Reviewerly/exHarmony huggingface dataset:
A Policy Enforcement Framework for Secure Data Dissemination in Vehicular Ad Hoc Network

In vehicular ad hoc networks (VANETs), enforcing expressive and suitable access policy is of great importance to achieve desirable and accurate data dissemination services. Recently, secure data dissemination schemes based on attribute-based encryption in VANETs have received considerable attention for their ability of fine-grained access control. Therefore, how to enforce accurate access policy in dynamic VANETs becomes significant yet very challenging. In order to achieve timely, flexible, and accurate data dissemination, we propose a policy enforcement framework for secure data dissemination, which enables data-sender vehicles with high mobility and RSUs with broad perception ability to co-design access control policy. Also, we design a policy combination method, which enforces an expressive access control policy based on the disjunctive norm form (DNF) formed by different attributes. Then we propose a policy conflict resolution based on confidence weights to deal with conflict policies. Finally, the experimental results demonstrate that our proposed scheme is applicable and efficient for secure data dissemination in VANETs.
Recognizing misogynous memes: Biased models and tricky archetypes

Warning: This paper contains examples of language and images which may be offensive. Misogyny is a form of hate against women and has been spreading exponentially through the Web, especially on social media platforms. Hateful content towards women can be conveyed not only by text but also using visual and/or audio sources or their combination, highlighting the necessity to address it from a multimodal perspective. One of the predominant forms of multimodal content against women is represented by memes, which are images characterized by pictorial content with an overlaying text introduced a posteriori. Its main aim is originally to be funny and/or ironic, making misogyny recognition in memes even more challenging. In this paper, we investigated 4 unimodal and 3 multimodal approaches to determine which source of information contributes more to the detection of misogynous memes. Moreover, a bias estimation technique is proposed to identify specific elements that compose a meme that could lead to unfair models, together with a bias mitigation strategy based on Bayesian Optimization. The proposed method is able to push the prediction probabilities towards the correct class for up to 61.43% of the cases. Finally, we identified the most challenging archetypes of memes that are still far to be properly recognized, highlighting the most relevant open research directions.
India’s CBDC for digital public infrastructure

The present study examines factors influencing the adoption of Central Bank Digital Currency (CBDC) in India. Based on the survey of prospective users, we find that significant factors influencing the acceptance of CBDC are trust, transparency, security, and customer support.
Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia’s top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube’s features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments.
Defending Fake via Warning: Universal Proactive Defense Against Face Manipulation

The emergence of deep learning has led to the rise of malicious face manipulation applications, which pose a significant threat to face security. In order to prevent the generation of forgery fundamentally, researchers have proposed proactive methods to disrupt the process of manipulation models. However, these methods output distorted images, which exhibit unacceptable black shadows or distorted facial features causing facial stigmatization. To address this issue, we propose a Universal Proactive Warning Defense (UPWD) method, which leads fake images to present a warning pattern against multiple manipulation models. Specifically, we proposed an Invisible Protection Module that generates protection messages and a feature-level measure strategy that enhances the salience of warning patterns. Furthermore, we improve the universality of the method based on Hard Model Meta-learning. Extensive experimental results on CelebA and LFWA datasets demonstrate that our proposed UPWD method effectively defends against multiple manipulation models and outperforms existing methods.
Decoding quantum field theory with machine learning

We demonstrate how one can use machine learning techniques to bypass the technical difficulties of designing an experiment and translating its outcomes into concrete claims about fundamental features of quantum fields. In practice, all measurements of quantum fields are carried out through local probes. Despite measuring only a small portion of the field, such local measurements have the capacity to reveal many of the field's global features. This is because, when in equilibrium with their environments, quantum fields store global information locally, albeit in a scrambled way. We show that neural networks can be trained to unscramble this information from data generated from a very simple one-size-fits-all local measurement protocol. To illustrate this general claim we will consider three non-trivial features of the field as case studies: a) how, as long as the field is in a stationary state, a particle detector can learn about the field's boundary conditions even before signals have time to propagate from the boundary to the detector, b) how detectors can determine the temperature of the quantum field even without thermalizing with it, and c) how detectors can distinguish between Fock states and coherent states even when the first and second moments of all their quadrature operators match. Each of these examples uses the exact same simple fixed local measurement protocol and machine-learning ansatz successfully. This supports the claim that the framework proposed here can be applied to nearly any kind of local measurement on a quantum field to reveal nearly any of the field's global properties in a one-size-fits-all manner.
Chatbots in frontline services and customer experience: An anthropomorphism perspective

