Reviewerly/exHarmony huggingface dataset:
Approaching the Real-World

Recently, IMUTube introduced a paradigm change for bootstrapping human activity recognition (HAR) systems for wearables. The key idea is to utilize videos of activities to support training activity recognizers based on inertial measurement units (IMUs). This system retrieves video from public repositories and subsequently generates virtual IMU data from this. The ultimate vision for such a system is to make large amounts of weakly labeled videos accessible for model training in HAR and, as such, to overcome one of the most pressing issues in the field: the lack of significant amounts of labeled sample data. In this paper we present the first in-detail exploration of IMUTube in a realistic assessment scenario: the analysis of free-weight gym exercises. We make significant progress towards a flexible, fully-functional IMUTube system by extending it such that it can handle a range of artifacts that are common in unrestricted online videos, including various forms of video noise, non-human poses, body part occlusions, and extreme camera and human motion. By overcoming these real-world challenges, we are able to generate high-quality virtual IMU data, which allows us to employ IMUTube for practical analysis tasks. We show that HAR systems trained by incorporating virtual sensor data generated by IMUTube significantly outperform baseline models trained only with real IMU data. In doing so we demonstrate the practical utility of IMUTube and the progress made towards the final vision of the new bootstrapping paradigm.
Reachable sets for two-level open quantum systems driven by coherent and incoherent controls

In this work, we study controllability in the set of all density matrices for a two-level open quantum system driven by coherent and incoherent controls. In [A. Pechen, Phys. Rev. A 84, 042106 (2011)] an approximate controllability, i.e., controllability with some precision, was shown for generic $N$-level open quantum systems driven by coherent and incoherent controls. However, the explicit formulation of this property, including the behavior of this precision as a function of transition frequencies and decoherence rates of the system, was not known. The present work provides a rigorous analytical study of reachable sets for two-level open quantum systems. First, it is shown that for $N=2$ the presence of incoherent control does not affect the reachable set (while incoherent control may affect the time necessary to reach particular state). Second, the reachable set in the Bloch ball is described and it is shown that already just for one coherent control any point in the Bloch ball can be achieved with precision $\delta\sim \gamma/\omega$, where $\gamma$ is the decoherence rate and $\omega$ is the transition frequency. Typical values are $\delta\lesssim10^{-3}$ that implies high accuracy of achieving any density matrix. Moreover, we show that most points in the Bloch ball can be exactly reached, except of two lacunae of size $\sim\delta$. For two coherent controls, the system is shown to be completely controllable in the set of all density matrices. Third, the reachable set as a function of the final time is found and shown to exhibit a non-trivial structure.
Parameter selection for CLAHE using multi-objective cuckoo search algorithm for image contrast enhancement

Contrast enhancement techniques that are proposed in the literature are devised to enhance image quality so as to provide better details for different image processing tasks. Histogram Equalization (HE) is a widely exploited technique to enhance contrast of the images. Histograms are used to measure the frequency of intensity levels in an image. The objective of HE is to distribute the intensity values in an image such that the lower contrast areas can gain higher contrast. HE techniques can be global or local. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a local HE method. Thus, CLAHE can handle the problems of the classical HE algorithm. CLAHE employs two parameters, which are, respectively, number of tiles and clip limit. The efficiency of the CLAHE depends on these two parameters. Hence, the results can be changed if the parameters of the CLAHE is adjusted. The value of the optimal parameters can vary according to the image type. In this paper, Multi-objective Cuckoo Search (MOCS) is employed to determine optimal parameters for CLAHE to enhance the contrast of images. For MOCS, first fitness function is set for computing Entropy and second fitness function is set for computing Fast Noise Variance Estimation (FNVE) so as to provide a good local detail preservation and prevent noise amplification in the output image. Image dataset is taken from Contrast Enhancement Evaluation Database (CEED2016), which can be found in Mendeley data repository. This database is used for evaluating the overall performance of the proposed method. Evaluation results of the proposed method are given in terms of Absolute Mean Brightness Error (AMBE), Peak Signal-to-noise Ratio (PSNR), Mean Squared Error (MSE), Maximum Difference (MD), Mean Absolute Error (MAE) and Computing Time (CT) .
Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

