Reviewerly/exHarmony huggingface dataset:
Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS

The operational challenge of a photovoltaic (PV) integrated system is the uncertainty (irregularity) of the future power output. The integration and correct operation can be carried out with accurate forecasting of the PV output power. A distinct artificial intelligence method was employed in the present study to forecast the PV output power and investigate the accuracy using endogenous data. Discrete wavelet transforms were used to decompose PV output power into approximate and detailed components. The decomposed PV output was fed into an adaptive neuro-fuzzy inference system (ANFIS) input model to forecast the short-term PV power output. Various wavelet mother functions were also investigated, including Haar, Daubechies, Coiflets, and Symlets. The proposed model performance was highly correlated to the input set and wavelet mother function. The statistical performance of the wavelet-ANFIS was found to have better efficiency compared with the ANFIS and ANN models. In addition, wavelet-ANFIS coif2 and sym4 offer the best precision among all the studied models. The result highlights that the combination of wavelet decomposition and the ANFIS model can be a helpful tool for accurate short-term PV output forecasting and yield better efficiency and performance than the conventional model.
Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning

Deep reinforcement learning (DRL) has great potential for acquiring the optimal action in complex environments such as games and robot control. However, it is difficult to analyze the decision-making of the agent, i.e., the reasons it selects the action acquired by learning. In this work, we propose Mask-Attention A3C (Mask A3C), which introduces an attention mechanism into Asynchronous Advantage Actor-Critic (A3C), which is an actor-critic-based DRL method, and can analyze the decision-making of an agent in DRL. A3C consists of a feature extractor that extracts features from an image, a policy branch that outputs the policy, and a value branch that outputs the state value. In this method, we focus on the policy and value branches and introduce an attention mechanism into them. The attention mechanism applies a mask processing to the feature maps of each branch using mask-attention that expresses the judgment reason for the policy and state value with a heat map. We visualized mask-attention maps for games on the Atari 2600 and found we could easily analyze the reasons behind an agent's decision-making in various game tasks. Furthermore, experimental results showed that the agent could achieve a higher performance by introducing the attention mechanism.
Unsupervised Non-Rigid Image Distortion Removal via Grid Deformation

Many computer vision problems face difficulties when imaging through turbulent refractive media (e.g., air and water) due to the refraction and scattering of light. These effects cause geometric distortion that requires either handcrafted physical priors or supervised learning methods to remove. In this paper, we present a novel unsupervised network to recover the latent distortion-free image. The key idea is to model non-rigid distortions as deformable grids. Our network consists of a grid deformer that estimates the distortion field and an image generator that outputs the distortion-free image. By leveraging the positional encoding operator, we can simplify the network structure while maintaining fine spatial details in the recovered images. Our method doesn't need to be trained on labeled data and has good transferability across various turbulent image datasets with different types of distortions. Extensive experiments on both simulated and real-captured turbulent images demonstrate that our method can remove both air and water distortions without much customization.
Low-rank and sparse representation based learning for cancer survivability prediction

Cancer survivability prediction has been of great interest to health professionals and researchers. The task refers to the procedure of estimating the potential survivability according to an individual's medical history. The difficulty is that raw data is usually subject to some noise, such as missing values. To address this issue, we propose a novel low-rank and sparse representation-based learning algorithm, which consists of two main stages of data self expressiveness and classification. Firstly, in the data self expressiveness stage, raw inputs have been decomposed into one dictionary (which is enforced with a low-rank constraint) and one coefficient matrix (which is sparsely coded), respectively. Secondly, this sparse coefficient matrix is paired with sample labels for training during the classification stage. We further integrate these two stages and formulate them into an optimization problem, which is then solved using an iterative computational strategy. Theoretically, we analyze the convergence of the proposed algorithm. The relationship between the proposed algorithm and existing approaches are also discussed. The efficiency of the proposed algorithm is experimentally verified using several benchmarking classification problems and a public longitudinal dataset. Experimental results demonstrate that the proposed algorithm achieves superior performance in terms of affordable computational complexity and high prediction accuracy, compared to state-of-the-art approaches.
Swapping Face Images with Generative Neural Networks for Deepfake Technology – Experimental Study

Generative neural networks are usually the most essential part of deepfake, a technique of image-to-image translation, designed to combine and overlay objects in images or videos creating deceptively realistic counterfeits. In this paper, four leading methods used for deepfake generation: autoencoders, variational autoencoders, variational autoencoders generative adversarial networks and cycle generative adversarial networks, in problem of face-to-face conversion, are analysed. We present results of experiments conducted on face-swapping task, performed on specially preprocessed data from VoxCeleb2 dataset. Due to the lack of numerical methods for deepfake comparison, a descriptive assessment method was proposed, and all obtained results were rated in a visual evaluation process. General conclusions concerning applicability of considered approaches to deepfake generation problem were formulated.
Harmonize: a shared environment for extended immersive entertainment