Abstract This study measures the effects of chatbot anthropomorphic language on customers' perception of chatbot competence and authenticity on customer engagement while taking into consideration the moderating roles of humanlike appearance and brand credibility. We conducted two experimental studies to examine the conceptual framework. Study 1 tests the moderating effect of a chatbot's anthropomorphic appearance on the relationship between chatbots' language and customer engagement. Study 2 tests the moderating effect of brand credibility on the relationship between a chatbot's anthropomorphic language and customer engagement. The findings confirm that the interaction between humanlike appearance via the use of avatars and anthropomorphic language, such as using emojis, in conversations with customers influences customer engagement, and that this effect is mediated by perceived chatbot competence and authenticity. Further, the positive effect of anthropomorphic language on perceived competence, and subsequently on authenticity and engagement, is only significant when the brand credibility was low (vs. high). This study offers insights into the effect of chatbots' anthropomorphic language and provides suggestions on how to devise efficient strategies for engaging customers using chatbots.
Theory Matters

Abstract: Following an extended perspective of evidence-centered design, this study provides a methodological exemplar of the theory-based construction of process indicators from log data. We investigated decision-making processes in web search as the target construct, assuming that individuals follow a heuristic search (focusing on search results vs. websites as a primary information source) and stopping rule (following a satisficing vs. sampling strategy). Drawing on these assumptions, we describe our reasoning for identifying the empirical evidence needed and selecting an assessment to obtain this evidence to derive process indicators that represent groups differentiated by search and stopping rule combinations. To evaluate our approach, we reanalyzed the processing behavior of 150 university students who were requested in four tasks to select a specific website from a list of five search results. We determined the process indicators per item and conducted multiple cluster analyses to investigate group recovery. For each item, we found three clusters, two of which matched our assumptions. Additionally, we explored the consistency of students’ cluster membership across items and investigated their relationship with students’ skills in evaluating online information. Based on the results, we discuss the tradeoff between construct breadth and process elaboration for deriving meaningful process indicators.
JUMLA-QSL-22: Creation and Annotation of a Qatari Sign Language Corpus for Sign Language Processing

Sign language processing (SLP) is essential to creating digital assistive technology for deaf people and requires the availability of large-scale corpus for accurate systems. However, large-scale publicly available datasets for sign languages, particularly Qatari Sign language, are limited. The aim of this paper is to present the development of the first Qatari Sign Language (QSL) corpus, named JUMLA-QSL-22, and the annotation process used to facilitate the SLP research. The corpus supplies a large sample of Arabic sign language in the Qatari dialect, with 6300 records collected over a period of five months. The study involved seven participants, including five hearing-impaired individuals and two sign language interpreters. The participants were recorded signing a sentence from a list of 900 sentences classified into 22 intents at a time. Furthermore, a new tool is proposed for sign language annotation, called "Jumla Annotation Tool" to build a Qatar Sign Bank. The videos were annotated using four types of annotations, namely glosses, translation, signer identity, and signer location. A consensus-based annotation approach was adopted to ensure the accuracy of annotations. This paper also discusses the challenges faced during the corpus development and annotation process, as well as the proposed solutions.
Dehallucinating Large Language Models Using Formal Methods Guided Iterative Prompting

Large language models (LLMs) such as ChatGPT have been trained to generate human-like responses to natural language prompts. LLMs use a vast corpus of text data for training, and can generate coherent and contextually relevant responses to a wide range of questions and statements. Despite this remarkable progress, LLMs are prone to hallucinations making their application to safety-critical applications such as autonomous systems difficult. The hallucinations in LLMs refer to instances where the model generates responses that are not factually accurate or contextually appropriate. These hallucinations can occur due to a variety of factors, such as the model’s lack of real-world knowledge, the influence of biased or inaccurate training data, or the model’s tendency to generate responses based on statistical patterns rather than a true understanding of the input. While these hallucinations are a nuisance in tasks such as text summarization and question-answering, they can be catastrophic when LLMs are used in autonomy-relevant applications such as planning. In this paper, we focus on the application of LLMs in autonomous systems and sketch a novel self-monitoring and iterative prompting architecture that uses formal methods to detect these errors in the LLM response automatically. We exploit the dialog capability of LLMs to iteratively steer them to responses that are consistent with our correctness specification. We report preliminary experiments that show the promise of the proposed approach on tasks such as automated planning.
Predicting Out-of-Distribution Performance of Deep Neural Networks Using Model Conformance

