Reviewerly/exHarmony huggingface dataset:
Cross-View Image Sequence Geo-localization

Cross-view geo-localization aims to estimate the GPS location of a query ground-view image by matching it to images from a reference database of geo-tagged aerial images. To address this challenging problem, recent approaches use panoramic ground-view images to increase the range of visibility. Although appealing, panoramic images are not readily available compared to the videos of limited Field-Of-View (FOV) images. In this paper, we present the first cross-view geo-localization method that works on a sequence of limited FOV images. Our model is trained end-to-end to capture the temporal structure that lies within the frames using the attention-based temporal feature aggregation module. To robustly tackle different sequences length and GPS noises during inference, we propose to use a sequential dropout scheme to simulate variant length sequences. To evaluate the proposed approach in realistic settings, we present a new large-scale dataset containing ground-view sequences along with the corresponding aerial-view images. Extensive experiments and comparisons demonstrate the superiority of the proposed approach compared to several competitive baselines.
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series

In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved. Deep learning-based state-of-the-art methods for LiDAR MOS strongly depend on annotated ground truth data, which is expensive to obtain and scarce in existence. To close this gap in the stationary setting, we propose a novel 4D LiDAR representation based on multivariate time series that relaxes the problem of unsupervised MOS to a time series clustering problem. More specifically, we propose modeling the change in occupancy of a voxel by a multivariate occupancy time series (MOTS), which captures spatio-temporal occupancy changes on the voxel level and its surrounding neighborhood. To perform unsupervised MOS, we train a neural network in a self-supervised manner to encode MOTS into voxel-level feature representations, which can be partitioned by a clustering algorithm into moving or stationary. Experiments on stationary scenes from the Raw KITTI dataset show that our fully unsupervised approach achieves performance that is comparable to that of supervised state-of-the-art approaches.
Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning

Deep neural networks (DNNS) are prone to miscalibrated predictions, often exhibiting a mismatch between the predicted output and the associated confidence scores. Contemporary model calibration techniques mitigate the problem of overconfident predictions by pushing down the confidence of the winning class while increasing the confidence of the remaining classes across all test samples. However, from a deployment perspective an ideal model is desired to (i) generate well calibrated predictions for high-confidence samples with predicted probability say > 0.95 and (ii) generate a higher proportion of legitimate high-confidence samples. To this end, we propose a novel regularization technique that can be used with classification losses, leading to state-of-the-art calibrated predictions at test time; From a deployment standpoint in safety critical applications, only high-confidence samples from a well-calibrated model are of interest, as the remaining samples have to undergo manual inspection. Predictive confidence reduction of these potentially "high-confidence samples" is a downside of existing calibration approaches. We mitigate this via proposing a dynamic traintime data pruning strategy which prunes low confidence samples every few epochs, providing an increase in confident yet calibrated samples. We demonstrate state-of-the-art calibration performance across image classification benchmarks, reducing training time without much compromise in accuracy. We provide insights into why our dynamic pruning strategy that prunes low confidence training samples leads to an increase in high-confidence samples at test time.
VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge

There has been a growing interest in solving Visual Question Answering (VQA) tasks that require the model to reason beyond the content present in the image. In this work, we focus on questions that require commonsense reasoning. In contrast to previous methods which inject knowledge from static knowledge bases, we investigate the incorporation of contextualized knowledge using Commonsense Transformer (COMET), an existing knowledge model trained on human-curated knowledge bases. We propose a method to generate, select, and encode external commonsense knowledge alongside visual and textual cues in a new pre-trained Vision-Language-Commonsense transformer model, VLC-BERT. Through our evaluation on the knowledge-intensive OK-VQA and A-OKVQA datasets, we show that VLC-BERT is capable of outperforming existing models that utilize static knowledge bases. Furthermore, through a detailed analysis, we explain which questions benefit, and which don’t, from contextualized commonsense knowledge from COMET. Code: https://github.com/aditya10/VLC-BERT
Graph-Augmented Social Translation Model for Next-Item Recommendation

