Reviewerly/exHarmony huggingface dataset:
Hybrid Blockchain and IPFS for Secure Industry 4.0 Framework of IoT-based Skin Monitoring System

The blockchain concept is already gaining traction in the healthcare industry, with significant ramifications. According to reports, IoT-based healthcare technologies are shifting the healthcare industry's focus from hospitals to patients. A recent development uses shared data to communicate, identify, and treat people with skin diseases: Intelligent Skin Monitoring Systems based on the Internet of Things (IoT). It is possible to equip healthcare vehicles with skin and image sensors to measure the care given to those who live in rural locations or have limited access to medical institutions. Conversely, divulging sensitive information through a network threatens the security and privacy of medical and personal data. It's a huge difficulty to provide security and privacy without jeopardising the trust of a trustworthy third party. Another significant issue is the safe storage of vast amounts of medical data. These problems can be resolved by using IPFS in conjunction with blockchain technology. In order to securely share medical transactions and verify individuals with skin diseases in a distributed setting, the study suggests a hybrid blockchain and IPFS-based solution. The work presented here provides a safer framework for interacting with IPFS transactions. The goal is to develop a system that is low-cost, high-efficiency, and secure.
Application of virtual simulation technology in sports decision training: a systematic review

Introduction Sports decision-making is a complex process and plays a decisive role in sports performance. Virtual simulation technology is one of the popular sports decision making training tools. The application of virtual reality technology in sports decision making training has received widespread attention. The purpose of this study is to evaluate the scientific evidence of the application of virtual simulation technology in sports decision-making training, and summarize its application advantages and limitations. Methods The research literature databases of Web of science, PubMed, SCOPUS and Medline were searched, and the results were screened to determine the application of virtual reality technology in motion decision-making. We identified 10 articles and coded them to record the research design, research object characteristics, VR task setting, experimental intervention and research results. Results Through the review, it is found that virtual simulation technology has important value for sports decision-making training. In sports practice, virtual simulation technology can simulate sports decision-making tasks, measure and analyze athletes’ sports decision-making performance. We still need to design a more scientific virtual simulation environment for sports decision-making. In this environment, we can better use virtual simulation technology to improve sports decision-making ability.
High-threshold fault-tolerant quantum computation with the Gottesman-Kitaev-Preskill qubit under noise in an optical setup

To implement fault-tolerant quantum computation (FTQC) with continuous variables, continuous variables need to be digitized using an appropriate code such as the Gottesman-Kitaev-Preskill (GKP) qubit. The scheme introduced in [Fukui et al. Phys. Rev. X 8, 021054 (2018)] has reduced the threshold of the squeezing level required for continuous-variable FTQC to less than 10 dB, assuming noise derived from the GKP qubit itself. In this paper, we propose a scheme to improve noise tolerance during the construction of a large-scale cluster state used for FTQC with the GKP qubits. In our scheme, a small-scale cluster state is prepared by employing maximum-likelihood estimation, the entanglement generation via the Bell measurement, and probabilistic reliable measurement. Then, a large-scale cluster state is constructed from the small-scale cluster states via the reliable encoded Bell measurement. In the numerical calculations, we assume errors derived from the two-mode gate and loss in the homodyne measurement in addition to noise from the GKP qubit itself. The results show that the thresholds of a squeezing level are around 8.1, 9.6, and 12.4 dB for loss in the homodyne measurement 0, 5, and 10%, respectively. Hence, this paper provides a way toward continuous-variable FTQC with a feasible squeezing level.
Use and Design of Chatbots for the Circular Economy

The fact that advanced technologies and their economic applications have generated increasing resource costs justifies the transition from a linear approach to a circular one in order to control these costs. From this perspective, this study presents how artificial intelligence can help achieve this goal. Therefore, at the beginning of this article, we begin with an introduction and brief review of the literature on the subject. Our research procedure involved the combination of qualitative and quantitative forms of research using mixed methods. In this study, we presented and analyzed five chatbot solutions used in the field of the circular economy. The analysis of these five chatbots helped us design, in the second part of this paper, the procedures for data collection, training, development, and testing of a chatbot using various natural language processing (NLP) and deep processing (DP) techniques. Additionally, we include discussions and some conclusions regarding all aspects of the subject to see how they can help us in future studies. Furthermore, our future research with this topic will have as the goal the effective construction of a chatbot dedicated to the circular economy.
A Survey on Explainable Artificial Intelligence for Cybersecurity

