Reviewerly/exHarmony huggingface dataset:
Position-Aware Relation Learning for RGB-Thermal Salient Object Detection

RGB-Thermal salient object detection (SOD) combines two spectra to segment visually conspicuous regions in images. Most existing methods use boundary maps to learn the sharp boundary. These methods ignore the interactions between isolated boundary pixels and other confident pixels, leading to sub-optimal performance. To address this problem,we propose a position-aware relation learning network (PRLNet) for RGB-T SOD based on swin transformer. PRLNet explores the distance and direction relationships between pixels to strengthen intra-class compactness and inter-class separation, generating salient object masks with clear boundaries and homogeneous regions. Specifically, we develop a novel signed distance map auxiliary module (SDMAM) to improve encoder feature representation, which takes into account the distance relation of different pixels in boundary neighborhoods. Then, we design a feature refinement approach with directional field (FRDF), which rectifies features of boundary neighborhood by exploiting the features inside salient objects. FRDF utilizes the directional information between object pixels to effectively enhance the intra-class compactness of salient regions. In addition, we constitute a pure transformer encoder-decoder network to enhance multispectral feature representation for RGB-T SOD. Finally, we conduct quantitative and qualitative experiments on three public benchmark datasets.The results demonstrate that our proposed method outperforms the state-of-the-art methods.
A novel policy based on action confidence limit to improve exploration efficiency in reinforcement learning

Reinforcement learning has been used to solve many intelligent decision-making problems. However, reinforcement learning still faces a challenge of the low exploration efficiency problem in practice, limiting its widespread application. To address this issue, in this paper, a novel exploration policy based on Q value and exploration value is proposed. The exploration value adopts action confidence limit to measure the uncertainty of the action, which guides the agent to adaptively explore the uncertainty region of the environment. This method can improve exploration efficiency and is beneficial for the agent to make optimal decisions. Then, in order to make our proposed policy applicable to discrete and continuous environments, we combine the proposed policy with classic reinforcement learning algorithms (Q-learning and deep Q-network), and propose two novel algorithms, respectively. Moreover, the convergence of the algorithm is analyzed. Furthermore, a deep auto-encoder network model is used to establish the mapping relationship on state-action in discrete environments, which can avoid a large number of state-action pairs stored in Q-learning stage. Our proposed method can achieve adaptive and effective exploration, which is beneficial for the agent to make intelligent decisions. Finally, the results are verified in discrete and continuous simulation environments. Experimental results demonstrate that our method improves the average reward value and reduces the number of catastrophic actions.
A fairness-aware graph contrastive learning recommender framework for social tagging systems

Personalized recommendations for social tagging systems aim to deliver high-quality recommendations for users and annotate meaningful characteristics on items. However, compared to some state-of-the-art general recommender systems, current tag-aware recommender systems (TRS) have no advantage in either performance or efficiency. We discover that tagging information not only boost performance, but also promote fairness caused by biased data. In our work, we propose a fairness-aware graph contrastive learning framework named Tag-aware Graph Contrastive Learning (TAGCL). The design of its graph structure is composed of two bipartite graphs: user-tag graph and item-tag graph. Leveraging contrastive learning paradigm, our proposed TAGCL is able to encode stable and high-quality representations. In the fairness-aware learning, we jointly optimize the model through negative tag learning and TransT regularization. Extensive experiments on three public datasets and two sizable real-world datasets show that TAGCL outperforms some state-of-the-art general recommender methods and TRS in terms of accuracy of recommendations and dramatically reduces bias on some heavily skewed dataset. We also carry out comprehensive ablation studies to verify the validity of our proposed framework.
Secure and Lightweight User Authentication Scheme for Cloud-Assisted Internet of Things

Cloud-assisted Internet of Things (IoT) overcomes the resource-constrained nature of the traditional IoT and is developing rapidly in such fields as smart grids and intelligent transportation. In a cloud-assisted IoT system, users can remotely control the IoT devices and send specific instructions to them. If the users’ identities are not verified, adversaries can pretend as legitimate users to send fake and malicious instructions to IoT devices, thereby compromising the security of the entire system. Thus, a sound authentication mechanism is indispensable to ensure security. At the same time, it should be noted that a gateway may connect to massive IoT devices with the exponential growth of interconnected devices in a cloud-assisted IoT system. The efficiency of authentication schemes is easily impacted by the computation capability of the gateway. Recently, several schemes have been designed for cloud-assisted IoT systems, but they have problems of one kind or another, making them not very suitable for cloud-assisted IoT systems. In this paper, we take a typical scheme (proposed at IEEE TDSC 2020) as an example to identify the common weaknesses and challenges of designing a user authentication scheme for cloud-assisted IoT systems. In addition, we propose a new secure user authentication scheme with lightweight computation on gateways. The proposed scheme provides secure access between remote users and IoT devices with many ideal attributions, such as forward secrecy and multi-factor security. Meanwhile, the security of this scheme is proved under the random-oracle model, heuristic analysis, the ProVerif tool and BAN logic. Compared with ten state-of-the-art schemes in security and performance, the proposed scheme achieves all the listed twelve security requirements with minimum computation and storage costs on gateways.
Character Recognition Technique Implementation for Complicated Deteriorated Scene

