Reviewerly/exHarmony huggingface dataset:
Multi-modal multi-task feature fusion for RGBT tracking

RGBT tracking has received more and more attention in recent years, and in this paper, we propose a multi-task auxiliary learning framework for RGBT tracking. Specifically, we simplify the tracking task to an instance classification task and make it the primary task of the framework. We designed three auxiliary tasks and used a hard-parameter sharing approach to jointly train multiple tasks, hoping that the primary task would benefit from them. The three auxiliary tasks are contrastive instance discrimination, one-shot instance segmentation, and instance semantic matching. The contrastive instance discrimination method promotes the classification process of the primary task by constraining the features in the representation space. One-shot instance segmentation trains the network in a weakly supervised way to focus on more fine-grained features. In addition, in order to make the network pay more attention to the invariant features of instance target during tracking, we introduce a semantic matching task to alleviate the model drift problem caused by time change. Based on the results on three RGBT tracking benchmarks, the proposed framework is not inferior to the state-of-the-art trackers.
IYDSE: Ameliorated Young’s double-slit experiment optimizer for applied mechanics and engineering

The Young’s Double-Slit Experiment (YDSE) optimizer is a newly developed swarm intelligence algorithm inspired by Young’s double-slit experiment that reveals the wave nature of light. Due to its simple structure and efficiency, the researchers employed the algorithm to address real-world optimization problems in multiple domains. However, in large-scale mechanical and engineering applications, it still needs to balance exploration and exploitation effectively and easily fall into local optimum. Therefore, in order to ameliorate these shortcomings, a multi-strategy improved YDSE optimizer named IYDSE is developed in this study. In IYDSE, introducing the Bernoulli map strategy ensures a higher quality initial population in the initialization, thus improving the convergence speed. Then, Levy flight is utilized as a local development engine to avoid local search stagnation of the optimal solution during optimization. In addition, mirror reflection learning is designed to enhance group diversity. Finally, the information entanglement strategy is introduced to enhance the information exchange between light and dark fringes, thus balancing the exploration-development capability. The superiority of the proposed IYDSE is comprehensively demonstrated by comparing it with state-of-the-art algorithms on the CEC2019 and CEC2020 test suites, respectively. Meanwhile, the practicability of IYDSE is also verified by solving five specific engineering design cases and 50 different types of engineering optimization suites. Finally, the IYDSE algorithm is applied to three truss topology optimization problems. The experimental results demonstrate the applicability and development potential of the proposed algorithm in practical engineering applications. Therefore, IYDSE is potentially an effective and competitive algorithm for solving applied mechanics and engineering.
Privacy risks of whole-slide image sharing in digital pathology

Access to large volumes of so-called whole-slide images-high-resolution scans of complete pathological slides-has become a cornerstone of the development of novel artificial intelligence methods in pathology for diagnostic use, education/training of pathologists, and research. Nevertheless, a methodology based on risk analysis for evaluating the privacy risks associated with sharing such imaging data and applying the principle "as open as possible and as closed as necessary" is still lacking. In this article, we develop a model for privacy risk analysis for whole-slide images which focuses primarily on identity disclosure attacks, as these are the most important from a regulatory perspective. We introduce a taxonomy of whole-slide images with respect to privacy risks and mathematical model for risk assessment and design . Based on this risk assessment model and the taxonomy, we conduct a series of experiments to demonstrate the risks using real-world imaging data. Finally, we develop guidelines for risk assessment and recommendations for low-risk sharing of whole-slide image data.
A smart energy IoT model based on the Itsuku PoW technology

Based on the blockchain + software defined network(SDN) technology, we explore and research a new smart energy networks IoT architecture model of distributed trust to solve the “stuck neck” problems such as bandwidth constraints and trust obstruction of the current centralized network architecture of smart energy networks. In order to achieve efficient data flow and sharing and co-governance. Based on the MTP-Argon2 hash function algorithm in the Itsuku PoW technical solution, we add computing power optimization and correction multi-objective optimization operations, and use the network control right separation technology of the FS-Open Security SDN model to establish local database access policies and storage credentials. The traditional hardware devices are separated from the centralized network architecture of the smart energy network into a hybrid network architecture model of distributed trust. The results show that the distributed trust hybrid network architecture model achieves low latency and circumvents network bandwidth limitations, reflecting the energy efficiency gain and security stability of the model, as well as network load robustness.
Stereoscopic image super-resolution with interactive memory learning

