Reviewerly/exHarmony huggingface dataset:
Unsupervised Domain Adaptation Based on Progressive Transfer for Ship Detection: From Optical to SAR Images

In recent years, Synthetic Aperture Radar (SAR) ship detection methods based on convolutional neural networks have attracted wide attention in remote sensing fields. However, these methods require a large number of labeled SAR images to train the network, where labeling for SAR images is more expensive and time-consuming than that for optical images. To address the problem of lacking labeled SAR images, in this paper, we proposed an unsupervised domain adaptation framework based on progressive transfer for SAR ship detection by transferring knowledge from the optical domain to the SAR domain. Due to the prominent difference between the optical and SAR images, our approach progressively transfers knowledge from three levels: pixel level, feature level and prediction level. At the pixel level, considering the difference in imaging mechanism, we propose a special data augmentation method for ship targets and build the generator with skip-connection based on generative adversarial networks (GANs) to generate transition domain, which can reduce the appearance discrepancy between the optical and SAR images. At the feature level, the detector is trained to learn the domain-invariant features with adversarial alignment. At the prediction level, we further use the predicted pseudo-labels obtained by the feature-aligned detector to learn more discriminative features of the SAR images directly and propose the robust self-training (RST) method to reduce the influence of noisy pseudo-labels on detector training. Specially, RST is formulated as a loss minimization problem for object detection. The experimental results based on the domain adaptation from optical dataset to SAR dataset demonstrate that our approach achieves superior SAR ship detection performance with unlabeled SAR images.
Dropout-VGG Based Convolutional Neural Network for Traffic Sign Categorization

In the modern era of motor vehicles where number of cars running on road are increasing exponentially, the safety of the people driving or walking along the road is being endangered. Traffic signs plays the most important role in ensuring their safety. The signs provide the necessary warning and information to help the driver to drive in order and prevent any potential danger. With the rise in modern technology, the concept of Self-Driving cars is the new hot topic. To ensure the feasibility of such vehicles, the concept of autonomous traffic sign detection and classification needs to be implemented with maximum efficiency and accuracy in real-time. Thus, from the past few years, researchers have shown keen interest in solving as well as optimizing traffic sign classification problem. Numerous approaches were intended in the past to deal with this problem, yet there is still an immense scope of performance optimization to meet the needs in real-time scenarios. Among all solutions proposed, convolutional neural networks (CNN) have emerged to be the most successful approach to classify traffic signs. In this paper, we have proposed a novel CNN model termed as dVGG. This technique is inspired by the Visual Geometry Group-16 (VGG-16) architecture. VGG-16 is based on dropout regularization approach. Moreover, other data processing techniques like shuffling, normalization and gray scaling are applied, resulting in a more consistent dataset which led to faster model generalization. “dVGG” is able to perform better than the VGG-16 model which was implemented through Transfer Learning. We have applied the proposed model on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The model proposed have gave an average accuracy of 98.44% on the GTRSB dataset.
Motion Planning for Closed-Chain Constraints Based on Probabilistic Roadmap With Improved Connectivity

Multiarm systems can perform complex and difficult tasks, such as manipulating a heavy or large object, that cannot be accomplished by a single manipulator owing to workspace and payload limitations. However, the motion planning problem for performing such a task is challenging because of the need to consider the closed-chain constraint. This article proposes an efficient motion planner that considers the closed-chain constraint based on a probabilistic roadmap. The proposed planner utilizes the following strategies. First, the planner obtains feasible nodes by randomly sampling the object pose, followed by computing the inverse kinematics (IK) solution of the multiarm. This can directly find a collision-free node satisfying the closed-chain constraint. Second, the planner repeatedly updates the new IK solution of the multiarm for the start and goal object pose. The IK solution is computed as close as possible to the joint configuration of the neighbor node. Consequently, the planner is more efficient than the existing methods that generate a node by sampling the joint configuration with projection method and have one pair of the start and goal node. Therefore, the planner can efficiently compute the path for object manipulation using a multiarm under a closed-chain constraint. The effectiveness of the proposed planner is validated by comparison with the existing planners in several scenarios. A video clip of the experiments in various scenarios can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/PR9aFf3juu4</uri> .
Single-Server Private Information Retrieval with Sublinear Amortized Time

