Reviewerly/exHarmony huggingface dataset:
Enhancing Pseudo Label Quality for Semi-supervised Domain-Generalized Medical Image Segmentation

Generalizing the medical image segmentation algorithms to unseen domains is an important research topic for computer-aided diagnosis and surgery. Most existing methods require a fully labeled dataset in each source domain. Although some researchers developed a semi-supervised domain generalized method, it still requires the domain labels. This paper presents a novel confidence-aware cross pseudo supervision algorithm for semi-supervised domain generalized medical image segmentation. The main goal is to enhance the pseudo label quality for unlabeled images from unknown distributions. To achieve it, we perform the Fourier transformation to learn low-level statistic information across domains and augment the images to incorporate cross-domain information. With these augmentations as perturbations, we feed the input to a confidence-aware cross pseudo supervision network to measure the variance of pseudo labels and regularize the network to learn with more confident pseudo labels. Our method sets new records on public datasets, i.e., M&amp;Ms and SCGM. Notably, without using domain labels, our method surpasses the prior art that even uses domain labels by 11.67% on Dice on M&amp;Ms dataset with 2% labeled data. Code is available at https://github.com/XMed-Lab/EPL SemiDG.
VulCNN

Since deep learning (DL) can automatically learn features from source code, it has been widely used to detect source code vulnerability. To achieve scalable vulnerability scanning, some prior studies intend to process the source code directly by treating them as text. To achieve accurate vulnerability detection, other approaches consider distilling the program semantics into graph representations and using them to detect vulnerability. In practice, text-based techniques are scalable but not accurate due to the lack of program semantics. Graph-based methods are accurate but not scalable since graph analysis is typically time-consuming.
Hybrid quantum-classical algorithms in the noisy intermediate-scale quantum era and beyond

Hybrid quantum-classical algorithms are central to much of the current research in quantum computing, particularly when considering the noisy intermediate-scale quantum (NISQ) era, with a number of experimental demonstrations having already been performed. In this perspective, we discuss in a very broad sense what it means for an algorithm to be hybrid quantum-classical. We first explore this concept very directly, by building a definition based on previous work in abstraction/representation theory, arguing that what makes an algorithm hybrid is not directly how it is run (or how many classical resources it consumes), but whether classical components are crucial to an underlying model of the computation. We then take a broader view of this question, reviewing a number of hybrid algorithms and discussing what makes them hybrid, as well as the history of how they emerged, and considerations related to hardware. This leads into a natural discussion of what the future holds for these algorithms. To answer this question, we turn to the use of specialized processors in classical computing.The classical trend is not for new technology to completely replace the old, but to augment it. We argue that the evolution of quantum computing is unlikely to be different: hybrid algorithms are likely here to stay well past the NISQ era and even into full fault-tolerance, with the quantum processors augmenting the already powerful classical processors which exist by performing specialized tasks.
The role of emotions in the consumer meaning-making of interactions with social robots

The interaction with social robots is supposed to be a unique and emotionally charged activity. Based on the diffusion of innovations literature, subjective feelings represent a driver of the innovation diffusion process. Yet, to date, no study has comprehensively assessed consumers' emotional responses over time to interactions with social robots. Thus, the study aims to address this research gap by combining innovation diffusion and psychology literature. The emotional content of customers' self-reported communication on social robots deployed across international hotels is categorized through Plutchik's wheel of emotions by using advanced text analytics techniques to track and analyze its evolution over time. Findings show that consumers generally express positive emotions towards social robots. Trust, anticipation and joy are the most frequently expressed emotions. Empirical results from multivariate regression analysis indicate that joy has the greatest magnitude and that anticipation and surprise do not significantly influence consumers' opinions and comments. Negative emotions are less frequent but have a significantly negative impact, which might be considered by hotel managers willing to introduce social robots.
A Collaborative Alignment Framework of Transferable Knowledge Extraction for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to utilize knowledge from a label-rich source domain to understand a similar yet distinct unlabeled target domain. Notably, global distribution statistics across domains and local semantic characteristics across samples, are two essential factors of data analysis that should be fully explored. Most existing UDA approaches either harness only one of them or fail to closely associate them for efficient adaptation. In this work, we propose a unified framework, called Collaborative Alignment Framework (CAF), which simultaneously reduces the global domain discrepancy and preserves the local semantic consistency for cross-domain knowledge transfer in a collaborative manner. Specifically, for domain-oriented alignment, we utilize adversarial training or minimize the Wasserstein distance between the two distributions to learn domain-level invariant representations. For semantic-oriented matching, we capture the semantic discrepancy between the predictions of two diverse task-specific classifiers and enhance the features of target data to be near the support of the source data class-wisely, which promotes semantic consistency across domains effectively. These two adaptation processes can be deeply intertwined in CAF via collaborative training, thus CAF can learn domain-invariant and semantic-consistent feature representations. Extensive experiments on four popular benchmarks, including DomainNet, VisDA-2017, Office-31, and ImageCLEF, demonstrate the proposed methods significantly outperform the existing methods, especially on the large-scale dataset. The code is available at <uri>https://github.com/BIT-DA/CAF</uri>.
Sport-Related Activity Recognition from Wearable Sensors Using Bidirectional GRU Network