The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of network cybersecurity, XAI has the potential to revolutionize the way we approach network security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity in network systems and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of network-driven cybersecurity threats and issues. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.
Image Processing for Automatic Cell Nucleus Segmentation Using Super pixel and Clustering Methods on Histopathological Images

On a daily basis, it appears that the number of cancer cases and cancer-related deaths are increasing. Early detection and treatment of the malignant region are critical for successful treatment. Early detection of sick cells is made possible with the use of computer-assisted programmes, which are then diagnosed by experienced pathologists due to their efforts. Using computer-aided programmes, this research found that cell nuclei could be automatically detected in high-resolution histopathological images using global segmentation methods such as k-Means and Fuzzy C Means and algorithms from superpixel segmentation methods such as SLIC, Quick-shift, Felzenszwalb, Watershed, and ERS. Using high-quality histopathology pictures, the researchers discovered that the k-means and FCM algorithms performed significantly better than the baseline techniques in the study. In terms of precision, the Quickshift and SLIC approaches produced superior outcomes. The K-means and FCM algorithms perform best in the F-M test, and the true negative ratio is more successful in the Quickshift and SLIC methods than in the F-M test.&#x0D;
Unsupervised domain adaptation via reliable pseudolabeling based memory module and dynamic distance threshold learning

An important challenge in unsupervised domain adaptation (UDA) is how to capture domain-specific discriminative information in the target domain data. For the labels in the target domain are unavailable, some methods uncover the intrinsic discriminative information by clustering the target domain features in the embedding space. However, most of existing works only consider filtering reliable target domain samples by setting a fixed distance threshold, ignoring the potential intraclass and interclass relationships and cannot set appropriate distance thresholds for different transfer tasks and different classes. To this end, we propose a new pseudolabeling framework named the dynamic distance threshold learning framework (DDT) for UDA. In our framework, we construct distance memory module to encode intraclass and interclass distribution information, and select reliable target domain samples satisfying intraclass compactness and interclass independence via dynamic intraclass and interclass distance thresholds. Specifically, we define class centroid memory modules, intraclass and interclass distance memory modules in the embedding space. Then, we retrieve the intraclass compactness distance threshold and interclass independence distance threshold from the distance memory based on the class and classpair information of target domain sample. Finally, we use reliable target domain samples to update the class centroid and the distance memory module, so that the distance memory module is dynamically updated following the model training. Benefiting from DDT, the UDA model can be updated with reliable target domain samples for training, and the joint distribution can be further aligned at the category level. As a lightweight technique, DDT constructs class center and distance memory modules to transfer the intraclass and interclass distribution relationships from the source domain to the target domain, and sets dynamic intraclass and interclass distances for different classes in different transfer tasks. Without any additional training parameters, the framework can be easily plugged into various domain adaptation paradigms. Experiments on benchmark datasets show that our method can filter the noise in pseudolabels and confers a competitive advantage compared with existing state-of-the-art domain adaptation methods.
Multimodal Learning Analytics: Rationale, Process, Examples, and Direction

This chapter is an introduction to the use of multiple modalities of learning trace data to better understand and feedback learning processes that occur both in digital and face-to-face contexts. First, it will explain the rationale behind the emergence of this type of study, followed by a brief explanation of what Multimodal Learning Analytics (MmLA) is based on current conceptual understandings and current state-of-the-art implementations. The majority of this chapter is dedicated to describing the general process of MmLA from the mapping of learning constructs to low-level multimodal learning traces to the reciprocal implementation of multimedia recording, multimodal feature extraction, analysis, and fusion to detect behavioral markers and estimate the studied constructs. This process is illustrated by the detailed dissection of a real-world example. This chapter concludes with a discussion of the current challenges facing the field and the directions in which the field is moving to address them.
A novel hermit crab optimization algorithm

