Reviewerly/exHarmony huggingface dataset:
Adaptive Mutual Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to unlabeled target domain.The semi-supervised method based on mean-teacher framework is one of the main stream approaches.By enforcing consistency constraints, it is hopeful that the teacher network will distill useful source domain knowledge to the student network.However, in practice negative transfer often emerges because the performance of the teacher network is not guaranteed to be always better than the student network.To address this limitation, a novel Adaptive Mutual Learning (AML) strategy is proposed in this paper.Specifically, given a target sample, the network with worse prediction will be optimized by pushing its prediction close to the better prediction.This is in the spirit of traditional knowledge distillation.On the other hand, the network with better prediction is further refined by requiring its prediction to stay away from the worse prediction.This can be regarded conceptually as reverse knowledge distillation.In this way, two networks learn from each other according to their respective performance.At inference phase, the averaged output of these two networks can be taken as the final prediction.Experimental results demonstrate that our AML achieves competitive results.
Adaptive Skeleton Construction for Accurate DAG Learning

Directed acyclic graph (DAG) learning plays a key role in causal discovery and many machine learning tasks. Learning a DAG from high-dimensional data always faces scalability problems. A local-to-global DAG learning approach can be scaled to high-dimensional data, however, existing local-to-global DAG learning algorithms employ either the AND-rule or the OR-rule for constructing a DAG skeleton. Simply using either rule, existing local-to-global methods may learn an inaccurate DAG skeleton, leading to unsatisfactory DAG learning performance. To tackle this problem, in this paper, we propose an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> daptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> AG <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning (ADL) algorithm. The novel contribution of ADL is that it can simultaneously and adaptively use the AND-rule and the OR-rule to construct an accurate global DAG skeleton. We conduct extensive experiments on both benchmark and real-world datasets, and the experimental results show that ADL is significantly better than some existing local-to-global and global DAG learning algorithms.
Everything Under Control: Secure Data Sharing Mechanism for Cloud-Edge Computing

Cloud-edge computing is a new paradigm for data sharing. Many computation tasks are assigned to multiple edge nodes to mitigate the computing burden of the cloud and data is also outsourced to them to provide real-time services for IoT devices. However, two major issues remain, namely data privacy and real-world deployment. According to the data privacy rights and principles that stated by General Data Protection Regulation (GDPR), data access control, restriction of data processing and finding inaccuracy data are critical issues that should be tackled in cloud-edge computing. Besides, since there are various types of devices and many of them are resource-constrained, how to efficiently apply deployment in cloud-edge computing is challenging for practice. In this work, we propose a new cryptographic primitive Controllable Outsourced Attribute-Based Proxy Re-Encryption (COAB-PRE) and a universal WebAssembly-based implementation framework for cross-platform deployment. In particular, COAB-PRE achieves bilateral and distributed access control whereby data producers and data consumers can both specify policies the other party must satisfy without a centralized access control server. The property, that we called controllable delegation, restricts the data processing on the edge nodes. COAB-PRE also supports comprehensive verifiability to find out a wrong result produced by the edge nodes and locate the misbehaved one. Moreover, we further discussed the potential property of COAB-PRE and put forward an improved scheme with high efficiency on devices. We also implemented our scheme using the approach and deployed it on different devices for experiment. All theoretical and experimental results indicate that our solution is secure and practical, and our implementation is suitable for cloud-edge computing.
An Improved Combination of Faster R-CNN and U-Net Network for Accurate Multi-Modality Whole Heart Segmentation

Detailed information of substructures of the whole heart is usually vital in the diagnosis of cardiovascular diseases and in 3D modeling of the heart. Deep convolutional neural networks have been demonstrated to achieve state-of-the-art performance in 3D cardiac structures segmentation. However, when dealing with high-resolution 3D data, current methods employing tiling strategies usually degrade segmentation performances due to GPU memory constraints. This work develops a two-stage multi-modality whole heart segmentation strategy, which adopts an improved Combination of Faster R-CNN and 3D U-Net (CFUN+). More specifically, the bounding box of the heart is first detected by Faster R-CNN, and then the original Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images of the heart aligned with the bounding box are input into 3D U-Net for segmentation. The proposed CFUN+ method redefines the bounding box loss function by replacing the previous Intersection over Union (IoU) loss with Complete Intersection over Union (CIoU) loss. Meanwhile, the integration of the edge loss makes the segmentation results more accurate, and also improves the convergence speed. The proposed method achieves an average Dice score of 91.1% on the Multi-Modality Whole Heart Segmentation (MM-WHS) 2017 challenge CT dataset, which is 5.2% higher than the baseline CFUN model, and achieves state-of-the-art segmentation results. In addition, the segmentation speed of a single heart has been dramatically improved from a few minutes to less than 6 seconds.
Gray level residual field: an effective metric for pixelwise matching quality evaluation in local digital image correlation