Numerous learning-based techniques for effective human activity recognition (HAR) have recently been developed. Wearable inertial sensors are critical for HAR studies to characterize sport-related activities. Smart wearables are now ubiquitous and can benefit people of all ages. HAR investigations typically involve sensor-based evaluation. Sport-related activities are unpredictable and have historically been classified as complex, with conventional machine learning (ML) algorithms applied to resolve HAR issues. The efficiency of machine learning techniques in categorizing data is limited by the human-crafted feature extraction procedure. A deep learning model named MBiGRU (multimodal bidirectional gated recurrent unit) neural network was proposed to recognize everyday sport-related actions, with the publicly accessible UCI-DSADS dataset utilized as a benchmark to compare the effectiveness of the proposed deep learning network against other deep learning architectures (CNNs and GRUs). Experiments were performed to quantify four evaluation criteria as accuracy, precision, recall and F1-score. Following a 10-fold cross-validation approach, the experimental findings indicated that the MBiGRU model presented superior accuracy of 99.55% against other benchmark deep learning networks. The available evidence was also evaluated to explore ways to enhance the proposed model and training procedure.
Blockchain-Enabled Smart Grid Applications: Architecture, Challenges, and Solutions

The conventional electrical grid is undergoing substantial growth for reliable grid operation and for more efficient and sustainable energy use. The traditional grid is now metamorphosing into a smart grid (SG) that incorporates a diverse, heterogeneous blend of operating measures such as smart appliances, meters, and renewable energy resources. With better efficient results and dependability, the SG can be described as a modern electric power grid architecture. The SG is one of the greatest potential advances as a promising solution for the energy crisis. However, it is complex and its decentralization could be of tremendous benefit. Moreover, digitalization and integration of a large number of growing connections make it a target of cyber-attacks. In this sense, blockchain is a promising SG paradigm solution that offers several excellent features. There has been considerable effort put into using blockchains in the smart grid for its decentralization and enhanced cybersecurity; however, it has not been thoroughly studied in both application and architectural perspectives. An in-depth study was conducted on blockchain-enabled SG applications. Blockchain architectures for various applications, such as the synchrophasor applications, electric vehicles, energy management systems, etc., were proposed. The purpose of this article is to provide directions for future research efforts aimed at secure and decentralized SG applications using blockchain.
Novel enhanced Salp Swarm Algorithms using opposition-based learning schemes for global optimization problems

Salp Swarm Algorithm (SSA) is a recent approach with a simple implementation, few parameters, and low computational cost. SSA has been used in different optimization problems and provided competitive results compared to other popular metaheuristics. However, like other metaheuristics, SSA also faces problems such as lack of population diversity and premature convergence in local optima. Its other drawback is that it uses a random population initialization procedure that may produce solutions in a less effective starting point (i.e., initial population) with a poor diversity. In SSA, a single spatial search mode is used to update the position of followers, which makes it easy to fall in the local optima. SSA is more inclined to the exploitation phase, so it cannot always conduct a global search well and, in some cases, cannot find the global optimal solution. This work proposes an in-depth investigation of applying Opposition-based Learning (OBL) and its impact on the search space’s coverage, accuracy, exploration and exploitation, and convergence of the SSA’s search process. Hence, five enhanced hybrid SSA-OBL algorithms were developed and compared to the traditional SSA, Chimp Optimization Algorithm (ChOA), Giza Pyramids Construction (GPC), and Arithmetic Optimization Algorithm (AOA) in 15 IEEE CEC-2015 benchmark global minimization functions with 30, 50, and 100 dimensions. The Wilcoxon Signed Rank Test is used to test the statistical significance of the results. The proposed opposition-based SSAs statistically outperform basic SSA, ChOA, GPC, and AOA, respectively, with an average of 7.6, 15, 13.4, 15 out of 15 functions with 30 dimensions, 8, 15, 15, 15 with 50 dimensions, and 9.2, 15, 15, and 15 with 100 dimensions. Besides, we also applied the proposal in solving real-world optimization problems such as circular antenna array design problem and spacecraft trajectory optimization problem, and the opposition-based SSAs statistically outperform other competitive algorithms. The experiment results showed that incorporating different OBL schemes in SSA enhanced its exploration ability leading to better performance.
Minimization of Latency Using Multitask Scheduling in Industrial Autonomous Systems

