Reviewerly/exHarmony huggingface dataset:
Monte Carlo denoising via auxiliary feature guided self-attention

While self-attention has been successfully applied in a variety of natural language processing and computer vision tasks, its application in Monte Carlo (MC) image denoising has not yet been well explored. This paper presents a self-attention based MC denoising deep learning network based on the fact that self-attention is essentially non-local means filtering in the embedding space which makes it inherently very suitable for the denoising task. Particularly, we modify the standard self-attention mechanism to an auxiliary feature guided self-attention that considers the by-products (e.g., auxiliary feature buffers) of the MC rendering process. As a critical prerequisite to fully exploit the performance of self-attention, we design a multi-scale feature extraction stage, which provides a rich set of raw features for the later self-attention module. As self-attention poses a high computational complexity, we describe several ways that accelerate it. Ablation experiments validate the necessity and effectiveness of the above design choices. Comparison experiments show that the proposed self-attention based MC denoising method outperforms the current state-of-the-art methods.
Measurement and modeling of diffuse irradiance masking on tilted planes for solar engineering applications

Photovoltaic and solar thermal projects require accurate solar irradiance data and models for the design, assessment, and forecasting of power output. For panels and solar collectors installed in arrays, the view toward the sky and ground is restricted by the surrounding rows. This paper analyzes the impact of view obstructions on the diffuse irradiance distribution in fixed-tilt arrays, addressing the insufficient research coverage to date. Irradiance was measured using high-precision pyranometers placed at ten positions throughout an experimental solar collector array for 3.5 and 6 m row spacings and 30°, 37.5°, and 45° tilt angles (relative row spacings between 0.68 and 1.51). A novel anisotropic transposition model was developed to calculate the diffuse tilted irradiance along the collector height, accounting for diffuse irradiance masking due to view obstructions. The model was also solved inversely, obtaining local global horizontal irradiance from global tilted irradiance. The validation showed that the model accurately captures the distinct shape of the diffuse irradiance distribution. Results indicate a strong effect of view obstructions for narrow row spacings and steep tilt angles. For the most extreme configuration (3.5 m row spacing, 45° tilt angle, 1.67 relative row spacing), the diffuse irradiance was reduced to 64% at the bottom and 89% on average, relative to the top of the collector. As a consequence, measurements at the top can result in biased performance assessments.
Combining transformer and CNN for object detection in UAV imagery

Combining multiple models is a well-known technique to improve predictive performance in challenging tasks such as object detection in UAV imagery. In this paper, we propose fusion of transformer-based and convolutional neural network-based (CNN) models with two approaches. First, we ensemble Swin Transformer and DetectoRS with ResNet backbone, and conduct performance comparison on four typical methods for combining predictions of multiple object detection models. Second, we design a hybrid architecture by combining Swin Transformer backbone with a neck of DetectoRS. We show that the fusion of the transformer and the CNN-based models performs better compared to the respective baseline model.
Cooperative Multi-Robot Task Allocation with Reinforcement Learning

This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems.
FastHand: Fast monocular hand pose estimation on embedded systems

Hand pose estimation is a fundamental task in many human–robot interaction-related applications. However, previous approaches suffer from unsatisfying hand landmark predictions in real-world scenes and high computation burden. This paper proposes a fast and accurate framework for hand pose estimation, dubbed as “FastHand”. Using a lightweight encoder–decoder network architecture, FastHand fulfills the requirements of practical applications running on embedded devices. The encoder consists of deep layers with a small number of parameters, while the decoder uses spatial location information to obtain more accurate results. The evaluation took place on two publicly available datasets demonstrating the improved performance of the proposed pipeline compared to other state-of-the-art approaches. FastHand offers high accuracy scores while reaching a speed of 25 frames per second on an NVIDIA Jetson TX2 graphics processing unit.
Optimal pricing-based computation offloading and resource allocation for blockchain-enabled beyond 5G networks

