Reviewerly/exHarmony huggingface dataset:
MNER-QG: An End-to-End MRC Framework for Multimodal Named Entity Recognition with Query Grounding

Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods either (1) obtain named entities with coarse-grained visual clues from attention mechanisms, or (2) first detect fine-grained visual regions with toolkits and then recognize named entities. However, they suffer from improper alignment between entity types and visual regions or error propagation in the two-stage manner, which finally imports irrelevant visual information into texts. In this paper, we propose a novel end-to-end framework named MNER-QG that can simultaneously perform MRC-based multimodal named entity recognition and query grounding. Specifically, with the assistance of queries, MNER-QG can provide prior knowledge of entity types and visual regions, and further enhance representations of both text and image. To conduct the query grounding task, we provide manual annotations and weak supervisions that are obtained via training a highly flexible visual grounding model with transfer learning. We conduct extensive experiments on two public MNER datasets, Twitter2015 and Twitter2017. Experimental results show that MNER-QG outperforms the current state-of-the-art models on the MNER task, and also improves the query grounding performance.
Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.
The Dance of the Doppelgängers: AI and the cultural heritage community

There are severe issues that bring the impact of AI enabled systems into sharp focus for the cultural heritage sector, especially when these platforms are open, extremely tempting, and now accessible for everyone who may wish to create art or write a novel at the keyboard.Creating the verbal cue and waiting for the sublimity of every desire is mesmerizing, enthralling, even addicting but the impact on human creativity somehow feels like an accident waiting to happen.This profuse human/machine creativity resulting in AI artifacts sets up enormous challenges for all players in the creative industries, from the struggling artist or designer; to the public-funded institutions such as museums who will be wandering the labyrinth of copyright policies, trying to inscribe the metadata of the provenance of AI production.The paper will discuss Chat GPT and present AI imagery as doppelgängers that seemingly emerge out of an uncanny valley.The process is almost immediate with DALL-E2, Midjourney, and Stable Diffusion all providing instant results at the drop of a prompt.With a series of case studies, we will discuss the challenging copyright and provenance issues that are taking place at a dizzying pace as a sector that is striving to come to terms with the highly problematic issues of authenticity and ownership.
Smart Smile: Revolutionizing Dentistry With Artificial Intelligence

Artificial intelligence (AI) has emerged as a transformative technology in various industries, and its potential in dentistry is gaining significant attention.This abstract explores the future prospects of AI in dentistry, highlighting its potential to revolutionize clinical practice, improve patient outcomes, and enhance the overall efficiency of dental care.The application of AI in dentistry encompasses several key areas, including diagnosis, treatment planning, image analysis, patient management, and personalized care.AI algorithms have shown promising results in the automated detection and diagnosis of dental conditions, such as caries, periodontal diseases, and oral cancers, aiding clinicians in early intervention and improving treatment outcomes.Furthermore, AI-powered treatment planning systems leverage machine learning techniques to analyze vast amounts of patient data, considering factors like medical history, anatomical variations, and treatment success rates.These systems provide dentists with valuable insights and support in making evidence-based treatment decisions, ultimately leading to more predictable and tailored treatment approaches.While the potential of AI in dentistry is immense, it is essential to address certain challenges, including data privacy, algorithm bias, and regulatory considerations.Collaborative efforts between dental professionals, AI experts, and policymakers are crucial to developing robust frameworks that ensure the responsible and ethical implementation of AI in dentistry.Moreover, AI-driven robotics has introduced innovative approaches to dental surgery, enabling precise and minimally invasive procedures, and ultimately reducing patient discomfort and recovery time.Virtual reality (VR) and augmented reality (AR) applications further enhance dental education and training, allowing dental professionals to refine their skills in a realistic and immersive environment.AI holds tremendous promise in shaping the future of dentistry.Through its ability to analyze vast amounts of data, provide accurate diagnoses, facilitate treatment planning, improve image analysis, streamline patient management, and enable personalized care, AI has the potential to enhance dental practice and significantly improve patient outcomes.Embracing this technology and its future development will undoubtedly revolutionize the field of dentistry, fostering a more efficient, precise, and patient-centric approach to oral healthcare.Overall, AI represents a powerful tool that has the potential to revolutionize various aspects of society, from improving healthcare outcomes to optimizing business operations.Continued research, development, and responsible implementation of AI technologies will shape our future, unlocking new possibilities and transforming the way we live and work.
The Use of Mind Mapping Technique in Descriptive Writing among Primary School Students

