Reviewerly/exHarmony huggingface dataset:
Band

The rapid development of deep learning algorithms, as well as innovative hardware advancements, encourages multi-DNN workloads such as augmented reality applications. However, existing mobile inference frameworks like TensorFlow Lite and MNN fail to efficiently utilize heterogeneous processors available on mobile platforms, because they focus on running a single DNN on a specific processor. As mobile processors are too resource-limited to deliver reasonable performance for such workloads by their own, it is challenging to serve multi-DNN workloads with existing frameworks.
Denoised Maximum Classiﬁer Discrepancy for Source-Free Unsupervised Domain Adaptation

Source-Free Unsupervised Domain Adaptation(SFUDA) aims to adapt a pre-trained source model to an unlabeled target domain without access to the original labeled source domain samples. Many existing SFUDA approaches apply the self-training strategy, which involves iteratively selecting confidently predicted target samples as pseudo-labeled samples used to train the model to fit the target domain. However, the self-training strategy may also suffer from sample selection bias and be impacted by the label noise of the pseudo-labeled samples. In this work, we provide a rigorous theoretical analysis on how these two issues affect the model generalization ability when applying the self-training strategy for the SFUDA problem. Based on this theoretical analysis, we then propose a new Denoised Maximum Classifier Discrepancy (D-MCD) method for SFUDA to effectively address these two issues. In particular, we first minimize the distribution mismatch between the selected pseudo-labeled samples and the remaining target domain samples to alleviate the sample selection bias. Moreover, we design a strong-weak self-training paradigm to denoise the selected pseudo-labeled samples, where the strong network is used to select pseudo-labeled samples while the weak network helps the strong network to filter out hard samples to avoid incorrect labels. In this way, we are able to ensure both the quality of the pseudo-labels and the generalization ability of the trained model on the target domain. We achieve state-of-the-art results on three domain adaptation benchmark datasets, which clearly validates the effectiveness of our proposed approach. Full code is available at https://github.com/kkkkkkon/D-MCD.
Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting

Time series data appears in many real-world fields such as energy, transportation, communication systems. Accurate modelling and forecasting of time series data can be of significant importance to improve the efficiency of these systems. Extensive research efforts have been taken for time series problems. Different types of approaches, including both statistical-based methods and machine learning-based methods, have been investigated. Among these methods, ensemble learning has shown to be effective and robust. However, it is still an open question that how we should determine weights for base models in the ensemble. Sub-optimal weights may prevent the final model from reaching its full potential. To deal with this challenge, we propose a reinforcement learning (RL) based model combination (RLMC) framework for determining model weights in an ensemble for time series forecasting tasks. By formulating model selection as a sequential decision-making problem, RLMC learns a deterministic policy to output dynamic model weights for non-stationary time series data. RLMC further leverages deep learning to learn hidden features from raw time series data to adapt fast to the changing data distribution. Extensive experiments on multiple real-world datasets have been implemented to showcase the effectiveness of the proposed method.
Exponential Particle Swarm Optimization for Global Optimization

Nature-inspired metaheuristics have been extensively investigated to solve challenging optimization problems. Particle Swarm Optimization (PSO) is one of the most famous nature-inspired algorithms owing to its simplicity and ability to be used in a wide range of applications. This paper presents an extended PSO variant, namely, Exponential Particle Swarm Optimization (ExPSO). To effectively explore the whole search space, the proposed algorithm divides the swarm population into three equal subpopulations and employs a new search strategy based on an exponential function (permitting particles to make leaps in the search space) and an adapted control of the velocity range of each particle (to balance the exploration and exploitation search phases). The leaping strategy is integrated into the velocity equation and a new linear decreasing cognitive parameter (including a dynamic inertia weight strategy) is integrated into the proposed method. The developed algorithm allows large jumps at the beginning of the search, and then small jumps for further improvements in specific regions of the solution search space. Our variant approach, ExPSO, has been intensively tested through a comparison with eight other well-known heuristic search algorithms, over 29 benchmark problems, and real optimization engineering problems. The Wilcoxon signed-rank test and Friedman rank have been applied to analyze the search performance of the algorithms. The comparisons and statistical results show that the exponential search strategy significantly contributes to the search process and proves the superiority of ExPSO in terms of the convergence velocity and optimization accuracy.
A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem

Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness and precision of processed primary sources. However, recycling operations are often labor intensive, and computer vision and deep learning (DL) techniques aid in automatically detecting and classifying trash types during recycling chores. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This research applies a new meta-heuristic algorithm called the artificial hummingbird algorithm (AHA) to solving the waste classification problem based on feature selection. However, the performance of the AHA is barely satisfactory; it may be stuck in optimal local regions or have a slow convergence. To overcome these limitations, this paper develops two improved versions of the AHA called the AHA-ROBL and the AHA-OBL. These two versions enhance the exploitation stage by using random opposition-based learning (ROBL) and opposition-based learning (OBL) to prevent local optima and accelerate the convergence. The main purpose of this paper is to apply the AHA-ROBL and AHA-OBL to select the relevant deep features provided by two pre-trained models of CNN (VGG19 &amp; ResNet20) to recognize a waste classification. The TrashNet dataset is used to verify the performance of the two proposed approaches (the AHA-ROBL and AHA-OBL). The effectiveness of the suggested methods (the AHA-ROBL and AHA-OBL) is compared with that of 12 modern and competitive optimizers, namely the artificial hummingbird algorithm (AHA), Harris hawks optimizer (HHO), Salp swarm algorithm (SSA), aquila optimizer (AO), Henry gas solubility optimizer (HGSO), particle swarm optimizer (PSO), grey wolf optimizer (GWO), Archimedes optimization algorithm (AOA), manta ray foraging optimizer (MRFO), sine cosine algorithm (SCA), marine predators algorithm (MPA), and rescue optimization algorithm (SAR). A fair evaluation of the proposed algorithms’ performance is achieved using the same dataset. The performance analysis of the two proposed algorithms is applied in terms of different measures. The experimental results confirm the two proposed algorithms’ superiority over other comparative algorithms. The AHA-ROBL and AHA-OBL produce the optimal number of selected features with the highest degree of precision.
UAVformer: A Composite Transformer Network for Urban Scene Segmentation of UAV Images

Urban scenes segmentation based on UAV (Unmanned aerial vehicle) view is a fundamental task for the applications of smart city such as city planning, land use monitoring, traffic monitoring, and crowd estimation. While urban scenes in UAV image characteristic by large scale variation of objects size and complexity background, which posed challenges to urban scenes segmentation of UAV image. The feature extracting backbone of existing networks cannot extract complex features of UAV image effectively, which limits the performance of urban scenes segmentation. To design segmentation network capable of extracting features of large scale variation urban ground scenes, this study proposed a novel composite transformer network for urban scenes segmentation of UAV image. A composite backbone with aggregation windows multi-head self-attention transformer blocks is proposed to make the extracted features more representatives by adaptive multi-level features fusion, and the full utilisation of contextual information and local information. Position attention modules are inserted in each stage between encoder and decoder to further enhance the spatial attention of extracted feature maps. Finally, a V-shaped decoder which is capable of utilising multi-level features is designed to get accurately dense prediction. The accuracy of urban scenes segmentation could significantly be enhanced in this way and successfully segmented the large scale variation objects from UAV views. Extensive ablation experiments and comparative experiments for the proposed network have been conducted on the public available urban scenes segmentation datasets for UAV imagery. Experimental results have demonstrated the effectiveness of designed network structure and the superiority of proposed network over state-of-the-art methods. Specifically, reached 53.2% mIoU on the UAVid dataset and 77.6% mIoU on the UDD6 dataset, respectively.
BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Blood cells carry important information that can be used to represent a person's current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet's architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.
Show, Deconfound and Tell: Image Captioning with Causal Inference

The transformer-based encoder-decoder framework has shown remarkable performance in image captioning. However, most transformer-based captioning methods ever overlook two kinds of elusive confounders: the visual confounder and the linguistic confounder, which generally lead to harmful bias, induce the spurious correlations during training, and degrade the model generalization. In this paper, we first use Structural Causal Models (SCMs) to show how two confounders damage the image captioning. Then we apply the backdoor adjustment to propose a novel causal inference based image captioning (CIIC) framework, which consists of an interventional object detector (IOD) and an interventional transformer decoder (ITD) to jointly confront both confounders. In the encoding stage, the IOD is able to disentangle the region-based visual features by deconfounding the visual confounder. In the decoding stage, the ITD introduces causal intervention into the transformer decoder and deconfounds the visual and linguistic confounders simultaneously. Two modules collaborate with each other to alleviate the spurious correlations caused by the unobserved confounders. When tested on MSCOCO, our proposal significantly outperforms the state-of-the-art encoder-decoder models on Karpathy split and online test split. Code is published in https://github.com/CUMTGG/CIIC.
BokehMe: When Neural Rendering Meets Classical Rendering

We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape. To this end, we analyze the errors from the classical scattering-based method and derive a formulation to calculate an error map. Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer. The neural renderer employs a dynamic multi-scale scheme to efficiently handle arbitrary blur sizes, and it is trained to handle imperfect disparity input. Experiments show that our method compares favorably against previous methods on both synthetic image data and real image data with predicted disparity. A user study is further conducted to validate the advantage of our method.
A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection

Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.
Identifying the Most Efficient Natural Fibre for Common Commercial Building Insulation Materials with an Integrated PSI, MEREC, LOPCOW and MCRAT Model

Building insulation is the most respected among the compatible and effective energy conservation technologies available today, as it also reduces yearly energy costs and negative environmental effects. A building envelope is made up of various insulation materials that are important in determining a building’s thermal performance. Proper insulation material selection concludes in less energy requisition for operation. The purpose of this research is to supply information about natural fibre insulating materials used in construction insulation to maintain energy efficiency, as well as to recommend the most efficient natural fibre insulation material. As in most decision-making problems, several criteria and alternatives are involved in insulation material selection, too. Therefore, we used a novel integrated multi criteria decision making (MCDM) model including the preference selection index (PSI), method based on the removal effects of criteria (MEREC), logarithmic percentage change-driven objective weighting (LOPCOW), and multiple criteria ranking by alternative trace (MCRAT) methods to deal with the complexity of numerous criteria and alternatives. The contribution of this study is that a new hybrid MCDM method is developed. Additionally, the number of studies using the MCRAT method is very limited in the literature; therefore, this study will provide more insights into and results of this method to the literature.
Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report

We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography aim to reach the visual quality of full-frame cameras. Now, a goal in computational photography is to optimize the Bokeh effect itself, which is the aesthetic quality of the blur in out-of-focus areas of an image. Photographers create this aesthetic effect by benefiting from the lens optical properties.The aim of this work is to design a neural network capable of converting the the Bokeh effect of one lens to the effect of another lens without harming the sharp foreground regions in the image. For a given input image, knowing the target lens type, we render or transform the Bokeh effect accordingly to the lens properties. We build the BETD using two full-frame Sony cameras, and diverse lens setups.To the best of our knowledge, we are the first attempt to solve this novel task, and we provide the first BETD dataset and benchmark for it. The challenge had 99 registered participants. The submitted methods gauge the state-of-the-art in Bokeh effect rendering and transformation.
Harnessing the power of synthetic data in healthcare: innovation, application, and privacy

Abstract Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use.
A Framework for the Semi-Automatic Testing of Video Games

Game environments are complex interactive systems that require extensive analysis and testing to ensure that they are at a high enough quality to be released commercially. In particular, the last build of the product needs an additional and extensive beta test carried out by people that play the game in order to establish its robustness and playability. This entails additional costs from the viewpoint of a company as it requires the hiring of play testers. In the present work we propose a general software framework that integrates Artificial Intelligence (AI) Agents and Computer Vision (CV) technologies to support the test team and help to improve and accelerate the test process. We also present a prototype shadow alias detection algorithm that illustrates the effectiveness of the framework in developing automated visual debugging technology that will ease the heavy cost of beta testing games.
Improving Twitter Retrieval by Exploiting Structural Information

Most Twitter search systems generally treat a tweet as a plain text when modeling relevance. However, a series of conventions allows users to tweet in structural ways using combination of different blocks of texts.These blocks include plain texts, hashtags, links, mentions, etc. Each block encodes a variety of communicative intent and sequence of these blocks captures changing discourse. Previous work shows that exploiting the structural information can improve the structured document (e.g., web pages) retrieval. In this paper we utilize the structure of tweets, induced by these blocks, for Twitter retrieval. A set of features, derived from the blocks of text and their combinations, is used into a learning-to-rank scenario. We show that structuring tweets can achieve state-of-the-art performance. Our approach does not rely upon social media features, but when we do add this additional information, performance improves significantly.
Automatic Rule Ordering for Dynamic Scripting

The goal of adaptive game AI is to enhance computer-controlled game-playing agents with (1) the ability to self-correct mistakes, and (2) creativity in responding to new situations. Dynamic scripting is a reinforcement learning technique that realises fast and reliable online adaptation of game AI. It employs knowledge bases which contain rules that can be included in game scripts. To be successful, dynamic scripting requires a mechanism to order the rules that are selected for scripts. So far, rule ordering was achieved by a manually-tuned priority value for each rule. In the present research, we propose three mechanisms to order rules automatically for dynamic scripting. We performed experiments in which we let dynamic scripting, using each of the three mechanisms, play against manually-designed tactics. Our results show that dynamic scripting with automatic rule ordering generates game AI that is at least as effective as dynamic scripting with manually-tuned priority values. Moreover, it has the ability to generate novel game AI with significantly increased effectiveness. The costs are a slight decrease in learning efficiency. So, we may conclude that automatic rule ordering is a valuable enhancement for dynamic scripting.