Reviewerly/exHarmony huggingface dataset:
Learning comprehensive global features in person re-identification: Ensuring discriminativeness of more local regions

• A novel baseline for person re-identification is proposed to learn comprehensive global embedding, ensuring that more local regions (the number of local regions is manually defined) of global feature maps are discriminative. • A Non-parameterized Local Classifier (NLC) module is designed to obtain a score vector of each local region on feature maps in a non-parametric manner. • A Comprehensive Global Embedding (CGE) module is designed to revise the global logits such that the subsequent cross entropy loss up-weights the loss assigned to samples with hard-to-learn local regions. • The network achieves 65.9% mAP, 85.1% rank1 on MSMT17, 86.4% mAP, 87.4% rank1 on CUHK03 labeled, 84.2% mAP, 85.9% rank1 on CUHK03 detected, and 92.2% mAP, 96.3% rank1 on Market-1501. Person re-identification (Re-ID) aims to retrieve person images from a large gallery given a query image of a person of interest. Global information and fine-grained local features are both essential for the representation. However, global embedding learned by naive classification model tends to be trapped in the most discriminative local region, leading to poor evaluation performance. To address the issue, we propose a novel baseline network that learns strong global feature termed as Comprehensive Global Embedding (CGE), ensuring more local regions of global feature maps to be discriminative. In this work, two key modules are proposed including Non-parameterized Local Classifier (NLC) and Global Logits Revise (GLR). The NLC is designed to obtain a score vector of each local region on feature maps in a non-parametric manner. The GLR module directly revises the logits such that the subsequent cross entropy loss up-weights the loss assigned to samples with hard-to-learn local regions. The convergence of the deep model indicates more local regions (the number of local regions is manually defined) on the feature maps of each sample are discriminative. We implement these two modules on two strong baseline methods including the BagTricks (BOT) [1] and AGW [2]. The network achieves 65.9% mAP, 85.1% rank1 on MSMT17, 86.4% mAP, 87.4% rank1 on CUHK03 labeled, 84.2% mAP, 85.9% rank1 on CUHK03 detected, and 92.2% mAP, 96.3% rank1 on Market-1501. The results show that the proposed baseline achieves a new state-of-the-art when using only global embedding during inference without any re-ranking technique.
Efficient Dynamic Phishing Safeguard System Using Neural Boost Phishing Protection

The instances of privacy and security have reached the point where they cannot be ignored. There has been a rise in data breaches and fraud, particularly in banks, healthcare, and government sectors. In today’s world, many organizations offer their security specialists bug report programs that help them find flaws in their applications. The breach of data on its own does not necessarily constitute a threat or attack. Cyber-attacks allow cyberpunks to gain access to machines and networks and steal financial data and esoteric information as a result of a data breach. In this context, this paper proposes an innovative approach to help users to avoid online subterfuge by implementing a Dynamic Phishing Safeguard System (DPSS) using neural boost phishing protection algorithm that focuses on phishing, fraud, and optimizes the problem of data breaches. Dynamic phishing safeguard utilizes 30 different features to predict whether or not a website is a phishing website. In addition, the neural boost phishing protection algorithm uses an Anti-Phishing Neural Algorithm (APNA) and an Anti-Phishing Boosting Algorithm (APBA) to generate output that is mapped to various other components, such as IP finder, geolocation, and location mapper, in order to pinpoint the location of vulnerable sites that the user can view, which makes the system more secure. The system also offers a website blocker, and a tracker auditor to give the user the authority to control the system. Based on the results, the anti-phishing neural algorithm achieved an accuracy level of 97.10%, while the anti-phishing boosting algorithm yielded 97.82%. According to the evaluation results, dynamic phishing safeguard systems tend to perform better than other models in terms of uniform resource locator detection and security.
PV-RCNN++: semantical point-voxel feature interaction for 3D object detection

Large imbalance often exists between the foreground points (i.e., objects) and the background points in outdoor LiDAR point clouds. It hinders cutting-edge detectors from focusing on informative areas to produce accurate 3D object detection results. This paper proposes a novel object detection network by semantical point-voxel feature interaction, dubbed PV-RCNN++. Unlike most of existing methods, PV-RCNN++ explores the semantic information to enhance the quality of object detection. First, a semantic segmentation module is proposed to retain more discriminative foreground keypoints. Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas. Then, to make points and voxels interact efficiently, we utilize voxel query based on Manhattan distance to quickly sample voxel-wise features around keypoints. Such the voxel query will reduce the time complexity from O(N) to O(K), compared to the ball query. Further, to avoid being stuck in learning only local features, an attention-based residual PointNet module is designed to expand the receptive field to adaptively aggregate the neighboring voxel-wise features into keypoints. Extensive experiments on the KITTI dataset show that PV-RCNN++ achieves 81.60 $$\%$$ , 40.18 $$\%$$ , 68.21 $$\%$$ 3D mAP on Car, Pedestrian, and Cyclist, achieving comparable or even better performance to the state-of-the-arts.
Research data management in agricultural sciences in Germany: We are not yet where we want to be

