Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Role in Subclassifying Cytology of Thyroid Follicular Neoplasm

Objective: Fine needle aspiration cytology has higher sensitivity and predictive value for diagnosis of thyroid nodules than any other single diagnostic methods. In the Bethesda system for reporting thyroid, the category IV, encompasses both adenoma and carcinoma, but it is not possible to differentiate both lesions in the cytology practice and can be only differentiated after resection. In this work, we aim at exploring the ability of a convolutional neural network (CNN) model to sub-classifying cytological images of Bethesda category IV diagnosis into follicular adenoma and follicular carcinoma. Methods: We used a cohort of cytology cases n= 43 with extracted images n= 886 to train CNN model aiming to sub-classify follicular neoplasm (Bethesda category IV) into either follicular adenoma or follicular carcinoma. Result: In our study, the model subclassification of follicular neoplasm into follicular adenoma (n = 28/43, images n = 527/886) from follicular carcinoma (n = 15/43, images n= 359/886), has achieved an accuracy of 78%, with a sensitivity of 88.4%, and a specificity of 64% and an area under the curve (AUC) score of 0.87 for each of follicular adenoma and follicular carcinoma. Conclusion: Our CNN model has achieved high sensitivity in recognizing follicular adenoma amongest cytology smears of follciualr neoplasms, thus it can be used as an ancillary technique in the subcalssification of Bethesda Iv category cytology smears.
Quotation Recommendation for Multi-party Online Conversations Based on Semantic and Topic Fusion

Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for humans. This work studies automatic quotation recommendation for online conversations. Unlike the previous works that only consider semantic-level modeling, we adopt topic-level representation to facilitate the recommendation. A hierarchical architecture that is based on a pretrained language model is adopted to model the semantic-level conversation representation, and a neural topic model is employed to learn the topic-level representation. Moreover, the semantic-level conversation modeling is enhanced by a topic-aware attention mechanism, which is adopted to capture the interactive conversation structure from the perspective of word co-occurrence. The joint training of semantic- and topic-based recommendation leads to significantly better performance than the state-of-the-art models on two large-scale datasets. Apart from the novel and advanced recommendation framework, we conduct extensive quantitative experiments to investigate the difficulty of the quotation recommendation task, validate the topic-based recommendation assumption, and explore the stability of the recommendation. Some qualitative experiments and analyses are also included to interpret the quotation and topic distribution for some instances. All the extensive experiments and analyses provide persuasive explanations and interpretations of the module design and the recommendation results.
Procontext: Exploring Progressive Context Transformer for Tracking

Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a template. This causes tracking to inevitably fail in fast-changing and crowded scenes, as it cannot account for changes in object appearance between frames. To this end, we revamped the tracking framework with Progressive Context Encoding Transformer Tracker (ProContEXT), which coherently exploits spatial and temporal contexts to predict object motion trajectories. Specifically, ProContEXT leverages a context-aware self-attention module to encode the spatial and temporal context, refining and updating the multi-scale static and dynamic templates to progressively perform accurately tracking. It explores the complementary between spatial and temporal context, raising a new pathway to multi-context modeling for transformer-based trackers. In addition, ProContEXT revised the token pruning technique to reduce computational complexity. Extensive experiments on popular benchmark datasets such as GOT-10k and TrackingNet demonstrate that the proposed ProContEXT achieves state-of-the-art performance <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Classifier calibration: a survey on how to assess and improve predicted class probabilities

This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of uncertainty or confidence associated with its instance-wise predictions. This is essential for critical applications, optimal decision making, cost-sensitive classification, and for some types of context change. Calibration research has a rich history which predates the birth of machine learning as an academic field by decades. However, a recent increase in the interest on calibration has led to new methods and the extension from binary to the multiclass setting. The space of options and issues to consider is large, and navigating it requires the right set of concepts and tools. We provide both introductory material and up-to-date technical details of the main concepts and methods, including proper scoring rules and other evaluation metrics, visualisation approaches, a comprehensive account of post-hoc calibration methods for binary and multiclass classification, and several advanced topics.
Enhancing STEM learning with ChatGPT and Bing Chat as objects to think with: A case study