High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems.
DTCM: Joint Optimization of Dark Enhancement and Action Recognition in Videos

Recognizing human actions in dark videos is a useful yet challenging visual task in reality. Existing augmentation-based methods separate action recognition and dark enhancement in a two-stage pipeline, which leads to inconsistently learning of temporal representation for action recognition. To address this issue, we propose a novel end-to-end framework termed Dark Temporal Consistency Model (DTCM), which is able to jointly optimize dark enhancement and action recognition, and force the temporal consistency to guide downstream dark feature learning. Specifically, DTCM cascades the action classification head with the dark augmentation network to perform dark video action recognition in a one-stage pipeline. Our explored spatio-temporal consistency loss, which utilizes the RGB-Difference of dark video frames to encourage temporal coherence of the enhanced video frames, is effective for boosting spatio-temporal representation learning. Extensive experiments demonstrated that our DTCM has remarkable performance: 1) Competitive accuracy, which outperforms the state-of-the-arts on the ARID dataset by 2.32% and the UAVHuman-Fisheye dataset by 4.19% in accuracy, respectively; 2) High efficiency, which surpasses the current most advanced method [1] with only 6.4% GFLOPs and 71.3% number of parameters; 3) Strong generalization, which can be used in various action recognition methods (e.g., TSM, I3D, 3D-ResNext-101, Video-Swin) to promote their performance significantly.
Hierarchical block aggregation network for long-tailed visual recognition

It is usually supposed that training database is manually balanced in traditional visual recognition tasks. However, in nature, data tends to follow long-tailed distributions. In recent years, many plug-and-play methods based on data augmentation or representation learning have been proposed to tackle the long-tailed visual recognition task. Although these methods are effective, we find that when different plug-and-play methods are applied to the same long-tail recognition model, they sometimes fail to promote each other. The reason for this phenomenon may lie in the fact that the overall performance of the model is constrained by the insufficient capability of a traditional feature extractor. Motivated by this fact, we first propose Hierarchical Block Aggregation Network (HBAN), a network structure with stronger feature extraction capability. Then, we design a Quantity-Aware Balanced (QAB) loss and a decoupled training paradigm to optimize HBAN. The effectiveness of HBAN is demonstrated by extensive experiments. In particular, HBAN achieves significant improvements over our baseline on three benchmark datasets, and outperforms the state-of-the-art methods on CIFAR 100-LT.
Contrastive Learning Reduces Hallucination in Conversations

Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the problem of “hallucination:” they may generate plausible-looking statements that are irrelevant or factually incorrect. To address this problem, we propose a contrastive learning scheme, named MixCL. A novel mixed contrastive objective is proposed to explicitly optimize the implicit knowledge elicitation process of LMs, and thus reduce their hallucination in conversations. We also examine negative sampling strategies of retrieved hard negatives and model-generated negatives. We conduct experiments on Wizard-of-Wikipedia, a public, open-domain knowledge-grounded dialogue benchmark, and assess the effectiveness of MixCL. MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality. We show that MixCL achieves comparable performance to state-of-the-art KB-based approaches while enjoying notable advantages in terms of efficiency and scalability.
Attribute and Structure Preserving Graph Contrastive Learning

Graph Contrastive Learning (GCL) has drawn much research interest due to its strong ability to capture both graph structure and node attribute information in a self-supervised manner. Current GCL methods usually adopt Graph Neural Networks (GNNs) as the base encoder, which typically relies on the homophily assumption of networks and overlooks node similarity in the attribute space. There are many scenarios where such assumption cannot be satisfied, or node similarity plays a crucial role. In order to design a more robust mechanism, we develop a novel attribute and structure preserving graph contrastive learning framework, named ASP, which comprehensively and efficiently preserves node attributes while exploiting graph structure. Specifically, we consider three different graph views in our framework, i.e., original view, attribute view, and global structure view. Then, we perform contrastive learning across three views in a joint fashion, mining comprehensive graph information. We validate the effectiveness of the proposed framework on various real-world networks with different levels of homophily. The results demonstrate the superior performance of our model over the representative baselines.
MicroAST: Towards Super-fast Ultra-Resolution Arbitrary Style Transfer

