Reviewerly/exHarmony huggingface dataset:
DeGNN

Mining from graph-structured data is an integral component of graph data management. A recent trending technique, graph convolutional network (GCN), has gained momentum in the graph mining field, and plays an essential part in numerous graph-related tasks. Although the emerging GCN optimization techniques bring improvements to specific scenarios, they perform diversely in different applications and introduce many trial-and-error costs for practitioners. Moreover, existing GCN models often suffer from oversmoothing problem. Besides, the entanglement of various graph patterns could lead to non-robustness and harm the final performance of GCNs. In this work, we propose a simple yet efficient graph decomposition approach to improve the performance of general graph neural networks. We first empirically study existing graph decomposition methods and propose an automatic connectivity-ware graph decomposition algorithm, DeGNN. To provide a theoretical explanation, we then characterize GCN from the information-theoretic perspective and show that under certain conditions, the mutual information between the output after l layers and the input of GCN converges to 0 exponentially with respect to l. On the other hand, we show that graph decomposition can potentially weaken the condition of such convergence rate, alleviating the information loss when GCN becomes deeper. Extensive experiments on various academic benchmarks and real-world production datasets demonstrate that graph decomposition generally boosts the performance of GNN models. Moreover, our proposed solution DeGNN achieves state-of-the-art performances on almost all these tasks.
Secure File Storage using Hybrid Cryptography

Security is a major concern in a wide range of applications, from cloud storage to messaging via chat. Many different approaches have also been proposed to provide data protection in the cloud, such as AES, DES, and RSA, but Existing systems often fail when only a certain form of encoding is utilised, either AES, OR DES, OR RSA depending on a consumer requirement. However, the major issue with this scheme is that each encryption is done with encryption keys, and if these keys are leaked in some manner, the entire data is destroyed, so we need a solution that can have additional security. As a result, hybrid cryptography is used in this project, in which current encryption techniques are combined with three new methods. When a user uploads data, it is divided into three sections, the first of which is encrypted with AES, the second with DES, and the third with RSA. LSB steganography is used to store the keys in the image, and the three encrypted files are stored in the cloud. Users must first recover the keys from the image before they can import all data from the server. These keys are then used to decrypt the data once more with AES, DES, and RSA. This approach increases the security of records.
Adaptive Disparity Candidates Prediction Network for Efficient Real-Time Stereo Matching

Efficient real-time disparity estimation is critical for the application of stereo vision systems in various areas. Recently, stereo network based on coarse-to-fine method has largely relieved the memory constraints and speed limitations of large-scale network models. Nevertheless, all of the previous coarse-to-fine designs employ constant offsets and three or more stages to progressively refine the coarse disparity map, still resulting in unsatisfactory computation accuracy and inference time when deployed on mobile devices. This paper claims that the coarse matching errors can be corrected efficiently with fewer stages as long as more accurate disparity candidates can be provided. Therefore, we propose a dynamic offset prediction module to meet different correction requirements of diverse objects and design an efficient two-stage framework. In addition, a disparity-independent convolution is proposed to regularize the compact cost volume efficiently and further improve the overall performance. The disparity quality and efficiency of various stereo networks are evaluated on multiple datasets and platforms. Evaluation results demonstrate that, the disparity error rate of the proposed network achieves 2.66% and 2.71% on KITTI 2012 and 2015 test sets respectively, where the computation speed is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> faster than the state-of-the-art lightweight models on high-end and source-constrained GPUs.
Global solar radiation prediction: Application of novel hybrid data-driven model

One of the significant prerequisites for harvesting solar energy is precise global solar radiation (GHI) forecasts. However, variability and uncertainty are inherent characteristics of solar radiation. It is challenging to show better generalization using current data analysis approaches. Thus, this research presents a new intelligence framework by hybridizing Support Vector Regression (SVR) with the Grasshopper Optimization Algorithm (GOA) and the Boruta-based feature selection algorithm (BA) for forecasting GHI values at different sites of Saudi Arabia. Interestingly, the most significant distinction that differentiates this proposed prediction model (SVR-GOA-BAK) from other models is that the GOA is automatically employed to search for optimal SVR’s hyperparameters. In contrast, these hyperparameters are chosen randomly and manually in conventional models. Consequently, the contribution helps save time, reduce cost, and avoid the possibility of models’ overfitting or underfitting caused by random and manual selection. A diversity of statistical measures has justified the proposed model’s effectiveness and superiority. In terms of mean absolute percentage error (MAPE), the proposed model outperformed the standalone SVR models by 32.15–39.69% at different study sites. In tuning the SVR’s parameters, GOA outperforms popular optimization algorithms. All the simulation test results demonstrate the superiority of the proposed model. Hence, the proposed approach provides a foundation for precise solar radiation forecasting, which can aid in the growth of renewable-energy-based technologies.
My Site Knows Where You Are: A Novel Browser Fingerprint to Track User Position

