Reviewerly/exHarmony huggingface dataset:
A bi-level formulation for multiple kernel learning via self-paced training

• A novel optimization problem in a bi-level formulation for learning an affective multiple kernel. • Capturing the accurate and complex relationships of data via global and local kernel alignments in a shared design. • Evaluating the reliability of the training samples via self-paced learning. • Robustness improvement of the proposed model via automatically absorbing the reliable instances into the model. Multiple kernel learning (MKL) is a crucial issue which has been widely researched over the last two decades. Although existing MKL algorithms have achieved satisfactory performance in a broad range of applications, these methods do not adequately consider the adverse effects of unreliable or less reliable instances. To handle this shortcoming, we formulate multiple kernel learning in a bi-level learning paradigm consisting of the kernel combination weight learning (KWL) stage and the self-paced learning (SPL) stage, which alternatively negotiate with each other. The KWL stage dynamically absorbs reliable instances into model learning to accurately capture neighborhood relationships and obtains kernel coefficients via maximizing both global and local kernel alignment in a common schema. The SPL stage automatically evaluates the reliability of training samples via self-paced training. The extensive experiments indicate the robustness and superiority of the presented approach in comparison with existing MKL methods.
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study

Pseudo-Relevance Feedback (PRF) utilises the relevance signals from the top-k passages from the first round of retrieval to perform a second round of retrieval aiming to improve search effectiveness. A recent research direction has been the study and development of PRF methods for deep language model based rankers, and in particular in the context of dense retrievers. Dense retrievers provide a trade off between effectiveness, which is often reduced compared to more complex neural rankers, and query latency, which also is reduced making the retrieval pipeline more efficient. The introduction of PRF methods for dense retrievers has been motivated as an attempt to further improve their effectiveness. In this paper, we reproduce and study a recent method for PRF with dense retrievers, called ANCE-PRF. This method concatenates the query text and that of the top-k feedback passages to form a new query input, which is then encoded into a dense representation using a newly trained query encoder based on the original dense retriever used for the first round of retrieval. While the method can potentially be applied to any of the existing dense retrievers, prior work has studied it only in the context of the ANCE dense retriever. We study the reproducibility of ANCE-PRF in terms of both its training (encoding of the PRF signal) and inference (ranking) steps. We further extend the empirical analysis provided in the original work to investigate the effect of the hyper-parameters that govern the training process and the robustness of the method across these different settings. Finally, we contribute a study of the generalisability of the ANCE-PRF method when dense retrievers other than ANCE are used for the first round of retrieval and for encoding the PRF signal.
Source Camera Model Identification

Abstract Every camera model acquires images in a slightly different way. This may be due to differences in lenses and sensors. Alternatively, it may be due to the way each vendor applies characteristic image processing operations, from white balancing to compression.
Decision analysis related to solar farm investments based on analysis hierarchical process and fuzzy <scp>AHP</scp> for sustainable energy production

Renewable solar energy is one of the most preferable clean energy sources with no environmental impacts. The Marrakech Safi region (Moroccan context), with its arid and semi-arid climate, can ensure sustainable energy production-oriented for a green economy. This paper aims to perform a decision analysis using the analysis hierarchical process (AHP) and Fuzzy AHP to identify the suitable areas for potential solar farms. The adopted approaches used 15 factors to produce suitability maps. AHP and Fuzzy AHP were used to minimize the subjectivity related to the factor notation, and to improve the result of the models. The factors' weights were reevaluated using the sensitivity analysis to assess their influence by comparing the theoretical and the effective weights. The final map was validated by overlaying other solar projects implemented in the region. Results show that the suitable areas to host the solar farms cover more than 35% (1300 Ha) of the entire region's surface using AHP and Fuzzy AHP. According to the results, there was no significant difference in Decision-Making between the two methods in determining the five highest priorities of attributes selection for solar power plants. Moreover, the similarity analysis performed demonstrated that 32% of unequal areas between AHP and Fuzzy AHP can be explained by the factors' weights and the difference of the used approaches. The climatic potential and the geographic location of the study area offer a large suitable area for the installation of solar farms. These findings constitute decision-making support for solar farm investments in a green economy.
GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations

We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is important for domain scientists to understand the influence of input parameters (e.g., wind stress) on the simulation output (e.g., temperature). The exploration requires scientists to exhaust the complicated parameter space by running a batch of computationally expensive simulations. Our approach improves the efficiency of parameter space exploration with a surrogate model that predicts the simulation outputs accurately and efficiently. Specifically, GNN-Surrogate predicts the output field with given simulation parameters so scientists can explore the simulation parameter space with visualizations from user-specified visual mappings. Moreover, our graph-based techniques are designed for unstructured meshes, making the exploration of simulation outputs on irregular grids efficient. For efficient training, we generate hierarchical graphs and use adaptive resolutions. We give quantitative and qualitative evaluations on the MPAS-Ocean simulation to demonstrate the effectiveness and efficiency of GNN-Surrogate. Source code is publicly available at https://github.com/trainsn/GNN-Surrogate.
Hierarchical Prototype Refinement With Progressive Inter-Categorical Discrimination Maximization for Few-Shot Learning

Metric-based few-shot learning categorizes unseen query instances by measuring their distance to the categories appearing in the given support set. To facilitate distance measurement, prototypes are used to approximate the representations of categories. However, we find prototypical representations are generally not discriminative enough to represent the discrepancy of inter-categorical distribution of queries, thereby limiting the classification accuracy. To overcome this issue, we propose a new Progressive Hierarchical-Refinement (PHR) method, which effectively refines the discrimination of prototypes by conducting the Progressive Discrimination Maximization strategy based on the hierarchical feature representations. Specifically, we first encode supports and queries into the representation space of spatial level, global level, and semantic level. Then, the refining coefficients are constructed by exploring the metric information contained in these hierarchical embedding spaces simultaneously. Under the guidance of the refining coefficients, the meta-refining loss progressively maximizes the discrimination degree of inter-categorical prototypical representations. In addition, the refining vectors are adopted to further enhance the representations of prototypes. In this way, the metric-based classification can be more accurate. Our PHR method shows the competitive performance on the miniImagenet, CIFAR-FS, FC100, and CUB datasets. Moreover, PHR presents good compatibility. It can be incorporated with other few-shot learning models, making them more accurate.
FPGA-based reservoir computing system for ECG denoising

ECG is corrupted by various noises such as baseline wander, channel noise, Electromyogram (EMG) noise, and power line interference (PLI). These noises make diagnosis difficult. In this paper, we implement an inexpensive, portable device that can remove noise from the corrupted ECG signals with high performance, speed, and privacy. First, we apply a Single Node Reservoir Computing (SNRC) architecture to clean the corrupted ECG signal with high performance. Second, we implement our technique on a portable inexpensive FPGA device that enables us to achieve high speed and privacy. In this manuscript, we simulate two noises: the typical EMG and PLI noises. To evaluate our technique, we use three performance metrics, namely, the output SNR improvement (SNRimp), the mean square error (MSE), and the percentage root mean square difference (PRD). The data is collected from the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database. With input SNR = 0 dB, the proposed system achieves SNRimp of 15.8 and PRD of 24.6, in the case of EMG noise and SNRimp of 25.7and PRD of 4.9, in the case of PLI noise. Comparison results to the related works show the advantages of the proposed system.
Transparent Object Reconstruction Based on Compressive Sensing and Super-Resolution Convolutional Neural Network

Abstract The detection and reconstruction of transparent objects have remained challenging due to the absence of their features and variations in the local features with variations in illumination. In this paper, both compressive sensing (CS) and super-resolution convolutional neural network (SRCNN) techniques are combined to capture transparent objects. With the proposed method, the transparent object’s details are extracted accurately using a single pixel detector during the surface reconstruction. The resultant images obtained from the experimental setup are low in quality due to speckles and deformations on the object. However, the implemented SRCNN algorithm has obviated the mentioned drawbacks and reconstructed images visually plausibly. The developed algorithm locates the deformities in the resultant images and improves the image quality. Additionally, the inclusion of compressive sensing minimizes the measurements required for reconstruction, thereby reducing image post-processing and hardware requirements during network training. The result obtained indicates that the visual quality of the reconstructed images has increased from a structural similarity index (SSIM) value of 0.2 to 0.53. In this work, we demonstrate the efficiency of the proposed method in imaging and reconstructing transparent objects with the application of a compressive single pixel imaging technique and improving the image quality to a satisfactory level using the SRCNN algorithm.
Improved Artificial Bee Colony Algorithm for Multimodal Optimization Based on Crowding Method

