Reviewerly/exHarmony huggingface dataset:
Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep Learning

Three-dimensional (3D) shape acquisition of objects from a single-shot image has been highly demanded by numerous applications in many fields, such as medical imaging, robotic navigation, virtual reality, and product in-line inspection. This paper presents a robust 3D shape reconstruction approach integrating a structured-light technique with a deep learning-based artificial neural network. The proposed approach employs a single-input dual-output network capable of transforming a single structured-light image into two intermediate outputs of multiple phase-shifted fringe patterns and a coarse phase map, through which the unwrapped true phase distributions containing the depth information of the imaging target can be accurately determined for subsequent 3D reconstruction process. A conventional fringe projection technique is employed to prepare the ground-truth training labels, and part of its classic algorithm is adopted to preserve the accuracy of the 3D reconstruction. Numerous experiments have been conducted to assess the proposed technique, and its robustness makes it a promising and much-needed tool for scientific research and engineering applications.
RoAN: A relation-oriented attention network for temporal knowledge graph completion

In the last few years, the availability of temporal knowledge graphs (TKGs), which associate time information for each event, increased the need for completing in these TKGs. In order to solve the completion problem, the existing efforts extend static knowledge graph completion models to handle time-dependent representations, or encode the structural information of events for passing temporal message. However, the above efforts mostly construct TKGs in the form of multi-edges mesh and focus on entity features. They do not explore the power of relations for TKG completion task. In this paper, we introduce a form of relational multi-chains to reconstruct TKGs and propose a relation-oriented attention mechanism for embedding the features of relations. According to our attention mechanism, we build a Relation-oriented Attention Network (RoAN) to model temporal embeddings of relations. It is worth noting that our approach is model-agnostic and can be potentially combined with most existing TKG completion models. Experimental results show that our approach can be coupled with previous TKGC methods and can increase their performance accordingly. In addition, the analysis reveals the principle of our relation-oriented attention mechanism.
Encrypted Semantic Communication Using Adversarial Training for Privacy Preserving

Semantic communication is implemented based on shared background knowledge, but the sharing mechanism risks privacy leakage. In this letter, we propose an encrypted semantic communication system (ESCS) for privacy preserving, which combines universality and confidentiality. The universality is reflected in the fact that the structures of all network modules of the proposed ESCS are public, and the training database is shared, which is suitable for large-scale deployment in practical scenarios. Meanwhile, confidentiality is achieved through key encryption. Based on the adversarial training, we design an adversarial encryption training scheme to guarantee the accuracy of semantic communication in both encrypted and unencrypted modes. Experiment results show that the proposed ESCS with the adversarial encryption training scheme can perform well regardless of whether the semantic information is encrypted. It is difficult for the attacker to reconstruct the original semantic information from the eavesdropped message.
A pairing-free certificateless searchable public key encryption scheme for IoMT

To ensure confidentiality, medical information is usually encrypted before it is sent to a third party for processing. Encryption technology can protect the privacy of data, but it limits the ability to search for data. The problem is usually solved by using public key encryption with keyword search (PEKS). Recently, some certificateless PEKS (CPEKS) schemes have been proposed. Not only do they allow users to search encrypted data, but they do not have the problems of certificate management and key escrow. However, they rely on the high-consumption bilinear pairing, and some of them are vulnerable to Inside Keyword Guessing Attacks (IKGA). To address these issues, we design a lightweight CPEKS scheme for the Medical Internet of Things (IoMT) that does not include bilinear pairing. In the random oracle model, our scheme is proved to be secure. The analysis results show that the proposed scheme has better overall performance than the existing schemes according to the security property, computation cost, and communication cost. Finally, an IoMT application scenario of our scheme is given.
Visual Questions Answering Developments, Applications, Datasets and Opportunities: A State-of-the-Art Survey

Visual Question Answering (VQA) is an emerging field in Artificial Intelligence (AI) that aims to enable machines to understand and answer questions about visual content. In this survey paper, current state-of-the art research is extensively surveyed to highlight limitations and futuristic opportunities. Visual QA systems use Natural Language Processing and machine learning techniques to understand and respond to questions posed by users. The paper then reviews the recent advances in neural network-based models and pre-trained language models. The paper also discusses the challenges facing visual QA systems, including the need for large-scale training data, the ability to handle complex and open-ended questions, and the need for robust evaluation metrics. Further, different types of datasets and evaluation metrics used in the literature are summarized, as well as the challenges and open research problems that remain to be addressed. Overall, it is concluded that VQA is a challenging task that requires a combination of visual understanding and natural language processing skills, and that there is still much scope for improvement in terms of accuracy and generalization.
Site-optimized training image database development using web-crawled and synthetic images

