Reviewerly/exHarmony huggingface dataset:
FA-YOLO: An Improved YOLO Model for Infrared Occlusion Object Detection under Confusing Background

Infrared target detection is a popular applied field in object detection as well as a challenge. This paper proposes the focus and attention mechanism-based YOLO (FA-YOLO), which is an improved method to detect the infrared occluded vehicles in the complex background of remote sensing images. Firstly, we use GAN to create infrared images from the visible datasets to make sufficient datasets for training as well as using transfer learning. Then, to mitigate the impact of the useless and complex background information, we propose the negative sample focusing mechanism to focus on the confusing negative sample training to depress the false positives and increase the detection precision. Finally, to enhance the features of the infrared small targets, we add the dilated convolutional block attention module (dilated CBAM) to the CSPdarknet53 in the YOLOv4 backbone. To verify the superiority of our model, we carefully select 318 infrared occluded vehicle images from the VIVID-infrared dataset for testing. The detection accuracy-mAP improves from 79.24% to 92.95%, and the F1 score improves from 77.92% to 88.13%, which demonstrates a significant improvement in infrared small occluded vehicle detection.
Ensemble solar forecasting and post-processing using dropout neural network and information from neighboring satellite pixels

Ensemble weather forecasts are often found to be under-dispersed and biased. Post-processing using spatio-temporal information is, therefore, required if one wishes to improve the quality of the raw forecasts. It is on this account that the present article generates and post-processes ensemble solar forecasts using satellite-derived irradiance not only from the focal pixel but also from the neighboring pixels. The ensemble forecasting model of choice is a dropout neural network with Monte Carlo sampling, eliminating the need for training multiple models and ensuring parameter diversity in ensemble forecasting. Subsequently, ensemble forecasts are post-processed using both parametric and nonparametric post-processing techniques, such as nonhomogenous regression, generalized additive model, linear quantile regression, or quantile random forests. The proposed forecasting framework is demonstrated and verified using four years of half-hourly data, at seven locations in the United States. Continuous ranked probability skill scores as high as 66% have been obtained when comparing the proposed method to a conditional climatology reference. The content of this article may be useful to a wide range of stakeholders in the power system, including but not limited to: independent system operators, who aim at efficiently maintaining the system’s reliability; utility- and distributed-scale PV plant owners, who wish to avoid penalties for power deviation between the scheduled and real-time delivery; and forecast retailers, who can benefit from selling solar forecasts of higher quality.
Deep semi-supervised learning via dynamic anchor graph embedding in latent space

Recently, deep semi-supervised graph embedding learning has drawn much attention for its appealing performance on the data with a pre-specified graph structure, which could be predefined or empirically constructed based on given data samples. However, the pre-specified graphs often contain considerable noisy/inaccurate connections and have a huge size for large datasets. Most existing embedding algorithms just take the graph off the shelf during the whole training stage and thus are easy to be misled by the inaccurate graph edges, as well as may result in large model size. In this paper, we attempt to address these issues by proposing a novel deep semi-supervised algorithm for simultaneous graph embedding and node classification, utilizing dynamic graph learning in neural network hidden layer space. Particularly, we construct an anchor graph to summarize the whole dataset using the hidden layer features of a consistency-constrained network. The anchor graph is used for sampling node neighborhood context, which is then presented together with node labels as contextual information to train an embedding network. The outputs of the consistency network and the embedding networks are finally concatenated together to pass a softmax function to perform node classification. The two networks are optimized jointly using both labeled and unlabeled data to minimize a single semi-supervised objective function, including a cross-entropy loss, a consistency loss and an embedding loss. Extensive experimental results on popular image and text datasets have shown that the proposed method is able to improve the performance of existing graph embedding and node classification methods, and outperform many state-of-the-art approaches on both types of datasets.
Automated Runtime-Aware Scheduling for Multi-Tenant DNN Inference on GPU