Artificial potential field (APF) is the effective real-time guide, navigation, and obstacle avoidance for UAV Quadrotor. The main problem in APF is local minima in an obstacle or multiple obstacles. In this paper, some modifications and improvements of APF will be introduced to solve one-obstacle local minima, two-obstacle local minima, Goal Not Reachable Near Obstacle (GNRON) and dynamic obstacle. The result shows that the improved APF gave the best result because it made the system reach the goal position in all of the examinations. Meanwhile, the APF with virtual force has the fastest time to reach the goal; however, it still has a problem in GNRON. It can be concluded that the APF needs to be modified in its algorithm to pass all of the local minima problems.
Parity–time symmetric optical neural networks

Optical neural networks (ONNs), implemented on an array of cascaded Mach–Zehnder interferometers (MZIs), have recently been proposed as a possible replacement for conventional deep learning hardware. They potentially offer higher energy efficiency and computational speed when compared to their electronic counterparts. By utilizing tunable phase shifters, one can adjust the output of each of MZI to enable emulation of arbitrary matrix–vector multiplication. These phase shifters are central to the programmability of ONNs, but they require a large footprint and are relatively slow. Here we propose an ONN architecture that utilizes parity–time (PT) symmetric couplers as its building blocks. Instead of modulating phase, gain–loss contrasts across the array are adjusted as a means to train the network. We demonstrate that PT symmetric ONNs (PT-ONNs) are adequately expressive by performing the digit-recognition task on the Modified National Institute of Standards and Technology dataset. Compared to conventional ONNs, the PT-ONN achieves a comparable accuracy (67% versus 71%) while circumventing the problems associated with changing phase. Our approach may lead to new and alternative avenues for fast training in chip-scale ONNs.
Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems

Predictive monitoring—making predictions about future states and monitoring if the predicted states satisfy requirements—offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named Signal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on whether all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world CPS datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines.
Direct Unsupervised Super-Resolution Using Generative Adversarial Network (DUS-GAN) for Real-World Data

The deep learning models for the Single Image Super-Resolution (SISR) task have found success in recent years. However, one of the prime limitations of existing deep learning-based SISR approaches is that they need supervised training. Specifically, the Low-Resolution (LR) images are obtained through known degradation (for instance, bicubic downsampling) from the High-Resolution (HR) images to provide supervised data as an LR-HR pair. Such training results in a domain shift of learnt models when real-world data is provided with multiple degradation factors not present in the training set. To address this challenge, we propose an unsupervised approach for the SISR task using Generative Adversarial Network (GAN), which we refer to hereafter as DUS-GAN. The novel design of the proposed method accomplishes the SR task without degradation estimation of real-world LR data. In addition, a new human perception-based quality assessment loss, i.e., Mean Opinion Score (MOS), has also been introduced to boost the perceptual quality of SR results. The pertinence of the proposed method is validated with numerous experiments on different reference-based (i.e., NTIRE Real-world SR Challenge validation dataset) and no-reference based (i.e., NTIRE Real-world SR Challenge Track-1 and Track-2) testing datasets. The experimental analysis demonstrates committed improvement from the proposed method over the other state-of-the-art unsupervised SR approaches, both in terms of subjective and quantitative evaluations on different reference metrics (i.e., LPIPS, PI-RMSE graph) and no-reference quality measures such as NIQE, BRISQUE and PIQE. We also provide the implementation of the proposed approach (https://github.com/kalpeshjp89/DUSGAN) to support reproducible research.
Deep multi-scale and multi-modal fusion for 3D object detection

The perception of 3D objects in the scene is the basis of autonomous driving. Most autonomous driving cars are equipped with cameras and Lidar to obtain 3D spatial information. RGB images taken from the camera and point cloud produced by Lidar both have their own advantages for 3D object detection. In order to make better use of the advantages of image data and point cloud data, a 3D object detection method based on Deep Multi-scale and Multi-modal Fusion (DMMF) is proposed. Firstly, point cloud is projected to the Bird’s Eye View (BEV) and extract BEV map and RGB image feature with feature extractor, respectively. Then, fuse the multi-modal feature with the deep multi-scale fusion method and finally input to position regression and classification network for object classification and accurate positioning. The experimental results on the benchmark KITTI dataset show that the method reaches state-of-the-art in both car and pedestrian classes, especially for hard level data, the detection AP is significantly improved.
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures

Building robust deep learning-based models requires diverse training data, ideally from several sources. However, these datasets cannot be combined easily because of patient privacy concerns or regulatory hurdles, especially if medical data is involved. Federated learning (FL) is a way to train machine learning models without the need for centralized datasets. Each FL client trains on their local data while only sharing model parameters with a global server that aggregates the parameters from all clients. At the same time, each client's data can exhibit differences and inconsistencies due to the local variation in the patient population, imaging equipment, and acquisition protocols. Hence, the federated learned models should be able to adapt to the local particularities of a client's data. In this work, we combine FL with an AutoML technique based on local neural architecture search by training a "supernet". Furthermore, we propose an adaptation scheme to allow for personalized model architectures at each FL client's site. The proposed method is evaluated on four different datasets from 3D prostate MRI and shown to improve the local models' performance after adaptation through selecting an optimal path through the AutoML supernet.
An Expressive Fully Policy-Hidden Ciphertext Policy Attribute-Based Encryption Scheme With Credible Verification Based on Blockchain

As the public cloud becomes one of the leading ways in data-sharing nowadays, data confidentiality and user privacy are increasingly critical. Partially policy-hidden ciphertext policy attribute-based encryption (CP-ABE) can effectively protect data confidentiality while reducing privacy leakage by hiding part of the access structure. However, it cannot satisfy the need of data sharing in the public cloud with complex users and large amounts of data, both in terms of less expressive access structures and limited granularity of policy hiding. Moreover, the verification of access right to shared data and correctness of decryption are ignored or conducted by an untrusted third party, and the prime-order groups are seldom considered in the expressive policy-hidden schemes. This article proposes a fully policy-hidden CP-ABE scheme constructed on linear secret sharing scheme (LSSS) access structure and prime-order groups for public cloud data sharing. To help users decrypt, hidden vector encryption (HVE) with a “convert step” is applied, which is more compatible with CP-ABE. Meanwhile, decentralized credible verification of access right to shared data and correctness of decryption based on blockchain are also provided. We prove the security of our scheme rigorously and compare the scheme with others comprehensively. The results show that our scheme performs better.
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging

Lesion segmentation in medical imaging has been an important topic in clinical research. Researchers have proposed various detection and segmentation algorithms to address this task. Recently, deep learning-based approaches have significantly improved the performance over conventional methods. However, most state-of-the-art deep learning methods require the manual design of multiple network components and training strategies. In this paper, we propose a new automated machine learning algorithm, T-AutoML, which not only searches for the best neural architecture, but also finds the best combination of hyper-parameters and data augmentation strategies simultaneously. The proposed method utilizes the modern transformer model, which is introduced to adapt to the dynamic length of the search space embedding and can significantly improve the ability of the search. We validate T-AutoML on several large-scale public lesion segmentation data-sets and achieve state-of-the-art performance.
Towards Meaningful Statements in IR Evaluation: Mapping Evaluation Measures to Interval Scales

Information Retrieval (IR) is a discipline deeply rooted in evaluation since its inception.Indeed, experimentally measuring and statistically validating the performance of IR systems are the only possible ways to compare systems and understand which are better than others and, ultimately, more effective and useful for end-users.Since the seminal paper by Stevens [103], it is known that the properties of the measurement scales determine the operations you should or should not perform with values from those scales.For example, Stevens suggested that you can compute means and variances only when you are working with, at least, interval scales.It was recently shown that the most popular evaluation measures in IR are not intervalscaled.However, so far, there has been little or no investigation in IR on the impact and consequences of departing from scale assumptions.Taken to the extremes, it might even mean that decades of experimental IR research used potentially improper methods, which may have produced results needing further validation.However, it was unclear if and to what extent these findings apply to actual evaluations; this opened a debate in the community with researchers standing on opposite positions about whether this should be considered an issue (or not) and to what extent.In this paper, we first give an introduction to the representational measurement theory explaining why certain operations and significance tests are permissible only with scales of a certain level.For that, we introduce the notion of meaningfulness specifying the conditions under which the truth (or falsity) of a statement is invariant under permissible transformations of a scale.Furthermore, we show how the recall base and the length of the run may make comparison and aggregation across topics problematic.Then we propose a straightforward and powerful approach for turning an evaluation measure into an interval scale, and describe an experimental evaluation of the differences between the original measures and the intervalscaled ones.For all the regarded measures -namely Precision, Recall, Average Precision, (Normalized) Discounted Cumulative Gain, Rank-Biased Precision and Reciprocal Rank -we observe substantial effects, both on the order of average values and on the outcome of significance tests.For the latter, previously significant differences turn out to be insignificant, while insignificant ones become significant.The effect varies remarkably between the tests considered but on average, we observed a 25% change in the decision about which systems are significantly different and which are not.These experimental findings further support the idea that measurement scales matter and that departing from their assumptions has an impact.This not only suggests that, to the extent possible, it would be better to comply with such assumptions but it also urges us to clearly indicate when we depart from such assumptions and, carefully, point out the limitations of the conclusions we draw and under which conditions they are drawn.
Should firms use digital work instructions?—Individual learning in an agile manufacturing setting