Since most state-of-the-art vision technologies have recently originated from machine learning or deep learning algorithms, it has become very important to build a large, high-quality training database (DB). To this end, this paper proposes an automated framework that creates images using web crawling and virtual reality techniques, labels target objects, and generates a training DB for vision-based detection models. The framework contains three main processes: (1) image collection and labeling using web crawling; (2) image producing using a 3D modeling tool; and (3) foreground–background cross-oversampling. As a result, the framework constructed a training DB composed of 99,800 images in 42 min. The deep learning model was trained by the generated DB and showed macro F1-scores of up to 96.99%. These results imply that the framework successfully constructed a high-quality training DB within a short period of time. The findings can contribute to reducing time and effort in developing vision-based monitoring technologies.
Parallel Global Best-Worst Particle Swarm Optimization Algorithm for solving optimization problems

The range of applications of swarm optimization algorithms is very vast. However, high dimensions and more number of decision variables make these optimization problems more complex. Particle Swarm Optimization (PSO) is the most popular optimizer for performing such types of optimization. PSO is motivated from the movement and intelligence of swarms. However, the primary constraint with the PSO and other swarm algorithms is enormous computational time (CT) due to more number of decision variables in complex problem. The number of steps inside Swarm Intelligence Algorithms (SIAs) also increase the complexity of computation in the process of optimization. Many iterations of the procedure of SIA need more CT since these algorithms are iterative in nature. In this study, a new Global Best-Worst Particle Swarm Optimization (GBWPSO) algorithm has been proposed so as to provide a fully version of parallel algorithm. GBWPSO algorithm is the combination of PSO and Jaya algorithm that provides a refined version of parallel algorithm having more parallelism. The proposed algorithm is executed on three different computational hardware with various combinations of population size and maximum number of iteration on five different standard benchmark functions. The evaluation is done on the basis of performance metrics such as speedup (S), real speedup (RS), maximum speedup (MS), efficiency (E), and scalability. The proposed parallel algorithm (P-GBWPSO) outperforms both parallel version of PSO and Jaya algorithm in terms of less CT and better optimal solution. Based on the results, we found that system 03 (S3) is best on proposed GBWPSO algorithm with an efficiency of 1.2518 compare with system 01 (S1) and system 02 (S2).
Survey of prominent blockchain development platforms

Many developers have ideas to create blockchain applications but do not know where to begin. Often these developers default to using the first blockchain development platform they discover, which may not be the best platform for their project. Over 8000 new blockchain-related projects are added to GitHub a year. The near-constant influx of new projects can make it difficult for developers to search through existing projects and platforms to find the best platform for their projects. We considered 65 blockchain development platforms for this work and provided a brief yet comprehensive summary of the 23 most popular platforms. Our aim for this work is to assist developers in selecting the most appropriate platform for their blockchain projects.
Multi-target Knowledge Distillation via Student Self-reflection

Abstract Knowledge distillation is a simple yet effective technique for deep model compression, which aims to transfer the knowledge learned by a large teacher model to a small student model. To mimic how the teacher teaches the student, existing knowledge distillation methods mainly adapt an unidirectional knowledge transfer, where the knowledge extracted from different intermedicate layers of the teacher model is used to guide the student model. However, it turns out that the students can learn more effectively through multi-stage learning with a self-reflection in the real-world education scenario, which is nevertheless ignored by current knowledge distillation methods. Inspired by this, we devise a new knowledge distillation framework entitled multi-target knowledge distillation via student self-reflection or MTKD-SSR, which can not only enhance the teacher’s ability in unfolding the knowledge to be distilled, but also improve the student’s capacity of digesting the knowledge. Specifically, the proposed framework consists of three target knowledge distillation mechanisms: a stage-wise channel distillation (SCD), a stage-wise response distillation (SRD), and a cross-stage review distillation (CRD), where SCD and SRD transfer feature-based knowledge (i.e., channel features) and response-based knowledge (i.e., logits) at different stages, respectively; and CRD encourages the student model to conduct self-reflective learning after each stage by a self-distillation of the response-based knowledge. Experimental results on five popular visual recognition datasets, CIFAR-100, Market-1501, CUB200-2011, ImageNet, and Pascal VOC, demonstrate that the proposed framework significantly outperforms recent state-of-the-art knowledge distillation methods.
A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning

In recent years, there have been several solutions to medical image segmentation, such as U-shaped structure, transformer-based network, and multi-scale feature learning method. However, their network parameters and real-time performance are often neglected and cannot segment boundary regions well. The main reason is that such networks have deep encoders, a large number of channels, and excessive attention to local information rather than global information, which is crucial to the accuracy of image segmentation. Therefore, we propose a novel multi-branch medical image segmentation network MBSNet. We first design two branches using a parallel residual mixer (PRM) module and dilate convolution block to capture the local and global information of the image. At the same time, a SE-Block and a new spatial attention module enhance the output features. Considering the different output features of the two branches, we adopt a cross-fusion method to effectively combine and complement the features between different layers. MBSNet was tested on five datasets ISIC2018, Kvasir, BUSI, COVID-19, and LGG. The combined results show that MBSNet is lighter, faster, and more accurate. Specifically, for a [Formula: see text] input, MBSNet's FLOPs is 10.68G, with an F1-Score of [Formula: see text] on the Kvasir test dataset, well above [Formula: see text] for UNet++ with FLOPs of 216.55G. We also use the multi-criteria decision making method TOPSIS based on F1-Score, IOU and Geometric-Mean (G-mean) for overall analysis. The proposed MBSNet model performs better than other competitive methods. Code is available at https://github.com/YuLionel/MBSNet .
Source-Free Progressive Graph Learning for Open-Set Domain Adaptation

Open-set domain adaptation (OSDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain while addressing disturbances from irrelevant target classes not present in the source data. However, most OSDA approaches are limited due to the lack of essential theoretical analysis of generalization bound, reliance on the coexistence of source and target data during adaptation, and failure to accurately estimate model predictions' uncertainty. To address these limitations, the Progressive Graph Learning (PGL) framework is proposed. PGL decomposes the target hypothesis space into shared and unknown subspaces and progressively pseudo-labels the most confident known samples from the target domain for hypothesis adaptation. PGL guarantees a tight upper bound of the target error by integrating a graph neural network with episodic training and leveraging adversarial learning to close the gap between the source and target distributions. The proposed approach also tackles a more realistic source-free open-set domain adaptation (SF-OSDA) setting that makes no assumptions about the coexistence of source and target domains. In a two-stage framework, the SF-PGL model' uniformly selects the most confident target instances from each category at a fixed ratio, and the confidence thresholds in each class weigh the classification loss in the adaptation step. The proposed methods are evaluated on benchmark image classification and action recognition datasets, where they demonstrate superiority and flexibility in recognizing both shared and unknown categories. Additionally, balanced pseudo-labeling plays a significant role in improving calibration, making the trained model less prone to over- or under-confident predictions on the target data.
FusedAR: Energy-Positive Human Activity Recognition Using Kinetic and Solar Signal Fusion

Today’s wearable Internet of Things (IoT) devices, which have been fêted for numerous applications, suffer from the limited lifetime of batteries due to the high power consumption of conventional inertial activity sensors. Recently, kinetic energy harvesters have been employed as a source of energy as well as context information to replace conventional activity sensors. However, the harvested power from human movements using miniaturised kinetic transducers may not be sufficient to enable a perpetual and self-powered activity recognition system. In this paper, we propose a novel mechanism of Fused signal based human Activity Recognition (FusedAR), which employs miniaturised wearable solar and kinetic energy harvesters simultaneously as an energy source as well as an activity sensor. As human activities engender distinct movement patterns, and interact and interfere with the ambient light differently, the kinetic and solar energy harvesting signals incorporate unique information about the underlying activities while generating sufficient power. After detailed experiments, we find that FusedAR which employs both solar and kinetic energy signals, achieves superior activity recognition performance by up to 10%, particularly in outdoor and night-time contexts, and can recognise not only activities but also contexts, compared to the individual energy harvesting signals. Furthermore, our analysis demonstrates that FusedAR, in addition to significant energy generation, consumes up to 22% less power than the highly optimised conventional 3-axis accelerometer-based mechanisms, achieving energy-positive human activity recognition leading towards perpetual, uninterrupted and autonomous operation of wearable IoT devices.
A Coupled Compression Generation Network for Remote-Sensing Images at Extremely Low Bitrates

