Reviewerly/exHarmony huggingface dataset:
Chaotic color multi-image compression-encryption/ LSB data type steganography scheme for NFT transaction security

With the rapid development of blockchain technology, the security of non-fungible tokens (NFT) in the transaction process has attracted much attention. In the transaction process, the commoditized NFT images will inevitably involve display and leakage problems, which is likely to lead to economic losses drink copyright disputes between the trading parties. To protect transaction security, a chaotic color multi-image compression encryption/LSB data-type steganography scheme is proposed in the paper. A series of chaotic sequences are obtained by iterating the chaotic map, while compression sensing (CS) is introduced to compress multiple secret images and fuse the compressed secret images into one large secret image. Then this image is encrypted, and the encrypted large secret image is hidden on multiple cover images by steganography. This encryption can hide the change in the statistical properties caused by steganography. Finally, the cover image is combined with the 3D object model by using it as a texture for the 3D object model to realize the type steganography of the image data. The simulation and performance test results of the scheme illustrate that the scheme has a large enough key space and steganography capacity, as well as good resistance to differential attacks, statistical attacks, compression reconstruction quality, and robustness. This scheme can well protect the transaction security of NFT images, and at the same time open a channel connecting 2D images and 3D obj textured models, which provides a new idea for steganography.
Autonomous GIS: the next-generation AI-powered GIS

Large Language Models (LLMs), such as ChatGPT, demonstrate a strong understanding of human natural language and have been explored and applied in various fields, including reasoning, creative writing, code generation, translation, and information retrieval. By adopting LLM as the reasoning core, we introduce Autonomous GIS as an AI-powered geographic information system (GIS) that leverages the LLM's general abilities in natural language understanding, reasoning, and coding for addressing spatial problems with automatic spatial data collection, analysis, and visualization. We envision that autonomous GIS will need to achieve five autonomous goals: self-generating, self-organizing, self-verifying, self-executing, and self-growing. We developed a prototype system called LLM-Geo using the GPT-4 API, demonstrating what an autonomous GIS looks like and how it delivers expected results without human intervention using three case studies. For all case studies, LLM-Geo returned accurate results, including aggregated numbers, graphs, and maps.. Although still in its infancy and lacking several important modules such as logging and code testing, LLM-Geo demonstrates a potential path toward the next-generation AI-powered GIS. We advocate for the GIScience community to devote more efforts to the research and development of autonomous GIS, making spatial analysis easier, faster, and more accessible to a broader audience.
Gender Bias in Virtual Doctor Interactions: Gender Matching Effects of Chatbots and Users on Communication Satisfactions and Future Intentions to Use the Chatbot

A healthcare provider's gender not only influences patients' health-related behaviors but also the reliability of health information. Grounded in the Computers Are Social Actors (CASA) theory and prior health literature, the current study examines how healthcare chatbots' gender cues and user gender affect intentions to use the chatbot and chatbot expertise perceptions. Using a 3 (Chatbot Gender Cues: Chatbot vs. Male Doctor vs. Female Doctor) X 2 (User Gender: Male vs. Female) between-subjects experiment, this study indicates that the female-doctor design cues led to significantly higher perceived warmth and communication satisfaction, which subsequently increased social presence and future intentions to use the chatbot. Results also indicated a significant gender congruence effect between female users and the female-doctor design cue chatbot to yield greater communication satisfaction. This study, however, did not find a significant difference in perceived expertise between male versus female doctor design cues. Theoretical and practical implications are discussed.
Feeding the Coffee Habit: A Longitudinal Study of a Robo-Barista

Studying Human-Robot Interaction over time can provide insights into what really happens when a robot becomes part of people’s everyday lives. “In the Wild” studies inform the design of social robots, such as for the service industry, to enable them to remain engaging and useful beyond the novelty effect and initial adoption. This paper presents an “In the Wild” experiment where we explored the evolution of interaction between users and a Robo-Barista. We show that perceived trust and prior attitudes are both important factors associated with the usefulness, adaptability and likeability of the Robo-Barista. A combination of interaction features and user attributes are used to predict user satisfaction. Qualitative insights illuminated users’ Robo-Barista experience and contribute to a number of lessons learned for future long-term studies.
I³: Interactive Iterative Improvement for Few-Shot Action Segmentation