Utilizing browsers to identify and track users has become a routine on the Web in recent years. It is easy for the browser to collect sensitive information and construct comprehensive user profiles while the users are still unaware. As the problem mentioned above, several anti-fingerprint mechanisms have been adopted to protect user privacy. However, our research finds a novel method based on localization fingerprints that may still threaten user privacy. The location fingerprint obtains the response delay of data transmission over the link between the users and the third-party sites. Since the physical link state information between the host and the remote website is distinct and steady, it can be used to extract statistical features and construct user profiles. We implement a multilateration cross-site image resource request scheme to collect link-state information of users and develop a prototype called PingLoc to evaluate the effectiveness. About 1,093 users from all over the world are involved in our experiment. The evaluation shows that the delay features collected are stable, and the accuracy of the localization fingerprint is up to 98%. Pressure testing shows that the PingLoc is robust against various anti-fingerprint mechanisms and achieves 93.5% accuracy for browser switching, 80.6% accuracy for virtual machine disguising, and 88.2% accuracy for IP rotation.
A novel chaos based generating function of the Chebyshev polynomials and its applications in image encryption

In this paper, we propose a generating function for Chebyshev polynomials with typical period-doubling to chaos. In this context, the bifurcation diagram and Lyapunov exponent proved that the proposed generating function is a deterministic system that exhibits chaotic behavior for specific values of the control parameters. As an application, this proposed generating function is used as a chaos-based cryptosystem to encrypt different images. Security analysis demonstrated that the proposed generating function of the Chebyshev polynomials presents an excellent performance in image encryption against various attacks.
Atrous spatial pyramid convolution for object detection with encoder-decoder

Recently, atrous spatial pyramid module and encoder-decoder structure are widely investigated for many computer vision tasks to address the scale variation challenge in deep convolutional networks. The atrous spatial pyramid module aims at capturing local and global cues together with various sampling rates in convolutional or pooling layers. At the same time, encoder-decoder structure propagates context features from low-resolution, strong-semantic features to high-resolution, weak-semantic ones, while maintaining the detail object boundaries. However, the aforementioned two strategies have their own drawbacks, and the previous object detectors only employ one of them to handle scale variation. In this work, we propose to cooperate atrous spatial pyramid convolution (ASPC) with encoder-decoder structure (ED) for object detection, termed ASPC-ED, which combines the complementary advantages from both modules in an end-to-endfashion. Specifically, the proposed method is consist of three components: encoder, ASPC and decoder. The extensive experiments on PASCAL VOC and MS COCO benchmarks demonstrate that our method with various backbone achieves the state-of-the-art results for object detection, instance segmentation and panoptic segmentation.
Towards Practical and Privacy-Preserving Multi-Dimensional Range Query Over Cloud

It is undeniable that Internet of Things (IoT) in big data era can provide us with huge volumes of multi-dimensional data, transforming our society into a much more intelligent one. In order to fit for the multi-dimensional data processing in big data era, multi-dimensional range queries, especially over cloud platform, have received considerable attention in recent years. However, as the cloud server is not fully trustable, designing multi-dimensional range queries over encrypted data becomes a research trend, and many solutions have been proposed in the literature. Nevertheless, most existing solutions suffer from the leakage of the single-dimensional privacy, and such leakage would severely put the data at risk. Although a few existing works have addressed the problem of single-dimensional privacy, they are impractical in some real scenarios due to the issues of inefficiency, inaccuracy, and two-cloud-server requirement. Aiming at solving these issues, in this article, we propose a practical and privacy-preserving multi-dimensional range query (PRQ) scheme. Specifically, in our proposed PRQ scheme, we first index the multi-dimensional dataset with an R-tree and reduce R-tree based range queries to the problem of point intersection and range intersection. Then, by employing the lightweight matrix encryption technique, we design two novel algorithms for PRQ, i.e., multi-dimensional point intersection predicate encryption (PIPE) and multi-dimensional range intersection predicate encryption (RIPE), which can preserve the privacy of the proposed point intersection algorithm and range intersection algorithm, and further preserve the single-dimensional privacy of the proposed PRQ scheme. Detailed security analysis shows that our proposed PRQ scheme is indeed privacy-preserving. In addition, extensive simulations are conducted, and the results also demonstrate its efficiency.
Embedding Regularizer Learning for Multi-View Semi-Supervised Classification