There has been an increase in interest in digitizing and preserving old books and papers in the last few years. The quick advancement of data innovation and the Internet’s quick spread has also contributed to the enormous volume of image and video data. The texts that are included in the image and video assist us in analyzing them and are also utilized for indexing, archiving, and retrieval. Different noises, such as Gaussian noise, salt and pepper noise, speckle noise, etc., can readily damage an image. Several image filtering algorithms, including the Gaussian filter, mean filter, median filter, etc., are employed to eliminate these various noises from images. This article analyses the impact of several pre-processing approaches, such as thresholding, morphology, and blurring procedures, to maximise text extraction strategies. The experiment’s findings demonstrate that pre-processing approaches unquestionably improve the document’s structural and visual quality.
Product market competition with crypto tokens and smart contracts

This paper models benefits of quoting output price in units of crypto token under duopolistic product market competition with switching costs. Pricing output in tokens provides a firm with a de facto second-mover advantage, raising its equilibrium profit. In addition, the firm can further increase its equilibrium profit by committing via a smart contract to the number of tokens sold. By focusing on utility tokens used at the product market competition stage, the paper highlights potential benefits of issuing crypto tokens that extend beyond the advantages of issuing security tokens at the venture financing stage.
Sentiment spin: Attacking financial sentiment with GPT-3

In this study, we explore the susceptibility of financial sentiment analysis to adversarial attacks that manipulate financial texts. With the rise of AI readership in the financial sector, companies are adapting their language and disclosures to fit AI processing better, leading to concerns about the potential for manipulation. In the finance literature, keyword-based methods, such as dictionaries, are still widely used for financial sentiment analysis due to their perceived transparency. However, our research demonstrates the vulnerability of keyword-based approaches by successfully generating adversarial attacks using the sophisticated transformer model, GPT-3. With a success rate of nearly 99% for negative sentences in the Financial Phrase Bank, a widely used database for financial sentiment analysis, we highlight the importance of incorporating robust methods, such as context-aware approaches such as BERT, in financial sentiment analysis.
GriDB: Scaling Blockchain Database via Sharding and Off-Chain Cross-Shard Mechanism

Blockchain databases have attracted widespread attention but suffer from poor scalability due to underlying non-scalable blockchains. While blockchain sharding is necessary for a scalable blockchain database, it poses a new challenge named on-chain cross-shard database services. Each cross-shard database service (e.g., cross-shard queries or inter-shard load balancing) involves massive cross-shard data exchanges, while the existing cross-shard mechanisms need to process each cross-shard data exchange via the consensus of all nodes in the related shards (i.e., on-chain) to resist a Byzantine environment of blockchain, which eliminates sharding benefits. To tackle the challenge, this paper presents GriDB, the first scalable blockchain database, by designing a novel off-chain cross-shard mechanism for efficient cross-shard database services. Borrowing the idea of off-chain payments, GriDB delegates massive cross-shard data exchange to a few nodes, each of which is randomly picked from a different shard. Considering the Byzantine environment, the untrusted delegates cooperate to generate succinct proof for cross-shard data exchanges, while the consensus is only responsible for the low-cost proof verification. However, different from payments, the database services' verification has more requirements (e.g., completeness, correctness, freshness, and availability); thus, we introduce several new authenticated data structures (ADS). Particularly, we utilize consensus to extend the threat model and reduce the complexity of traditional accumulator-based ADS for verifiable cross-shard queries with a rich set of relational operators. Moreover, we study the necessity of inter-shard load balancing for a scalable blockchain database and design an off-chain and live approach for both efficiency and availability during balancing. An evaluation of our prototype shows the performance of GriDB in terms of scalability in workloads with queries and updates.
How do emotions elicited in virtual reality affect our memory? A systematic review