Benefiting from the excellent texture recovery capability of generative adversarial networks (GANs), generated images are capable of maintaining clear texture features even when compressed into extremely low-bit streams. In recent years, the GAN has made great progress in extremely low-bit compression for natural images. However, a few studies have been conducted on extremely low-bit compression for the remote-sensing (RS) field. We find that a single GAN tends to generate visually pleasing texture information, and this characteristic may affect the visual effect and accuracy of other computer vision tasks. Therefore, we propose a coupled compression generation network (CCGN) that reconstructs the image content and detailed textures separately and fuses them to achieve a balanced image reconstruction task at extremely low bitrates for remote-sensing images. Specifically, a multidimensional residual attention mechanism (MRAM) is adopted to achieve extremely low-bit stream generation, whereas contentwise images and texturewise images are reconstructed using the same generator with different training strategies. We further optimize the texture generation strategy, and an enhanced perceptual-guided refinement stage (EPGRS) and a multiscale fusion discriminator (MSFD) are developed for a more realistic texture. The proposed method achieves outstanding results on compression tasks on the dataset for object detection in aerial images (DOTA), and the fused results of extremely low-bit streams also perform well in object detection tasks, significantly alleviating pressure from bandwidth and storage space.
FACapsnet: A fusion capsule network with congruent attention for cyberbullying detection

As an extension of bullying on social networks, cyberbullying has seriously affected the security of the online social environment and infringed on mental health. Taking appropriate measures to detect online bullying reviews is crucial. Existing studies usually classify the whole content as cyberbullying and non-cyberbullying. However, they do not fully exploit the interaction of multi-dimensional features and precisely distinguish the types of cyberbullying. To automatically extract features of bullying words and further identify fine-grained types of cyberbullying better, we propose a fusion capsule network with congruent attention for cyberbullying detection. In the proposed algorithm, a novel similarity weighting scheme based on word2vec is designed to soft highlight bullying features in word embeddings. Meanwhile, to leverage the respective advantages of extracted multiple subspace features, we construct a novel extensible congruent attention to balance the fusion of complex correlations between different subspace representations and retain the independence of context features. The fused features are updated iteratively with dynamic routing to aggregate and generate fine-grained category capsules for cyberbullying prediction. A series of experiments on the tweets cyberbullying benchmark demonstrate that our architecture matches or exceeds the performance of the compared baseline models and the results of extensive experiments prove the effectiveness of different strategies.
BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pre-trained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features the superior modeling capability of Transformer to capture the dynamic sequential patterns inherent in Ethereum transactions, and addresses the challenges of pre-training a BERT model for Ethereum with three practical and effective strategies, namely repetitiveness reduction, skew alleviation and heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH outperforms state-of-the-art methods with significant enhancements in terms of the phishing account detection and de-anonymization tasks. The code for BERT4ETH is available at: https://github.com/git-disl/BERT4ETH.
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning

Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding represents KGs in a continuous vector space, serving as the backbone of many knowledge-driven applications. As a promising combination, federated KG embedding can fully take advantage of knowledge learned from different clients while preserving the privacy of local data. However, realistic problems such as data heterogeneity and knowledge forgetting still remain to be concerned. In this paper, we propose FedLU, a novel FL framework for heterogeneous KG embedding learning and unlearning. To cope with the drift between local optimization and global convergence caused by data heterogeneity, we propose mutual knowledge distillation to transfer local knowledge to global, and absorb global knowledge back. Moreover, we present an unlearning method based on cognitive neuroscience, which combines retroactive interference and passive decay to erase specific knowledge from local clients and propagate to the global model by reusing knowledge distillation. We construct new datasets for assessing realistic performance of the state-of-the-arts. Extensive experiments show that FedLU achieves superior results in both link prediction and knowledge forgetting.