Reviewerly/exHarmony huggingface dataset:
Heterogeneous self-supervised interest point matching for multi-modal remote sensing image registration

The implementation of multi-modal remote sensing image registration is still a challenge in various applications. In this paper, a novel framework called Heterogeneous SuperPoint Network is proposed for multi-modal remote sensing image registration. Heterogeneous SuperPoint Network operates on full-sized images and produces multi-modal interest points accompanied with fixed length descriptors in a single forward pass. Moreover, we develop a strategy for training a base detector that used in conjunction with heterogeneous homographic adaptation to generate pseudo-ground truth interest point labels for unlabeled images in a self-supervised fashion. Then, the interest points and point correspondences obtained by Heterogeneous SuperPoint Network are used to do the final matching with the nearest neighbor (NN) matching and Random sample consensus (RANSAC). Various image sets have been considered in this paper for the evaluation of the proposed approach. Experimental results demonstrate the superior performance of the proposed method over the existing design for the whole data set.
Medical images lossless recovery based on POB number system and image compression

To protect the information integrity of the medical images, this paper proposes a secure lossless recovery scheme for medical images based on image block compression and permutation ordered binary (POB) number system, which includes two parts: share generation, and lossless recovery. In the shares generation stage, the region of interest (ROI) of the original medical image and the image block compression coding algorithm JPEG-LS, are firstly adopted to generate the compressed data, which can use limited storage space to place the data repeatedly. Then, after separating the bit-plane, the compressed data are executed by data reorganization and data encryption respectively on the high-plane and the low-plane. Finally, two authentication bits are extracted by 8-bit pixel of two planes to be inserted into the pixel itself, and then the 10-bit pixel is converted into an 8-bit POB value to produce two shares of ShareH and ShareL, respectively. In the lossless recovery stage, data of placing repeatedly can restore the original image. At the same time, three attacks are carried in the two shares, which contain content cropping, content exchange, and text addition. Some comparisons with other schemes present that the proposed scheme implements a better performance under some criteria. Theoretical analysis and experimental results demonstrate that the original image can be recovered losslessly even if two shares are tampered at the ration more than 50%.
A Survey of Research on Crowd Abnormal Behavior Detection Algorithm Based on YOLO Network

Thanks to the rapid development of computer vision technology, methods based on in-depth learning have gradually replaced counting methods based on traditional machine learning, and substantial progress has been made in the accuracy and real-time detection of abnormal crowd behavior detection. Firstly, it introduces the structure and application of the YOLO network of the one-stage detection system. Secondly, according to the development of the YOLO network model, it specifically introduces the abnormal behavior detection algorithms and research based on the YOLO v3 network, the YOLOv4 network and the YOLO v5 network. Finally, the summary The shortcomings of the current One-stage target detection network are analyzed, and the future research directions are prospected.
Blockchain Technology and Green Supply Chain Management (GSCM) – Improving Environmental and Energy Performance in Multi-echelon Supply Chains

Abstract In this competitive and technology driven world, businesses are striving to gain benefits of environmental sustainability and energy conservation by implementing the latest technologies in their supply chains. A promising way of evaluating the environmental performance of a product or a process is Life Cycle Assessment (LCA) modelling, but the reliability of LCA results is mostly compromised due to the lack of real data from upstream and downstream supply chains. Our research aim is to drive LCA modelling through Blockchain Technology (BCT) and internet-of-things which have the capability to record reliable, transparent, and secure data from across the supply chain. The modified LCA model will provide various industries with the quantified benefits of BCT in terms of industrial emissions reduction. This conference paper reflects the 1st stage of research where we carried out a detailed literature review on potential use of blockchain technology in green supply chain management and based on the findings, we developed an integrative framework architecture for the supply chain of a supermarket product. In the next phase, the integration of BCT and LCA will be studied in food supply chains using a supermarket case study of Walmart-IBM developed blockchain consortium.
Markov-Driven Graph Convolutional Networks for Social Spammer Detection