Emotion has been shown to have a significant influence on memory. However, most of the investigations that led to this conclusion used non-virtual reality procedures. Virtual reality, as an emotional medium, is suitable for the study of the interplay between emotion and cognition as it has the potential to induce complex emotions. Employing VR could further enhance the intensity of the emotions elicited, which in turn might improve and expand the quality and the possibility of paradigms involving both emotions and cognition. This manuscript systematically reviewed studies investigating the relationship between memory and emotions using immersive technologies. PRISMA and PICO guidelines were applied to carry out the systematic search. The 22 studies reviewed show a complex panorama: research areas range from basic research, with studies regarding episodic, spatial, traumatic, autobiographical, and emotional memories, to applied research in the field of cinema, education, and rehabilitation. Navigation and interaction, embodiment, presence and immersion, semantic aspects, sounds or music, narration were the elements that characterized the VR experience across the studies and might work as a trigger for emotions. These findings can contribute to enhancing knowledge of either how VR works as an affective medium and how emotions elicited affect memory.
CCPF-RRT*: An improved path planning algorithm with consideration of congestion

Path planning is essential for robots to efficiently execute jobs in challenging settings. In this paper, we propose a new algorithm, called potential function-based sampling heuristic optimal path planning considering congestion (CCPF-RRT*), extending the standard Rapidly-exploring Random Tree star (RRT*) to address the issues of slow convergence, high path cost, and how to plan suitable paths in the presence of congested regions in the environment. First, the CCPF-RRT* algorithm adds the artificial potential field to RRT*, which can drastically cut down on the number of iterations and speed up convergence. Second, a movement cost function that takes congestion intensity and path length into account when evaluating a connection between two path nodes is created. This function is then used to direct the growth of new nodes and the updating of parent nodes so that the algorithm takes congestion intensity and path length into account when generating paths. Third, in order to reduce the path cost, the algorithm establishes parent nodes for random nodes by combining the benefits of the F-RRT* algorithm. The movement cost function is added to the process of establishing parent nodes to optimize the path cost in order to prevent the creation of parent nodes that force the path to traverse through crowded areas. In experimental simulation results, the algorithm outperforms RRT*, Q-RRT*, PQ-RRT*, and F-RRT* not only in terms of initial solution and quick convergence speed, but is also able to design an ideal path with the lowest movement cost in a crowded environment using the movement cost function.
Deep learning based active learning technique for data annotation and improve the overall performance of classification models

Deep learning (DL) models achieved a lot of success due to the availability of labeled training data. In contrast, labeling a huge amount of data by a human is a time-consuming and expensive solution. Active Learning (AL) efficiently addresses the issue of labeled data collection at a low cost by picking the most useful samples from a large number of unlabeled datasets. However, current AL techniques largely depend on regular human involvement to annotate the most uncertain/informative samples in the collection. Therefore, we introduce a Deep Learning Based Active Learning (DLBAL) method that may incrementally learn from a small number of annotated training samples to build an effective classifier with optimum feature representation. The proposed DLBAL method improves the contemporary AL approaches in two steps. First, EfficientNet-B0 networks are integrated into AL. The incrementally annotated useful samples may be used to update both the representation of the feature and the classification algorithm concurrently. Second, we provide an effective technique for sample selection that improves the performance of the classifier with fewer manual annotations at a lower cost. In contrast to previous approaches that emphasize only uncertain instances with low prediction confidence, we obtain a significant number of instances with high-confidence from the unannotated set for pattern learning. Particularly, these instances are randomly picked and assigned labels to them continuously. Detailed experiments are conducted to show that the proposed DLBAL system obtained reasonable results on two difficult image classification datasets, namely Cross-Age Celebrity face recognition Dataset (CACD) and Caltech-256.
Enhancing Public Engagement in Architectural Design: A Comparative Analysis of Advanced Virtual Reality Approaches in Building Information Modeling and Gamification Techniques

Purpose: This paper investigates the potential of virtual reality (VR) technologies—specifically, building information modeling (BIM) (“Autodesk Revit”) and game engines (“Unreal Engine”)—to enhance public involvement in the design and execution of architecture and urban projects. The main research question focuses on comparing the effectiveness of these two methods in creating an interactive design model for participatory design in public spaces. Methods: The study employed a VR exploratory experiment with 33 participants, followed by semi-structured interviews to analyze two recent developments in London: the Sky Garden, and Crossrail Place Roof Garden. Participants interacted with the design models and provided feedback on their experiences. Results: The findings demonstrate that integrating VR with BIM software using the Enscape plugin effectively enhances user involvement, enabling real-time generation and testing of design alternatives. While both methods were found to be beneficial, participants reported a preference for the direct implementation of VR in BIM software. Conclusions: This research highlights the potential of VR technologies—specifically, BIM and game engines—as a co-design approach for public and social spaces in urban environments. It also identifies limitations and future research opportunities in adopting these methods for participatory design.
Breast cancer prediction using gated attentive multimodal deep learning