With the fast development of deep neural networks (DNNs), many real-world applications are adopting multiple models to conduct compound tasks, such as co-running classification, detection, and segmentation models on autonomous vehicles. Such multi-tenant DNN inference cases greatly exacerbate the computational complexity and call for comprehensive collaboration for graph-level operator scheduling, runtime-level resource awareness, as well as hardware scheduler support. However, the current scheduling support for such multi-tenant inference is still relatively backward. In this work, we propose a resource-aware scheduling framework for efficient multi-tenant DNN inference on GPU, which automatically coordinates DNN computing in different execution levels. Leveraging the unified scheduling intermediate representation and the automated ML-based searching algorithm, optimal schedules could be generated to wisely adjust model concurrency and interleave DNN model operators, maintaining a continuously balanced resource utilization across the entire inference process, and eventually improving the runtime efficiency. Experiments show that we could consistently achieve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.3\times\sim 1.7\times$</tex> speed-up, comparing to regular DNN runtime libraries (e.g., CuDNN, TVM) and particular concurrent scheduling methods (e.g., NVIDIA Multi-Stream).
Streamlining Evaluation with ir-measures

We present ir-measures, a new tool that makes it convenient to calculate a diverse set of evaluation measures used in information retrieval. Rather than implementing its own measure calculations, ir-measures provides a common interface to a handful of evaluation tools. The necessary tools are automatically invoked (potentially multiple times) to calculate all the desired metrics, simplifying the evaluation process for the user. The tool also makes it easier for researchers to use recently-proposed measures (such as those from the C/W/L framework) alongside traditional measures, potentially encouraging their adoption.
Quantum simulation of molecules without fermionic encoding of the wave function

Molecular simulations generally require fermionic encoding in which fermion statistics are encoded into the qubit representation of the wave function. Recent calculations suggest that fermionic encoding of the wave function can be bypassed, leading to more efficient quantum computations. Here we show that the energy can be expressed as a functional of the two-electron reduced density matrix (2-RDM) where the 2-RDM is a unique functional of the unencoded $N$-qubit-particle wave function. Contrasts are made with current hardware-efficient methods. An application to computing the ground-state energy and 2-RDM of H$_{4}$ is presented.
Faster Region-based Convolutional Neural Network for Mask Face Detection

We present two-stage detection approach, Faster Region-based Convolutional Network, Faster R-CNN for masked face detection. In this face detection, we localize the face object in the image and classify the face object based on mask occlusion on the face. There are three classes: a face without any mask, the second is an incorrectly masked face, and the third is a face with correctly masking. The first stage of the detection is finding the candidate regions of the target object. This stage uses the Region Proposal Network (RPN). Then, the candidate regions are fed into the last pooling layer of the Faster R-CNN identified as the ROI Pooling layer. The model is trained using MAFA and AFLW datasets. The mean Average Precision of trained model for all classes is 0.73, with the highest accuracy is obtained by the face without mask class, and the lowest accuracy is the incorrectly masked face class.
Joint image denoising with gradient direction and edge-preserving regularization

Joint image denoising algorithms use the structures of the guidance image as a prior to restore the noisy target image. While the provided guidance images are helpful to improve the denoising performance, the denoised edges are most likely to be blurred especially when the edges of the guidance image are weak or inexistent. To address this weakness, this paper proposes a new gradient-direction-based joint image denoising method in which the absolute cosine value of the angle between two gradient vectors of the guidance image and those of the image to recover is employed as the parallel measurement to ensure that the gradient directions of the denoised image are approximately the same as or opposite to those of the guidance image. Besides, a new edge-preserving regularization term is developed to alleviate the effects of the unreliable prior information from guidance image. To simplify the resultant complex nonconvex and nonlinear fractional model, the logarithm function is employed to convert the multiplication operation into addition operation. Then, we construct the surrogate function for the logarithmic term of l2-norm, and separate the variables to transform the objective function into convex one with high numerical stability while retaining high efficiency. Finally, the optimal solutions can be obtained by directly minimizing the convex functions. Experimental results on public datasets and from nine benchmark methods consistently demonstrate the effectiveness of the proposed method both visually and quantitatively.
Backward movement oriented shark smell optimization-based audio steganography using encryption and compression strategies

Audio steganography is the process of hiding secret information such as images, text, or video into the audio file. Data hiding in audio signals has been utilized in diverse applications like secret communication, hiding of data that may affect the safety and security of personnel and governments. Most of the recently introduced audio steganography approaches are content non-adaptive that may suffer low embedding capability and poor security. This paper proposes the audio steganography model for secure audio transmission during communication based on the meta-heuristic concept. In the embedding phase, the optimized instant selection is made for embedding the secret message (image) into the input audio. Further, the secret message to be embedded is encrypted using the optimized 2D-Logistic Chaotic Map. Here, the development of optimal instant selection and the optimized 2D-Logistic Chaotic Map are performed by the improved Shark Smell Optimization (SSO) called Backward Movement oriented SSO (BM-SSO). Once the secret message is encrypted, the compression of the message is done by Modified Huffman Encoding (MHE). Finally, the extraction phase uses the optimized 2D-Logistic Chaotic Map-based decryption. The proposed audio steganography by optimized instant selection for audio embedding minimizes the distortion error between the stego audio and original audio in terms of Mean Squared Error (MSE), and optimized 2D-Logistic Chaotic Map-based encryption aims to maximize the entropy of cipher image. The performance of the developed model has been estimated extensively against attacks, and simulation results are presented to prove the robustness of the proposed algorithm.
A systematic process for Mining Software Repositories: Results from a systematic literature review