Next-item recommendation has been a hot research topic in academia and industry, which aims to help users discover the next interesting item. In this article, we propose a novel solution, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graph-augmented social translation model</i> (GAST), which investigates the utility of dynamic social influence for the task of next-item recommendation. Specifically, we introduce a gated graph convolution module to better model long-term user preference. Furthermore, we design a cogating module to capture dynamic patterns at both sequential level and social level. In addition, a social-enhanced translation mechanism is devised to measure the intensity of user–item relationships. Extensive experiments under different recommendation scenarios demonstrate the rationality and effectiveness of our proposed GAST method over several state-of-the-art methods.
FMvC: Fast Multi-View Clustering

In multi-view clustering, an eigen-decomposition of the Laplacian matrix of the graph is usually necessary.This leads to a significant increase in time cost and also requires post-processing such as k-means.In addition, some methods require learning a uniform graph matrix.In large-scale data, this process significantly increase time and memory costs.To address these problems, this paper proposes Fast Multi-view Clustering (FMvC).First, non-negative constraints are added to the objective function from the unified view of relaxed normalized and ratio cuts.Then, graph reconstruction is performed on the similarity matrix using an indication matrix to ensure that the obtained graph has robust intra-cluster and weak intercluster connectivity.Besides, the operation speed of the method can be further enhanced by setting a common labeling matrix.Finally, the problem is solved optimally based on the strategy of alternating directional multipliers.Experimental results on eight real-world datasets demonstrate the effectiveness of the proposed algorithm, which can always outperform eleven existing baseline algorithms.
Incorporating Extended Reality Technology for Delivering Computer Aided Design and Visualisation Modules

The basic requirement in curriculum design is to review and constructively align programme modules with state-of-the art and trends in the subject area. Extended reality (XR), an umbrella term for emerging technologies such as augmented reality (AR), mixed reality (MR) and virtual reality (VR), is becoming a prominent aspect of design and visualization for architecture, engineering, and construction (AEC) industry. Engineering programmes are primary feeders for the AEC industry, delivery of CAD and visualisation modules provide wider opportunities for adapting such emerging technologies in the subject area, as well as use of these technologies for developing novel pedagogical practices. This paper provides a rational behind revising traditional CAD and visualisation modules designed for Engineering undergraduate programmes, and constructively incorporate XR within programme modules. A critical literature review is provided on XR subject area as well as XR based pedagogical practices. This review identifies elements of XR as a subject area that can be incorporated in AEC programmes. It also highlights academic and operational considerations in adapting XR technology for delivering CAD and visualisation modules. Similar approach is extended to evaluate integration of XR technologies for Electrical and Power Engineering Programmes.
Parallel photonic accelerator for decision making using optical spatiotemporal chaos

Photonic accelerators have attracted increasing attention for use in artificial intelligence applications. The multi-armed bandit problem is a fundamental problem of decision making using reinforcement learning. However, to the best of our knowledge, the scalability of photonic decision making has not yet been demonstrated in experiments because of the technical difficulties in the physical realization. We propose a parallel photonic decision-making system to solve large-scale multi-armed bandit problems using optical spatiotemporal chaos. We solved a 512-armed bandit problem online, which is larger than those in previous experiments by two orders of magnitude. The scaling property for correct decision making is examined as a function of the number of slot machines, evaluated as an exponent of 0.86. This exponent is smaller than that in previous studies, indicating the superiority of the proposed parallel principle. This experimental demonstration facilitates photonic decision making to solve large-scale multi-armed bandit problems for future photonic accelerators.
Edge-enhanced Global Disentangled Graph Neural Network for Sequential Recommendation

Sequential recommendation has been a widely popular topic of recommender systems. Existing works have contributed to enhancing the prediction ability of sequential recommendation systems based on various methods, such as recurrent networks and self-attention mechanisms. However, they fail to discover and distinguish various relationships between items, which could be underlying factors which motivate user behaviors. In this article, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning. At the global level, we build a global-link graph over all sequences to model item relationships. Then a channel-aware disentangled learning layer is designed to decompose edge information into different channels, which can be aggregated to represent the target item from its neighbors. At the local level, we apply a variational auto-encoder framework to learn user intention over the current sequence. We evaluate our proposed method on three real-world datasets. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines and is able to distinguish item features.
Improving Classification Performance with Statistically Weighted Dimensions and Dimensionality Reduction