We construct new private-information-retrieval protocols in the single-server setting. Our schemes allow a client to privately fetch a sequence of database records from a server, while the server answers each query in average time sublinear in the database size. Specifically, we introduce the first single-server private-information-retrieval schemes that have sublinear amortized server time, require sublinear additional storage, and allow the client to make her queries adaptively. Our protocols rely only on standard cryptographic assumptions (decision Diffie-Hellman, quadratic residuosity, learning with errors, etc.). They work by having the client first fetch a small "hint" about the database contents from the server. Generating this hint requires server time linear in the database size. Thereafter, the client can use the hint to make a bounded number of adaptive queries to the server, which the server answers in sublinear time—yielding sublinear amortized cost. Finally, we give lower bounds proving that our most efficient scheme is optimal with respect to the trade-off it achieves between server online time and client storage.
Automatic Short-Answer Grading via BERT-Based Deep Neural Networks

Automatic short-answer grading (ASAG) is a key component of intelligent tutoring systems. Deep learning is an advanced method to deal with recognizing textual entailment tasks in an end-to-end manner. However, deep learning methods for ASAG still remain challenging mainly because of the following two major reasons: 1) high-precision scoring requires a deep understanding of the answer text; and 2) ASAG's corpus is usually small and cannot provide enough training data for deep learning. To address these challenges, in this article, we propose a novel bidirectional encoder representation from transformer (BERT)-based deep neural network framework for ASAG. First, we use a pretrained and fine-tuned BERT model to dynamically encode the answer text, which can effectively overcome the problem of a too small corpus in the ASAG task. Second, to generate a powerful semantic representation for ASAG, we construct a semantic refinement layer to refine the semantics of the BERT outputs, which consists of a bidirectional-Long Short-Term Memory (LSTM) network and a Capsule network with position information in parallel. Third, we propose a triple-hot loss strategy for regression tasks in ASAG, which changes the gold label representation in the standard cross-entropy loss function from one-hot to triple-hot. Experiments demonstrate that our proposed model is effective and outperforms most of the state-of-the-art systems on both the SemEval-2013 dataset and the Mohler dataset. The code is available online at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wuhan-1222/ASAG</uri> .
IEMask R-CNN: Information-Enhanced Mask R-CNN

The instance segmentation task is relatively difficult in computer vision, which requires not only high-quality masks but also high-accuracy instance category classification. Mask R-CNN has been proven to be a feasible method. However, due to the Feature Pyramid Network (FPN) structure lack useful channel information, global information and low-level texture information, and mask branch cannot obtain useful local-global information, Mask R-CNN is prevented from obtaining high-quality masks and high-accuracy instance category classification. Therefore, we proposed the Information-enhanced Mask R-CNN, called IEMask R-CNN. In the FPN structure of IEMask R-CNN, the information-enhanced FPN will enhance the useful channel information and the global information of the feature maps to solve the issues that the high-level feature map loses useful channel information and inaccurate of instance category classification, meanwhile the bottom-up path enhancement with adaptive feature fusion will ultilize the precise positioning signal in the lower layer to enhance the feature pyramid. In the mask branch of IEMask R-CNN, an encoding-decoding mask head will strength local-global information to gain a high-quality mask. Without bells and whistles, IEMask R-CNN gains significant gains of about 2.60%, 4.00%, 3.17% over Mask R-CNN on MS COCO2017, Cityscapes and LVIS1.0 benchmarks respectively.
MANet: Multi-Scale Aware-Relation Network for Semantic Segmentation in Aerial Scenes

Semantic segmentation is an important yet unsolved problem in aerial scenes understanding. One of the major challenges is the intense variations of scenes and object scales. In this paper, we propose a novel multi-scale aware-relation network (MANet) to tackle this problem in remote sensing. Inspired by the process of human perception of multi-scale information, we explore discriminative and diverse multi-scale representations. For discriminative multi-scale representations, we propose an inter-class and intra-class region refinement method (IIRR) to reduce feature redundancy caused by fusion. IIRR utilizes the refinement maps with intra- and inter-class scale variation to guide multi-scale fine-grained features. Then, we propose multi-scale collaborative learning (MCL) to enhance the diversity of multi-scale feature representations. The MCL constrains the diversity of multi-scale feature network parameters to obtain diverse information. And the segmentation results are rectified according to the dispersion of the multi-level network predictions. In this way, MANet can learn multi-scale features by collaboratively exploiting the correlation among different scales. Extensive experiments on image and video datasets which have large scale variations have demonstrated the effectiveness of our proposed MANet.
A Momentum-Accelerated Hessian-Vector-Based Latent Factor Analysis Model