Mining Software Repositories (MSR) is a growing area of Software Engineering (SE) research. Since their emergence in 2004, many investigations have analysed different aspects of these studies. However, there are no guidelines on how to conduct systematic MSR studies. There is a need to evaluate how MSR research is approached to provide a framework to do so systematically. To identify how MSR studies are conducted in terms of repository selection and data extraction. To uncover potential for improvement in directing systematic research and providing guidelines to do so. A systematic literature review of MSR studies was conducted following the guidelines and template proposed by Mian et al. (which refines those provided by Kitchenham and Charters). These guidelines were extended and revised to provide a framework for systematic MSR studies. MSR studies typically do not follow a systematic approach for repository selection, and many do not report selection or data extraction protocols. Furthermore, few manuscripts discuss threats to the study’s validity due to the selection or data extraction steps followed. Although MSR studies are evidence-based research, they seldom follow a systematic process. Hence, there is a need for guidelines on how to conduct systematic MSR studies. New guidelines and a template have been proposed, consolidating related studies in the MSR field and strategies for systematic literature reviews.
Cloud Computing Task Scheduling Model Based on Improved Whale Optimization Algorithm

The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling.
MDA + RRT: A general approach for resolving the problem of angle constraint for hyper-redundant manipulator

In this paper, a cable-driven hyper-redundant manipulator with 17 degrees of freedom is presented for narrow and complex environments firstly. Based on the mechanical structure, the MDA + RRT algorithms is proposed for path planning considering the maximum deflection angle of joint. First, the transformation relationship between the path parameters and manipulator parameters is obtained through the theorem of sine of triangles and two extreme points, which can convert limitation of the deflection angle of the joint to the limitation of the deflection angle of the path. Then, three corresponding improved algorithms are proposed: MDA-RRT, MDA-RRT*, MDA-QRRT* on the basis of the traditional RRT, RRT* and Q-RRT* algorithms. Finally, six algorithms are applied to plan the path of avoiding three different obstacles respectively by simulation. The results demonstrate that, three improved algorithms can guarantee that the planned path satisfies the constraint of deflection angle of joint without increasing the computational amount compared with the three traditional algorithms, only by limiting the selection range of the next random vertex. Among the six algorithms, the feasibility and optimization of the MDA-QRRT* algorithm are the best and the feasibility and optimization of the three improved algorithms are also better than the corresponding traditional algorithms.
A robust model predictive path following controller for an Autonomous Underwater Vehicle

A robust model predictive controller (RMPC) based on the H∞ criterion is proposed for accomplishing effective path following motion plan of an Autonomous Underwater Vehicle (AUV) in face of uncertainties, external disturbances, and actuator constraints. A factorized Extreme Learning Machine (FELM) dynamic model of an AUV is proposed in this paper to identify the dynamics of AUV. Further, the parameters of the FELM model are updated at every sampling instant to accommodate any parameter variations in the AUV dynamics. The aforesaid FELM dynamic model is used to design a robust model predictive controller using Linear Matrix Inequality. The simulation and experimental results envisage that a robust path following performance is achieved in face of uncertainties and disturbances whilst avoiding actuator saturation. A comparison in terms of tracking performance is carried out between the proposed controller and the H∞ state feedback controller. From the obtained simulation and experimental results, it is inferred that, in face of actuator constraints and external disturbances, the proposed controller demonstrates efficient tracking performance compared to the H∞ state feedback controller.
Addressing scale imbalance for small object detection with dense detector