Abstract Virtual reality (VR) and augmented reality (AR) applications are very diffuse nowadays. Moreover, recent technology innovations led to the diffusion of commercial head-mounted displays for immersive VR: users can enjoy entertainment activities that fill their visual fields, experiencing the sensation of physical presence in these virtual immersive environments. Even if AR and VR are mostly used separately, they can be effectively combined to provide a multi-user shared environment (SE), where two or more users perform some specific tasks in a cooperative or competitive way, providing a wider set of interactions and use cases compared to immersive VR alone. However, due to the differences between the two technologies, it is difficult to develop SEs offering a similar experience for both AR and VR users. This paper presents Harmonize, a novel framework to deploy applications based on SEs with a comparable experience for both AR and VR users. Moreover, the framework is hardware-independent, and it has been designed to be as much extendable to novel hardware as possible. An immersive game has been designed to test and to evaluate the validity of the proposed framework. The assessment of the system through the System Usability Scale questionnaire and the Game Experience Questionnaire shows a positive evaluation.
Distilling Effective Supervision for Robust Medical Image Segmentation with Noisy Labels

Despite the success of deep learning methods in medical image segmentation tasks, the human-level performance relies on massive training data with high-quality annotations, which are expensive and time-consuming to collect. The fact is that there exist low-quality annotations with label noise, which leads to suboptimal performance of learned models. Two prominent directions for segmentation learning with noisy labels include pixel-wise noise robust training and image-level noise robust training. In this work, we propose a novel framework to address segmenting with noisy labels by distilling effective supervision information from both pixel and image levels. In particular, we explicitly estimate the uncertainty of every pixel as pixel-wise noise estimation, and propose pixel-wise robust learning by using both the original labels and pseudo labels. Furthermore, we present an image-level robust learning method to accommodate more information as the complements to pixel-level learning. We conduct extensive experiments on both simulated and real-world noisy datasets. The results demonstrate the advantageous performance of our method compared to state-of-the-art baselines for medical image segmentation with noisy labels.
SPEX: A Generic Framework for Enhancing Neural Social Recommendation

Social Recommender Systems (SRS) have attracted considerable attention since its accompanying service, social networks, helps increase user satisfaction and provides auxiliary information to improve recommendations. However, most existing SRS focus on social influence and ignore another essential social phenomenon, i.e., social homophily. Social homophily, which is the premise of social influence, indicates that people tend to build social relations with similar people and form influence propagation paths. In this article, we propose a generic framework Social PathExplorer (SPEX) to enhance neural SRS. SPEX treats the neural recommendation model as a black box and improves the quality of recommendations by modeling the social recommendation task, the formation of social homophily, and their mutual effect in the manner of multi-task learning. We design a Graph Neural Network based component for influence propagation path prediction to help SPEX capture the rich information conveyed by the formation of social homophily. We further propose an uncertainty based task balancing method to set appropriate task weights for the recommendation task and the path prediction task during the joint optimization. Extensive experiments have validated that SPEX can be easily plugged into various state-of-the-art neural recommendation models and help improve their performance. The source code of our work is available at: https://github.com/XMUDM/SPEX.
Semi-Supervised Anomaly Detection in Business Process Event Data using Self-Attention based Classification

The analysis of business processes has become increasingly important in recent years, not least due to the emergence of analysis tools that enable data-centric views of processes and thus provide increasingly operational support for process flows. In this work, a semi-supervised classification model is presented that takes into account different developments in deep learning (e.g., deep generative models), time series analysis (e.g., long short-term memory) and sequence processing (e.g., attention mechanism) and combines them in one approach. The results of the experimental implementation of the classification model show that it is able to filter activity-related and time-related anomalies from the event data and outperform existing approaches in its classification accuracy (F1 score). The classification model achieves an F1 score of up to 93%.
Subset Multivariate Collective and Point Anomaly Detection

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Quantum Codesign

Codesign has been an integral part of computer architecture since the very first systems were brought online. From the early days of the field until now, end-user applications inevitably shape the design and capabilities of subsequent generations of hardware. Likewise, the characteristics and capabilities of new computational hardware and systems often impact the algorithms and software that run on them. Quantum computing (QC) is similarly reliant on codesign approaches, particularly now in its resource constrained early days. This article discusses what codesign means in a QC setting, gives examples of its value to QC, and proposes key attributes of QC codesign approaches going forward.
Survival Prediction Based on Histopathology Imaging and Clinical Data: A Novel, Whole Slide CNN Approach

Current methods for whole slide image (WSI) histopathology subregion classification and survival prediction rely on phenotype clustering from randomly sampled image tiles or on analyzing key tiles selected by experts from the much larger in size WSIs. These approaches do not capture the whole tissue region present in a histopathology image, also missing the spatial distribution of features that could be critical for good survival predictors. We propose a novel method that extracts a whole slide feature map (WSFM) in the first step and then uses it to train the survival prediction model. Specifically, we partition the WSI into tiles, and for each tile extract InceptionV3 features followed by PCA dimension reduction. The low dimension features of each tile are stored as the channel information in the WSFM. The resulting WSFM preserves the tile adjacency information and captures the entire tissue in the WSI. To overcome the small-size data set concern inherent to previous methods, we design a siamese survival convolutional neural network (SSCNN) that takes the WSFM and multivariate clinical features as input and predicts the survival score. We train the SSCNN using a novel loss function that combines a modified pairwise ranking loss and a bounded inverse term. The key advantages of the proposed method are that it does not require pixel-level annotations, a notorious bottleneck, and it can be easily adapted for any type of tumor without performance dependence on other parameters like the number of clusters. Experimental results demonstrate the effectiveness of the proposed SSCNN over other state-of-the-art survival analysis approaches.
Automated HW/SW co-design for edge AI