Service-oriented applications commonly involve high-dimensional and sparse (HiDS) interactions among users and service-related entities, e.g., user-item interactions from a personalized recommendation services system. How to perform precise and efficient representation learning on such HiDS interactions data is a hot yet thorny issue. An efficient approach to it is latent factor analysis (LFA), which commonly depends on large-scale non-convex optimization. Hence, it is vital to implement an LFA model able to approximate second-order stationary points efficiently for enhancing its representation learning ability. However, existing second-order LFA models suffer from high computational cost, which significantly reduces its practicability. To address this issue, this paper presents a Momentum-accelerated Hessian-vector algorithm (MH) for precise and efficient LFA on HiDS data. Its main ideas are two-fold: a) adopting the principle of a Hessian-vector-product-based method to utilize the second-order information without manipulating a Hessian matrix directly, and b) incorporating a generalized momentum method into its parameter learning scheme for accelerating its convergence rate to a stationary point. Experimental results on nine industrial datasets demonstrate that compared with state-of-the-art LFA models, an MH-based LFA model achieves gains in both accuracy and convergence rate. These positive outcomes also indicate that a generalized momentum method is compatible with the algorithms, e.g., a second-order algorithm, which implicitly rely on gradients.
Cone-Shaped Space Target Inertia Characteristics Identification by Deep Learning With Compressed Dataset

An effective method for identifying inertia characteristics of cone-shaped space target based on deep learning is proposed. The inertia ratio is determined by the time-varying scattering fields from the cone-shaped targets. The multistatic method is introduced to reduce the evaluation time of time-varying scattering fields. The micro-Doppler spectrogram (MDS) dataset is constructed by the time–frequency analysis with numerical simulation method, point scattering model, and experimental tests. The compressed dataset is further achieved by truncated singular value decomposition (SVD). Finally, the micromotion parameter identification model is constructed to identify the inertia ratio for the cone-shaped space target. The interaction loss function and the feedforward denoising convolutional neural networks (DnCNNs) are employed to improve the identification accuracy. Parameters identification of the precession frequency, precession angle, spin frequency, and inertia ratio with both simulation and experiment datasets demonstrate the validity of the proposed method.
Intelligent Computation Offloading for MEC-Based Cooperative Vehicle Infrastructure System: A Deep Reinforcement Learning Approach

In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy.
Self-Guided Proposal Generation for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) in remote sensing images remains a challenging task when learning object detectors with only image-level labels. As we know, object proposal generation plays a crucial role in WSOD. At present, the proposal generation of most existing WSOD methods mainly relies on heuristic strategies such as selective search and Edge Boxes. However, the proposals obtained by the above methods cannot well cover the entire objects, severely hindering the performance of WSOD. To address this issue, this paper proposes a Self-guided Proposal Generation approach, termed SPG. It can be easily implemented with most WSOD methods in a unified framework. To this end, we first introduce a confidence propagation approach to obtain the objectness confidence map for each image, which on the one hand highlights informative object locations, and on the other hand aggregates discriminative feature representation by combining the objectness confidence map with the deep features. Then, the proposal generation is implemented by mining informative regions as proposals on the objectness confidence map. Extensive evaluations on two challenging datasets demonstrate that our SPG significantly improves the baseline methods Online Instance Classifier Refinement (OICR) and Min-Entropy Latent Model (MELM) by large margins (for OICR: 15.86% mAP and 12.89% CorLoc gains on the NWPU VHR-10.v2 dataset, 3.65% mAP and 4.87% CorLoc gains on the DIOR dataset; for MELM: 20.51% mAP and 23.54% CorLoc gains on the NWPU VHR-10.v2 dataset, 7.11% mAP and 4.96% CorLoc gains on the DIOR dataset) and achieves the state-of-the-art results compared with existing methods.
Neuro-Symbolic Models for Sentiment Analysis

