protect data privacy and use Merkle hash tree structure to achieve dynamic update of data. At the same time, our scheme can avoid the complex certificate management in public key infrastructure. We proved the safety of our scheme based on the discrete logarithm assumption and the computational Diffie-Hellman assumption. Theoretical analysis and experimental results show that our scheme is feasible and effective in practical applications.
Dual Attention and Question Categorization-Based Visual Question Answering

Visual question answering (VQA) aims at predicting an answer to a natural language question associated with an image. This work focuses on two important issues pertaining to VQA, which is a complex multimodal AI task: First, the task of answer prediction in a large output answer space, and second, to obtain enriched representation through cross-modality interactions. This work aims to address these two issues by proposing a dual attention (DA) and question categorization (QC)-based visual question answering model (DAQC-VQA). DAQC-VQA has three main network modules: First, a novel dual attention mechanism that helps toward the objective of obtaining an enriched cross-domain representation of the two modalities; second, a question classifier subsystem for identifying input (natural language) question category. The second module of question categorizer helps in reducing the answer search space; and third, a subsystem for predicting answer depending on the question category. All component networks of DAQC-VQA are trained in an end-to-end manner with a joint loss function. The performance of DAQC-VQA is evaluated on two widely used VQA datasets, viz., TDIUC and VQA2.0. Experimental results demonstrate competitive performance of DAQC-VQA against the recent state-of-the-art VQA models. An ablation analysis indicates that the enriched representation obtained using the proposed dual-attention mechanism helps improve performance.