Using enhanced ant colony optimization, this study proposes an efficient heuristic scheduling technique for cloud infrastructure that addresses the issues with nonlinear loads, slow processing complexity, and incomplete shared memory asset knowledge that plagued earlier resource supply implementations. The cloud-based planning architecture has been tailored for dynamic planning. Therefore, to determine the best task allocation method, a contentment factor was developed by integrating these three objectives of the smallest waiting period, the extent of commodity congestion control, and the expense of goal accomplishment. Ultimately, the incentive and retribution component would be used to modify the ant colony calculation perfume-generating criteria that accelerate a solution time. In particular, they leverage an activity contributed of the instability component to enhance the capabilities of such a method, and they include a virtual desktop burden weight component in the operation of regional pheromone revamping to assure virtual computers’ immense. Experiences with the routing protocol should be used to explore or demonstrate the feasibility of our methodology. In comparison with traditional methods, the simulation results show that the proposed methodology has the most rapid generalization capability, and it has the shortest duration of the project, the most distributed demand, and the best utilization of the capabilities of the virtual computer. Consequently, their hypothetical technique of optimizing the supply of resources exceeds world competition.
Multimodal Sentiment Analysis

Multimodal sentiments have become the challenge for the researchers and are equally sophisticated for an appliance to understand. One of the studies that support MS problems is a MSA, which is the training of emotions, attitude, and opinion from the audiovisual format. This survey article covers the comprehensive overview of the last update in this field. Many recently proposed algorithms and various MSA applications are presented briefly in this survey. The article is categorized according to their contributions in the various MSA techniques. The main purpose of this survey is to provide a full image of the MSA opportunities and difficulties and related field with brief details. The main contribution of this article includes the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the MSA and its related areas.
Light-Guided and Cross-Fusion U-Net for Anti-Illumination Image Super-Resolution

The learning-based methods for single image super- resolution (SISR) can reconstruct realistic details, but they suffer severe performance degradation for low-light images because of their ignorance of negative effects of illumination, and even produce overexposure for unevenly illuminated images. In this paper, we pioneer an anti-illumination approach toward SISR named Light-guided and Cross-fusion U-Net (LCUN), which can simultaneously improve the texture details and lighting of low-resolution images. In our design, we develop a U-Net for SISR (SRU) to reconstruct super- resolution (SR) images from coarse to fine, effectively suppressing noise and absorbing illuminance information. In particular, the proposed Intensity Estimation Unit (IEU) generates the light intensity map and innovatively guides SRU to adaptively brighten inconsistent illumination. Further, aiming at efficiently utilizing key features and avoiding light interference, an Advanced Fusion Block (AFB) is developed to cross-fuse low-resolution features, reconstructed features and illuminance features in pairs. Moreover, SRU introduces a gate mechanism to dynamically adjust its composition, overcoming the limitations of fixed-scale SR. LCUN is compared with the retrained SISR methods and the combined SISR methods on low-light and uneven-light images. Extensive experiments demonstrate that LCUN advances the state-of-the-arts SISR methods in terms of objective metrics and visual effects, and it can reconstruct relatively clear textures and cope with complex lighting.
Tracing the evolution of AI in the past decade and forecasting the emerging trends

The past decade has witnessed the rapid development of Artificial Intelligence (AI), especially the explosion of deep learning-related connectionist approaches. This study combines traditional literature review, bibliometric methods, and the Science of Science (SciSci) theory to scrutinize the development context of AI in the last decade on AMiner. 4 4 www.aminer.cn , AMiner is an academic mining system, which will be introduced in the following section. With the assistance of AMiner tools and datasets, this paper aims to describe a further explicit context and evolution of AI in the past decade from the development of connectionist approaches. Five aspects of the past decade are highlighted: self-learning and self-coding algorithms, Recurrent Neural Networks (RNN) algorithms, reinforcement learning, pre-trained models, and other typical deep learning algorithms, which represent the significant progress of this field. By combing these critical parts, we then summarize the current limitations and corresponding future of AI trends in the next decade and discuss some topics about the next generation of AI. Discoveries in this paper will benefit AI research in promoting understanding of the current critical stage and future trends of AI development and the AI industry in the dramatic ascendant for the academic research results transformation and its industrial layout.
Consortium blockchain-enabled smart ESG reporting platform with token-based incentives for corporate crowdsensing