With the growing popularity of social media, malicious users (spammers) unfairly overpower legitimate users with unwanted or fake content to achieve their illegal purposes, which encourages research on spammer detection. The existing spammer detection methods can be characterized into feature-based detection and propagation-based detection. However, feature-based methods (e.g., GCN) cannot capture the user’s following relations, while propagation-based methods cannot utilize the rich text features. To this end, we consider combining these two methods and propose an Adaptive Reward Markov Random Field (ARMRF) layer. ARMRF layer models three intuitions on user label relations and assign them different learnable rewards. Besides, we learn the reward weights by stacking the ARMRF layer on top of GCN for end-to-end training, and we call the stacked model ARMGCN. To further improve the expressive power of ARMGCN, we propose the Markov-Driven Graph Convolutional Network (MDGCN), which integrates conditional random fields (CRF) and ARMGCN. CRF establishes the label joint probability distribution conditioned features for learning user dependencies, and the distribution can be optimized by a variational EM algorithm. We extensively evaluate the proposed method on two real-world Twitter datasets, and the experimental results demonstrate that MDGCN outperforms the state-of-the-art baselines. In addition, the ARMRF layer is model-independent, so it can be integrated with existing advanced detection methods to improve detection performance further.
Global and local attention-based multi-label learning with missing labels

In multi-label learning algorithms, the classification performance can be significantly improved using global and local label correlation. However, the incompleteness of the label space leads to difficulties in measuring the label correlation. In the process of label recovery, many multi-label learning algorithms focus on label correlation, but ignore the queried instance information. In this paper, we introduce an attention mechanism to jointly exploit label and instance information in order to improve the quality of the recovered labels. Firstly, the attention mechanism is used to encode the label and the instance information for label space reconstruction. Secondly, attention computations are performed on the reconstructed label space to obtain the label completion matrix. Finally, global and local features of label correlation are used to improve the model robustness, and label prediction is completed. Through the analysis of the experimental results of multiple benchmark multi-label datasets, it is demonstrated that the proposed method has certain advantages over other state-of-the-art algorithms.
Efficient Counterfactual Debiasing for Visual Question Answering

Despite the success of neural architectures for Visual Question Answering (VQA), several recent studies have shown that VQA models are mostly driven by superficial correlations that are learned by exploiting undesired priors within training datasets. They often lack sufficient image grounding or tend to overly-rely on textual information, failing to capture knowledge from the images. This affects their generalization to test sets with slight changes in the distribution of facts. To address such an issue, some bias mitigation methods have relied on new training procedures that are capable of synthesizing counterfactual samples by masking critical objects within the images, and words within the questions, while also changing the corresponding ground truth. We propose a novel model-agnostic counterfactual training procedure, namely Efficient Counterfactual Debiasing (ECD), in which we introduce a new negative answer-assignment mechanism that exploits the probability distribution of the answers based on their frequencies, as well as an improved counterfactual sample synthesizer. Our experiments demonstrate that ECD is a simple, computationally-efficient counterfactual sample-synthesizer training procedure that establishes itself as the new state of the art for unbiased VQA.
DeepLTSC: Long-Tail Service Classification via Integrating Category Attentive Deep Neural Network and Feature Augmentation

With the explosive growth in the number and diversity of Web services, correlative research has been investigated on Web service classification, as it fundamentally promotes advanced service-oriented applications, such as service discovery, selection, composition and recommendation. However, conventional approaches are restricted to indiscriminatingly classify Web services, which can trigger many challenges. First, they have not made full advantage of the implicit relationships among multi-dimensional information of Web services, such as the increasing number of service categories. Thus, it leads to low effectiveness of learning and representing service features, failing to ensure the overall accuracy of service classification. Second, the imbalance of service distributions has been ignored, while it is observed that service categories reveal distinct long-tail characteristics. That results in low accuracy on service classification for those categories that contain fewer Web services. To handle the challenges of more effectively learning implicit service features across the service repository, and with a particular concentration on those tail categories that contain fewer Web services, we propose a novel framework called DeepLTSC to more accurately perform the task of Web service classification under long-tail distributions. In DeepLTSC, we first present an improved label attentive convolutional deep neural network (LACNN) with service categories, which can generate deep service features to improve the overall classification performance. Then, a proposed service feature augmentation model (SFA) together with focal loss function is integrated into DeepLTSC to further optimize service features, aiming to boost the classification accuracy on tail service categories. Extensive experiments are conducted on three large-scale real-world services datasets with different long-tail distributions. The results demonstrate that DeepLTSC significantly outperforms state-of-the-art approaches for Web service classification on both overall and tail categories.
Secure and Reliable Data sharing scheme using Attribute-based Encryption with weighted attribute-based Encryption in Cloud Environment