This study investigates the potential of ChatGPT and Bing Chat, advanced conversational AIs, as "objects-to-think-with," resources that foster reflective and critical thinking, and concept comprehension in enhancing STEM education, using a constructionist theoretical framework. A single-case study methodology was used to analyse extensive interaction logs between students and both AI systems in simulated STEM learning experiences. The results highlight the ability of ChatGPT and Bing Chat to help learners develop reflective and critical thinking, creativity, problem-solving skills, and concept comprehension. However, integrating AIs with collaborative learning and other educational activities is crucial, as is addressing potential limitations like concerns about AI information accuracy and reliability of the AIs' information and diminished human interaction. The study concludes that ChatGPT and Bing Chat as objects-to-think-with offer promising avenues to revolutionise STEM education through a constructionist lens, fostering engagement in inclusive and accessible learning environments.
Active contour model based on local Kullback–Leibler divergence for fast image segmentation

The inhomogeneity of image intensity and noise are the main factors that affect the segmentation results. To overcome these challenges, a new active contour model is designed based on level set method and Kullback–Leibler Divergence. First of all, a new regional measurement of information scale is applied to construct energy functional, instead of Euclidean distance. Test results demonstrate that the Kullback–Leibler Divergence achieves a truly better segmentation. Then, a new Heaviside function has been proposed in this paper, which gives rise to a faster zero-crossing slope than traditional function. In this sense, it can stimulate the evolution of the level set function faster and allocate internal and external energy reasonably. In addition, the activation function has also been improved, which makes itself fluctuates over a smaller range than former activation function. Experiments reveal that the 'Local Kullback–Leibler Divergency' (LKLD) model has desired segmentation results both on real-world and medical images. Also, it owns a better noise robustness and is not limited to position of initial contour.
HeterPS: Distributed deep learning with reinforcement learning based scheduling in heterogeneous environments

Deep neural networks (DNNs) exploit many layers and a large number of parameters to achieve excellent performance. The training process of DNN models generally handles large-scale input data with many sparse features, which incurs high Input/Output (IO) cost, while some layers are compute-intensive. The training process generally exploits distributed computing resources to reduce training time. While heterogeneous computing resources, e.g., CPUs, GPUs of multiple types, are available for the distributed training process, the scheduling of multiple layers to diverse computing resources remains critical for the training process. To efficiently train a DNN model using the heterogeneous computing resources, we propose a distributed framework, i.e., Heterogeneous Parameter Server (HeterPS), composed of a distributed architecture and a Reinforcement Learning (RL)-based scheduling method. The advantages of HeterPS are three-fold compared with existing frameworks. First, HeterPS enables efficient training process of diverse workloads with heterogeneous computing resources. Second, HeterPS exploits an RL-based method to efficiently schedule the workload of each layer to appropriate computing resources to minimize the cost while satisfying throughput constraints. Third, HeterPS manages data storage and data communication among distributed computing resources. We carry out extensive experiments to show that HeterPS significantly outperforms state-of-the-art approaches in terms of throughput (14.5 times higher) and monetary cost (312.3% smaller).
NFT luxury brand marketing in the metaverse: Leveraging blockchain‐certified NFTs to drive consumer behavior

Abstract Industry 4.0 technology enables luxury fashion brands in the virtual market to quantify the value of digital items in the metaverse; thus, brands can maintain their reputations, ensure consistent and integrated luxury brand marketing, and attract new consumers in the virtual market. Understanding consumer behavior toward buying digital assets (i.e., nonfungible tokens [NFTs]) is important. By using blockchain‐based NFTs as a way to verify the authenticity of digital assets in the virtual market, luxury brands can maintain their reputations and help consumers protect their digital assets. Thus, developing global marketing strategies supported by this technology is important for the success of luxury fashion brands in the metaverse. We conducted analyses to explore consumer behavior in the metaverse with regard to blockchain‐based luxury NFTs. The findings reveal the psychological evaluation process as a mechanism that drives consumer behavior toward NFT luxury brand fashion items in global virtual markets. The empirical findings also extend the application of game theory and prospect theory by revealing the psychological evaluation of risks associated with (not) buying luxury fashion NFTs as another mechanism driving consumer behavior in the metaverse.
ESO: An enhanced snake optimizer for real-world engineering problems