Abstract Gray level residual (GLR), also known as correlation residual field or image difference, reflects the intensity differences between reference and deformed images. In finite element-based global digital image correlation (DIC), GLR field can be readily computed and has been successfully used for crack or damage detection. The computation of pixelwise GLR field requires dense displacement results, which can be obtained using mesh interpolation in global DIC. To obtain the GLR field in subset-based local DIC, in which results are obtained at discrete calculation points, a simple, efficient and practical computation method for GLR field is presented, which accounts for the influence of illumination variations unavoidably occurred in real DIC tests. The proposed method can be easily implemented on both central process unit and graphics process unit to realize real-time visualization. Experiments showed that the GLR field can quantitatively and intuitively evaluate the pixelwise matching quality and provide clearer and finer locations for new components or textures (e.g. cracks) presented in deformed images. By fully leveraging the pixelwise matching quality information provided by the GLR field, the capability and robustness of local DIC is expected to be further enhanced.
Crowd counting from single images using recursive multi-pathway zooming and foreground enhancement

Crowd counting is a challenging task due to many challenges such as scale variations and noisy background. To handle these challenges, we propose a novel framework named Multi-Pathway Zooming Network (MZNet) in this paper. The proposed framework recursively optimizes multi-scale features using multiple zooming pathways and progressively enhances the foreground information to improve crowd counting performance. Each zooming pathway comprises two zooming directions, zooming in and zooming out. Convolutional features at different resolutions are propagated to optimize the context information at each specific level. By sequentially integrating and interacting multi-observation information, the optimized features are powerful in handling the scale variation issue, and thus the crowd counting performance can be enhanced. To address the noisy background in many scenarios, we also introduce a new scheme to enhance the foreground information by incorporating a masked input image into the network, which is formed by a mask that element-wise multiplies with the original image. Finally, the context information, incorporated with an output density map, is recursively finetuned in our network to boost the counting performance. Extensive experiments evaluated on challenging benchmark datasets show competitive performances for both crowded and sparse scenarios.
Augmented Reality for Supporting Workers in Human–Robot Collaboration

This paper discusses the potential benefits of using augmented reality (AR) technology to enhance human–robot collaborative industrial processes. The authors describe a real-world use case at Siemens premises in which an AR-based authoring tool is used to reduce cognitive load, assist human workers in training robots, and support calibration and inspection tasks during assembly tasks. The study highlights the potential of AR as a solution for optimizing human–robot collaboration and improving productivity. The article describes the methodology used to deploy and evaluate the ARContent tool, which demonstrated improved usability, reduced task load, and increased efficiency in the assembly process. However, the study is limited by the restricted availability of workers and their knowledge of assembly tasks with robots. The authors suggest that future work should focus on testing the ARContent tool with a larger user pool and improving the authoring tool based on the shortcomings identified during the study. Overall, this work shows the potential for AR technology to revolutionize industrial processes and improve collaboration between humans and robots.
Restoration of Complex Signals Distorted by Aliasing as a Result of Bandpass Sub-Nyquist Sampling

The purpose of this article is to develop a technique for recovering a complex signal distorted by aliasing as a result of sub-Nyquist bandpass sampling. The developed technique is based on the use of multichannel sampling. Its essence is that the signal is processed in several channels. All channels are analog-to-digital converted with different sampling rates, each of which leads to aliasing in its channel. For each channel, a system of equations is compiled that describes the corresponding aliasing. The joint solution of these systems of equations makes it possible to restore the distorted signal. Consideration in this article is limited only to first-order aliasing, in which no more than two aliases overlap at each frequency. This work is a continuation of the approach developed by the authors, called multichannel multirate sampling.
BasicTAD: An astounding RGB-Only baseline for temporal action detection