Classification remains challenging when confronted with the existence of multi-view data with limited labels. In this paper, we propose an embedding regularizer learning scheme for multi-view semi-supervised classification (ERL-MVSC). The proposed framework integrates diversity, sparsity and consensus to dexterously manipulate multi-view data with limited labels. To encourage diversity, ERL-MVSC recasts a linear regression model to derive view-specific embedding regularizers and automatically determines their weights. This is able to tactfully incorporate complementary information of different views. To ensure sparsity, ERL-MVSC imposes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm on a fused embedding regularizer to exploit the sparse local structure of samples, thereby conveying valuable classification information and enhancing the robustness against noise/outliers. To enhance consensus, ERL-MVSC learns a shared predicted label matrix, which serves as the comment target of multi-view classification. With these techniques, we formulate ERL-MVSC as a joint optimization problem of an embedding regularizer and a predicted label matrix, which can be solved by a coordinate descent method. Extensive experimental results on real-world datasets demonstrate the effectiveness and superiority of the proposed algorithm.
A survey of immersive technologies and applications for industrial product development

With the expanded digitalization of manufacturing and product development process, research into the use of immersive technology in smart manufacturing has increased. The use of immersive technology is theorized to increase the productivity of all steps in the product development process, from the start of the concept generation phase to assembling the final product. Many aspects of immersive technology are considered, including techniques for CAD model conversion and rendering, types of VR/AR displays, interaction modalities, as well as its integration with different areas of product development. The purpose of this survey paper is to investigate the potential applications of immersive technology and advantages and potential drawbacks that should be considered when integrating the technology into the workplace. The potential application is broad, and the possibilities are continuing to expand as the technology used becomes more advanced and more affordable for commercial business to implement on a large scale. The technology is currently being utilized in the concept generation and in the design or engineering of new products. Additionally, the immersive technology have great potential to increase the productivity of assembly line workers and of the factory layout/functionality, and could provide a more hands-on form of training, which leads to the conclusion that immersive technology is the step to the future in terms of smart product development strategies to implement for employers.
TS4Net: Two-stage sample selective strategy for rotating object detection

Rotating object detection has recently attracted increasing attention in aerial photographs, remote sensing images, etc. In this paper, we propose a rotating object detector TS4Net, which contains anchor refinement module (ARM) and two-stage sample selective strategy (TS4). The ARM can convert the preset horizontal anchors into high-quality rotated anchors through two-stage refinement, which also adopts a rotated Intersection-over-Union(IoU) prediction branch to improve localization accuracy in the second stage. TS4 module utilizes different constrained sample selective strategies to allocate positive and negative samples, which is adaptive to the regression task in different stages. Benefiting from the ARM and TS4, the TS4Net can achieve superior performance for rotating object detection solely with one preset horizontal anchor. Considering that most rotating object detection datasets mainly focus on the field of remote sensing and are shot in high-altitude scenes. We present a low-altitude drone-based dataset, named UAV-ROD, aiming to promote the research and development in rotating object detection and UAV applications. The UAV-ROD dataset can be used in rotating object detection, vehicle orientation recognition, and object counting tasks. Extensive experiments on the UAV-ROD dataset and four datasets demonstrate that our method achieves competitive performance against most state-of-the-art methods.
A primer on partially observable Markov decision processes (POMDPs)

Abstract Partially observable Markov decision processes (POMDPs) are a convenient mathematical model to solve sequential decision‐making problems under imperfect observations. Most notably for ecologists, POMDPs have helped solve the trade‐offs between investing in management or surveillance and, more recently, to optimise adaptive management problems. Despite an increasing number of applications in ecology and natural resources, POMDPs are still poorly understood. The complexity of the mathematics, the inaccessibility of POMDP solvers developed by the Artificial Intelligence (AI) community, and the lack of introductory material are likely reasons for this. We propose to bridge this gap by providing a primer on POMDPs, a typology of case studies drawn from the literature, and a repository of POMDP problems. We explain the steps required to define a POMDP when the state of the system is imperfectly detected (state uncertainty) and when the dynamics of the system are unknown (model uncertainty). We provide input files and solutions to a selected number of problems, reflect on lessons learned applying these models over the last 10 years and discuss future research required on interpretable AI. Partially observable Markov decision processes are powerful decision models that allow users to make decisions under imperfect observations over time. This primer will provide a much‐needed entry point to ecologists.
Sharing the hate? Memes and transnationality in the far right’s digital visual culture

