Reviewerly/exHarmony huggingface dataset:
Modeling comorbidity of chronic diseases using coupled hidden Markov model with bivariate discrete copula

A range of chronic diseases have a significant influence on each other and share common risk factors. Comorbidity, which shows the existence of two or more diseases interacting or triggering each other, is an important measure for actuarial valuations. The main proposal of the study is to model parallel interacting processes describing two or more chronic diseases by a combination of hidden Markov theory and copula function. This study introduces a coupled hidden Markov model with the bivariate discrete copula function in the hidden process. To estimate the parameters of the model and deal with the numerical intractability of the log-likelihood, we use a variational expectation maximization algorithm. To perform the variational expectation maximization algorithm, a lower bound of the model's log-likelihood is defined, and estimators of the parameters are computed in the M-part. A possible numerical underflow occurring in the computation of forward-backward probabilities is solved. The simulation study is conducted for two different levels of association to assess the performance of the proposed model, resulting in satisfactory findings. The proposed model was applied to hospital appointment data from a private hospital. The model defines the dependency structure of unobserved disease data and its dynamics. The application results demonstrate that the model is useful for investigating disease comorbidity when only population dynamics over time and no clinical data are available.
An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imbalanced distribution of classes. Many popular oversampling techniques have been designed for imbalanced batch data rather than a continuous stream. This work proposes a self-adjusting window to improve the adaptive classification of an imbalanced data stream based on minimizing cluster distortion. It includes two models; the first chooses only the previous data instances that preserve the coherence of the current chunk’s samples. The second model relaxes the strict filter by excluding the examples of the last chunk. Both models include generating synthetic points for oversampling rather than the actual data points. The evaluation of the proposed models using the Siena EEG dataset showed their ability to improve the performance of several adaptive classifiers. The best results have been obtained using Adaptive Random Forest in which Sensitivity reached 96.83% and Precision reached 99.96%.
A Comparative Study on the Effectiveness of Traditional and Modern Teaching Methods

The rapid development of the times has also brought more advanced educational methods and concepts.Many experts advocate replacing traditional educational methods with modern educational methods.The difference between the two educational methods depends on three aspects: educational method, educational purpose, and educational focus.This article mainly lists and compares the advantages and disadvantages of modern education and traditional education methods by integrating the literature.The result shows that from a macro perspective, the advantages of modern educational methods are greater, but teachers should evaluate and choose which teaching method to use according to the student's situation, teaching purpose, and potential obstacles.
Why we need biased AI: How including cognitive biases can enhance AI systems

This paper stresses the importance of biases in the field of artificial intelligence (AI). To foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the implementation of human cognitive biases in learning algorithms. We use insights from cognitive science and apply them to the AI field, combining theoretical considerations with tangible examples depicting promising bias implementation scenarios. Ultimately, this paper is the first tentative step to explicitly putting the idea forth to implement cognitive biases into machines.
Smart Contract Obfuscation Technique to Enhance Code Security and Prevent Code Reusability

Along with the advancements in blockchain technology, many blockchain-based successful projects have been done mainly on the ethereum platform, most of which deal with transactions.Still, it also carries various risks when it comes to security, as evident from past attacks.Most big projects like uniswap, decentraland, and others use smart contracts, deployed on the ethereum platform, leading to similar projects via code reuse.Code reuse practice is quite frequent as a survey suggests 26% of contract code deployed is via code reuse.Smart contract code obfuscation techniques can be used on solidity code that is publicly verified, published (in the case of Ethereum), and on the deployment address.All the above techniques work by replacing characters with their random counterpart, known as statistical substitution.A statistical substitution is a process of transforming an input string into a new string where each character has been replaced by a random character drawn from a stock of all possible 'random' characters.Therefore, we proposed numerous methods in this paper to solve the above problems using various smart contract code obfuscation techniques.These techniques can be really useful in blockchain projects and can save millions of dollars to investors & companies by enhancing code security and preventing code reusability.Techniques mentioned in this paper when compared with other techniques.Our methods are not expensive to implement, very easy to use, and provide a developer-friendly selective increment in code complexity.
Deep Convolution Neural Networks for CrossDataset Facial Expression Recognition System

