Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning

Social media platforms such as Twitter and Facebook have become popular channels for people to record and express their feelings, opinions, and feedback in the last decades. With proper extraction techniques such as sentiment analysis, this information is useful in many aspects, including product marketing, behavior analysis, and pandemic management. Sentiment analysis is a technique to analyze people’s thoughts, feelings and emotions, and to categorize them into positive, negative, or neutral. There are many ways for someone to express their feelings and emotions. These sentiments are sometimes accompanied by sarcasm, especially when conveying intense emotion. Sarcasm is defined as a positive sentence with underlying negative intention. Most of the current research work treats them as two distinct tasks. To date, most sentiment and sarcasm classification approaches have been treated primarily and standalone as a text categorization problem. In recent years, research work using deep learning algorithms have significantly improved performance for these standalone classifiers. One of the major issues faced by these approaches is that they could not correctly classify sarcastic sentences as negative. With this in mind, we claim that knowing how to spot sarcasm will help sentiment classification and vice versa. Our work has shown that these two tasks are correlated. This paper proposes a multi-task learning-based framework utilizing a deep neural network to model this correlation to improve sentiment analysis’s overall performance. The proposed method outperforms the existing methods by a margin of 3%, with an F1-score of 94%.
A systematic literature review of cybersecurity scales assessing information security awareness

Information Security Awareness (ISA) is a significant concept that got considerable attention recently and can assist in minimizing the risks associated with information security breaches. Several measurement scales have been developed in this regard, as measuring users’ ISA is paramount. Although ISA specific scales are very important, yet what methodological rigor they use in terms of initial conceptualization of ISA, data collection and analysis during the development, and scale validation of such scales are some unknown aspects. Therefore, we provide a comprehensive review of the existing ISA specific scales to address all the above concerns. A popular method, PRISMA, is utilized, and a total of 24 articles that match with criteria of this research are included for the final in-depth analysis. Also, a holistic evaluation framework is developed containing three phases and 19 criteria. Findings revealed that most studies treat ISA as a multi-dimensional construct, and ISA researchers rarely conduct both pilot testing and pre-text evaluation while validating and refining the initial scales. Additionally, several articles did not report some of the essential elements used for checking the rigor of factor analysis, and evidence for validities of the identified scales is inadequate. Consequently, existing ISA specific scales must be improved both in terms of the methodological thoroughness of the scale development procedure and their validities. Moreover, not only justifying why the development of a new scale is necessary, but also improving the quality of the existing scales by doing multiple iterations is significant in the future. Likewise, the inclusion of all the dimensions of ISA, while generating the initial items pool is an important aspect to be considered. A thorough discussion, recommendations for future research, conclusions, and study limitations are provided.
ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation

The performance of a semantic segmentation model for remote sensing (RS) images pre-trained on an annotated dataset greatly decreases when testing on another unannotated dataset because of the domain gap. Adversarial generative methods, e.g., DualGAN, are utilized for unpaired image-to-image translation to minimize the pixel-level domain gap, which is one of the common approaches for unsupervised domain adaptation (UDA). However, the existing image translation methods face two problems when performing RS image translation: (1) ignoring the scale discrepancy between two RS datasets, which greatly affects the accuracy performance of scale-invariant objects; (2) ignoring the characteristic of real-to-real translation of RS images, which brings an unstable factor for the training of the models. In this paper, ResiDualGAN is proposed for RS image translation, where an in-network resizer module is used for addressing the scale discrepancy of RS datasets and a residual connection is used for strengthening the stability of real-to-real images translation and improving the performance in cross-domain semantic segmentation tasks. Combined with an output space adaptation method, the proposed method greatly improves the accuracy performance on common benchmarks, which demonstrates the superiority and reliability of ResiDualGAN. At the end of the paper, a thorough discussion is conducted to provide a reasonable explanation for the improvement of ResiDualGAN. Our source code is also available.
A survey on image enhancement for Low-light images