Abstract Women are prone to breast cancer, which is a major cause of death. One out of every eight women has a lifetime risk of developing this cancer. Early diagnosis of this disease is critical and enhances the success rate of cure. It is extremely important to determine which genes are associated with the disease. However, too many features make studies on gene data challenging. In this study, an attention-based multimodal deep learning model was created by combining data from clinical, copy number alteration and gene expression sources. Attention-based deep learning models can analyze mammography images and identify subtle patterns or abnormalities that may indicate the presence of cancer. These models can also integrate patient data, such as age and family history, to improve the accuracy of predictions. The objective of this study is to help breast cancer prediction tasks and improve efficiency by incorporating attention mechanisms. Our suggested methodology employs multimodal data and generates insightful characteristics to improve the prediction of the prognosis for breast cancer. It is a two-phase model; the first phase generates the stacked features using a sigmoid gated attention convolutional neural network, and the second phase uses flatten, dense and dropout processes for bi-modal attention. Based on our findings, the proposed model produced successful results and has the potential to significantly improve breast cancer detection and diagnosis, ultimately leading to better patient outcomes.
A simple color image encryption algorithm based on a discrete memristive hyperchaotic map and time-controllable operation

Chaotic maps are widely used in image encryption due to their characteristics, but many chaotic maps have the weakness of narrow and discontinuous chaotic range. To solve the problem, a new chaotic map, is constructed by combining the modular sinusoidal discrete memristor and classical sine map. An image encryption algorithm is designed based on the proposed chaotic map, and it can control the time by adjusting the block size. The proposed encryption algorithm consists of two parts: random-cross permutation and alternate-plane diffusion. Random-cross permutation shuffles the pixels on the three color planes by accessing each block. Alternate-plane diffusion uses chaotic sequence to process the image from both positive and negative directions, so that the pixel information of any point can be extended to the whole plane. Simulation experiments and performance analysis show that the proposed encryption algorithm is effective and reliable.
NeRFshop

Neural Radiance Fields (NeRFs) have revolutionized novel view synthesis for captured scenes, with recent methods allowing interactive free-viewpoint navigation and fast training for scene reconstruction. However, the implicit representations used by these methods---often including neural networks and complex encodings---make them difficult to edit. Some initial methods have been proposed, but they suffer from limited editing capabilities and/or from a lack of interactivity, and are thus unsuitable for interactive editing of captured scenes. We tackle both limitations and introduce NeRFshop, a novel end-to-end method that allows users to interactively select and deform objects through cage-based transformations. NeRFshop provides fine scribble-based user control for the selection of regions or objects to edit, semi-automatic cage creation, and interactive volumetric manipulation of scene content thanks to our GPU-friendly two-level interpolation scheme. Further, we introduce a preliminary approach that reduces potential resulting artifacts of these transformations with a volumetric membrane interpolation technique inspired by Poisson image editing and provide a process that "distills" the edits into a standalone NeRF representation.
TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

Collaborative filtering (CF) is a widely applied method to perform recommendation tasks in a wide range of domains and applications. Dictionary learning (DL) models, which are highly important in CF-based recommender systems (RSs), are well represented by rating matrices. However, these methods alone do not resolve the cold start and data sparsity issues in RSs. We observed a significant improvement in rating results by adding trust information on the social network. For that purpose, we proposed a new dictionary learning technique based on trust information, called TrustDL, where the social network data were employed in the process of recommendation based on structural details on the trusted network. TrustDL sought to integrate the sources of information, including trust statements and ratings, into the recommendation model to mitigate both problems of cold start and data sparsity. It conducted dictionary learning and trust embedding simultaneously to predict unknown rating values. In this paper, the dictionary learning technique was integrated into rating learning, along with the trust consistency regularization term designed to offer a more accurate understanding of the feature representation. Moreover, partially identical trust embedding was developed, where users with similar rating sets could cluster together, and those with similar rating sets could be represented collaboratively. The proposed strategy appears significantly beneficial based on experiments conducted on four frequently used datasets: Epinions, Ciao, FilmTrust, and Flixster.