Stereo image super-resolution aims to exploit the complementary information between image pairs and generate images with high resolution and rich details. However, existing methods explicitly calculate the similarity between image patches or pixels to build correspondence between different views. These hard-matching methods leave deep semantic information between image pairs unexplored. In this paper, a stereo image super-resolution method with interactive memory learning is designed to take advantage of the complementary information of different views in an implicit way. Specifically, we propose an interactive memory learning strategy to implicitly capture the semantic similarity between different views and design a feature dual-aggregation module for feature refinement. Extensive experiments on different datasets achieve state-of-the-art results, demonstrating that our method effectively boosts the quantitative and qualitative results of stereoscopic image pairs. Code can be found at: https://github.com/zhuxiangyuan1/IMLnet.
Log-Can: Local-Global Class-Aware Network For Semantic Segmentation of Remote Sensing Images

Remote sensing images are known of having complex backgrounds, high intra-class variance and large variation of scales, which bring challenge to semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) modules to remote sensing images. Specifically, the GCA module captures the global representations of class-wise context modeling to circumvent back-ground interference; the LCA modules generate local class representations as intermediate aware elements, indirectly associating pixels with global class representations to reduce variance within a class; and a multi-scale architecture with GCA and LCA modules yields effective segmentation of objects at different scales via cascaded refinement and fusion of features. Through the evaluation on the ISPRS Vaihingen dataset and the ISPRS Potsdam dataset, experimental results indicate that LoG-CAN outperforms the state-of-the-art methods for general semantic segmentation, while significantly reducing network parameters and computation. Code is available at https://github.com/xwmaxwma/rssegmentation.
A Novel Efficient Multi-View Traffic-Related Object Detection Framework

With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception. However, performing video analytics efficiently by exploiting the spatial-temporal redundancy from video data remains challenging. Accordingly, we propose a novel traffic-related framework named CEVAS to achieve efficient object detection using multi-view video data. Briefly, a fine-grained input filtering policy is introduced to produce a reasonable region of interest from the captured images. Also, we design a sharing object manager to manage the information of objects with spatial redundancy and share their results with other vehicles. We further derive a content-aware model selection policy to select detection methods adaptively. Experimental results show that our framework significantly reduces response latency while achieving the same detection accuracy as the state-of-the-art methods.
PCF: ECAPA-TDNN with Progressive Channel Fusion for Speaker Verification

ECAPA-TDNN is currently the most popular TDNN-series model for speaker verification, which refreshed the state-of-the-art (SOTA) performance of TDNN models. However, one-dimensional convolution has a global receptive field over the feature channel. It destroys the time-frequency relevance of the spectrogram. Besides, as ECAPA-TDNN only has five layers, a much shallower structure compared to ResNet restricts the capability to generate deep representations. To further improve ECAPA-TDNN, we propose a progressive channel fusion strategy that splits the spectrogram across the feature channel and gradually expands the receptive field through the network. Secondly, we enlarge the model by extending the depth and adding branches. Our proposed model achieves EER with 0.718 and minDCF(0.01) with 0.0858 on vox1o, relatively improved 16.1% and 19.5% compared with ECAPA-TDNN(C=1024).
GS-InGAT: An interaction graph attention network with global semantic for knowledge graph completion

Knowledge graph completion (KGC) aims to infer missing links between entities based on the observed ones. Current KGC methods primarily focus on KG embedding models, which project entities and relations as low-dimensional vectors. Recently, the combination of textual information with graph neural network models has drawn extensive attention due to their superiority in utilizing topological structures, benefiting from the message passing mechanism, and their effectiveness in supplementing structural information. Nevertheless, previous methods suffer from the following two limitations. First, they always treat the textual information as an independent instance to enhance the corresponding entities, without considering the global semantic within the KG. Second, Graph Neural Networks (GNNs) typically assume that the neighbors of a node are independent of each other, ignoring the possible interactions between them. To eliminate these limitations, we creatively propose a KGC method called GS-InGAT (Interaction Graph ATtention Network with Global Semantic). Concretely, we utilize a semantic graph to model the semantic relationships and obtain the global semantic representations for entities based on it. Furthermore, we introduce an efficient Interaction Graph ATtention network (InGAT) that can simultaneously capture both the interaction and local information of entities, which can be fused to generate structural representations. Finally, we feed the combination of the semantic and structural representations, along with relation representations, into the decoder to score triples. Experimental results demonstrate that the GS-InGAT consistently attains comparable performance on benchmark datasets, verifying the effectiveness of considering the global semantic and interactions between neighbors.
Towards Simultaneous Segmentation Of Liver Tumors And Intrahepatic Vessels Via Cross-Attention Mechanism