There are severe challenges on small object detection when using general object detector, especially scale imbalance on samples and features. Anchor-based detector performs poorly on small object detection because IoUs are too low to regress the objects. Key-point detector is hard to detect small objects for containing little semantic information. In contrast, a dense detector which directly learns an object via dense points is potential for small object detection. But they only catch fewer small objects because few of them can address scale imbalance brought by label assignment and feature extraction. We propose a dense detector named Libra EBox which includes Libra Ellipse Sampling (LES) and Residual Low-level Feature Enhancement (RiLFE). LES regulates positive regions of various objects by rescaling an ellipse box to catch samples of small object as much as possible. RiLFE is designed by several low-level feature maps to enhance small object’s feature representation. Experimental results show that our Libra EBox outperforms FoveaBox, FCOS and RetinaNet by 2.0% AP, 1.7% AP and 2.2% AP respectively for small objects on MS-COCO test-dev, and also outperforms most popular dense detectors on VisDrone-DET2018 and TinyPerson.
Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

In this paper, we tackle the task of similar question retrieval (QR) which is essential for Community Question Answering (cQA) and aims to retrieve historical questions that are semantically equivalent to the new queries. Over time, with the sharp increase of community archives and the accumulation of duplicated questions, the QR problem has become increasingly challenging due to the shortness of the community questions as well as the word mismatch problem as users can formulate the same query using different wording. Although many efforts have been devoted to address this problem, existing methods mostly relied on supervised models which significantly depend on massive training data sets and manual feature engineering. Such methods are chiefly constrained by their specificities that ignore the word order and do not capture enough syntactic and semantic information in questions. In this paper, we rely on Neural Networks (NNs) which use a deep analysis of words and questions to take into consideration the semantics as well as the structure of questions to predict the semantic text similarity. We propose a deep learning approach based on a Siamese architecture with Long Short-Term Memory (LSTM) networks, augmented with an attention mechanism to let the model give different words different attention while modeling questions. We also explore the use of Convolutional Neural Networks (CNN) nested within the Siamese architecture to retrieve relevant questions. Different similarity measures were tested to predict the semantic similarity between the pairs of questions. To evaluate the proposed approach, we conducted experiments on large-scale datasets in English and Arabic.
Multi-label modality enhanced attention based self-supervised deep cross-modal hashing

The recent deep cross-modal hashing (DCMH) has achieved superior performance in effective and efficient cross-modal retrieval and thus has drawn increasing attention. Nevertheless, there are still two limitations for most existing DCMH methods: (1) single labels are usually leveraged to measure the semantic similarity of cross-modal pairwise instances while neglecting that many cross-modal datasets contain abundant semantic information among multi-labels. (2) several DCMH methods utilized the multi-labels to supervise the learning of hash functions. Nevertheless, the feature space of multi-labels suffers the weakness of sparse, resulting in sub-optimization for the hash functions learning. Thus, this paper proposed a multi-label modality enhanced attention-based self-supervised deep cross-modal hashing (MMACH) framework. Specifically, a multi-label modality enhanced attention module is designed to integrate the significant features from cross-modal data into multi-labels feature representations, aiming to improve its completion. Moreover, a multi-label cross-modal triplet loss is defined based on the criterion that the feature representations of cross-modal pairwise instances with more common categories should preserve higher semantic similarity than other instances. To the best of our knowledge, the multi-label cross-modal triplet loss is the first time designed for cross-modal retrieval. Extensive experiments on four multi-label cross-modal datasets demonstrate the effectiveness and efficiency of our proposed MMACH. Moreover, the MMACH also achieved superior performance and outperformed several state-of-the-art methods on the task of cross-modal retrieval. The source code of MMACH is available at https://github.com/SWU-CS-MediaLab/MMACH.
AL-Net: Attention Learning Network Based on Multi-Task Learning for Cervical Nucleus Segmentation

Cervical nucleus segmentation is a crucial and challenging issue in automatic pathological diagnosis due to uneven staining, blurry boundaries, and adherent or overlapping nuclei in nucleus images. To overcome the limitation of current methods, we propose a multi-task network based on U-Net for cervical nucleus segmentation. This network consists of a primary task and an auxiliary task. The primary task is employed to predict nuclei regions. The auxiliary task, which predicts the boundaries of nuclei, is designed to improve the feature extraction of the main task. Furthermore, a context encoding layer is added behind each encoding layer of the U-Net. The output of each context encoding layer is processed by an attention learning module and then fused with the features of the decoding layer. In addition, a codec block is used in the attention learning module to obtain saliency-based attention and focused attention simultaneously. Experiment results show that the proposed network performs better than the state-of-the-art methods on the 2014 ISBI dataset, BNS, MoNuSeg, and our nucluesSeg dataset.