In image classification, various techniques have been developed to enhance the performance of principal component analysis (PCA) dimension reduction techniques with guiding weighting features to remove redundant and irrelevant features. This study proposes the statistically weighted dimension technique based on three distribution-related class behaviors; collection-class, inter-class, and intra-class to enhance the feature-extraction ability before using PCA for feature selection. The data from the statistics-weighted dimension spaces is utilized to reduce dimensionality by reducing the large index data into smaller index data using PCA. The new principal component from the weighted training part by an unlabeled dataset is constructed and then the image is classified efficiently. Additionally, the weighting direction investigates the pros and cons of promoting and demoting to determine the worst or best option utilizing the exponents of three proposed weighted scheme. The experiment is conducted using three datasets, MNIST, E-MNIST, and F-MNIST, along with three image classification algorithms, logistic Regression, KNN, and SVM (RBF). The results clearly demonstrate that the statistically weighted dimension feature can improve the conventional classification accuracy in lower dimensions with an appropriate combination of weighting nearly 3% for the best solution on dimensionality reduction by more than 50%.
Architecting the Metaverse: Blockchain and the Financial and Legal Regulatory Challenges of Virtual Real Estate

There has been disagreement over the value of purchasing space in the metaverse, but many businesses including Nike, The Wendy's Company, and McDonald's have jumped in headfirst.While the metaverse land rush has been called an "illusion" given underdeveloped infrastructure, including inadequate software and servers, and the potential opportunities for economic and legal abuse, the "real estate of the future" shows no signs of slowing.While the current virtual space of the metaverse is worth $6.30 billion, that is expected to grow to $84.09 billion by the end of 2028.But the long-term legal and regulatory considerations of capitalizing on the investment, as well as the manner in which blockchain technology can secure users' data and digital assets, has yet to be properly investigated.With the metaverse still in a conceptual phase, building a new 3D social environment capable of digital transactions will represent most of the initial investment in time in human capital.Digital twin technologies, already well-established in industry, will be ported to support the need to architect and furnish the new digital world.The return on and viability of investing in the "real estate of the future" raises questions fundamental to the success or failure of the enterprise.As such this paper proposes a novel framing of the issue and looks at the intersection where finance, technology, and law are converging to prevent another Dot-com bubble of the late 1990s in metaverse-based virtual real estate transactions.Furthermore, the paper will argue that these domains are technologically feasible, but the main challenges for commercial users remain in the legal and
An <scp>encoder‐decoder</scp> based <scp>CNN</scp> architecture using end to end dehaze and detection network for proper image visualization and detection

Abstract Industrial sectors are reinventing in automation, stability, and robustness due to the rapid development of artificial intelligence technologies, resulting in significant increases in quality and production. Visual‐based sensor networks capture various views of the surrounding environment and are used to monitor industrial and transportation sectors. However, due to unclean suspended air particles that damage the whole monitoring and transportation systems, the visual quality of the images is degraded under adverse weather conditions. This research proposed a convolutional neural network‐based image dehazing and detection approach, called end to end dehaze and detection network (EDD‐N), for proper image visualization and detection. This network is trained on real‐time hazy images that are directly used to recover dehaze images without a transmission map. EDD‐N is robust, and accuracy is higher than any other proposed model. Finally, we conducted extensive experiments using real‐time foggy images. The quantitative and qualitative evaluations of the hazy dataset verify the proposed method's superiority over other dehazing methods. Moreover, the proposed method validated real‐time object detection tasks in adverse weather conditions and improved the intelligent transportation system.
A Brief Review on Cloud Computing Authentication Frameworks

Cloud computing is among the most widely used technologies worldwide as it helps individual users and organizations to store and share information conveniently and cost-effectively. However, data security is a major concern in cloud computing. Security involves guaranteed access to the data only to authorized users and protection against various threats and attacks. Security is generally achieved through the appropriate and efficient implementation of access control, authentication, and authorization mechanisms. Various frameworks have been proposed and implemented for authentication and access control in cloud computing. This paper discusses some of the main authentication frameworks in cloud computing, highlighting their methodologies, algorithms, and problems and identifying the issues that should form the focus of research in the area.
Building an Application that reads Secure Information Stored on the Chip of the Citizen Identity Card in Vietnam