The field of data management has been reformed by the Cloud computing technologies which offered valuable establishments and amended the storage restrictions barriers for its users. In large enterprises the cloud has been extensively used for implementation due to its benefits. There are still lot of security threats for the data in the cloud. The data owners suffer from its privacy issues which are considered as one of the major concerns. Data privacy can be secured by employing some of the existing methods such as Attribute-based Encryption (ABE). Yet, the security issues are prevailing largely over the cloud. In this research a secured data access control is proposed using the Advanced Encryption Standard (AES) combined with a weighted attribute-based Encryption (AES-WABE). To encrypt the data, the access control policies are used and weight is assigned according to its significance of each attribute. The outsourced data is stored by the cloud service provider and the attribute authority based on the weight that updates the attributes. To minimize the computational overload the data file is accessed by the receiver corresponding to its weight. The proposed procedure provides resistance for collusion, multiple user security with control of fine-grained access based on protection, reliability and efficiency. On concerning the data collaboration and confidentiality, the performance rating is done related with the Cipher-text Policy–Attribute-based Encryption (CP-ABE) and the hybrid attribute-based encryption (HABE) scheme, access control flexibility, limited decryption, full delegation, verification and partial signing.
An Improved Federated Learning Algorithm for Privacy Preserving in Cybertwin-Driven 6G System

With the expected explosive use of the Internet of Everything in sixth generation (6G), the cybertwin network is able to convert user information to digital assets and provide extensive services. However, protecting and enhancing privacy of the processed and transmitted data in cybertwin-driven 6G is still in its infancy. Federated learning (FL) is a nascent distributed machine learning paradigm that is able to facilitate privacy protection in cybertwin networks. In a cybertwin network, imbalanced data distribution of the clients can increase the bias of the global model and sacrifice the performance of the FL model. Prior research work dealing with imbalanced data requires extra data information exchanged between clients and the server, which increases the risk of privacy leakage. To avoid privacy leakage, we design an estimation algorithm to determine the distribution of local data collected at the clients without the awareness of specific raw data. We consider two scenarios in FL: 1) the server could receive the individual trained model for each selected device and 2) the server could receive the aggregated model from the selected clients. We formulate two device selection problems to improve the training performance of the aforementioned scenarios. We develop two online learning algorithms to tackle the selection problems for both individual model uploading and aggregated model uploading. The proposed algorithms are conducted on the server, thereby avoiding privacy leakage and extra computation at the clients. We validate the effectiveness of the proposed client selection algorithms with sufficient experiments in cybertwin-driven 6G networks.
Topological Analysis of Vector-Field Guided Path Following on Manifolds

A path-following control algorithm enables a system’s trajectories under its guidance to converge to and evolve along a given geometric desired path. There exist various such algorithms, but many of them can only guarantee local convergence to the desired path in its neighborhood. In contrast, the control algorithms using a well-designed guiding vector field can ensure almost global convergence of trajectories to the desired path; here, “almost” means that in some cases, a measure-zero set of trajectories converge to the singular set where the vector field becomes zero (with all other trajectories converging to the desired path). In this article, we first generalize the guiding vector field from the Euclidean space to a general smooth Riemannian manifold. This generalization can deal with path-following in some abstract configuration space (such as robot arm joint space). Then, we show several theoretical results from a topological viewpoint. Specifically, we are motivated by the observation that singular points of the guiding vector field exist in many examples where the desired path is homeomorphic to the unit circle, but it is unknown whether the existence of singular points always holds in general (i.e., is inherent in the topology of the desired path). In the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional Euclidean space, we provide an affirmative answer, and conclude that it is not possible to guarantee global convergence to desired paths that are homeomorphic to the unit circle. Furthermore, we show that there always exist nonpath-converging trajectories (i.e., trajectories that do not converge to the desired path) starting from the boundary of a ball containing the desired path in an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional Euclidean space where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n \geq 3$</tex-math></inline-formula> . Examples are provided to illustrate the theoretical results.
Research on Effective Path Planning Algorithm Based on Improved A* Algorithm

Abstract In recent years, with the rapid development of technology, mobile robots are being applied in all aspects of production and life. The A* algorithm can be used for global path planning of mobile robots. Aimed at the problem of the traditional A* algorithm planning path having many turning points and do not satisfying the global optimality, an A* algorithm based on the adaptive neighborhood search and steering cost has been proposed. Based on the information of the surrounding obstacles, the algorithm adaptively selects the appropriate neighborhood to search for the optimal child node. By establishing the steering cost model of the mobile robot, the steering cost is joined into the evaluation function of the A* algorithm. Based on the adaptive neighborhood search, the A* algorithm with steering cost breaks through the constraint of eight neighborhood search nodes, shortens the path length, and uses the steering cost to search for the global optimal path with fewer turning points. The simulation results show that compared to the traditional A* algorithm, the total path cost of the A* algorithm based on adaptive neighborhood search and steering cost is reduced by 19.3%, and the number of turning points is reduced by 44.4%.
CrossLocate: Cross-modal Large-scale Visual Geo-Localization in Natural Environments using Rendered Modalities