Extracting modular segments from raw video demonstrations of high-level actions is important to understand the underlying building blocks for different tasks in human-robot interaction. While (data-hungry) supervised learning approaches for Action Segmentation show good performance when the underlying segments are predefined, their performance degrades when unseen actions are introduced on-the-go as new data samples are scarce. In this regard, Zero-and Few-Shot Learning approaches have shown good performance in generalizing to unseen examples. In Action Segmentation, where each frame needs to be labeled, annotating new data even for a few tasks can become tedious as the number of tasks scale. In this work, we propose Interactive Iterative Improvement $(I^{3})$ for Few-Shot Action Segmentation, a Semi-Supervised Interactive Meta-Learning approach for Zero-Shot Learning on unlabeled videos and Few-Shot Learning on small amounts of labeled videos. $I^{3}$ consists of a Prototypical Network model for frame-wise prediction coupled with a Hidden-Semi-Markov-Model to prevent over-segmentation. The model is iteratively improved in an interactive manner through users’ annotations provided via a webinterface. This is done in a task-agnostic manner that, in theory, can be reused for a number of different actions. Our model provides sequentially accurate segmentations using only a limited amount of labeled data which shows the efficacy of our learning approach. A lower edit distance compared to baselines indicates a lower number of required user edits making it well suited for non-expert users to smoothly provide annotations enabling them to have more control over the learned model.
Indirect Politeness of Disconfirming Answers to Humans and Robots

Politeness is a social and linguistic phenomenon that humans use in communication to build and maintain relationships and spare others' feelings.Research on whether humans also apply politeness strategies when interacting with robots -artifacts that lack feelings -yields contradictory findings.This paper presents a human-robot interaction study (N = 40) and compares participants' use of face saving politeness strategies in their responses to disconfirmation eliciting and face-threatening questions asked either by a robot or a human.An analysis of the linguistic properties of participants' answers (response type, use of politeness markers) shows a higher use of indirect politeness in disconfirming answers directed at humans than at robots.This contradicts previous theories on the automatic and 'mindless' application of social strategies towards artificial agents.Alternative explanations for the differences in politeness behavior are discussed.
End-To-End Resource Analysis for Quantum Interior-Point Methods and Portfolio Optimization

A detailed resource analysis of an established quantum algorithm for convex optimization reveals that the practical run times implied by this quantum solution are impractical for problems of interest in finance.
Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module

Multiple-object tracking (MOT) is a fundamental task in computer vision and is widely applied across various domains. However, its algorithms remain somewhat immature in practical applications. To address the challenges presented by complex scenarios featuring instances of missed detections, false alarms, and frequent target switching leading to tracking failures, we propose an approach to multi-object tracking utilizing KC-YOLO detection and an identity validity discrimination module. We have constructed the KC-YOLO detection model as the detector for the tracking task, optimized the selection of detection frames, and implemented adaptive feature refinement to effectively address issues such as incomplete pedestrian features caused by occlusion. Furthermore, we have introduced an identity validity discrimination module in the data association component of the tracker. This module leverages the occlusion ratio coefficient, denoted by “k”, to assess the validity of pedestrian identities in low-scoring detection frames following cascade matching. This approach not only enhances pedestrian tracking accuracy but also ensures the integrity of pedestrian identities. In experiments on the MOT16, MOT17, and MOT20 datasets, MOTA reached 75.9%, 78.5%, and 70.1%, and IDF1 reached 74.8%, 77.8%, and 72.4%. The experimental results demonstrate the superiority of the methodology. This research outcome has potential applications in security monitoring, including public safety and fire prevention, for tracking critical targets.
An Improved SIFT Underwater Image Stitching Method

Underwater image stitching is a technique employed to seamlessly merge images with overlapping regions, creating a coherent underwater panorama. In recent years, extensive research efforts have been devoted to advancing image stitching methodologies for both terrestrial and underwater applications. However, existing image stitching methods, which do not utilize detector information, heavily rely on matching feature pairs and tend to underperform in situations where underwater images contain regions with blurred feature textures. To address this challenge, we present an improved scale-invariant feature transform (SIFT) underwater image stitching method. This method enables the stitching of underwater images with arbitrarily acquired images featuring blurred feature contours and that do not require any detector information. Specifically, we perform a coarse feature extraction between the reference and training images, and then we acquire the target image and perform an accurate feature extraction between the reference and target images. In the final stage, we propose an improved fade-in and fade-out fusion method to obtain a panoramic underwater image. The experimental results show that our proposed method demonstrates enhanced robustness, particularly in scenarios where detecting feature points is challenging, when compared to traditional SIFT methods. Additionally, our method achieves higher matching accuracy and produces higher-quality results in the stitching of underwater images.
Research on wearable technologies for learning: a systematic review