Many real-world problems can be transformed into multimodal functional optimization. Each of these problems may include several globally optimal solutions, rendering the solution of the problem progressively more difficult. In our study, we present a crowding artificial bee colony, called IABC, which exploits the concepts of crowding and explores search solutions. A crowding approach formed in niches is used to make it capable of tracking and maintaining multiple optima, resulting in good convergence of the search space with a better chance of locating multiple optima. Two new solution search mechanisms are proposed to increase population diversity and explore new search spaces. Experiments were carried out on 14 benchmark functions selected from previous literature. The results of our experiments show that our method is both effective and efficient. In terms of the quality of the success rate, the average number of optima found, and the maximum peak ratio, IABC performs better, or at least comparably, to other cutting-edge approaches.
A robust Poisson multi-Bernoulli filter for multi-target tracking based on arithmetic average fusion

The coalescence and missed detection are two key challenges in Multi-Target Tracking (MTT). To balance the tracking accuracy and real-time performance, the existing Random Finite Set (RFS) based filters are generally difficult to handle the above problems simultaneously, such as the Track-Oriented marginal Multi-Bernoulli/Poisson (TOMB/P) and Measurement-Oriented marginal Multi-Bernoulli/Poisson (MOMB/P) filters. Based on the Arithmetic Average (AA) fusion rule, this paper proposes a novel fusion framework for the Poisson Multi-Bernoulli (PMB) filter, which integrates both the advantages of the TOMB/P filter in dealing with missed detection and the advantages of the MOMB/P filter in dealing with coalescence. In order to fuse the different PMB distributions, the Bernoulli components in different Multi-Bernoulli (MB) distributions are associated with each other by Kullback-Leibler Divergence (KLD) minimization. Moreover, an adaptive AA fusion rule is designed on the basis of the exponential fusion weights, which utilizes the TOMB/P and MOMB/P updates to solve these difficulties in MTT. Finally, by comparing with the TOMB/P and MOMB/P filters, the performance of the proposed filter in terms of accuracy and efficiency is demonstrated in three challenging scenarios.
The Best of Both Worlds: Combining Model-based and Nonparametric Approaches for 3D Human Body Estimation

Nonparametric based methods have recently shown promising results in reconstructing human bodies from monocular images while model-based methods can help correct these estimates and improve prediction. However, estimating model parameters from global image features may lead to noticeable misalignment between the estimated meshes and image evidence. To address this issue and leverage the best of both worlds, we propose a framework of three consecutive modules. A dense map prediction module explicitly establishes the dense UV correspondence between the image evidence and each part of the body model. The inverse kinematics module refines the key point prediction and generates a posed template mesh. Finally, a UV inpainting module relies on the corresponding feature, prediction and the posed template, and completes the predictions of occluded body shape. Our framework leverages the best of non-parametric and model-based methods and is also robust to partial occlusion. Experiments demonstrate that our framework outperforms existing 3D human estimation methods on multiple public benchmarks.
Privacy protected internet of unmanned aerial vehicles for disastrous site identification

Abstract Disastrous site identification through the internet of UAVs is a current research area that aims to improve data sharing by connecting servers to the internet. Internet‐connected unmanned aerial vehicles (UAVs) for aerial image classification necessitated the sharing of large datasets between the number of connected flying machines. The artificial intelligence model training to predict images need to compromise data privacy, consume a lot of energy, and high requirement of data communication. This article aims to propose and implement federated deep learning trained aerial image classification for disastrous sites through the internet of UAVs. The proposed model provides privacy‐preserving and resource efficient deep learning by sharing only models rather than large datasets of images. Multisystem client–server federated learning UAV architecture was implemented and comparatively analyzed on basis of parameters namely, training–testing accuracy, train‐testing loss, RAM‐CPU utilization.
Ontology-based approach to support life cycle engineering: Development of a data and knowledge structure

The data supported consideration of the whole life cycle of a product or component for an optimization of the life cycle and single life cycle stages is a promising research area in Life Cycle Engineering. Here, the acquisition of data along the life cycle is essential and can be achieved through the use of emerging so-called Life Cycle Technologies. For a value-adding use of the data and a target-oriented data acquisition it is important to derive a standardized data and knowledge structure. In this paper, an ontology-based approach is presented to support Life Cycle Engineering. The focus lies on structuring the necessary data and knowledge for approaches to optimize the whole life cycle or single life cycle stages. The approach supports an efficient, application-based use of the data by connecting the required knowledge and the acquired data and is a first step towards a generalized ontology for Life Cycle Engineering.
Garbage Detection Algorithm Based on YOLO v3

