Reviewerly/exHarmony huggingface dataset:
Interaction in eXtended Reality Applications for Cultural Heritage

Digital technologies in the modern era are almost mandatory for the presentation of all types of cultural heritage. Virtual depictions of crafts and traditions offer the users the possibility of time travel, taking them to the past through the use of 3D reconstructions of cultural monuments and sites. However, digital resources alone are not enough to adequately present cultural heritage. Additional information on the historical context in the form of stories, virtual reconstructions, and digitized objects is needed. All of this can be implemented using a digital multimedia presentation technique called digital storytelling. Nowadays, an integral part of many museum exhibitions is interactive digital storytelling. This paper gives an overview of the techniques and discusses different means of facilitating interaction on digital storytelling applications for virtual cultural heritage presentations. We describe the ways in which natural interaction and interaction via eXtended Reality (Virtual and Augmented Reality) applications for cultural heritage are made possible. Users will find the stories told through these applications educational and entertaining at the same time. Through user-experience studies, we measure the user edutainment level and present how users react to implemented interactions.
Context- and Sentiment-Aware Networks for Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) has promising potential in many fields, such as recommendation systems, man–machine interaction, and medical care. In contrast to other emotion identification tasks, conversation is essentially a process of dynamic interaction in which people often convey emotional messages relying on context and common-sense knowledge. In this article, we propose a context- and sentiment-aware framework, termed Sentic GAT, to solve this challenge. In Sentic GAT, common-sense knowledge is dynamically represented by the context- and sentiment-aware graph attention mechanism based on sentimental consistency, and context information is captured by the dialogue transformer (DT) with hierarchical multihead attention (HMAT), where HMAT is used to obtain the dependency of historical utterances on themselves and other utterances for better context representation. Additionally, we explore a contrastive loss to discriminate context-free and context-sensitive utterances in emotion identification to enhance context representation in straightforward conversations that directly express ideas. The experimental results show that context and sentimental information can promote the representation of common-sense knowledge, and the intra- and inter-dependency of contextual utterances effectively improve the performance of Sentic GAT. Moreover, our Sentic GAT using emotional intensity outperforms the most advanced model on the tested datasets.
Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions

Learning human languages is a difficult task for a computer. However, Deep Learning (DL) techniques have enhanced performance significantly for almost all-natural language processing (NLP) tasks. Unfortunately, these models cannot be generalized for all the NLP tasks with similar performance. NLU (Natural Language Understanding) is a subset of NLP including tasks, like machine translation, dialogue-based systems, natural language inference, text entailment, sentiment analysis, etc. The advancement in the field of NLU is the collective performance enhancement in all these tasks. Even though MTL (Multi-task Learning) was introduced before Deep Learning, it has gained significant attention in the past years. This paper aims to identify, investigate, and analyze various language models used in NLU and NLP to find directions for future research. The Systematic Literature Review (SLR) is prepared using the literature search guidelines proposed by Kitchenham and Charters on various language models between 2011 and 2021. This SLR points out that the unsupervised learning method-based language models show potential performance improvement. However, they face the challenge of designing the general-purpose framework for the language model, which will improve the performance of multi-task NLU and the generalized representation of knowledge. Combining these approaches may result in a more efficient and robust multi-task NLU. This SLR proposes building steps for a conceptual framework to achieve goals of enhancing the performance of language models in the field of NLU.
TorchMetrics - Measuring Reproducibility in PyTorch

A main problem with reproducing machine learning publications is the variance of metric implementations across papers.A lack of standardization leads to different behavior in mechanisms such as checkpointing, learning rate schedulers or early stopping, that will influence the reported results.For example, a complex metric such as Fréchet inception distance (FID) for synthetic image quality evaluation (Heusel et al., 2017) will differ based on the specific interpolation method used.
Linked Data Visualization: Techniques, Tools, and Big Data

Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domain
Segmentation of Ovarian Cancer using Active Contour and Random Walker Algorithm

Image Processing nowadays has been commonly used in different medical fields and includes many types of techniques such as storage, communication, presentation, extraction, detection, image gaining, and surgical planning which helps in improving early detection and treatment stage. Early detection is easier in the treatment especially in the case of ovarian cancer. Ovarian cancer has become one among the frequently occuring diseases in 1 in every 8 women these days, this increase in the number of women getting this cancer has drawn the attention of many medical communities. Statistics indicate that ovarian cancer is one of the 10 usual cancers in women. It is the deadliest gynecologic cancer and takes many lives. Early identification of this cancer is very much important for an efficacious diagnosis. There have been many algorithms used to detect ovarian cancer. Here are two algorithms active contour and random walker that are used to detect ovarian cancer and to find the better approach out of them.
Chatbots in retail: How do they affect the continued use and purchase intentions of Chinese consumers?