A good amount of research has explored the use of wearables for educational or learning purposes. We have now reached a point when much literature can be found on that topic, but few attempts have been made to make sense of that literature from a holistic perspective. This paper presents a systematic review of the literature on wearables for learning. Literature was sourced from conferences and journals pertaining to technology and education, and through an ad hoc search. Our review focuses on identifying the ways that wearables have been used to support learning, and provides perspectives on that issue from a historical dimension, and with regards to the types of wearables used, the populations targeted, and the settings addressed. Seven different ways of how wearables have been used to support learning were identified. We propose a framework identifying five main components that have been addressed in existing research on how wearables can support learning, and present our interpretations of unaddressed research directions based on our review results.
IVNet: Transfer Learning Based Diagnosis of Breast Cancer Grading Using Histopathological Images of Infected Cells

Breast cancer constitutes a significant global health concern that impacts millions of women across the world. The diagnosis of breast cancer involves categorizing grades based on the histopathological characteristics of tumor cells. While histopathological assessment remains the established benchmark for breast cancer diagnosis, it is hampered by time-consuming procedures, subjectivity, and susceptibility to human errors. This study introduces a novel approach called ImageNet-VGG16 (IVNet) for the real-time diagnosis of breast cancer within a hospital environment. The research experiments are conducted using a benchmark dataset known as Jimma University Medical Center (JUMC) breast cancer grading. Advanced image processing techniques are applied to preprocess the data, enhancing performance. This preprocessing involves the utilization of Holistically Nested Edge Detection (HED) and Contrast Limited Adaptive Histogram Equalization (CLAHE) for transformation and stain normalization. We employ advanced neural network-based transfer learning techniques to analyze the preprocessed histopathological images and identify affected cells. Various pre-trained models are utilized, including convolutional neural networks (CNN) such as VGG16, ResNet50, InceptionNetv3, ImageNet, MobileNetv3, and EfficientNetV3, in a comparative framework. The principal objective of this research is the accurate classification of breast cancer images into Grade-1, Grade-2 and Grade-3. Through extensive experimental research, we achieved a commendable 97% correct classification rate by utilizing a hybrid of VGG16 and ImageNet as the proposed feature engineering method, IVNet. We also validate our proposed approach performance using other state-of-the-art study data and statistical t-test analysis. Furthermore, we develop a user-friendly Graphical User Interface (GUI) that facilitates real-time cell tracking in histopathological images. Our real-time diagnosing application offers valuable insights for treatment planning and assists medical professionals in making prognoses. Moreover, our approach can serve as a reliable decision support system for pathologists and clinicians, particularly in settings constrained by limited resources and restricted access to expertise and equipment.
An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images

Deep learning-based solutions for histological image classification have gained attention in recent years due to their potential for objective evaluation of histological images. However, these methods often require a large number of expert annotations, which are both time-consuming and labor-intensive to obtain. Several scholars have proposed generative models to augment labeled data, but these often result in label uncertainty due to incomplete learning of the data distribution.To alleviate these issues, a method called InceptionV3-SMSG-GAN has been proposed to enhance classification performance by generating high-quality images. Specifically, images synthesized by Multi-Scale Gradients Generative Adversarial Network (MSG-GAN) are selectively added to the training set through a selection mechanism utilizing a trained model to choose generated images with higher class probabilities. The selection mechanism filters the synthetic images that contain ambiguous category information, thus alleviating label uncertainty.Experimental results show that compared with the baseline method which uses InceptionV3, the proposed method can significantly improve the performance of pathological image classification from 86.87% to 89.54% for overall accuracy. Additionally, the quality of generated images is evaluated quantitatively using various commonly used evaluation metrics.The proposed InceptionV3-SMSG-GAN method exhibited good classification ability, where histological image could be divided into nine categories. Future work could focus on further refining the image generation and selection processes to optimize classification performance.
CCSD: cross-camera self-distillation for unsupervised person re-identification