Meta-heuristic algorithms are an essential way to solve realistic optimization problems. Developing effective, accurate, and stable meta-heuristic algorithms has become the goal of optimization research. Snake Optimizer (SO) is a novel algorithm with good optimization results. However, due to the limitations of natural laws, the parameters are more fixed values in the exploration and exploitation phase, so the SO algorithm quickly falls into local optimization and slowly converges. This paper proposes an Enhanced Snake Optimizer (ESO) by introducing a novel opposition-based learning strategy and new dynamic update mechanisms (parameter dynamic update strategy, sine–cosine composite perturbation factors, Tent-chaos & Cauchy mutation) to achieve better performance. The effectiveness of ESO has been tested on 23 classic benchmark functions and the CEC 2019 function set. 13 functions of 23 classic benchmark functions have variants that belong to multiple dimensions (Dim = 30, 100, 500, 1000, and 2000). In addition, ESO is also used to solve four real-world engineering design problems. Experimental results and two statistical tests show that the proposed ESO performs better than the other 13 state-of-the-art algorithms, including SO. The MATLAB code of ESO is available from: https://ww2.mathworks.cn/matlabcentral/fileexchange/120173-eso-an-enhanced-snake-optimizer.
Toward Metaverse of everything: Opportunities, challenges, and future directions of the next generation of visual/virtual communications

The emergence of Metaverse, a digital environment built on Web 3.0 technologies, has garnered significant attention from various industries and fields, including science and technology. This technology offers the potential to create virtual versions of physical objects, resulting in a digital twin (DT) of the physical world. This paper aims to explore the Metaverse-based Internet of everything (IoE), which we have termed the Metaverse of everything (MoE), as a three-dimensional (3D) virtual communication and relationship platform. The MoE includes Web 3.0 components, generated data, virtual individuals called Metaversians, and all virtual things in Metaverse, forming a comprehensive network of interconnected entities. Our research suggests that Metaverse will become increasingly popular, with the virtual world incorporating more aspects of the physical world. We have conducted a SWOT (Strengths - Weaknesses - Opportunities - Threats) analysis of the MoE concept and have identified several opportunities and challenges. Finally, we propose potential future directions for MoE that academic researchers, industry owners, and IT leaders can pursue. In particular, we suggest the implementation of a new service called Metaverse-as-a-creator (MaaC) based on an artificial intelligence (AI)-enabled Metaverse as a significant achievement in future Internet-based communication.
A Heuristic Approach to Improve the Data Processing in Big Data using Enhanced Salp Swarm Algorithm (ESSA) and MK-means Algorithm

Improving data processing in big data is a delicate procedure in our current digital era due to the massive amounts of data created by humans and machines in daily life. Handling this data, creating a repository for storage, and retrieving photos from internet platforms is a difficult issue for businesses and industries. Currently, clusters have been constructed for many types of data, such as text, documents, audio, and video files, but the extraction time and accuracy during data processing remain stressful. Hadoop Distributed File System (HDFS) is a system that provides a large storage area in big data for managing large datasets, although the accuracy level is not as high as desired. Furthermore, query optimization was used to produce low latency and high throughput outcomes. To address these concerns, this study proposes a novel technique for query optimization termed the Enhanced Salp Swarm Algorithm (ESSA) in conjunction with the Modified K-Means Algorithm (MKM) for cluster construction. The process is separated into two stages: data collection and organization, followed by data extraction from the repository. Finally, numerous experiments with assessments were carried out, and the outcomes were compared. This strategy provides a more efficient method for enhancing data processing speed in a big data environment while maintaining an accuracy level of 98% while processing large amounts of data.
Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey

Artificial intelligence (AI) and machine learning (ML) are widely employed to make the solutions more accurate and autonomous in many smart and intelligent applications in the Internet of Things (IoT). In these IoT applications, the performance and accuracy of AI/ML models are the main concerns; however, the transparency, interpretability, and responsibility of the models’ decisions are often neglected. Moreover, in AI/ML-supported next-generation IoT applications, there is a need for more reliable, transparent, and explainable systems. In particular, regardless of whether the decisions are simple or complex, how the decision is made, which features affect the decision, and their adoption and interpretation by people or experts are crucial issues. Also, people typically perceive unpredictable or opaque AI outcomes with skepticism, which reduces the adoption and proliferation of IoT applications. To that end, explainable AI (XAI) has emerged as a promising research topic that allows ante-hoc and post-hoc functioning and stages of black-box models to be transparent, understandable, and interpretable. In this article, we provide an in-depth and systematic review of recent studies that use XAI models in the scope of the IoT domain. We classify the studies according to their methodology and application areas. Additionally, we highlight the challenges and open issues and provide promising future directions to lead the researchers in future investigations.
Breaking a Fifth-Order Masked Implementation of CRYSTALS-Kyber by Copy-Paste