To meet the future challenges and foster integrated and holistic research approaches in agricultural sciences, new and sustainable methods in research data management (RDM) are needed. The involvement of scientific users is a critical success factor for their development. We conducted an online survey in 2020 among different user groups in agricultural sciences about their RDM practices and needs. In total, the questionnaire contained 52 questions on information about produced and (re-)used data, data quality aspects, information about the use of standards, publication practices and legal aspects of agricultural research data, the current situation in RDM in regards to awareness, consulting and curricula as well as needs of the agricultural community in respect to future developments. We received 196 (partially) completed questionnaires from data providers, data users, infrastructure and information service providers. In addition to the diversity in the research data landscape of agricultural sciences in Germany, the study reveals challenges, deficits and uncertainties in handling research data in agricultural sciences standing in the way of access and efficient reuse of valuable research data. However, the study also suggests and discusses potential solutions to enhance data publications, facilitate and secure data re-use, ensure data quality and develop services (i.e. training, support and bundling services). Therefore, our research article provides the basis for the development of common RDM, future infrastructures and services needed to foster the cultural change in handling research data across agricultural sciences in Germany and beyond.
Multidimensional Convolution Operation with Synthetic Frequency Dimensions in Photonics

The convolution operation is widely used in signal and image processing and represents the most computationally intensive step in convolutional neural networks. We introduce a scheme to achieve arbitrary convolution kernels in the synthetic frequency dimension with a simple setup consisting of a ring resonator incorporating a phase and an amplitude modulator. This scheme can be used to perform multidimensional convolutions. We provide an analytic approach that determines the required modulation profile for any convolution kernel. Our work points to a direction of using optical computing to remove the computational bottleneck in traditional electronic circuits and may be useful in improving machine-learning hardware in artificial-intelligence applications.
FLNet: A Near-shore Ship Detection Method Based on Image Enhancement Technology

In the past few years, Synthetic Aperture Radar (SAR) has been widely used to detect marine ships due to its ability to work in various weather conditions. However, due to the imaging mechanism of SAR, there is a lot of background information and noise information similar to ships in the images, which seriously affects the performance of ship detection models. To solve the above problems, this paper proposes a new ship detection model called Feature enhancement and Land burial Net (FLNet), which blends traditional image processing methods with object detection approaches based on deep learning. We first design a SAR image threshold segmentation method, Salient Otsu (S-Otsu), according to the difference between the object and the noise background. To better eliminate noise in SAR images, we further combine image processing methods such as Lee filtering. These constitute a Feature Enhancement Module (FEM) that mitigates the impact of noise data on the overall performance of a ship detection model. To alleviate the influence of land information on ship detection, we design a Land Burial Module (LBM) according to the morphological differences between ships and land areas. Finally, these two modules are added to You Only Look Once V5 (YOLO V5) to form our FLNet. Experimental results on the SAR Ship Detection Dataset (SSDD) dataset show that FLNet comparison with YOLO V5 accuracy when performing object detection is improved by 7% and recall rate by 6.5%.
CRCNet: Few-Shot Segmentation with Cross-Reference and Region–Global Conditional Networks

Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. In this paper, we propose a Cross-Reference and Local–Global Conditional Networks (CRCNet) for few-shot segmentation. Unlike previous works that only predict the query image’s mask, our proposed model concurrently makes predictions for both the support image and the query image. Our network can better find the co-occurrent objects in the two images with a cross-reference mechanism, thus helping the few-shot segmentation task. To further improve feature comparison, we develop a local-global conditional module to capture both global and local relations. We also develop a mask refinement module to refine the prediction of the foreground regions recurrently. Experiments on the PASCAL VOC 2012, MS COCO, and FSS-1000 datasets show that our network achieves new state-of-the-art performance.
Question classification using limited labelled data