Abstract The application of artificial intelligence in the retail industry has exposed many innovation opportunities and significantly impacts consumer behavior. Based on the social presence, retailer innovativeness, and self‐determination theories, this study constructs an integrated and original framework for elucidating the influence mechanism of the social characteristics of anthropomorphic intelligent services (chatbots) on consumer behavioral intentions. An empirical study of Chinese consumers ( n = 442) reveals the following: first, the social presence of chatbots exerts a direct and positive impact on retailer experience innovativeness and intimacy; second, retailer experiential innovativeness, intimacy, and sense of empowerment mediate the influence of the social presence of chatbots on consumers' behavioral intentions; third, the cute communication styles in the low‐level social presence are very conducive for promoting the relationships between social presence and experiential innovativeness. By improving the level of social presence, formal communication styles can play a key role in promoting relationships. Theoretically, this study contributes to the social presence theory by extending it to the interaction with retailer experiential innovativeness and exploring the chain‐like mediating role of experiential innovativeness, as well as the self‐determined satisfaction between the social presence and consumer behavior intentions. Furthermore, this study also examines how different chatbot communication styles moderate the influence of social presence on consumers' behavioral intentions, thereby revealing the differential effects and boundary conditions of chatbot social presence on consumer behaviors. These findings will offer retailers insights into consumers' psychological activities to practically design chatbots and conduct smart retail innovation activities.
Engaging the Avatar: The effects of authenticity signals during chat-based service recoveries

Marketing practitioners and consumers can benefit from more efficient communication by using online chats for customer service. However, consumers desire a genuine experience and if the online chat experience is not believed to be authentic, it can detract from the benefits offered by using these new technologies. In four studies, including one electroencephalogram (EEG) measurement study, the authors show that firms can increase perceived authenticity of chat agents by sending signals via the avatars used in the chat system. The results indicate that avatar authenticity can be enhanced when the avatar is female, and these effects are amplified when the avatar is dressed professionally or a different race than the consumer. This increased authenticity is shown to drive engagement, loyalty, and satisfaction. The results offer fresh insight on how the use of avatars could help firms improve customer perceptions of service for either human- or bot-supported chat experiences.
Distilling Virtual Examples for Long-tailed Recognition

We tackle the long-tailed visual recognition problem from the knowledge distillation perspective by proposing a Distill the Virtual Examples (DiVE) method. Specifically, by treating the predictions of a teacher model as virtual examples, we prove that distilling from these virtual examples is equivalent to label distribution learning under certain constraints. We show that when the virtual example distribution becomes flatter than the original input distribution, the under-represented tail classes will receive significant improvements, which is crucial in long-tailed recognition. The proposed DiVE method can explicitly tune the virtual example distribution to become flat. Extensive experiments on three benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed DiVE method can significantly outperform state-of-the-art methods. Further-more, additional analyses and experiments verify the virtual example interpretation, and demonstrate the effectiveness of tailored designs in DiVE for long-tailed problems.
A wavelet based medical image watermarking scheme for secure transmission in telemedicine applications

Watermarking is one of the techniques used to protect multimedia data, and images in particular, from malicious attacks by inserting a signature into these images. However, traditional watermarking schemes encounter limitations for sensitive images such as medical images. Indeed, the inserted mark often alters the image in an irreversible way and can hide subtle details. In order to overcome these limitations, we propose in this paper a watermarking approach for patient identification and watermark integrity verification. In this approach, the watermark consists of two parts, the first part contains the patient's information fingerprint, and the second part contains the patient's encrypted photography. For the integration process, a discrete wavelet transform is applied to divide the medical image into four sub-bands. The watermark bits are then integrated by modulating the obtained mid-frequency coefficients. This approach's capacity allows the integration of the patient's photography and fingerprint, but will also be sufficient for a possible error-correcting code addition. The coefficient modulation performed by the integration process allowed hiding the watermark in an imperceptible way. This allowed us to generate a watermarked image structurally similar to the original. Performed experiments on the test images allowed to evaluate the performance of this method and to assess its robustness against JPEG compression and noise addition. Nevertheless, these values are sometimes a little far from the ideal value of 1 for normalized correlation.
Elaborative Rehearsal for Zero-shot Action Recognition