In real scenes, due to the problems of low light and unsuitable views, the images often exhibit a variety of degradations, such as low contrast, color distortion, and noise. These degradations affect not only visual effects but also computer vision tasks. This paper focuses on the combination of traditional algorithms and machine learning algorithms in the field of image enhancement. The traditional methods, including their principles and improvements, are introduced from three categories: gray level transformation, histogram equalization, and Retinex methods. Machine learning based algorithms are not only divided into end-to-end learning and unpaired learning, but also concluded to decomposition-based learning and fusion based learning based on the applied image processing strategies. Finally, the involved methods are comprehensively compared by multiple image quality assessment methods, including mean square error, natural image quality evaluator, structural similarity, peak signal to noise ratio, etc.
Current and Emerging Trends in Medical Image Segmentation With Deep Learning

In recent years, the segmentation of anatomical or pathological structures using deep learning has experienced a widespread interest in medical image analysis. Remarkably successful performance has been reported in many imaging modalities and for a variety of clinical contexts to support clinicians in computer-assisted diagnosis, therapy, or surgical planning purposes. However, despite the increasing amount of medical image segmentation challenges, there remains little consensus on which methodology performs best. Therefore, we examine in this article the numerous developments and breakthroughs brought since the rise of U-Net-inspired architectures. Especially, we focus on the technical challenges and emerging trends that the community is now focusing on, including conditional generative adversarial and cascaded networks, medical Transformers, contrastive learning, knowledge distillation, active learning, prior knowledge embedding, cross-modality learning, multistructure analysis, federated learning, or semi-supervised and self-supervised paradigms. We also suggest possible avenues to be further investigated in future research efforts.
Digital Art and the Metaverse: Benefits and Challenges

As a concept that is somewhat under emergence, the notion of the Metaverse varies across different academic articles. Yet there is a shared view on the benefits to its ongoing implementation, particularly for digital art, where the technology can provide a new metric for artists to showcase and sell their artwork to a global audience with minimal barriers, and for consumers to have an unbounded experience not limited by physical space or museum entry fees. In this article, a contribution is provided to a broader conversation about the future of the digital art and the Metaverse and its role in shaping our online culture. We discuss the concept of the Metaverse, its structure, the role of artificial intelligence and the benefits (and limitations) the technology holds for digital art. For a case study, we develop a 3D art gallery housing an art collection generated using artificial-intelligence-based techniques such as diffusion models. A total of 67 individuals are surveyed from three pools (two in-person and one online-based), with questions relating to the future of digital art, the Metaverse and artificial intelligence. Findings include that the majority of participants were familiar with the concept of the Metaverse and overall, they had a predominately optimistic view of both the use artificial intelligence for art, and the use of the Metaverse to support digital art, with 85.3% of the participants having already seen artificial-intelligence-based artwork. The identification of consumer segments further highlights the importance of finding customised solutions, considering consumers’ heterogenous preferences for AI-generated art. Research presented in this article will be beneficial for those looking to explore the Metaverse for artwork and develop virtual galleries, and the findings further highlight the Metaverse as a potential democratising force in the art world.
Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

Abstract The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field.
BEVStereo: Enhancing Depth Estimation in Multi-View 3D Object Detection with Temporal Stereo

Restricted by the ability of depth perception, all Multi-view 3D object detection methods fall into the bottleneck of depth accuracy. By constructing temporal stereo, depth estimation is quite reliable in indoor scenarios. However, there are two difficulties in directly integrating temporal stereo into outdoor multi-view 3D object detectors: 1) The construction of temporal stereos for all views results in high computing costs. 2) Unable to adapt to challenging outdoor scenarios. In this study, we propose an effective method for creating temporal stereo by dynamically determining the center and range of the temporal stereo. The most confident center is found using the EM algorithm. Numerous experiments on nuScenes have shown the BEVStereo's ability to deal with complex outdoor scenarios that other stereo-based methods are unable to handle. For the first time, a stereo-based approach shows superiority in scenarios like a static ego vehicle and moving objects. BEVStereo achieves the new state-of-the-art in the camera-only track of nuScenes dataset while maintaining memory efficiency. Codes have been released.
Bridging the Gap Between Urological Research and Patient Understanding: The Role of Large Language Models in Automated Generation of Layperson’s Summaries