Question classification (QC) involves classifying given question based on the expected answer type and is an important task in the Question Answering(QA) system. Existing approaches for question classification use full training dataset to fine-tune the models. It is expensive and requires more time to develop labelled datasets in huge size. Hence, there is a need to develop approaches that can achieve comparable or state of the art performance using limited training instances. In this paper, we propose an approach that uses data augmentation as a tool to generate additional training instances. We evaluate our proposed approach on two question classification datasets namely TREC and ICHI datasets. Experimental results show that our proposed approach reduces the requirement of labelled instances (a) up to 81.7% and achieves new state of the art accuracy of 98.11 on TREC dataset and (b) up to 75% and achieves 67.9 on ICHI dataset.
SHARE: Designing multiple criteria-based personalized research paper recommendation system

Extraneous growth of scientific information over the Internet makes the searching task non-trivial and as a consequence researchers are facing difficulties in finding relevant papers from the millions of research papers in digital repositories. The research paper recommendation systems have been advocated to address this problem. The existing research paper recommendation systems lack in exploiting prominent information of papers, such as relevancy with the current time, novelty, scientific contribution, writing complexity of the papers, etc. Further, the existing models emphasize only on user's preference rather than user's intention that may change with time. Furthermore, the existing models do not consider a sound ranking strategy to unleash the personalization aspect and relevancy of papers. This work aims to address the existing limitations and proposes a systematic hidden attribute-based recommendation engine (SHARE). SHARE utilizes a feature engineering technique to unfold valuable insights of papers through multiple hidden features. These features are used as a context for users as well as multiple criteria for ranking papers. Additionally, SHARE predicts a user's intention beyond the user's preference to capture the dynamic notion of a user. Finally, a novel ranking strategy is proposed to retrieve personalized and the most important papers. SHARE is flexible for recommending both old and new users. In order to evaluate the effectiveness of SHARE both user studies and system evaluations were performed. Experimental results substantiate the efficacy of the proposed approach and are comparable to the existing systems.
Dynamically aggregating individuals’ social influence and interest evolution for group recommendations

The rapid development of social networks has made group activities part of people's daily lives. It is in high demand to recommend items for a group of users that meet their common preferences, known as group recommendations (GRs). Recently, some studies have focused on GRs and adopted deep neural networks to model the process of group decision-making. These approaches can be further divided into two phases: the individual preference learning phase and the group decision phase, both of which face several challenges although recent research has some achievements. In the first individual preference learning phase, the challenges are how to effectively incorporate social networks to overcome the sparsity issue of user interaction data and how to capture the dynamic evolutions of users' preferences over time. The challenge for the second group decision phase is how to adjust group members' preference weights dynamically since each group member may carry different deciding power in different situations, depending on their roles in the group and domain expertise. In this paper, to address these challenges, we propose a novel group recommendation framework that dynamically aggregates the social influence diffusion and interest evolution (DASIIE). Specifically, DASIIE is composed of three main modules: 1) a social influence diffusion (SID) module which stacks multi-layer graph neural networks to simulate the influence propagation in social networks; 2) an interest evolution learning (IEL) component which learns individuals' interest evolutions over time from their behavior sequences; 3) a two-way attention (TWA) module which learns group members' role weights and expertise weights during the decision-making process simultaneously. We conduct extensive experiments on two real-world datasets, and the results show the better performance of our model compared to the state-of-the-art baseline models.
Distribution-Sensitive Information Retention for Accurate Binary Neural Network

Model binarization is an effective method of compressing neural networks and accelerating their inference process, which enables state-of-the-art models to run on resource-limited devices. Recently, advanced binarization methods have been greatly improved by minimizing the quantization error directly in the forward process. However, a significant performance gap still exists between the 1-bit model and the 32-bit one. The empirical study shows that binarization causes a great loss of information in the forward and backward propagation which harms the performance of binary neural networks (BNNs). We present a novel distribution-sensitive information retention network (DIR-Net) that retains the information in the forward and backward propagation by improving internal propagation and introducing external representations. The DIR-Net mainly relies on three technical contributions: (1) Information Maximized Binarization (IMB): minimizing the information loss and the binarization error of weights/activations simultaneously by weight balance and standardization; (2) Distribution-sensitive Two-stage Estimator (DTE): retaining the information of gradients by distribution-sensitive soft approximation by jointly considering the updating capability and accurate gradient; (3) Representation-align Binarization-aware Distillation (RBD): retaining the representation information by distilling the representations between full-precision and binarized networks. The DIR-Net investigates both forward and backward processes of BNNs from the unified information perspective, thereby providing new insight into the mechanism of network binarization. The three techniques in our DIR-Net are versatile and effective and can be applied in various structures to improve BNNs. Comprehensive experiments on the image classification and objective detection tasks show that our DIR-Net consistently outperforms the state-of-the-art binarization approaches under mainstream and compact architectures, such as ResNet, VGG, EfficientNet, DARTS, and MobileNet. Additionally, we conduct our DIR-Net on real-world resource-limited devices which achieves $$11.1\times $$ storage saving and $$5.4\times $$ speedup.
A survey on blockchain-enabled federated learning and its prospects with digital twin