Tony Buzan first developed mind mapping, which has since become a popular learning technique. It solves various educational tasks by combining information from various sources and displaying it as keywords in a bright, colorful manner. Mind maps have been said to be a good way to study when applied to written material. This research aimed to guide and integrate mind mapping into descriptive writing with primary students. Ninety-four fifth-grade students at a primary school were chosen to participate in the study. Two groups of students from a primary school were recruited for the experiment. The proposed method was to be taught in one class, while the traditional method was to be taught in the other. The results showed that the students in the experimental group who learned using the mind map technique did better in writing than in the control group. Mind mapping has numerous educational applications and can assist students in their writing process. Teachers can use mind mapping tools to expand their knowledge and lay the groundwork for their students' learning. Teachers should be encouraged to take training courses on mind mapping to learn and practice the skills they need to use mind mapping in their classrooms.&#x0D; &#x0D; Received: 20 February 2023 / Accepted: 12 June 2023 / Published: 5 July 2023
Investigating OpenAI’s ChatGPT Potentials in Generating Chatbot's Dialogue for English as a Foreign Language Learning

Lack of opportunities is a significant hurdle for English as a Foreign Language (EFL) for students during their learning journey.Previous studies have explored the use of chatbots as learning partners to address this issue.However, the success of chatbot implementation depends on the quality of the reference dialogue content, yet research focusing on this subject is still limited.Typically, human experts are involved in creating suitable dialogue materials for students to ensure the quality of such content.Research attempting to utilize artificial intelligence (AI) technologies for generating dialogue practice materials is relatively limited, given the constraints of existing AI systems that may produce incoherent output.This research investigates the potential of leveraging OpenAI's ChatGPT, an AI system known for producing coherent output, to generate reference dialogues for an EFL chatbot system.The study aims to assess the effectiveness of ChatGPT in generating high-quality dialogue materials suitable for EFL students.By employing multiple readability metrics, we analyze the suitability of ChatGPTgenerated dialogue materials and determine the target audience that can benefit the most.Our findings indicate that ChatGPT's dialogues are well-suited for students at the Common European Framework of Reference for Languages (CEFR) level A2 (elementary level).These dialogues are easily comprehensible, enabling students at this level to grasp most of the vocabulary used.Furthermore, a substantial portion of the dialogues intended for CEFR B1 (intermediate level) provides ample stimulation for learning new words.The integration of AIpowered chatbots in EFL education shows promise in overcoming limitations and providing valuable learning resources to students.
Automated Cognitive Health Assessment Based on Daily Life Functional Activities

Dementia is increasing day-by-day in older adults. Many of them are spending their life joyfully due to smart home technologies. Smart homes contain several smart devices which can support living at home. Automated assessment of smart home residents is a significant aspect of smart home technology. Detecting dementia in older adults in the early stage is the basic need of this time. Existing technologies can detect dementia timely but lacks performance. In this paper, we proposed an automated cognitive health assessment approach using machines and deep learning based on daily life activities. To validate our approach, we use CASAS publicly available daily life activities dataset for experiments where residents perform their routine activities in a smart home. We use four machine learning algorithms: decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP). Furthermore, we use deep neural network (DNN) for healthy and dementia classification. Experiments reveal the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>96</mn> <mo>%</mo> </math> accuracy using the MLP classifier. This study suggests using machine learning classifiers for better dementia detection, specifically for the dataset which contains real-world data.
Challenges and Issues in Sentiment Analysis: A Comprehensive Survey