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world using edge sensors and actuators. For IoT systems, there is now a strong trend to move the intelligence from the cloud to the edge or the extreme edge (known as TinyML). Yet, this shift to edge AI systems requires to design powerful machine learning systems under very strict resource constraints. This poses a difficult design task that needs to take the complete system stack from machine learning algorithm, to model optimization and compression, to software implementation, to hardware platform and ML accelerator design into account. This paper discusses the open research challenges to achieve such a holistic Design Space Exploration for a HW/SW Co-design for Edge AI Systems and discusses the current state with three currently developed flows: one design flow for systems with tightly-coupled accelerator architectures based on RISC-V, one approach using loosely-coupled, application-specific accelerators as well as one framework that integrates software and hardware optimization techniques to built efficient Deep Neural Network (DNN) systems.
Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment

During the last years, convolutional neural networks (C-NNs) have triumphed over video quality assessment (VQA) tasks. However, CNN-based approaches heavily rely on annotated data which are typically not available in VQA, leading to the difficulty of model generalization. Recent advances in domain adaptation technique makes it possible to adapt models trained on source data to unlabeled target data. However, due to the distortion diversity and content variation of the collected videos, the intrinsic subjectivity of VQA tasks hampers the adaptation performance. In this work, we propose a curriculum-style unsupervised domain adaptation to handle the cross-domain no-reference VQA problem. The proposed approach could be divided into two stages. In the first stage, we conduct an adaptation between source and target domains to predict the rating distribution for target samples, which can better reveal the subjective nature of VQA. From this adaptation, we split the data in target domain into confident and uncertain subdomains using the proposed uncertainty-based ranking function, through measuring their prediction confidences. In the second stage, by regarding samples in confident subdomain as the easy tasks in the curriculum, a fine-level adaptation is conducted between two subdomain-s to fine-tune the prediction model. Extensive experimental results on benchmark datasets highlight the superiority of the proposed method over the competing methods in both accuracy and speed. The source code is released at https://github.com/cpf0079/UCDA.
Josephson Traveling Wave Parametric Amplifiers as non-classical light source for Microwave Quantum Illumination

Detection of low-reflectivity objects can be enriched via the so-called quantum illumination procedure.In order that this quantum procedure outperforms classical detection protocols, entangled states of microwave radiation are initially required.In this paper, we discuss the role of Josephson Traveling Wave Parametric Amplifiers (JTWPAs), based on circuit-QED components, as suitable sources of a two-mode squeezed vacuum state, a special signal-idler entangled state.The obtained wide bandwidth makes the JTWPA an ideal candidate for generating quantum radiation in quantum metrology and information processing applications.
Exploiting Group Information for Personalized Recommendation with Graph Neural Networks

Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared
Impacts of software community patterns on process and product: An empirical study

Software engineering projects are now more than ever a community effort. In the recent past, researchers have shown that their success not only depends on source code quality, but also on other aspects like the balance of power distance, culture, and global engineering practices, and more. In such a scenario, understanding the characteristics of the community around a project and foresee possible problems may be the key to develop successful systems. In this paper, we focus on this research problem and propose an exploratory study on the relation between community patterns, i.e.,recurrent mixes of organizational or social structure types, and aspects related to the quality of software products and processes by mining open-source software repositories hosted on GitHub. We first exploit association rule mining to discover frequent relations between community pattern and community smells, i.e.,sub-optimal patterns across the organizational structure of a software development community that may be precursors of some form of social debt. Further on, we use statistical analyses to understand their impact on software maintainability and on the community engagement, in terms of contributions and issues. Our findings show that different organizational patterns are connected to different forms of socio-technical problems; further on, specific combinations are set in equally specific contextual conditions. Findings support two possible conclusions: (1) practitioners should put in place specific preventive actions aimed at avoiding the emergence of community smells and (2) such actions should be drawn according to the contextual conditions of the organization and the project.
PFF-FPN: A Parallel Feature Fusion Module Based on FPN in Pedestrian Detection

Feature extraction in pedestrian detection is a challenging problem due to the different sizes of pedestrians and occlusion in pedestrians. Currently, Feature Pypyramid Networks(FPN) structure is usually used in pedestrian detection networks for feature extraction but aiming at the characteristics of pedestrian detection tasks it may not be effective in extracting important layer feature information. Therefore, this paper proposes a module based on PFN structure with parallel feature fusion named PFF-FPN. PFF-FPN uses three different FPNs to extract feature and fuses the corresponding layer feature to reinforce the focused layer feature information. In pedestrian detection task PFF-FPN can be adapted to different network frameworks and it also gets a good performance.