This study assessed ChatGPT's ability to generate readable, accurate, and clear layperson summaries of urological studies, and compared the performance of ChatGPT-generated summaries with original abstracts and author-written patient summaries to determine its effectiveness as a potential solution for creating accessible medical literature for the public.Articles from the top 5 ranked urology journals were selected. A ChatGPT prompt was developed following guidelines to maximize readability, accuracy, and clarity, minimizing variability. Readability scores and grade-level indicators were calculated for the ChatGPT summaries, original abstracts, and patient summaries. Two MD physicians independently rated the accuracy and clarity of the ChatGPT-generated layperson summaries. Statistical analyses were conducted to compare readability scores. Cohen's κ coefficient was used to assess interrater reliability for correctness and clarity evaluations.A total of 256 journal articles were included. The ChatGPT-generated summaries were created with an average time of 17.5 (SD 15.0) seconds. The readability scores of the ChatGPT-generated summaries were significantly better than the original abstracts, with Global Readability Score 54.8 (12.3) vs 29.8 (18.5), Flesch Kincade Reading Ease 54.8 (12.3) vs 29.8 (18.5), Flesch Kincaid Grade Level 10.4 (2.2) vs 13.5 (4.0), Gunning Fog Score 12.9 (2.6) vs 16.6 (4.1), Smog Index 9.1 (2.0) vs 12.0 (3.0), Coleman Liau Index 12.9 (2.1) vs 14.9 (3.7), and Automated Readability Index 11.1 (2.5) vs 12.0 (5.7; P < .0001 for all except Automated Readability Index, which was P = .037). The correctness rate of ChatGPT outputs was >85% across all categories assessed, with interrater agreement (Cohen's κ) between 2 independent physician reviewers ranging from 0.76-0.95.ChatGPT can create accurate summaries of scientific abstracts for patients, with well-crafted prompts enhancing user-friendliness. Although the summaries are satisfactory, expert verification is necessary for improved accuracy.
Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition

Abstract In the field of machine intelligence and ubiquitous computing, there has been a growing interest in human activity recognition using wearable sensors. Over the past few decades, researchers have extensively explored learning-based methods to develop effective models for identifying human behaviors. Deep learning algorithms, known for their powerful feature extraction capabilities, have played a prominent role in this area. These algorithms can conveniently extract features that enable excellent recognition performance. However, many successful deep learning approaches have been built upon complex models with multiple hyperparameters. This paper examines the current research on human activity recognition using deep learning techniques and discusses appropriate recognition strategies. Initially, we employed multiple convolutional neural networks to determine an effective architecture for human activity recognition. Subsequently, we developed a hybrid convolutional neural network that incorporates a channel attention mechanism. This mechanism enables the network to capture deep spatio-temporal characteristics in a hierarchical manner and distinguish between different human movements in everyday life. Our investigations, using the UCI-HAR, WISDM, and IM-WSHA datasets, demonstrated that our proposed model, which includes cross-channel multi-size convolution transformations, outperformed previous deep learning architectures with accuracy rates of 98.92%, 98.80%, and 98.45% respectively. These results indicate that the suggested model surpasses state-of-the-art approaches in terms of overall accuracy, as supported by the research findings.
Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers

Large pretrained language models have shown surprising in-context learning (ICL) ability.With a few demonstration input-label pairs, they can predict the label for an unseen input without parameter updates.Despite the great success in performance, its working mechanism still remains an open question.In this paper, we explain language models as metaoptimizers and understand in-context learning as implicit finetuning.Theoretically, we figure out that Transformer attention has a dual form of gradient descent.On top of it, we understand ICL as follows: GPT first produces meta-gradients according to the demonstration examples, and then these meta-gradients are applied to the original GPT to build an ICL model.We comprehensively compare the behaviors of in-context learning and explicit finetuning on real tasks to provide empirical evidence that supports our understanding.Experimental results show that in-context learning behaves similarly to explicit finetuning from multiple perspectives.Inspired by the dual form between Transformer attention and gradient descent, we design a momentum-based attention by analogy with gradient descent with momentum.The improved performance over vanilla attention further supports our understanding from another perspective, and more importantly, shows the potential to utilize our understanding for future model design.
Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation

Capturing emotions within a conversation plays an essential role in modern dialogue systems.However, the weak correlation between emotions and semantics brings many challenges to emotion recognition in conversation (ERC).Even semantically similar utterances, the emotion may vary drastically depending on contexts or speakers.In this paper, we propose a Supervised Prototypical Contrastive Learning (SPCL) loss for the ERC task.Leveraging the Prototypical Network, the SPCL targets at solving the imbalanced classification problem through contrastive learning and does not require a large batch size.Meanwhile, we design a difficulty measure function based on the distance between classes and introduce curriculum learning to alleviate the impact of extreme samples.We achieve state-of-the-art results on three widely used benchmarks.Further, we conduct analytical experiments to demonstrate the effectiveness of our proposed SPCL and curriculum learning strategy.We release the code at https://github.com/caskcsg/SPCL.
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement

Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions.Due to its sophisticated nature, it is usually challenging to be detected from the text itself.As a result, multi-modal sarcasm detection has received more attention in both academia and industries.However, most existing techniques only modeled the atomic-level inconsistencies between the text input and its accompanying image, ignoring more complex compositions for both modalities.Moreover, they neglected the rich information contained in external knowledge, e.g., image captions.In this paper, we propose a novel hierarchical framework for sarcasm detection by exploring both the atomic-level congruity based on multi-head cross attention mechanism and the composition-level congruity based on graph neural networks, where a post with low congruity can be identified as sarcasm.In addition, we exploit the effect of various knowledge resources for sarcasm detection.Evaluation results on a public multi-modal sarcasm detection dataset based on Twitter demonstrate the superiority of our proposed model.
Implicit Identity Driven Deepfake Face Swapping Detection

In this paper, we consider the face swapping detection from the perspective of face identity. Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake. We argue that the fake face contains the explicit identity and implicit identity, which respectively corresponds to the identity of the source face and target face during face swapping. Note that the explicit identities of faces can be extracted by regular face recognizers. Particularly, the implicit identity of real face is consistent with the its explicit identity. Thus the difference between explicit and implicit identity of face facilitates face swapping detection. Following this idea, we propose a novel implicit identity driven framework for face swapping detection. Specifically, we design an explicit identity contrast (EIC) loss and an implicit identity exploration (IIE) loss, which supervises a CNN backbone to embed face images into the implicit identity space. Under the guidance of EIC, real samples are pulled closer to their explicit identities, while fake samples are pushed away from their explicit identities. More-over, IIE is derived from the margin-based classification loss function, which encourages the fake faces with known target identities to enjoy intra-class compactness and inter-class diversity. Extensive experiments and visualizations on several datasets demonstrate the generalization of our method against the state-of-the-art counterparts.
Workplace Deviance Among Hotel Employees : An Exploratory Survey

This exploratory study examined workplace deviance among hotel employees in Langkawi, Malaysia. Specifically, it attempted to provide empirical answers to two main research questions: (1) what are the types of workplace deviance employees exhibit at the workplace, and (2) what work-related factors account for the exhibition of those acts. In order to address these questions, data were collected from 308 hotel employees. The findings showed that workplace deviance exists in organisations and takes on a variety of forms. The results also depicted that workplace deviance is influenced by a number of work-related factors, such as pay, supervision, co-workers, and management practices. In addition, employee deviance was also significantly related to the way they were treated by hotel guests. The implications of the study’s findings on practice and theory are discussed.
Mining and Visualizing Online Web Content Using BAM: Brand Association Map

In this paper, we describe our Brand Association Map (BAM) tool which maps and visualizes the way consumers naturally think and talk about brands across billions of unaided conversations online. BAM is a semi-supervised tool that leverages text-mining algorithms to identify key correlated phrases, terms and issues out of millions of candidate terms which were derived from billions of online conversations. The most correlated phrases with a given brand are then projected and plotted onto visual bull's eye representation. BAM's visualization illustrates both the correlation level between a brand (appears in the center of the visualization) and each of the highly correlated terms as well as the inner correlations among all presented terms, where terms on the same radial angel represent a "clustered" discussion of terms frequently mentioned together. We found BAM useful for extracting various intuitions and beliefs that are highly correlated with brands to better grasp how consumers really contextualize them, out of massive consumer generated media (CGM) documents.