Abstract Existing unsupervised person re-identification (Re-ID) methods have achieved remarkable performance by adopting an alternate clustering-training manner. However, they still suffer from camera variation, which results in an inconsistent feature space and unreliable pseudo labels that severely degrade the performance. In this paper, we propose a cross-camera self-distillation (CCSD) framework for unsupervised person Re-ID to alleviate the effect of camera variation. Specifically, in the clustering phase, we propose a camera-aware cluster refinement mechanism, which first splits each cluster into multiple clusters according to the camera views, and then refines them into more compact clusters. In the training phase, we first obtain the similarity between the samples and the refined clusters from the same and different cameras, and then transfer the knowledge of similarity distribution from intra-camera to cross-camera. Since the intra-camera similarity is free from camera variation, our knowledge distillation approach is able to learn a more consistent feature space across cameras. Extensive experiments demonstrate the superiority of our proposed CCSD against the state-of-the-art approaches on unsupervised person Re-ID.
Enhancing Cloud Security—Proactive Threat Monitoring and Detection Using a SIEM-Based Approach

With the escalating frequency of cybersecurity threats in public cloud computing environments, there is a pressing need for robust security measures to safeguard sensitive data and applications. This research addresses growing security concerns in the cloud by proposing an innovative security information and event management system (SIEM) that offers automated visibility of cloud resources. Our implementation includes a virtual network comprising virtual machines, load balancers, Microsoft Defender for Cloud, and an application gateway that functions as a web application firewall (WAF). This WAF scans incoming Internet traffic and provides centralized protection against common exploits and vulnerabilities, securing web applications within the cloud environment. We deployed the SIEM system to automate visibility and incident response for cloud resources. By harnessing the power of this employed SIEM, the developed system can continuously monitor, detect security incidents, and proactively mitigate potential security threats. Microsoft Defender for Cloud consistently assesses the configuration of cloud resources against industry standards, regulations, and benchmarks to ensure compliance requirements are met. Our findings highlight the practicality and effectiveness of deploying such solutions to safeguard cloud resources, offering valuable insights to organizations and security professionals seeking sustainable and resilient security measures in the cloud computing environment.
Racist Hate Speech at School and its Association with Bystanders’ Active Defending – The Protective Role of Immigrant Background

There are scarce studies on bystanders’ responses to racist hate speech. The present study was rooted in the general aggression model to examine the direct and indirect associations between witnessing racist hate speech and bystanders’ active defending behaviors. Moreover, we examined if these associations varied as a function of the immigration background of bystanders. The sample included 3,225 7th, 8th, and 9th graders (51.7% girls, 37.2% with an immigrant background) from 40 schools in Switzerland and Germany. A multilevel mediation path model revealed that witnessing racist hate speech negatively predicted bystanders’ active defending. It also had indirect associations with bystanders’ active defending via low levels of empathy. Finally, associations were not observed in students with an immigrant background. The findings regarding the potential contribution of these results to preventing hate speech in school are discussed.
Expanding the metamorphic devolatilization model: Komatiites as a source for orogenic gold deposits in high-grade metamorphic rocks

Abstract Orogenic gold deposits contribute the largest proportion of the world’s gold reserves, and the source of their ore-forming components has been recognized as the metamorphic devolatilization of metapelites or metabasites across the greenschist- to amphibolite-facies transition. However, hypozonal orogenic gold deposits represent an enigma in this context. Some of these apparently formed in higher-grade metamorphic rocks when temperatures were beyond the wet solidus of quartz-feldspar–bearing rocks; it is therefore puzzling how these fluids were generated in the source and migrated through the crust without causing partial melting. Here, we show that devolatilization of hydrated komatiites, a volumetrically significant lithological unit in Precambrian greenstone belts, is a viable model that can plausibly lead to gold mineralization at amphibolite-facies conditions. Our thermodynamic simulations indicate that subsolidus metamorphic devolatilization of komatiites at ~700 °C (upper amphibolite facies) can unlock significant amounts of gold via dehydration of talc and chlorite. This genetic model is supported by the geochemical characteristics of, and estimated pressure-temperature (P-T) formation conditions of, hypozonal gold deposits and the intimate spatiotemporal association between hypozonal deposits and komatiites in greenstone belts. This work expands the P-T range of the metamorphic devolatilization model and enhances its robustness in explaining gold mineralization in metamorphic terranes.