Computation offloading has been considered as a promising solution for resource-constrained mobile devices supporting computation-intensive mobile collaborative applications in beyond 5G networks. By leveraging network-assisted device-to-device (D2D) collaboration, resource-constrained mobile devices are able to offload computation tasks to nearby resource-sharing mobile devices. Meanwhile, blockchain technology is developing rapidly and has been applied to mobile scenarios with massive information interaction for establishing trust between mobile devices. However, the computing power of mobile devices is limited by the physical size and battery capacity, which is not enough to cope with the high computation overhead for blockchain mining process. Thus, offloading mining tasks to the edge servers becomes a viable option. This paper investigates both of the D2D task offloading problem and the mining task offloading problem in a blockchain-enabled beyond 5G network. For the purpose of efficient resource allocation, the former is formulated as a double auction market and the latter is formulated as a Stackelberg game. Moreover, we propose two pricing based schemes to solve these two computation offloading problems, among which Bayes-Nash equilibrium and Stackelberg equilibrium are analyzed. At last, by comparison with the benchmark, the efficiency and effectiveness of our proposed schemes are evaluated. Numerical results show that both of the double auction based normal task offloading scheme and the Stackelberg game based mining task offloading scheme are able to improve the utility of resource demanders and providers.
NWP-Miner: Nonoverlapping weak-gap sequential pattern mining

Nonoverlapping sequential pattern mining (SPM) is a type of SPM with gap constraints that can mine valuable information in sequences. One of the disadvantages of nonoverlapping SPM is that any characters can match with gap constraints. Hence, there can be a significant difference between the trend of a pattern and those of its occurrences. To tackle this issue, we propose nonoverlapping weak-gap sequential pattern (NWP) mining, where characters are divided into two types: weak and strong. This allows discovering frequent patterns more accurately by limiting the gap constraints to match only weak characters. To discover NWPs, we propose NMP-Miner which involves two key steps: support calculation and candidate pattern generation. To efficiently calculate the support of candidate patterns, depth-first search and backtracking strategies based on a simplified Nettree structure are adopted, which effectively reduce the time and space complexities of the algorithm. Moreover, a pattern join approach is applied to effectively reduce the number of candidate patterns. The experimental results show that NWP-Miner is more efficient than other competitive algorithms. More importantly, the case study of time series shows that NWP-Miner can effectively filter out noise patterns and discover more meaningful patterns. Algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/NWP-Miner.
Attribute-Modulated Generative Meta Learning for Zero-Shot Learning

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to semantically related unseen classes, which are absent during training. The promising strategies for ZSL are to synthesize visual features of unseen classes conditioned on semantic side information and to incorporate meta-learning to eliminate the model’s inherent bias towards seen classes. While existing meta generative approaches pursue a common model shared across task distributions, we aim to construct a generative network adaptive to task characteristics. To this end, we propose an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ttribute- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> odulated gener <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> tive meta-model for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Z</b> ero-shot learning (AMAZ). Our model consists of an attribute-aware modulation network, an attribute-augmented generative network, and an attribute-weighted classifier. Given unseen classes, the modulation network adaptively modulates the generator by applying task-specific transformations so that the generative network can adapt to highly diverse tasks. The weighted classifier utilizes the data quality to enhance the training procedure, further improving the model performance. Our empirical evaluations on four widely-used benchmarks show that AMAZ outperforms state-of-the-art methods by 3.8% and 3.1% in ZSL and generalized ZSL settings, respectively, demonstrating the superiority of our method. Our experiments on a zero-shot image retrieval task show AMAZ’s ability to synthesize instances that portray real visual characteristics.
CRACT: Cascaded Regression-Align-Classification for Robust Tracking