In recent years, garbage classification has become the focus of the world. YOLOv3 has good real-time performance and accurate detection accuracy, which can meet the requirements of garbage sorting equipment. In this paper, we introduce a lightweight cross-channel interaction Attention mechanism in the residual unit of YOLOv3, and propose a new target detection algorithm, namely Efficient Channel Attention YOLO (ECA-YOLO) detection algorithm. So the target detection algorithm can get more complete and more effective feature information. Experimental results show that the improved model performs better than YOLOv3 in the garbage image data set of &#x201C;Huawei Garbage Classification Challenge Cup&#x201D;. The improved model has a high accuracy and recall rate, and the detection mAP is improved by 1.07&#x0025;, while the detection speed is kept unchanged and the number of parameters is introduced.
Spatiotemporal dynamics of agricultural areas with central pivot using orbital remote sensing in the Brazilian semiarid

It is essential to know the dynamics of agricultural areas through surface energy balance components such as radiation balance, evapotranspiration, and soil moisture for the sustainable management of water use in irrigation. In the present study, we undertook to monitor and evaluate the spatiotemporal dynamics of an irrigated area with a center pivot in a region of the Brazilian semiarid region, based on physical and water parameters of the energy balance, using remote sensing techniques. The work was developed using three Landsat-8 satellite images, implementing the Surface Energy Balance Algorithms for Land (SEBAL), responsible for extracting geospatial information. In addition, surface meteorological data were used for image corrections and calibrations. The results presented, from thematic maps, monitoring of the spatiotemporal dynamics of irrigated areas, specifying mainly the production of irrigated corn with pivot. Thus, because of the physical-water parameters, photosynthetically active radiation irrigated areas and water deficit were identified, with higher and lower values of the radiation balance, evapotranspiration, and soil moisture, respectively. Therefore, spatiotemporal monitoring of the dynamics of irrigated areas can help in planning and managing the proper management of agricultural systems for the sustainable use of water in the Brazilian semiarid region. The MNDWI proved to be sensitive and efficient to analyze the dynamics of the presence of water in center pivot irrigation and can be an effective part of irrigation management in the region.
A Novel Gray Image Denoising Method Using Convolutional Neural Network

In order to make the image denoising more effective in high noise level environment, we propose a gray image denoising method using convolutional neural network (ConvNet). By constructing symmetric and dilated convolutional residual network and combining leaky ReLU (Rectified Linear Unit) and ReLU dual-functions. Experimental data show that this method achieves better objective results based on small resolution input & large resolution input and small dataset & large dataset compared with other methods. A series of subjective visual comparison results also show that proposed method can perform image denoising well in high noise level environment, and there are no problems such as damaged edge or texture details, boundary artifacts and poor definition of the denoised image. The proposed method not only improves the image denoising ability, but also improves the denoising productivity to some extent. In the related work, we have made a comprehensive discussion on the current mainstream image denoising methods based on traditional machine learning and deep learning, which to some extent makes up for the lack of such reviews. In addition, the existing problems and future development direction of image denoising are discussed in detail.
A Novel Tree-based Neural Network for Android Code Smells Detection

The quality of mobile applications has received broad attention due to the booming development of mobile devices. Detection of Android code smells to refactor codes is an effective way to eliminate potential risks of applications. Besides traditional methods based on manually selected features, deep learning models provide an additional perspective of detection solutions. Though deep learning models are dispensed with artificial decisions, the paucity of dedicated datasets in Android code smells. The inadequate embedded syntax and semantics information acquired from source codes by token-based neural networks become new challenges. These obstacles will seriously affect the prediction performance of models. This paper put forward ACS-TNN, a novel Tree-based Neural Network approach for Android Code Smells detection. ACS-TNN took advantage of RvNN and LSTM while introducing ASTs for the first time in order to retain maximum semantic and structural information for extracting features of source codes. We constructed a dataset from practical open-source Android projects and labeled Java files data with popular Android code smells. The dataset would help researchers to train and test their deep learning models. Numerous experiments were conducted to investigate the prediction performance of ACS-TNN. The conclusions were that compared with two state-of-the-art token-based deep neural networks and another natural language tree-based model, ACS-TNN gained the best prediction performance. Further, ACS-TNN and our dataset are conducive to the maintenance of mobile applications.