Temporal action detection (TAD) is extensively studied in the video understanding community by generally following the object detection pipeline in images. However, complex designs are not uncommon in TAD, such as two-stream feature extraction, multi-stage training, complex temporal modeling, and global context fusion. In this paper, we do not aim to introduce any novel technique for TAD. Instead, we study a simple, straightforward, yet must-known baseline given the current status of complex design and low detection efficiency in TAD. In our simple baseline (BasicTAD), we decompose the TAD pipeline into several essential components: data sampling, backbone design, neck construction, and detection head. We extensively investigate the existing techniques in each component for this baseline and, more importantly, perform end-to-end training over the entire pipeline thanks to the simplicity of design. As a result, this simple BasicTAD yields an astounding and real-time RGB-Only baseline very close to the state-of-the-art methods with two-stream inputs. In addition, we further improve the BasicTAD by preserving more temporal and spatial information in network representation (termed as PlusTAD). Empirical results demonstrate that our PlusTAD is very efficient and significantly outperforms the previous methods on the datasets of THUMOS14 and FineAction. Meanwhile, we also perform in-depth visualization and error analysis on our proposed method and try to provide more insights into the TAD problem. Our approach can serve as a strong baseline for future TAD research. The code and model are released at https://github.com/MCG-NJU/BasicTAD.
Saturating quantum advantages in postselected metrology with the positive Kirkwood-Dirac distribution

Weak value amplification and other postselection-based metrological protocols can enhance precision while estimating small parameters, outperforming postselection-free protocols. In general, these enhancements are largely constrained because the protocols yielding higher precision are rarely obtained due to a lower probability of successful postselection. It is shown that this precision can further be improved with the help of quantum resources like entanglement and negativity in the quasiprobability distribution. However, these quantum advantages in attaining considerable success probability with large precision are bounded irrespective of any accessible quantum resources. Here we derive a bound of these advantages in postselected metrology, establishing a connection with weak value optimization where the latter can be understood in terms of geometric phase. We introduce a scheme that saturates the bound, yielding anomalously large precision. Usually, negative quasiprobabilities are considered essential in enabling postselection to increase precision beyond standard optimized values. In contrast, we prove that these advantages can indeed be achieved with positive quasiprobability distribution. We also provide an optimal metrological scheme using three level non-degenerate quantum system.
Modelling the dynamics of cryptocurrency prices for risk hedging: The case of Bitcoin, Ethereum, and Litecoin

Cryptocurrencies have, over the years, gained an unprecedented prominence in financial discourse, with the market fielding over 5,300 digital currencies and reaching over $2 trillion in market capitalisation in 2022. The surge in market values of digital currencies and their popularity in the world of e-commerce have remained unabated and equally received special attention from researchers focusing on identifying the underlying factors that drive changes in their market values. Thus, this study models the dynamics of the prices of cryptocurrencies alongside their interconnectedness, focusing on Bitcoin, Ethereum, and Litecoin along the time and frequency dimensions of monthly data from 1 March 2016 to 05/31/2022. Based on the ARDL model, results show that the volume of transactions of Bitcoin, Ethereum, and Litecoin, oil prices, and gold prices exert a more significant positive influence on their prices in the longrun than in the shortrun. However, the publicity of the selected cryptocurrencies (google search rates) does not significantly influence their prices. Interestingly, results from the Wavelet Granger causality tests show no causality between the raw series of Bitcoin, Ethereum, and Litecoin prices. However, a bi-directional causality exists between Bitcoin and Ethereum prices during the longrun in their low frequencies, a unidirectional causality running from Bitcoin to Litecoin prices during the longrun in their low frequencies, and a unidirectional causality running from Litecoin to Ethereum prices during the shortrun, medium run and longrun in their high, medium, and low frequencies. These findings have profound implications for the global financial market and investor decisions.
Multimodal fusion for autonomous navigation via deep reinforcement learning with sparse rewards and hindsight experience replay

The multimodal perception of intelligent robots is essential for achieving collision-free and efficient navigation. Autonomous navigation is enormously challenging when perception is acquired using only vision or LiDAR sensor data due to the lack of complementary information from different sensors. This paper proposes a simple yet efficient deep reinforcement learning (DRL) with sparse rewards and hindsight experience replay (HER) to achieve multimodal navigation. By adopting the depth images and pseudo-LiDAR data generated by an RGB-D camera as input, a multimodal fusion scheme is used to enhance the perception of the surrounding environment compared to using a single sensor. To alleviate the misleading way for the agent to navigate with dense rewards, the sparse rewards are intended to identify its tasks. Additionally, the HER technique is introduced to address the sparse reward navigation issue for accelerating optimal policy learning. The results show that the proposed model achieves state-of-the-art performance in terms of success, crash, and timeout rates, as well as generalization capability.
Image Encryption Algorithm Based on Arnold Transform and Chaos Theory in the Multi-wavelet Domain