High quality object proposals are crucial in visual tracking algorithms that utilize region proposal network (RPN). Refinement of these proposals, typically by box regression and classification in parallel, has been popularly adopted to boost tracking performance. However, it still meets problems when dealing with complex and dynamic background. Thus motivated, in this paper we introduce an improved proposal refinement module, Cascaded Regression-Align-Classification (CRAC), which yields new state-of-the-art performances on many benchmarks.First, having observed that the offsets from box regression can serve as guidance for proposal feature refinement, we design CRAC as a cascade of box regression, feature alignment and box classification. The key is to bridge box regression and classification via an alignment step, which leads to more accurate features for proposal classification with improved robustness. To address the variation in object appearance, we introduce an identification-discrimination component for box classification, which leverages offline reliable fine-grained template and online rich background information to distinguish the target from background. Moreover, we present pyramid RoIAlign that benefits CRAC by exploiting both the local and global cues of proposals. During inference, tracking proceeds by ranking all refined proposals and selecting the best one. In experiments on seven benchmarks including OTB-2015, UAV123, NfS, VOT-2018, TrackingNet, GOT-10k and LaSOT, our CRACT exhibits very promising results in comparison with state-of-the-art competitors and runs in real-time at 28 fps.
UAV Path Planning Based on Multi-Stage Constraint Optimization

Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.
A CNN-based Human Activity Recognition System Combining a Laser Feedback Interferometry Eye Movement Sensor and an IMU for Context-aware Smart Glasses

Smart glasses are considered the next breakthrough in wearables. As the successor of smart watches and smart ear wear, they promise to extend reality by immersive embedding of content in the user's field of view. While advancements in display technology seems to fulfill this promises, interaction concepts are derived from established wearable concepts like touch interaction or voice interaction, preventing full immersion as they require the user to frequently interact with the glasses. To minimize interactions, we propose to add context-awareness to smart glasses through human activity recognition (HAR) by combining head- and eye movement features to recognize a wide range of activities. To measure eye movements in unobtrusive way, we propose laser feedback interferometry (LFI) sensors. These tiny low power sensors are highly robust to ambient light. We combine LFI sensors and an IMU to collect eye and head movement features from 15 participants performing 7 cognitive and physical activities, leading to a unique data set. To recognize activities we propose a 1D-CNN model and apply transfer learning to personalize the classification, leading to an outstanding macro-F1 score of 88.15 % which outperforms state of the art methods. Finally, we discuss the applicability of the proposed system in a smart glasses setup.
An attentive joint model with transformer-based weighted graph convolutional network for extracting adverse drug event relation

Adverse drug event (ADE) relation extraction is a crucial task for drug safety surveillance which aims to discover potential relations between ADE mentions from unstructured medical texts. To date, the graph convolutional networks (GCN) have been the state-of-the-art solutions for improving the ability of relation extraction task. However, there are many challenging issues that should be addressed. Among these, the syntactic information is not fully exploited by GCN-based methods, especially the diversified dependency edges. Still, these methods fail to effectively extract complex relations that include nested, discontinuous and overlapping mentions. Besides, the task is primarily regarded as a classification problem where each candidate relation is treated independently which neglects the interaction between other relations. To deal with these issues, in this paper, we propose an attentive joint model with transformer-based weighted GCN for extracting ADE Relations, called ADERel. Firstly, the ADERel system formulates the ADE relation extraction task as an N-level sequence labelling so as to model the complex relations in different levels and capture greater interaction between relations. Then, it exploits our neural joint model to process the N-level sequences jointly. The joint model leverages the contextual and structural information by adopting a shared representation that combines a bidirectional encoder representation from transformers (BERT) and our proposed weighted GCN (WGCN). The latter assigns a score to each dependency edge within a sentence so as to capture rich syntactic features and determine the most influential edges for extracting ADE relations. Finally, the system employs a multi-head attention to exchange boundary knowledge across levels. We evaluate ADERel on two benchmark datasets from TAC 2017 and n2c2 2018 shared tasks. The experimental results show that ADERel is superior in performance compared with several state-of-the-art methods. The results also demonstrate that incorporating a transformer model with WGCN makes the proposed system more effective for extracting various types of ADE relations. The evaluations further highlight that ADERel takes advantage of joint learning, showing its effectiveness in recognizing complex relations.
Design of Online and Offline Integration Teaching System for Body Sense Dance Based on Cloud Computing