We propose a novel approach to visual geo-localization in natural environments. This is a challenging problem due to vast localization areas, the variable appearance of outdoor environments and the scarcity of available data. In order to make the research of new approaches possible, we first create two databases containing "synthetic" images of various modalities. These image modalities are rendered from a 3D terrain model and include semantic segmentations, silhouette maps and depth maps. By combining the rendered database views with existing datasets of photographs (used as "‘queries" to be localized), we create a unique benchmark for visual geo-localization in natural environments, which contains correspondences between query photographs and rendered database imagery. The distinct ability to match photographs to synthetically rendered databases defines our task as "cross-modal". On top of this benchmark, we provide thorough ablation studies analysing the localization potential of the database image modalities. We reveal the depth information as the best choice for outdoor localization. Finally, based on our observations, we carefully develop a fully-automatic method for large-scale cross-modal localization using image retrieval. We demonstrate its localization performance outdoors in the entire state of Switzerland. Our method reveals a large gap between operating within a single image domain (e.g. photographs) and working across domains (e.g. photographs matched to rendered images), as gained knowledge is not transferable between the two. Moreover, we show that modern localization methods fail when applied to such a cross- modal task and that our method achieves significantly better results than state-of-the-art approaches. The datasets, code and trained models are available on the project website: http://cphoto.fit.vutbr.cz/crosslocate/.
CARD-B: A stacked ensemble learning technique for classification of encrypted network traffic

Classification of network traffic data into different applications, services, or types is critical for network service providers to monitor networks and maintain Quality of Service (QoS). With the continuous evolution of technological advancements and with the rapid increase in security and user privacy concerns, encryption techniques are commonly used. Encrypted network traffic makes it challenging for network service providers to monitor a network using classical network monitoring techniques and tools. Due to security and privacy reasons, data cannot be decrypted. The dynamics of encrypted network traffic data cannot be interpreted, and it makes the task of classifying the encrypted network traffic a major challenge. This presents Internet Service Providers with challenges as varying Quality of Service is being provided to clients along with the security and privacy concerns to monitor network traffic. Machine learning and deep learning techniques are being utilized to classify encrypted network traffic data. This paper presents an ensemble learning technique that is based on existing data pre-processing machine learning and deep learning techniques. We examine different models and identify the best and relevant statistical features from encrypted network traffic for the classification of non-VPN encrypted network traffic data. We performed multiple experiments that led us to developing an ensemble learning model based on the existing deep learning and machine learning models for the classification of non-VPN encrypted network traffic data. The proposed solution (named as CARD-B) is composed of Capsule Neural Networks, Artificial Neural Networks, Random Forest, Decision Trees, along with Boosting techniques such as Adaptive Boosting and Extreme Gradient Boosting. The techniques are stacked using Random Forest Classifier. The result of the experiment shows that the proposed model achieved an overall accuracy of 96% and an AUC of 98% using different possible extracted statistical features.
Do You Need Another Hand? Investigating Dual Body Representations During Anisomorphic 3D Manipulation

In virtual reality, several manipulation techniques distort users' motions, for example to reach remote objects or increase precision. These techniques can become problematic when used with avatars, as they create a mismatch between the real performed action and the corresponding displayed action, which can negatively impact the sense of embodiment. In this paper, we propose to use a dual representation during anisomorphic interaction. A co-located representation serves as a spatial reference and reproduces the exact users' motion, while an interactive representation is used for distorted interaction. We conducted two experiments, investigating the use of dual representations with amplified motion (with the Go-Go technique) and decreased motion (with the PRISM technique). Two visual appearances for the interactive representation and the co-located one were explored. This exploratory study investigating dual representations in this context showed that people globally preferred having a single representation, but opinions diverged for the Go-Go technique. Also, we could not find significant differences in terms of performance. While interacting seemed more important than showing exact movements for agency during out-of-reach manipulation, people felt more in control of the realistic arm during close manipulation.