Environmental, social and governance (ESG) issues arouse wide concern in both industry and academia to promote sustainable development. Listed companies assume the responsibility to submit annual reports to disclose their ESG outcomes genuinely. However, the challenge is that companies may overstate or slur their actual sustainable performance, and the entire ESG reporting process is hidden backward, thereby making the report untrustworthy. Accordingly, this paper proposes an architecture of smart ESG reporting platform leveraging the Internet of Things (IoT) and blockchain technologies to enable corporate crowdsensing for environmental data and enhance the security, transparency and creditability of ESG reporting process. In addition, with the aim of motivating firms to upload massive ESG raw data of high quality, we devise an incentive mechanism that grants them crypto tokens as a reputation for sustainable performance disclosure, exposed to industry and investors for reference. The maximum tokens settlement for each environmental key performance indicator (KPI) is regarded as a cooperative game for premiums, and Shapley value is applied to fairly distribute the tokens in line with the disclosure significance scored by experienced investors. Here, we take the ESG reporting guide issued by the Hong Kong exchange (HKEX) and the practice in the Hong Kong apparel industry as the context. An experimental simulation is conducted to illustrate the feasibility and effectiveness of the proposed platform architecture and approach of token allocation. This study holds the promise of providing a precedent for adopting the advanced technologies to address the greenwashing in firms and actualize intelligent and trustable ESG reporting.
Explainable Artificial Intelligence (XAI) in auditing

Artificial Intelligence (AI) and Machine Learning (ML) are gaining increasing attention regarding their potential applications in auditing. One major challenge of their adoption in auditing is the lack of explainability of their results. As AI/ML matures, so do techniques that can enhance the interpretability of AI, a.k.a., Explainable Artificial Intelligence (XAI). This paper introduces XAI techniques to auditing practitioners and researchers. We discuss how different XAI techniques can be used to meet the requirements of audit documentation and audit evidence standards. Furthermore, we demonstrate popular XAI techniques, especially Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), using an auditing task of assessing the risk of material misstatement. This paper contributes to accounting information systems research and practice by introducing XAI techniques to enhance the transparency and interpretability of AI applications applied to auditing tasks.
Extended analysis on the global Mittag-Leffler synchronization problem for fractional-order octonion-valued BAM neural networks

In this paper, a new case of neural networks called fractional-order octonion-valued bidirectional associative memory neural networks (FOOVBAMNNs) is established. First, the higher dimensional models are formulated for FOOVBAMNNs with general activation functions and the special linear threshold ones, respectively. On one hand, employing Cayley-Dichson construction in octonion multiplication which is essentially neither commutative nor associative, the system of FOOVBAMNNs is divided into four fractional-order complex-valued ones. On the other hand, Caputo fractional derivative's character and BAM's interactive feature are also properly dealt with. Second, the general criteria are obtained by the new design of LKFs, the application of the related inequalities and the construction of the linear feedback controllers for the global Mittag-Leffler synchronization problem of FOOVBAMNNs. Finally, we present two numerical examples to show the realizability and progress of the derived results.
Contrastive autoencoder for anomaly detection in multivariate time series

With the proliferation of the Internet of Things, a large amount of multivariate time series (MTS) data is being produced daily by industrial systems, corresponding in many cases to life-critical tasks. The recent anomaly detection researches focus on using deep learning methods to construct a normal profile for MTS. However, without proper constraints, these methods cannot capture the dependencies and dynamics of MTS and thus fail to model the normal pattern, resulting in unsatisfactory performance. This paper proposes CAE-AD, a novel contrastive autoencoder for anomaly detection in MTS, by introducing multi-grained contrasting methods to extract normal data pattern. First, to capture the temporal dependency of series, a projection layer is employed and a novel contextual contrasting method is applied to learn the robust temporal representation. Second, the projected series is transformed into two different views by using time-domain and frequency-domain data augmentation. Last, an instance contrasting method is proposed to learn local invariant characteristics. The experimental results show that CAE-AD achieves an F1-score ranging from 0.9119 to 0.9376 on the three public datasets, outperforming the baseline methods.
A comprehensive overview of knowledge graph completion

Knowledge Graph (KG) provides high-quality structured knowledge for various downstream knowledge-aware tasks (such as recommendation and intelligent question-answering) with its unique advantages of representing and managing massive knowledge. The quality and completeness of KGs largely determine the effectiveness of the downstream tasks. But in view of the incomplete characteristics of KGs, there is still a large amount of valuable knowledge is missing from the KGs. Therefore, it is necessary to improve the existing KGs to supplement the missed knowledge. Knowledge Graph Completion (KGC) is one of the popular technologies for knowledge supplement. Accordingly, there has a growing concern over the KGC technologies. Recently, there have been lots of studies focusing on the KGC field. To investigate and serve as a helpful resource for researchers to grasp the main ideas and results of KGC studies, and further highlight ongoing research in KGC, in this paper, we provide a all-round up-to-date overview of the current state-of-the-art in KGC.