(2021). Sharing the hate? Memes and transnationality in the far right's digital visual culture. Information, Communication & Society: Vol. 24, The Playful Politics of Memes, Guest Editors: Mette Mortensen and Christina Neumayer, pp. 2502-2521.
Median Stable Clustering and Global Distance Classification for Cross-Domain Person Re-Identification

The person re-identification (ReID) method in a single-domain achieves appealing performance, but its reliance on label information greatly limits its extensibility. Therefore, the unsupervised cross-domain ReID method has received extensive attention. Its purpose is to optimize the model by using the labelled source domain and the unlabelled target domain and finally make the model well generalized in the target domain. We propose an unsupervised cross-domain ReID method based on median stable clustering (MSC) and global distance classification (GDC). Specifically, the measurement method used by MSC comprehensively considers the similarity between clusters, the number of samples in a cluster, and the combined similarity within a cluster. Different from the method based on triple loss, GDC can separate the distance distribution of positive and negative sample pairs in a global scope. In addition, considering that model performance is very sensitive to probability parameters when source domain memory is reconsolidated, we designed a dynamic memory reconsolidation (DMR) method to reduce the influence of parameters on performance. Extensive experiments on large-scale datasets (Market-1501, DukeMTMC-reID and MSMT17) demonstrate the superior performance of MSC-GDC over the state-of-the-art methods.
Hybrid Quantum Computing - Tabu Search Algorithm for Partitioning Problems: Preliminary Study on the Traveling Salesman Problem

Quantum Computing is considered as the next frontier in computing, and it is attracting a lot of attention from the current scientific community. This kind of computation provides to researchers with a revolutionary paradigm for addressing complex optimization problems, offering a significant speed advantage and an efficient search ability. Anyway, Quantum Computing is still in an incipient stage of development. For this reason, present architectures show certain limitations, which have motivated the carrying out of this paper. In this paper, we introduce a novel solving scheme coined as hybrid Quantum Computing - Tabu Search Algorithm. Main pillars of operation of the proposed method are a greater control over the access to quantum resources, and a considerable reduction of non-profitable accesses. To assess the quality of our method, we have used 7 different Traveling Salesman Problem instances as benchmarking set. The obtained outcomes support the preliminary conclusion that our algorithm is an approach which offers promising results for solving partitioning problems while it drastically reduces the access to quantum computing resources. We also contribute to the field of Transfer Optimization by developing an evolutionary multiform multitasking algorithm as initialization method.
Blockchain and other distributed ledger technologies: Where is the accounting?

• Blockchain and Distributed Ledgers. • Smart Contracting. • Accounting knowledge for blockchain implementation. In a recent survey of academic research, Fintech related topics, broadly classified as crypto-currency studies, were by far the most researched topics in the social sciences. However, we have observed that, perhaps surprisingly, even though crypto-currencies rely on a distributed accounting ledger technology, relatively few of those studies were conducted by accounting academics. While some of the features of a system like Bitcoin do not necessarily rely on a traditional accounting knowledge, this knowledge is key in designing effective real-world distributed systems. Building on a foundational framework developed by Risius and Spohrer (2017), we provide support for their hypothesis that to date, research in this area has been predominantly of a somewhat narrow focus (i.e., based upon exploiting existing programming solutions without adequately considering the fundamental needs of users). This is particularly reflected by the abundance of Bitcoin-like crypto-currency code-bases with little or no place for business applications. We suggest that this may severely limit an appreciation of the relevance and applicability of decentralized systems, and how they may support value creation and improved governance . We provide supporting arguments for this statement by considering four applied classes of problems where a blockchain/distributed ledger can add value without requiring a crypto-currency to be an integral part of the functioning system. We note that each class of problem has been viewed previously as part of accounting issues within the legacy centralized ledger systems paradigm. We show how accounting knowledge is still relevant in the shift from centralized to decentralized ledger systems. We advance the debate on the development of (crypto-currency free) value-creating distributed ledger systems by showing that applying accounting knowledge in this area has potentially a much wider impact than that currently being applied in areas limited to auditing and operations management. We develop a typology for general distributed ledger design which assists potential users to understand the wide range of choices when developing such systems.