With the increasingly high interest in using Deep Neural Networks (DNN) in safety-critical cyber-physical systems, such as autonomous vehicles, providing assurance about the safe deployment of these models becomes ever more important. The safe deployment of deep learning models in the real world where the inputs can vary from the training environment of the models requires characterizing the performance and the uncertainty in the prediction of these models, particularly on novel and out-of-distribution (OOD) inputs. This has motivated the development of methods to predict the accuracy of DNN in novel (unseen during training) environments. These methods, however, assume access to some labeled data from the novel environment which is unrealistic in many real-world settings. We propose an approach for predicting the accuracy of a DNN classifier under a shift from its training distribution without assuming access to labels of the inputs drawn from the shifted distribution. We demonstrate the efficacy of the proposed approach on two autonomous driving datasets namely the GTSRB dataset for image classification, and the ONCE dataset with synchronized feeds from LiDAR and cameras used for object detection. We show that the proposed approach is applicable for predicting accuracy on different modalities (image from camera, and point cloud from LiDAR) of the input data.
How risk messages influence tourist processing and sharing: The role of emojis

Understanding how tourists process and share risk messages during a crisis is critical for tourism risk communication. This research develops and tests a theoretical framework of tourist risk information processing and sharing in the context COVID-19 pandemic. A mixed-method design was employed, consisting of three separate studies conducted in different case contexts, involving different experimental subjects. The results showed that: a) risk message framing significantly impacted information sharing; b) heuristic and systematic processing and perceived value of information sharing were critical mediators that underpin the tourists' role shift from "receiver" to "sharer"; c) emojis strengthened tourists' processing of risk message, and improved their perceived value and information sharing in the risk amplifying frame, compared with the risk attenuating frame. This research advances the risk communication literature and provides implications for destinations to set effective risk communication strategies in times of crisis.
Hybrid CNN-Transformer model for medical image segmentation with pyramid convolution and multi-layer perceptron

Vision Transformer (ViT) has emerged as a potential alternative to convolutional neural networks for large datasets. However, applying ViT directly to medical image segmentation is challenging due to its lack of induction bias, which requires a large number of high-quality annotated medical images for effective model training. Recent studies have discovered that, in addition to the increased model capacity and generalization resulting from the lack of induction bias, the excellent performance of Transformer can also be attributed to its large receptive field. In this paper, we propose a U-shaped medical image segmentation model that combines large kernel convolutions with Transformers. Specifically, we construct a basic Transformer unit using pyramidal convolution modules with multi-scale kernels and multi-layer perceptron. In the pyramid convolution module, we employ grouped convolution to reduce parameter and computational complexity while utilizing multi-scale large kernel attention as a foundation for more efficient feature extraction. For different types of grouping, different sizes of convolutions are used to enhance the extraction of features with multiple receptive fields. To optimize the extracted features from the encoder, the U-shaped model integrates a variant of the pyramidal convolutional module into the skip connections. This variant utilizes multi-scale large kernel convolutional attention based on channel splitting. The incorporation of this variant enables efficient refinement of the feature representations within the skip connections. Through extensive comparisons on multi-modal medical image datasets, our model outperforms state-of-the-art methods across various evaluation metrics, with notable superiority observed on small-scale medical datasets. Our research findings suggest that the combination of large kernel convolutions and Transformer models introduces an advantageous inductive bias, resulting in enhanced performance specifically for small-scale medical image datasets. To facilitate accessibility, we have made our code openly accessible on our GitHub repository, which can be found at https://github.com/medical-images-process/CNN-Transformer.
Virtual reality supports perspective taking in cultural heritage interpretation