CRYSTALS-Kyber has been selected by the NIST as a public-key encryption and key encapsulation mechanism to be standardized. It is also included in the NSA’s suite of cryptographic algorithms recommended for national security systems. This makes it important to evaluate the resistance of CRYSTALS-Kyber’s implementations to side-channel attacks. The unprotected and first-order masked software implementations have been already analysed. In this paper, we present deep learning-based message recovery attacks on the ω -order masked implementations of CRYSTALS-Kyber in ARM Cortex-M4 CPU for ω ≤ 5. The main contribution is a new neural network training method called recursive learning. In the attack on an ω -order masked implementation, we start training from an artificially constructed neural network Mω whose weights are partly copied from a model Mω − 1 trained on the (ω − 1)-order masked implementation, and then extended to one more share. Such a method allows us to train neural networks that can recover a message bit with the probability above 99% from high-order masked implementations.
Mapping Metaverse Research: Identifying Future Research Areas Based on Bibliometric and Topic Modeling Techniques

The metaverse represents an immersive digital environment that has garnered significant attention as a result of its potential to revolutionize various industry sectors and its profound societal impact. While academic interest in the metaverse has surged, a dearth of comprehensive review articles employing bibliometric techniques remains. This study seeks to address this gap by analyzing 595 metaverse-related journal articles using bibliometric and topic modeling techniques, marking the first of its kind to investigate the bibliometric profile of metaverse research. The findings reveal exponential growth in metaverse research since 2020, identifying major trends, prolific authors, and the most active journals in the field. A keyword co-occurrence analysis further uncovers four significant clusters of metaverse-related interests, highlighting its unique facets and underscoring its far-reaching implications across various sectors, including education, healthcare, retail, and tourism. This study emphasizes the need for more research and collaboration in advancing the metaverse field and presents 27 research questions for future investigation. This comprehensive analysis serves as a foundation for understanding the current state of metaverse research and its potential trajectory.
Enhanced resource allocation in distributed cloud using fuzzy meta-heuristics optimization

Cloud computing is a modern technology that has become popular today. A large number of requests has made it essential to propose a resources allocation framework for arriving requests. The network can be made more efficient and less costly this way. The cloud–edge paradigm has been considered a growing research area in the computing industry in recent years. The increase in the number of customers and requests for cloud data centers (CDCs) has created the need for robust servers and low power consumption mechanisms. Ways to reduce energy in the CDC having appropriate algorithms for resource allocation. The purpose of this study was to develop an intelligent method for dynamic resource allocation using Takagi–Sugeno–Kang (TSK) neural-fuzzy systems and ant colony optimization (ACO) techniques to reduce energy consumption by optimizing resource allocation in cloud networks. It predicts future loads using a drop-down window to track CPU usage. By optimizing virtual machine migration, ACO can reduce energy consumption. Simulations are provided by examining the implementation and a variety of parameters such as the number of requests made wasted resources, and requests rejected. In this paper, we propose the use of virtual machine migration to accomplish two main goals: evacuating additional and non-optimal virtual machines (scaling and shutting down additional active physical machines) and solving the resource granulation problem. We evaluated and compared our results with literature for rejection rates of virtual and physical machine applications. The performances of our algorithms are compared to different criteria such as performance in request rejection, dynamic CPU resource allocation with reinforcement learning, multi-objective resource allocation, NSGAIII, Whale optimization and Forecast Particle Swarm allocation. A comparison of some evaluation criteria showed that the proposed method is more efficient than other methods.
Strategies for enhancing the performance of news article classification in Bangla: Handling imbalance and interpretation

The rapid increase in obtainable online text data has made text categorization an important tool for data analysts to extract relevant information on the web. However, incorrect or incomplete classification of marginalized groups may result from using biased text data. In order to remedy the disparity in available data, this research suggests a system for classifying and analyzing Bangla news articles. The suggested approach first uses both Random Under-Sampling (RUS) and Synthetic Minority Oversampling Techniques to balance the massive unbalanced Bangla News dataset consisting of 4,37,948 instances (SMOTE). Secondly, the proposed system employs three machine learning models: Logistic Regression, Decision Tree, and Stochastic Gradient Descent along with three deep learning models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Bidirectional Encoder Representations from Transformers (BERT) for Bangla text categorization. The experimental results signify the superior performance of BERT to other classification models of the system as well as other existing methods in this domain. The proposed system achieves the maximum accuracy of 99.04% in balanced dataset and 72.23% in imbalanced dataset using BERT. K-fold cross validation with varied K values is used to determine the performance consistency of BERT. Finally, both LIME (Local Interpretable Model agnostic Explanations and SHAP (SHapley Additive exPlanations) techniques are applied for interpreting each prediction made by BERT.