The growing number of action classes has posed a new challenge for video understanding, making Zero-Shot Action Recognition (ZSAR) a thriving direction. The ZSAR task aims to recognize target (unseen) actions without training examples by leveraging semantic representations to bridge seen and unseen actions. However, due to the complexity and diversity of actions, it remains challenging to semantically represent action classes and transfer knowledge from seen data. In this work, we propose an ER-enhanced ZSAR model inspired by an effective human memory technique Elaborative Rehearsal (ER), which involves elaborating a new concept and relating it to known concepts. Specifically, we expand each action class as an Elaborative Description (ED) sentence, which is more discriminative than a class name and less costly than manual-defined attributes. Besides directly aligning class semantics with videos, we incorporate objects from the video as Elaborative Concepts (EC) to improve video semantics and generalization from seen actions to unseen actions. Our ER-enhanced ZSAR model achieves state-of-the-art results on three existing benchmarks. Moreover, we propose a new ZSAR evaluation protocol on the Kinetics dataset to overcome limitations of current benchmarks and first compare with few-shot learning baselines on this more realistic setting. Our codes and collected EDs are released at https://github.com/DeLightCMU/ElaborativeRehearsal.
Global and Local Feature Reconstruction for Medical Image Segmentation

Learning how to capture long-range dependencies and restore spatial information of down-sampled feature maps are the basis of the encoder-decoder structure networks in medical image segmentation. U-Net based methods use feature fusion to alleviate these two problems, but the global feature extraction ability and spatial information recovery ability of U-Net are still insufficient. In this paper, we propose a Global Feature Reconstruction (GFR) module to efficiently capture global context features and a Local Feature Reconstruction (LFR) module to dynamically up-sample features, respectively. For the GFR module, we first extract the global features with category representation from the feature map, then use the different level global features to reconstruct features at each location. The GFR module establishes a connection for each pair of feature elements in the entire space from a global perspective and transfers semantic information from the deep layers to the shallow layers. For the LFR module, we use low-level feature maps to guide the up-sampling process of high-level feature maps. Specifically, we use local neighborhoods to reconstruct features to achieve the transfer of spatial information. Based on the encoder-decoder architecture, we propose a Global and Local Feature Reconstruction Network (GLFRNet), in which the GFR modules are applied as skip connections and the LFR modules constitute the decoder path. The proposed GLFRNet is applied to four different medical image segmentation tasks and achieves state-of-the-art performance.
GHRS: Graph-based hybrid recommendation system with application to movie recommendation

Research about recommender systems emerges over the last decade and comprises valuable services to increase different companies' revenue. Several approaches exist in handling paper recommender systems. While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches. Even though many algorithms are proposed using such methods, it is still necessary for further improvement. In this paper, we propose a recommender system method using a graph-based model associated with the similarity of users' ratings, in combination with users' demographic and location information. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. Using the new set of features for clustering users, our proposed approach (GHRS) has gained a significant improvement, which dominates other methods' performance in the cold-start problem. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.
MVD

Memory-related vulnerabilities constitute severe threats to the security of modern software. Despite the success of deep learning-based approaches to generic vulnerability detection, they are still limited by the underutilization of flow information when applied for detecting memory-related vulnerabilities, leading to high false positives. In this paper,we propose MVD, a statement-level Memory-related Vulnerability Detection approach based on flow-sensitive graph neural networks (FS-GNN). FS-GNN is employed to jointly embed both unstructured information (i.e., source code) and structured information (i.e., control- and data-flow) to capture implicit memory-related vulnerability patterns. We evaluate MVD on the dataset which contains 4,353 real-world memory-related vulnerabilities, and compare our approach with three state-of-the-art deep learning-based approaches as well as five popular static analysisbased memory detectors. The experiment results show that MVD achieves better detection accuracy, outperforming both state-of-theart DL-based and static analysis-based approaches. Furthermore, MVD makes a great trade-off between accuracy and efficiency.
Software fault prediction using data mining, machine learning and deep learning techniques: A systematic literature review