Sentiment analysis, a specialization of natural language processing (NLP), has witnessed significant progress since its emergence in the late 1990s, owing to the swift advances in deep learning techniques and the abundance of vast digital datasets. Though sentiment analysis has reached a relatively advanced stage in the area of NLP, it is erroneously assumed that sentiment analysis has reached its pinnacle, leaving no room for further improvement. However, it is important to acknowledge that numerous challenges that require attention persist. This survey paper provides a comprehensive overview of sentiment analysis, including its applications, approaches to sentiment classification, and commonly used evaluation metrics. The survey primarily focuses on the challenges associated with different types of data for sentiment classification, namely cross-domain data, multimodal data, cross-lingual data, and small-scale data, and provides a review of the state-of-the-art in sentiment analysis to address these challenges. The paper also addresses the challenges faced during sentiment classification irrespective of the type of data available. It aims at a better understanding of sentiment analysis to enable practitioners and researchers select suitable methods for sentiment classification depending on the type of data being analyzed.
A Blockchain Dynamic Sharding Scheme Based on Hidden Markov Model in Collaborative IoT

Sharded blockchain offers scalability, decentralization, immutability, and linear improvement, making it a promising solution for addressing the trust problem in large-scale collaborative IoT. However, a high proportion of cross-shard transactions can severely limit the performance of decentralized blockchain. Furthermore, the dynamic assemblage characteristic of collaborative sensing in sharded blockchain is often ignored. To overcome these limitations, we propose HMMDShard, a dynamic blockchain sharding scheme based on the Hidden Markov Model. HMMDShard leverages fine-grained blockchain sharding and fully embraces the dynamic assemblage characteristic of IoT collaborative sensing. By integrating the Hidden Markov Model, we achieve adaptive dynamic incremental updating of blockchain shards, effectively reducing cross-shard transactions across all shards. We conduct a comprehensive analysis of the security issues and properties of HMMDShard, and evaluate its performance through the implementation of a system prototype. The results demonstrate that HMMDShard significantly reduces the proportion of cross-shard transactions and outperforms other baselines in terms of system throughput and transaction confirmation latency.
An Efficient Numerical Scheme for Biological Models in the Frame of Bernoulli Wavelets

This article considers three types of biological systems: the dengue fever disease model, the COVID-19 virus model, and the transmission of Tuberculosis model.The new technique of creating the integration matrix for the Bernoulli wavelets is applied.Also, the novel method proposed in this paper is called the Bernoulli wavelet collocation scheme (BWCM).All three models are in the form system of coupled ordinary differential equations without an exact solution.These systems are converted into a system of algebraic equations using the Bernoulli wavelet collocation scheme.The numerical wave distributions of these governing models are obtained by solving the algebraic equations via the Newton-Raphson method.The results obtained from the developed strategy are compared to several schemes such as the Runge Kutta method, and ND solver in mathematical software.The convergence analyses are discussed through theorems.The newly implemented Bernoulli wavelet method improves the accuracy and converges when it is compared with the existing methods in the literature.
A deep learning model using hyperspectral image for <scp>EUS‐FNA</scp> cytology diagnosis in pancreatic ductal adenocarcinoma

Abstract Background and Aims Endoscopic ultrasonography‐guided fine‐needle aspiration/biopsy (EUS‐FNA/B) is considered to be a first‐line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)‐based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS‐FNA cytology specimens. Methods HSI images were captured of pancreatic EUS‐FNA cytological specimens from benign pancreatic tissues ( n = 33) and PDAC ( n = 39) prepared using a liquid‐based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF‐Visualization) was used to visualize the regions of important classification features identified by the model. Results A total of 1913 HSI images were obtained. Our ResNet18‐SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF‐Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei. Conclusions An HSI‐based model was developed to diagnose cytological PDAC specimens obtained using EUS‐guided sampling. Under the supervision of experienced cytopathologists, we performed multi‐staged consecutive in‐depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.
Digital pathology systems enabling quality patient care

Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.
LongEval-Retrieval: French-English Dynamic Test Collection for Continuous Web Search Evaluation

LongEval-Retrieval is a Web document retrieval benchmark that focuses on continuous retrieval evaluation. This test collection is intended to be used to study the temporal persistence of Information Retrieval systems and will be used as the test collection in the Longitudinal Evaluation of Model Performance Track (LongEval) at CLEF 2023. This benchmark simulates an evolving information system environment - such as the one a Web search engine operates in - where the document collection, the query distribution, and relevance all move continuously, while following the Cranfield paradigm for offline evaluation. To do that, we introduce the concept of a dynamic test collection that is composed of successive sub-collections each representing the state of an information system at a given time step. In LongEval-Retrieval, each sub-collection contains a set of queries, documents, and soft relevance assessments built from click models. The data comes from Qwant, a privacy-preserving Web search engine that primarily focuses on the French market. LongEval-Retrieval also provides a 'mirror' collection: it is initially constructed in the French language to benefit from the majority of Qwant's traffic, before being translated to English. This paper presents the creation process of LongEval-Retrieval and provides baseline runs and analysis.
Tolerance of immersive head-mounted virtual reality among older nursing home residents

Introduction Virtual Reality (VR) is a tool that is increasingly used in the aging population. Head-Mounted Displays (HMDs) are stereoscopic vision devices used for immersive VR. Cybersickness is sometimes reported after head-mounted display (HMD) VR exposure. Cybersickness severity and anxiety state reflect VR low tolerance. We aimed to evaluate HMD VR tolerance among older nursing home residents through cybersickness and anxiety state. Methods A total of 36 participants were included in this preliminary study, 33 of whom (mean age: 89.33 ± 5.48) underwent three individual HMD VR sessions with three different contents. Cybersickness occurrence and severity were scored by the Simulator Sickness Questionnaire (SSQ) after each session. Anxiety state was assessed by the State–Trait Anxiety Inventory form Y-A before and after each session. Anxiety trait (using State–Trait Anxiety Inventory form Y-B) was also evaluated before and after the experiment. In total, 92% (33/36) of patients completed all three sessions, of which 61% (20/33) did not report any cybersickness symptoms (SSQ = 0). Six participants reported significant cybersickness (defined by an SSQ score ⩾10) in at least one session. Discussion Only two participants stopped the study after the first exposure because of cybersickness. Age, cognitive function, anxiety trait, and well-being were not associated with cybersickness. The mean anxiety state decreased significantly from pre- to post-session. This immersive HMD VR experience was well tolerated among nursing home dwellers. Further larger studies in this population aiming to identify CS determinants are needed in order to use HMD VR on a standard basis.
Implications of Deep Compression with Complex Neural Networks

Deep learning and neural networks have become increasingly popular in the area of artificial intelligence. These models have the capability to solve complex problems, such as image recognition or language processing. However, the memory utilization and power consumption of these networks can be very large for many applications. This has led to research into techniques to compress the size of these models while retaining accuracy and performance. One of the compression techniques is the deep compression three-stage pipeline, including pruning, trained quantization, and Huffman coding. In this paper, we apply the principles of deep compression to multiple complex networks in order to compare the effectiveness of deep compression in terms of compression ratio and the quality of the compressed network. While the deep compression pipeline is effectively working for CNN and RNN models to reduce the network size with small performance degradation, it is not properly working for more complicated networks such as GAN. In our GAN experiments, performance degradation is too much from the compression. For complex neural networks, careful analysis should be done for discovering which parameters allow a GAN to be compressed without loss in output quality.