Digital Twin (DT) supports real time analysis and provides a reliable simulation platform in the Internet of Things (IoT). The creation and application of DT hinges on amounts of data, which poses pressure on the application of Artificial Intelligence (AI) for DT descriptions and intelligent decision-making. Federated Learning (FL) is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model training. Therefore, DT can benefit by combining with FL, successfully solving the "data island" problem in traditional AI. However, FL still faces serious challenges, such as enduring single-point failures, suffering from poison attacks, lacking effective incentive mechanisms. Before the successful deployment of DT, we should tackle the issues caused by FL. Researchers from industry and academia have recognized the potential of introducing Blockchain Technology (BT) into FL to overcome the challenges faced by FL, where BT acting as a distributed and immutable ledger, can store data in a secure, traceable, and trusted manner. However, to the best of our knowledge, a comprehensive literature review on this topic is still missing. In this paper, we review existing works about blockchain-enabled FL and visualize their prospects with DT. To this end, we first propose evaluation requirements with respect to security, fault-tolerance, fairness, efficiency, cost-saving, profitability, and support for heterogeneity. Then, we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation requirements. Finally, we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL, based on which we further propose some directions for future research.
A node trust evaluation method of vehicle-road-cloud collaborative system based on federated learning

As the vehicle-road-cloud collaboration system develops rapidly, it is accompanied by serious information security problems while solving the data transmission issues. For constructing secure transmission of data, trust is recommended as a relevant way to accomplish network security; that is, developing a trust model that can be used by sensor nodes to determine the reliability of another node is crucial. However, the heterogeneity of the network has different functional requirements for trust evaluation, and the openness of the network makes the nodes more vulnerable to attacks. Therefore, the research of trust evaluation model in the vehicle-road-cloud collaborative system is facing greater challenges than the traditional network. In this paper, a trust evaluation scheme of the vehicle-road-cloud collaborative system based on Federated Learning (FLT) is proposed. A hierarchical trust evaluation model is designed, and the complex model is simplified to an orderly hierarchical structure by using hierarchical analysis. The trust indexes of different layers are evaluated, and the influencing factors among different nodes are comprehensively considered. Combined with federated learning, it solves the problem of finding the most reliable route and realizes personalization at the level of equipment, data, and model. For the purpose of alleviating the heterogeneity and obtaining a high-quality personalized model for each device, trust values can be adaptively updated as changes in the topology of the network occur in real-time. The simulation findings demonstrate that, as compared to previous schemes, the energy consumption is lowered by 35%, and the accuracy is raised by 45% while maintaining trust stability.
Poisoning-Assisted Property Inference Attack Against Federated Learning

Federated learning (FL) has emerged as an ideal privacy-preserving learning technique which can train a global model in a collaborative way while preserving the private data in the local. However, recent advances have demonstrated that FL is still vulnerable to inference attacks, such as reconstruction attack and membership inference. Among these attacks, the property inference attack, aiming to infer properties of the training data that are irrelevant with the learning objective, has not received too much attention while resulting in severe privacy leakage. Existing property inference attack approaches either cannot achieve satisfactory performance when the global model has converged or under dynamic FL where participants can drop in and drop out freely. In this paper, we propose a novel poisoning-assisted property inference attack (PAPI-attack) against FL. The key insight is that there exists underlying discriminative ability in the periodic model updates, which reflects the change of the data distribution, especially the occurrence of the sensitive property. Thus, a binary attack model can be constructed by a malicious participant for inferring the unintended information. More importantly, we present a property-specific poisoning mechanism by modifying the label of training data from the adversary to distort the decision boundary of shared (global) model in FL. Consequently, benign participants are induced to disclose more information about the sensitive property. Extensive experiments on real-world datasets demonstrate that PAPI-attack outperforms the state-of-the-art property inference attacks against FL.