Accurate visualization of liver tumors and their surrounding blood vessels is essential for noninvasive diagnosis and prognosis prediction of tumors. In medical image segmentation, there is still a lack of in-depth research on the simultaneous segmentation of liver tumors and peritumoral blood vessels. To this end, we collect the first liver tumor, and vessel segmentation benchmark datasets containing 52 portal vein phase computed tomography images with liver, liver tumor, and vessel annotations. In this case, we propose a 3D U-shaped Cross-Attention Network (UCA-Net) that utilizes a tailored cross-attention mechanism instead of the traditional skip connection to effectively model the encoder and decoder feature. Specifically, the UCA-Net uses a channel-wise cross-attention module to reduce the semantic gap between encoder and decoder and a slice-wise cross-attention module to enhance the contextual semantic learning ability among distinct slices. Experimental results show that the proposed UCA-Net can accurately segment 3D medical images and achieve state-of-the-art performance on the liver tumor and intrahepatic vessel segmentation task.
DEHRFormer: Real-Time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes

Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges. Recent learning-based depth estimation methods are mainly targeted at dehazing first and estimating depth subsequently from haze-free scenes. This way, the inner connections between colored haze and scene depth are lost. In this paper, we propose a real-time transformer for simultaneous single image Depth Estimation and Haze Removal (DEHRFormer). DEHRFormer consists of a single encoder and two task-specific decoders. The transformer decoders with learnable queries are designed to decode coupling features from the task-agnostic encoder and project them into clean image and depth map, respectively. In addition, we introduce a novel learning paradigm that utilizes contrastive learning and domain consistency learning to tackle weak-generalization problem for real-world dehazing, while predicting the same depth map from the same scene with varicolored haze. Experiments demonstrate that DEHRFormer achieves significant performance improvement across diverse varicolored haze scenes over previous depth estimation networks and dehazing approaches.
Depth-Optimization of Quantum Cryptanalysis on Binary Elliptic Curves

This paper presents quantum cryptanalysis for binary elliptic curves from a time-efficient implementation perspective (i.e., reducing the circuit depth), complementing the previous research that focuses on the space-efficiency perspective (i.e., reducing the circuit width).To achieve depth optimization, we propose an improvement to the existing circuit implementation of the Karatsuba multiplier and FLT-based inversion, then construct and analyze the resource in the Qiskit quantum computer simulator.The proposed multiplier architecture, which improves the quantum Karatsuba multiplier from the previous study, reduces the depth and yields a lower number of CNOT gates that bound to O(n log 2 (3) ) while maintaining a similar number of Toffoli gates and qubits.Furthermore, our improved FLT-based inversion reduces CNOT count and overall depth, with a tradeoff of higher qubit size.Finally, we employ the proposed multiplier and FLTbased inversion for performing quantum cryptanalysis of binary point addition as well as the complete Shor's algorithm for the elliptic curve discrete logarithm problem (ECDLP).As a result, apart from depth reduction, we are also able to reduce up to 90% of the Toffoli gates required in a single-step point addition compared to prior work, leading to significant improvements and giving new insights on quantum cryptanalysis for a depth-optimized implementation.
Data-Centric AI for Healthcare Fraud Detection

Automated methods for detecting fraudulent healthcare providers have the potential to save billions of dollars in healthcare costs and improve the overall quality of patient care. This study presents a data-centric approach to improve healthcare fraud classification performance and reliability using Medicare claims data. Publicly available data from the Centers for Medicare & Medicaid Services (CMS) are used to construct nine large-scale labeled data sets for supervised learning. First, we leverage CMS data to curate the 2013-2019 Part B, Part D, and Durable Medical Equipment, Prosthetics, Orthotics, and Supplies (DMEPOS) Medicare fraud classification data sets. We provide a review of each data set and data preparation techniques to create Medicare data sets for supervised learning and we propose an improved data labeling process. Next, we enrich the original Medicare fraud data sets with up to 58 new provider summary features. Finally, we address a common model evaluation pitfall and propose an adjusted cross-validation technique that mitigates target leakage to provide reliable evaluation results. Each data set is evaluated on the Medicare fraud classification task using extreme gradient boosting and random forest learners, multiple complementary performance metrics, and 95% confidence intervals. Results show that the new enriched data sets consistently outperform the original Medicare data sets that are currently used in related works. Our results encourage the data-centric machine learning workflow and provide a strong foundation for data understanding and preparation techniques for machine learning applications in healthcare fraud.
The Metaverse as a Virtual Model of Platform Urbanism: Its Converging AIoT, XReality, Neurotech, and Nanobiotech and Their Applications, Challenges, and Risks