We propose and test multiple neuro-symbolic methods for sentiment analysis. They combine deep neural networks – transformers and recurrent neural networks – with external knowledge bases. We show that for simple models, adding information from knowledge bases significantly improves the quality of sentiment prediction in most cases. For medium-sized sets, we obtain significant improvements over state-of-the-art transformer-based models using our proposed methods: Tailored KEPLER and Token Extension. We show that the cases with the improvement belong to the hard-to-learn set.
Finding permission bugs in smart contracts with role mining

Smart contracts deployed on permissionless blockchains, such as Ethereum, are accessible to any user in a trustless environment. Therefore, most smart contract applications implement access control policies to protect their valuable assets from unauthorized accesses. A difficulty in validating the conformance to such policies, i.e., whether the contract implementation adheres to the expected behaviors, is the lack of policy specifications. In this paper, we mine past transactions of a contract to recover a likely access control model, which can then be checked against various information flow policies and identify potential bugs related to user permissions. We implement our role mining and security policy validation in tool SPCon. The experimental evaluation on labeled smart contract role mining benchmark demonstrates that SPCon effectively mines more accurate user roles compared to the state-of-the-art role mining tools. Moreover, the experimental evaluation on real-world smart contract benchmark and access control CVEs indicates SPCon effectively detects potential permission bugs while having better scalability and lower false-positive rate compared to the state-of-the-art security tools, finding 11 previously unknown bugs and detecting six CVEs that no other tool can find.
An accurate generation of image captions for blind people using extended convolutional atom neural network

Recently, the progress on image understanding and AIC (Automatic Image Captioning) has attracted lots of researchers to make use of AI (Artificial Intelligence) models to assist the blind people. AIC integrates the principle of both computer vision and NLP (Natural Language Processing) to generate automatic language descriptions in relation to the image observed. This work presents a new assistive technology based on deep learning which helps the blind people to distinguish the food items in online grocery shopping. The proposed AIC model involves the following steps such as Data Collection, Non-captioned image selection, Extraction of appearance, texture features and Generation of automatic image captions. Initially, the data is collected from two public sources and the selection of non-captioned images are done using the ARO (Adaptive Rain Optimization). Next, the appearance feature is extracted using SDM (Spatial Derivative and Multi-scale) approach and WPLBP (Weighted Patch Local Binary Pattern) is used in the extraction of texture features. Finally, the captions are automatically generated using ECANN (Extended Convolutional Atom Neural Network). ECANN model combines the CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) architectures to perform the caption reusable system to select the most accurate caption. The loss in the ECANN architecture is minimized using AAS (Adaptive Atom Search) Optimization algorithm. The implementation tool used is PYTHON and the dataset used for the analysis are Grocery datasets (Freiburg Groceries and Grocery Store Dataset). The proposed ECANN model acquired accuracy (99.46%) on Grocery Store Dataset and (99.32%) accuracy on Freiburg Groceries dataset. Thus, the performance of the proposed ECANN model is compared with other existing models to verify the supremacy of the proposed work over the other existing works.
Semi-Supervised Image Deraining Using Knowledge Distillation

Image deraining has achieved considerable progress based on supervised learning with synthetic training pairs, but is usually limited in handling real-world rainy images. Although semi-supervised methods are suggested to exploit real-world rainy images when training deep deraining models, their performances are still notably inferior. To address this crucial issue, this work proposes a semi-supervised image deraining network with knowledge distillation (SSID-KD) for better exploiting real-world rainy images. In particular, the consistency of feature distribution of rain streaks extracted from synthetic and real-world rainy images is enforced by adopting knowledge distillation. Moreover, as for the backbone in SSID-KD, we propose the multi-scale feature fusion module and the pyramid fusion module to better extract deep features of rainy images. SSID-KD can relieve the problem of over-deraining or under-deraining for real-world rainy images, while it can keep comparable performance with supervised deraining methods on several benchmark datasets. Extensive experiments on both synthetic and real-world rainy images have validated that our SSID-KD not only can achieve better deraining results than existing semi-supervised deraining methods but also are quantitatively comparable with state-of-the-art supervised deraining methods. Benefiting from the well exploration of real-world rainy images, our SSID-KD can obtain more visually plausible deraining results. The source code and trained models are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cuiyixin555/SSID-KD</uri> .