AbstractImage encryption is essential for ensuring data transmission security over open public networks. Using Multi-Wavelet Transform, Arnold transform, and two chaotic systems, a novel color image encryption technology is designed in this paper. In the proposed algorithm, the primary color components of the input image undergo a multi-wave transform before the Arnold Transform confounds the sub-bands of each color component. Each color component is then divided into blocks shuffled in a predetermined order. Finally, the encrypted image is generated using secret keys derived from Nahrain's and WAM's chaotic systems. Notably, the initial conditions of the chaotic maps are generated using image data to increase the algorithm's sensitivity to the input image. Security analyses conducted to validate the practicability of the new algorithm reveal that it possesses excellent encryption efficiency, high key sensitivity, and the ability to withstand a wide variety of attacks.KEYWORDS: CryptographyEncryptionChaotic mapMulti-wavelet TransformChaos TheoryCoding AcknowledgmentsMany thanks to the reviewers and editors for their insightful comments and innovative suggestions that significantly enhanced the work.Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of data and materialsThis section is irrelevant to this study.Competing interestsThe authors have no personal or financial relationships that could have appeared to influence the work described in this paper.Authors' contributionsProf. Waleed Ameen Mahmoud Al-Jawher SupervisorAli Akram AbdulKareem PhD StudentAdditional informationNotes on contributorsAli Akram Abdul-KareemAli Akram Abdul-Kareem earned a Bachelor from Middle Technical University in Baghdad, Iraq, in 2011 and a Master of Science in Computer Science from Middle East University in Amman, Jordan, in 2016. Currently, he is a Ph.D. candidate at the Iraqi Commission for Computers and Informatics, Information Institute for Postgraduate Studies in Baghdad, Iraq, and a Computer Programmer at Al Rafidain Bank in Baghdad, Iraq. He has more than ten years of experience implementing banking information systems projects. His research interests are image encryption, chaos, compressive sensing, and optimization algorithms.Waleed Ameen Mahmoud Al-JawherProfessor Waleed Ameen Mahmoud Al-Jawher President Assistance for scientific Affairs, University of Uruk, Iraq. He received a School of Research in Digital Signal Processing (2005). He received his Ph.D. in Digital Signal Processing from University of Wales, United Kingdom (1986). He has a teaching experience in Computer Science and Communication engineering for 44 years. A total of (15) National Awards. He Published over (290) papers, Supervised more than (210) MSc and PhD Students. He was the First professor Award of University of Baghdad, Iraq. His present areas of research interest are the field of Digital Signal Processing and their applications.
Policy gradients using variational quantum circuits

Abstract Variational quantum circuits are being used as versatile quantum machine learning models. Some empirical results exhibit an advantage in supervised and generative learning tasks. However, when applied to reinforcement learning, less is known. In this work, we considered a variational quantum circuit composed of a low-depth hardware-efficient ansatz as the parameterized policy of a reinforcement learning agent. We show that an 𝜖 -approximation of the policy gradient can be obtained using a logarithmic number of samples concerning the total number of parameters. We empirically verify that such quantum models behave similarly to typical classical neural networks used in standard benchmarking environments and quantum control, using only a fraction of the parameters. Moreover, we study the barren plateau phenomenon in quantum policy gradients using the Fisher information matrix spectrum.
The promise of convolutional neural networks for the early diagnosis of the Alzheimer’s disease

Alzheimer’s Disease (AD) is one of the most devastating neurologic disorders, if not the most, as there is no cure for this disease, and its symptoms eventually become severe enough to interfere with daily tasks. The early diagnosis of AD, which might be up to 8 years before the onset of dementia symptoms, comes with many promises. To this end, we propose a novel Convolutional Neural Network (CNN) as a cheap, fast, yet accurate solution. First, a gold-standard dataset, namely DARWIN, that was proposed for the detection of AD through handwriting and consisted of 1D features, was used to generate the 2D features, which were yielded into the proposed novel model. Then, the proposed novel model was trained and evaluated on this dataset. According to the experimental result, the proposed novel model obtained an accuracy as high as 90.4%, which was higher than the accuracies obtained by the state-of-the-art baselines, which covered a total of 17 widely-used classifiers.