With their exponentially rising computational power, digital platforms are heralding a new era of hybrid intelligence. There has recently been much enthusiasm and hype that the Metaverse has the potential to unlock hybrid intelligence. This is premised on the idea that the Metaverse represents an applied convergence of Artificial Intelligence of Things (AIoT) and Extended Reality (XR) that intersects with urbanism in terms of the distinctive features of platform-mediated everyday life experiences in cities. However, social interaction and its resulting social organization in the Metaverse are mediated and governed by algorithms and thus submitted to—a dream of—complete logical ordering. This raises a plethora of concerns related to the systemic collection and algorithmic processing of users’ personal, brain, and biometric data, i.e., profound societal—and the hardest to predict ethical—implications. Therefore, this study analyzes and synthesizes a large body of scientific literature on the unfolding convergence of AIoT and XR technologies, neurotechnology, and nanobiotechnology in the realm of the Metaverse in order to derive a novel conceptual framework for the Metaverse as an envisioned virtual model of platform urbanism. Further, it examines the key challenges and risks of these converging technologies in relation to the Metaverse and beyond. This study employs thematic analysis and synthesis to cope with multidisciplinary literature. The analysis identifies seven themes: (1) Platformization, (2) platform urbanism, (3) virtual urbanism, (4) XR technologies, (5) AIoT technologies, (6) neurotechnology, and (7) nanobiotechnology. The synthesized evidence reveals that, while neurotechnology and nanobiotechnology have numerous benefits and promising prospects, they raise contentions and controversies stemming from their potential use to inflict harm to human users—if left unchecked—through the black box of the algorithmic mediation underpinning the Metaverse. The findings serve to steer the Metaverse to contribute to human flourishing and wellbeing by adhering to and upholding ethical principles as well as leveraging its underlying disruptive technologies in meaningful ways. They also aid scholars, practitioners, and policymakers in assessing the pros and cons of these technologies, especially their inevitable ramifications.
Optimal entanglement distribution policies in homogeneous repeater chains with cutoffs

We study the limits of bipartite entanglement distribution using a chain of quantum repeaters that have quantum memories. To generate end-to-end entanglement, each node can attempt the generation of an entangled link with a neighbor, or perform an entanglement swapping measurement. A maximum storage time, known as cutoff, is enforced on the memories to ensure high-quality entanglement. Nodes follow a policy that determines when to perform each operation. Global-knowledge policies take into account all the information about the entanglement already produced. Here, we find global-knowledge policies that minimize the expected time to produce end-to-end entanglement. Our methods are based on Markov decision processes and value and policy iteration. We compare optimal policies to a policy in which nodes only use local information. We find that the advantage in expected delivery time provided by an optimal global-knowledge policy increases with increasing number of nodes and decreasing probability of successful swapping. Our work sheds light on how to distribute entangled pairs in large quantum networks using a chain of intermediate repeaters with cutoffs.
Trust chain driven bidirectional feedback mechanism in social network group decision making and its application in Metaverse virtual community

This paper investigates how to conduct bidirectional feedback mechanism by establishing trust chains in social network group decision making (SN-GDM), which is more applicable than the ordinary bidirectional feedback process, and further explores its application in the Metaverse virtual community. Firstly, a trust chain between decision makers (DMs) is constructed from the trust network, which can connect two individuals who do not have trust relationship and therefore assist the opinion interaction and consensus building. Then, this paper designs an opinion transmission model based on trust chain to realize the information transmission and bidirectional feedback process of DMs, and the characteristics of the proposed model are revealed through simulation analysis. Finally, the proposed trust chain driven bidirectional feedback mechanism is applied to an illustrative example of Metaverse virtual community decision-making problem to demonstrate the effectiveness and applicability of the model, and comparisons with other methods shows the advantage of our proposed approach.