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions.
Automatic Modulation Classification Using ResNeXt-GRU With Deep Feature Fusion

With the integrated design and application of radar, communication and electronic reconnaissance, automatic modulation classification (AMC) is becoming increasingly significant in various applications, e.g. spectrum monitoring and cognitive radio, and has entered a stage of remarkable development. Recently, the AMC technology is suffering from complex and diverse signal types, low signal-to-noise ratio, poor algorithm robustness, and so forth. To cope with these flaws, this paper presents a modulation classification scheme based on deep feature fusion. In this work, the ResNeXt network is utilized to extract the distinctive semantic feature of the signal, and the gated recurrent unit (GRU) is employed to extract the time-series representation characteristic, respectively. Considering the complementarity between different features, a feature fusion model using discriminant correlation analysis (DCA) is proposed to fuse the output responses of the ResNeXt and GRU. The simulation results reveal that the proposed method achieves the superior performance, which is conducive to promoting the application of feature fusion in the AMC.
Target-Aware Tracking with Long-Term Context Attention

Most deep trackers still follow the guidance of the siamese paradigms and use a template that contains only the target without any contextual information, which makes it difficult for the tracker to cope with large appearance changes, rapid target movement, and attraction from similar objects. To alleviate the above problem, we propose a long-term context attention (LCA) module that can perform extensive information fusion on the target and its context from long-term frames, and calculate the target correlation while enhancing target features. The complete contextual information contains the location of the target as well as the state around the target. LCA uses the target state from the previous frame to exclude the interference of similar objects and complex backgrounds, thus accurately locating the target and enabling the tracker to obtain higher robustness and regression accuracy. By embedding the LCA module in Transformer, we build a powerful online tracker with a target-aware backbone, termed as TATrack. In addition, we propose a dynamic online update algorithm based on the classification confidence of historical information without additional calculation burden. Our tracker achieves state-of-the-art performance on multiple benchmarks, with 71.1% AUC, 89.3% NP, and 73.0% AO on LaSOT, TrackingNet, and GOT-10k. The code and trained models are available on https://github.com/hekaijie123/TATrack.
Development and Application of the Branched and Isoprenoid GDGT Machine Learning Classification Algorithm (BIGMaC) for Paleoenvironmental Reconstruction

Abstract Glycerol dialkyl glycerol tetraethers (GDGTs), both archaeal isoprenoid GDGTs (isoGDGTs) and bacterial branched GDGTs (brGDGTs), have been used in paleoclimate studies to reconstruct environmental conditions. Since GDGTs are produced in many types of environments, their relative abundances also depend on the depositional setting. This suggests that the distribution of GDGTs also preserves useful information that can be used more broadly to infer these depositional environments in the geological past. Here, we combined existing iso‐ and brGDGT relative abundance data with newly analyzed samples to generate a database of 1,153 samples from several modern sedimentary settings. We observed a robust relationship between the depositional environment and the relative abundances of GDGTs in our samples. This data set was used to train and test the B ranched and isoGDGT Ma chine learning C lassification (BIGMaC) algorithm, which identifies the environment a sample comes from based on the distribution of GDGTs with high precision and recall ( F 1 = 0.95). We tested the model on the sedimentary record from the Giraffe kimberlite pipe, an Eocene maar in subantarctic Canada, and found that the BIGMaC reconstruction agrees with independent stratigraphic and palynological information, provides new information about the paleoenvironment of this site, and helps improve its paleotemperature reconstruction. In contrast, we also include an example from the PETM‐aged Cobham lignite as a cautionary example that illustrates the limitations of the algorithm. We propose that in cases where paleoenvironments are unknown or are changing, BIGMaC can be applied in concert with other proxies to generate more refined paleoclimate records.