Traditional teaching methods are limited to time and place, and the performance of dance teaching resource management is poor. This paper designs a dance teaching resource management system based on cloud computing. The functional structure of the system includes the core cloud computing teaching and teaching management application. The data management module is used to store the processed data in the data file, and respond to the retrieval request of dance teaching content publishing module and the remote image resource positioning request of multi-media management module. Design user courseware on demand process, using the existing dance teaching resources to learn. The experimental results show that the designed system has good performance, high stability and compatibility, and strong data storage capacity of teaching resources.
Experimental evaluation of filters used for removing speckle noise and enhancing ultrasound image quality

In the modern-days diagnostics, ultrasound is considered a significant non-invasive imaging technique. However, ultrasound images are frequently contaminated by multiplicative speckle noise. Speckle noise is produced by constructive and destructive interference between ultrasound waves when the object being measured is smaller than the wavelength of the beam. The removal of speckle noise in ultrasound images is possible by using various filters. The current experimental study is aimed at finding the most suitable de-speckling filters for enhancing ultrasound images. The present study involves 102-abdomen ultrasound images degraded by speckle noise and the analysis of eight de-speckling filters (i.e., Mean, Median, Kuan, Lee, Frost, Adaptive Homomorphic, Wiener and Anisotropic Diffusion) is developed to find the optimum de-speckling filter. The results of the given filters are analyzed using the five quality metrics (i.e., PSNR, RMSE, SSI, STM, and SSIM). The metrics are further investigated by using the statistical tests (i.e., one-way ANOVA and Tukey's post hoc test) to support the experimental evaluation. Simulation results showed that the Frost and adaptive Homomorphic filters work well for the ultrasound images degraded with higher value of speckle noise. From the proposed research, it is easy to select the optimal filter, which helps doctors to get fine details of the image.
Connecting Self-Sovereign Identity with Federated and User-centric Identities via SAML Integration

Self-sovereign identity provides a feasible alternative to login via username and password through an identity provider to access digital services. It allows identity subjects to control and own their data. Although this is an appealing approach, it requires a whole new infrastructure with almost no dependencies on the existing ones. We designed and implemented a solution that combines an existing federated identity access management solution with the new approach by enabling authentication via self-sovereign-identity-based credentials while the identity provider retains verification and communication with the service provider via Security Assertion Mark Up Language. Thanks to the standardized federated systems in the German higher education domain, the solution not only enables a smooth transition to self-sovereign identities but can also be easily transferred to other universities using the same federated identity framework.
A robust variational autoencoder using beta divergence

The presence of outliers can severely degrade learned representations and performance of deep learning methods and hence disproportionately affect the training process, leading to incorrect conclusions about the data. For example, anomaly detection using deep generative models is typically only possible when similar anomalies (or outliers) are not present in the training data. Here we focus on variational autoencoders (VAEs). While the VAE is a popular framework for anomaly detection tasks, we observe that the VAE is unable to detect outliers when the training data contains anomalies that have the same distribution as those in test data. In this paper we focus on robustness to outliers in training data in VAE settings using concepts from robust statistics. We propose a variational lower bound that leads to a robust VAE model that has the same computational complexity as the standard VAE and contains a single automatically-adjusted tuning parameter to control the degree of robustness. We present mathematical formulations for robust variational autoencoders (RVAEs) for Bernoulli, Gaussian and categorical variables. The RVAE model is based on beta-divergence rather than the standard Kullback-Leibler (KL) divergence. We demonstrate the performance of our proposed β-divergence-based autoencoder for a variety of image and categorical datasets showing improved robustness to outliers both qualitatively and quantitatively. We also illustrate the use of our robust VAE for detection of lesions in brain images, formulated as an anomaly detection task. Finally, we suggest a method to tune the hyperparameter of RVAE which makes our model completely unsupervised.
Bayesian Convolutional Neural Network-based Models for Diagnosis of Blood Cancer

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