Abstract New technological innovations in robotics, automation, and digitization enable agile manufacturing, smaller lot sizes, and quicker changes to the product and the production process. In such environments, learning is important, since workers must learn new tasks and adapt to frequent changes quickly. In an experimental study, we compare digital work instructions with traditional paper‐based ones, the latter still being common in today's quality management systems. Our analysis is based on subjects working in the demonstration factory of a large German university in a realistic work environment; it, therefore, guarantees high internal and external validity. We show that digitally animated, interactive work instructions are an effective way to foster faster learning and enhanced performance when new manufacturing tasks are being carried out. Our results indicate that a combination of digital and traditional paper‐based instructions does not yield any advantages over a sole reliance on digital learning.
Some critical and ethical perspectives on the empirical turn of AI interpretability

We consider two fundamental and related issues currently faced by Artificial Intelligence (AI) development: the lack of ethics and interpretability of AI decisions. Can interpretable AI decisions help to address ethics in AI? Using a randomized study, we experimentally show that the empirical and liberal turn of the production of explanations tends to select AI explanations with a low denunciatory power. Under certain conditions, interpretability tools are therefore not means but, paradoxically, obstacles to the production of ethical AI since they can give the illusion of being sensitive to ethical incidents. We also show that the denunciatory power of AI explanations is highly dependent on the context in which the explanation takes place, such as the gender or education level of the person to whom the explication is intended for. AI ethics tools are therefore sometimes too flexible and self-regulation through the liberal production of explanations do not seem to be enough to address ethical issues. We then propose two scenarios for the future development of ethical AI: more external regulation or more liberalization of AI explanations. These two opposite paths will play a major role on the future development of ethical AI.
3D Talking Face With Personalized Pose Dynamics

Recently, we have witnessed a boom in applications for 3D talking face generation. However, most existing 3D face generation methods can only generate 3D faces with a static head pose, which is inconsistent with how humans perceive faces. Only a few articles focus on head pose generation, but even these ignore the attribute of personality. In this article, we propose a unified audio-driven approach to endow 3D talking faces with personalized pose dynamics. To achieve this goal, we establish an original person-specific dataset, providing corresponding head poses and face shapes for each video. Our framework is composed of two separate modules: PoseGAN and PGFace. Given an input audio, PoseGAN first produces a head pose sequence for the 3D head, and then, PGFace utilizes the audio and pose information to generate natural face models. With the combination of these two parts, a 3D talking head with dynamic head movement can be constructed. Experimental evidence indicates that our method can generate person-specific head pose sequences that are in sync with the input audio and that best match with the human experience of talking heads.
Time-Aware Multi-Type Data Fusion Representation Learning Framework for Risk Prediction of Cardiovascular Diseases

Predicting the future risk of cardiovascular diseases from the historical Electronic Health Records (EHRs) is a significant research task in personalized healthcare fields. In recent years, many deep neural network-based methods have emerged, which model patient disease progression by capturing the temporal patterns in sequential visit data. However, existing methods usually cannot effectively integrate the features of heterogeneous clinical data, and do not fully consider the impact of patients age and irregular time interval between consecutive medical records on the patients disease development. To address these challenges, we propose a Time-Aware Multi-type Data fUsion Representation learning framework (TAMDUR) for CVDs risk prediction. In this framework, we design a time-aware decay function, which is based on the patients age and the elapsed time between visits, to model the disease progression pattern. A parallel combination of Bi LSTM and CNN is constructed to respectively learn the temporal and non-temporal features from various types of clinical data. Finally, a multi-type data fusion representation layer based on self-attention is utilized to integrate various features and their correlations to obtain the final patient representation. We evaluate our model on a real medical dataset, and the experimental results demonstrate that TAMDUR outperforms the state-of-the-art approaches.