Our research proves that virtual reality (VR) technology can be used to encourage users to adopt an alternative point of view through a virtual embodiment. Our results extend previous studies showing that emotional responses are evoked in virtual environments (Meuleman and Rudrauf, 2018) and arouse the sense of empathy (Ventura et al., 2020). To this end, we built a virtual museum, where different avatars can interact with experimental subjects to discuss emotional and value-driven interpretations related to artworks, aiming at bootstrapping user's interpretation-reflection loops (IRL) (Daga et al., 2022). IRL consists in encouraging the user to (a) provide a point of view, (b) to know about another point of view, and (c) to possibly take a different perspective. In line with recent literature, our results are based on the analysis of dialogue, and on the psychophysiological response of the users, showing that a VR-driven methodology can both develop a sense of embodiment, and maximize the human capacity to take another's point of view. Results also confirm that the use of immersive VR can be a valid tool to promote empathy through an embodied experience.
Research on an Underwater Object Detection Network Based on Dual-Branch Feature Extraction

Underwater object detection is challenging in computer vision research due to the complex underwater environment, poor image quality, and varying target scales, making it difficult for existing object detection networks to achieve high accuracy in underwater tasks. To address the issues of limited data and multi-scale targets in underwater detection, we propose a Dual-Branch Underwater Object Detection Network (DB-UODN) based on dual-branch feature extraction. In the feature extraction stage, we design a dual-branch structure by combining the You Only Look Once (YOLO) v7 backbone with the Enhanced Channel and Dilated Block (ECDB). It allows for the extraction and complementation of multi-scale features, which enable the model to learn both global and local information and enhance its perception of multi-scale features in underwater targets. Furthermore, we employ the DSPACSPC structure to replace the SPPCSPC structure in YOLOv7. The DSPACSPC structure utilizes atrous convolutions with different dilation rates to capture contextual information at various scales, compensating for potential information loss caused by pooling operations. Additionally, we utilize a dense connection structure to facilitate feature reuse and enhance the network’s representation and generalization capabilities. Experimental results demonstrate that the proposed DB-UODN outperforms the most commonly used object detection networks in underwater scenarios. On the URPC2020 dataset, the network achieves an average detection accuracy of 87.36%.
Digital Face Manipulation Creation and Detection: A Systematic Review

The introduction of publicly available large-scale datasets and advances in generative adversarial networks (GANs) have revolutionized the generation of hyper-realistic facial images, which are difficult to detect and can rapidly reach millions of people, with adverse impacts on the community. Research on manipulated facial image detection and generation remains scattered and in development. This survey aimed to address this gap by providing a comprehensive analysis of the methods used to produce manipulated face images, with a focus on deepfake technology and emerging techniques for detecting fake images. The review examined four key groups of manipulated face generation techniques: (1) attributes manipulation, (2) facial re-enactment, (3) face swapping, and (4) face synthesis. Through an in-depth investigation, this study sheds light on commonly used datasets, standard manipulated face generation/detection approaches, and benchmarking methods for each manipulation group. Particular emphasis is placed on the advancements and detection techniques related to deepfake technology. Furthermore, the paper explores the benefits of analyzing deepfake while also highlighting the potential threats posed by this technology. Existing challenges in the field are discussed, and several directions for future research are proposed to tackle these challenges effectively. By offering insights into the state of the art for manipulated face image detection and generation, this survey contributes to the advancement of understanding and combating the misuse of deepfake technology.
Dual Personalization on Federated Recommendation

Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used to combine distributed recommendation algorithms and privacy-preserving mechanisms. Thus it inherently takes the form of heavyweight models at the server and hinders the deployment of on-device intelligent models to end-users. This paper proposes a novel Personalized Federated Recommendation (PFedRec) framework to learn many user-specific lightweight models to be deployed on smart devices rather than a heavyweight model on a server. Moreover, we propose a new dual personalization mechanism to effectively learn fine-grained personalization on both users and items. The overall learning process is formulated into a unified federated optimization framework. Specifically, unlike previous methods that share exactly the same item embeddings across users in a federated system, dual personalization allows mild finetuning of item embeddings for each user to generate user-specific views for item representations which can be integrated into existing federated recommendation methods to gain improvements immediately. Experiments on multiple benchmark datasets have demonstrated the effectiveness of PFedRec and the dual personalization mechanism. Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings. The code is available.