Software fault/defect prediction assists software developers to identify faulty constructs, such as modules or classes, early in the software development life cycle. There are data mining, machine learning, and deep learning techniques used for software fault prediction. We perform analysis of previously published reviews, surveys, and related studies to distill a list of questions. These questions were either answered in the past but needed a fresh look or they were not considered at all. We justify why answers to newly added questions are important and divide previous work based on data mining, machine learning, and deep learning and compare their performance. We study which datasets were commonly used and what comparison criteria were mostly adopted for software fault prediction. We select 68 primary studies from a wide list of initially selected set following our quality assessment criteria and present answers to our research questions.
Adversarial attacks and defenses on AI in medical imaging informatics: A survey

In recent years, medical images have significantly improved and facilitated diagnosis in versatile tasks including classification of lung diseases, detection of nodules, brain tumor segmentation, and body organs recognition. On the other hand, the superior performance of machine learning (ML) techniques, specifically deep learning networks (DNNs), in various domains has lead to the application of deep learning approaches in medical image classification and segmentation. Due to the security and vital issues involved, healthcare systems are considered quite challenging and their performance accuracy is of great importance. Previous studies have shown lingering doubts about medical DNNs and their vulnerability to adversarial attacks. Although various defense methods have been proposed, there are still concerns about the application of medical deep learning approaches. This is due to some of medical imaging weaknesses, such as lack of sufficient amount of high quality images and labeled data, compared to various high-quality natural image datasets. This paper reviews recently proposed adversarial attack methods to medical imaging DNNs and defense techniques against these attacks. It also discusses different aspects of these methods and provides future directions for improving neural network’s robustness.
A Systematic Survey on Cybersickness in Virtual Environments

Virtual reality (VR) is an emerging technology with a broad range of applications in training, entertainment, and business. To maximize the potentials of virtual reality as a medium, the unwelcome feeling of cybersickness needs to be minimized. Cybersickness is a type of simulation sickness that is experienced in virtual reality. It is a significant challenge for the usability of virtual reality systems. Even with advancements in virtual reality, the usability concerns are barriers for a wide-spread acceptance. Several factors (hardware, software, human) play a part towards a pleasant virtual reality experience. In this paper, we review the potential factors which cause sickness and minimize the usability of virtual reality systems. The reviewed scientific articles are mostly part of documents indexed in digital libraries. We review the best practices from a developer’s perspective and some of the safety measures a user must follow while using the virtual reality systems from existing research. Even after following some of the guidelines and best practices virtual reality environments do not guarantee a pleasant experience for users. Limited research in virtual reality environments towards requirements specification, design, and development for maximum usability and adaptability was the main motive for this work.
Scams, Frauds, and Crimes in the Nonfungible Token Market

This article delves into scams, frauds, and deceits in the nonfungible token (NFT) market. It also proposes a typology of cyberattacks and other malicious behaviors in the NFT space.
Hate speech and offensive language detection in Dravidian languages using deep ensemble framework

Social networking platforms gained widespread popularity and are used for various activities like: promoting products, sharing news, achievements and many more. On the other hand, it is also used for spreading rumors, bullying people, and abusing certain groups of people with hateful words. The hate and offensive posts must be detected and removed as early as possible from the social platforms because such posts are spread very quickly and tend to have a lot of negative impacts on human beings. In the last few years, offensive content and hate speech detection has become popular topic of research. Detecting hate speech on social platforms has many challenges, one of them being the use of code-mixed language. Majority of the social media users usually post their messages in code-mixed languages such as Hindi–English, Tamil–English, Malayalam–English, Telugu–English and others. In this exhaustive study, we explore and compare the use of various machine learning and deep learning approaches. An ensemble model by combining the outcomes of transformer and deep learning-based models is suggested to detect hate speech and offensive language on social networking platforms. The experimental outcomes of the proposed weighted ensemble framework outperformed state-of-the-art models by achieving 0.802 and 0.933 weighted F1-score for Malayalam and Tamil code-mixed datasets. • Proposed a weighted ensemble framework for hate and offensive code-mixed posts identification on social platforms. • Two code-mixed datasets, namely Tamil and Malayalam, are used in this research. • The proposed model utilized the outcomes of deep learning and transformer-based models. • Transformer based models like m-BERT, distilBERT, xlm-RoBERTa performed better than the ML and DL based models.