Reading the information on the CIC/passport is very meaningful in serving the life activities of citizens in Vietnam and of foreign citizens visiting Vietnam. This research is based on the operating modes, such as BAC, FACE, and EAC to read the data contained in the chip put on the Citizen Identity Card (CIC) out securely. Specifically, the authors used the BAC mode to perform safe data reading from the CIC’s chip. BAC mode uses 3DES and SHA1 algorithms to encrypt data to ensure security, so when the data are transmitted from the chip they are encrypted and decryption is performed by the application. In this paper, a complete application has been built for reading personal information stored securely on CIC. This application is built based on the BAC reading mode corresponding to CIC in Vietnam and meets the requirements of the ICAO 9303 standard, so it is able to read passports or CICs of other countries that use this standard. The performance of this application when reading data stored on fields DG1, DG2, and DG13 is improved with a speed of about 1.2s - 1.3s for all 3 data fields.
An augmented reality maintenance assistant with real-time quality inspection on handheld mobile devices

Advances of industry 4.0 are enabling augmented reality (AR) devices to be deployed across the manufacturing sector to enhance worker engagement and performance. This evolution of AR in the workspace is driving changes in how workers on the factory floor interact with the enterprise. AR is evolving to provide work instructions for assembly and maintenance. Recent demonstrations show the capability to allow for quality inspection to be integrally evaluated as part of the AR workflow. Previous integrations of quality include the ability for manual assessment to be logged into the enterprise as part of completing work instructions; however, this is largely based on the perception of the user to determine if they accurately performed the AR instructions. This study is designed to demonstrate the ability of an enterprise-level AR maintenance experience to go beyond manual inspection by embedding real-time quality assessment into the work instructions. Essentially, the goal is to enable the enterprise to perform a self-assessment of the quality of the work performed by maintenance personnel. Furthermore, the AR instructions enable additional instruction to empower maintenance personnel to correct their work prior to moving on to other tasks. The novelty of this work is focused on deployment on a mobile device with integrated vision inspection using the same device that is being used to deploy the AR work instructions. This work enumerates several essential qualities of mobile quality inspection tools and outlines some of the challenges associated with the development of such a system. Finally, results from testing the system demonstrate that handheld mobile devices can be used to capture inspection images while simultaneously deploying AR work instructions. Analysis of the image correction, timing for vision process of the quality check, and overall performance of the integrated system are presented.
A Unified Model for Face Matching and Presentation Attack Detection using an Ensemble of Vision Transformer Features

A typical automated face recognition system is composed of three main component tasks: face detection and alignment (FDA), face presentation attack detection (FPAD), and face representation and matching (FRM). These tasks are often treated as standalone problems and deep neural network (DNN)-based solutions have been proposed to address them individually. However, in resource-constrained sce-narios it would be ideal to have a unified DNN model that can perform all the three tasks together. As a first step towards realizing this goal, this work attempts to perform joint FRM and FPAD based on a single Vision Transformer (ViT) backbone. Recent work demonstrating the ability of ViT to extract a diverse set of feature representations gives rise to the tantalising possibility of building an end-to-end face recognition system using a single ViT model. The standard approach for designing multi-task DNNs is to implement different classification heads (e.g., for FRM and FPAD) based on a common stem/base and learn these heads either individually or jointly. A key contribution of this work is to demonstrate that this naive multi-head approach results in sub-optimal performance for either FRM or FPAD, because the features required by these tasks are very different. While good FPAD performance depends on accurately characterizing the micro textures, face matching demands attention towards more global characteris-tics. Hence, we propose a novel feature ensemble approach, where an ensemble of local features extracted from the in-termediate blocks of a ViT are utilized for FPAD, while face matching is performed based on the ViT class token. Exper-iments demonstrate that the proposed ViT feature ensemble approach is able to achieve good performance for both face matching and FPAD compared to the multi-head approach.
Chaotic image encryption based on spiral traversal and finite field bidirectional diffusion

Abstract In this paper, a new sinusoidal cascade chaotic map model is proposed, which constructs chaotic system by sinusoidal cascade of two identical 1D chaotic maps. This model can generate 1D chaotic system with wider parameter range and wider chaotic region. To verify its application in image encryption, a chaotic image encryption algorithm based on spiral traversal and finite field bidirectional diffusion is proposed. The pseudo-random sequences generated by three kinds of 1D chaotic maps improved by the sinusoidal cascade chaotic map model are used as the ciphers of the cryptosystem. Among them, SHA-256, SHA-512 and plain image are combined to generate the initial value of the system. The experimental results and security analysis show that the encryption algorithm designed in this paper can effectively resist exhaustive attack, differential attack, noise attack and so on, and the proposed model has superior performance in encryption speed and security.