Facial Expressions are a true and obvious way to represent emotions in human beings.Understanding facial expression recognition (FER) is essential, and it is also useful in the area of Artificial Intelligence, Computing, Medical, Video games, e-Education, and many more.In the past, much research was conducted in the domain of FER using different approaches such as analysis through different sensor data, using machine learning and deep learning framework with static images and dynamic sequence.Researchers used machine learning-based techniques such as the Multi-layer Perceptron Model, k-Nearest Neighbors, and Support Vector Machines were used by researchers in solving the FER.These methods have extracted features such as Local Binary Patterns, Eigenfaces, Face-landmark features, and Texture features.Recently use of deep learning algorithms in FER has been considerable.State-of-the-art results show deep learning-based approaches are more potent than conventional FER approaches.This paper focuses on implementing three different Custom CNN Architecture training them on FER13 Dataset and testing them on CK+ and JAFFE Dataset including FER13 after fine-tuning.The three pre-trained models' on FER2013 after fine-tuning have significantly improved the accuracy of the resulting CNN on the target test sets between 65.12 % to 79.07% on the JAFFE dataset and 50.96% to 68.81% on the CK+ dataset.
Cell image instance segmentation based on PolarMask using weak labels

A PolarMask-based method for blood cell contour segmentation is proposed. The method is divided into two parts. One part is a weak label-based model pretraining method, which uses weak labels to train the model and obtain a pretraining weight. The training speed and accuracy of the segmentation model are accelerated. The other part is based on the PolarMask method to segment the white and red blood cells in blood cells and can obtain smoother cell contours. Thus, it improves the accuracy of blood cell segmentation. Our method can help medical personnel identify the number of cells and cell shape quickly, which reduces the workload for medical personnel. We improve PolarMask to make it more suitable for blood cell contour segmentation, and the improved method can be divided into two parts. In the first part, we use a weakly labeled dataset with the labeling type of bounding boxes for pretraining and then use the labels of the segmentation type for transfer learning of the cell segmentation model. In the second part, we add a smoothing constraint loss to the loss function of the mask to smoothen the segmented cell contours. We add the SE attention mechanism in the backbone network (ResNet18) to further improve the segmentation accuracy. Our method is mainly used for the segmentation of blood cell (erythrocyte and leukocyte) contours. Our method improves average precision (AP) by 8.4% and AP50 by 0.6% compared with PolarMask. The most significant improvement is in AP75, which improves by 8.8%. Our method models blood cell contours based on PolarMask and uses a weakly labeled training model to obtain pretrained weights that can segment red and white blood cells. Our method effectively improves the accuracy of the model in segmenting blood cells, and the segmented blood cell contours are smoother.
Artificial Intelligence Technology-Based Semantic Sentiment Analysis on Network Public Opinion Texts

Considering that the current social network text analysis works poorly in accurate and effective sentiment prediction and management, a deep learning (D-L)-based text sentiment analysis method is proposed for the big data environment. First, the autoregressive language model mode XLNet is used to capture bidirectional text information and a sentiment analysis model XLNet-Multi-Attention-BiGRU. Then, considering the context information of social network texts, the defect of traditional GRU units only reading texts in order is overcome by introducing a BiGRU model to extract features in both directions. Finally, a multi-headed attention layer is added between the BiGRU and CRF layers to better capture the key information in the sentence by integrating multiple single-head attention. The results show that the precision, recall, and F1 value of the method proposed in this paper are the largest, with the highest reaching 92.64%, 92.32%, and 91.25%, respectively, which are 12.40%, 10.17%, and 9.63% higher than the maximum values of the other three methods, respectively.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI &amp; AIM 2022 Challenge: Report

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 m. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.
A recommender system for occupational hygiene services using natural language processing

Although recommender systems have been used in a wide variety of products, they have not had the same adoption in the insurance sector due to the particular aspects of the industry. The present research proposes a novel model that uses natural language processing techniques and a deep learning-based model to recommend occupational hygiene services. The results of ranking methods showed that the model combining memorization and generalization of companies' activities preferences performed better compared to other models. These findings have significant implications for decision-making as they can improve the welfare of both companies and workers, while also limiting the options offered to clients based on their unique profiles.
The Kurdish Language Corpus: State of the Art

The notable growth of the digital communities and different online news streams led to the growing availability of online natural language content. However not all natural languages have the enough attention of being made readable and comprehendible to machines. Among these less resourced and paid attention languages is the Kurdish language. Creating the machine-readable text is the first step toward applications of text mining and semantic web, such as translation, information retrieval and recommendation systems. With the de facto challenges in the Kurdish language, such as the scarcity of linguistic sources and not having unified orthography rules, this language has a lack of the language processing tools. However, to overcome the mentioned challenges and enable intelligent applications the well organized and annotated Kurdish text corpora is needed. This review paper investigates the available textual corpora in the Kurdish language and its dialects and then determined challenges are discussed, open problems are listed and future directions suggested.
Uncertainty guidance in proton therapy planning visualization

We investigate uncertainty guidance mechanisms to support proton therapy (PT) planning visualization. Uncertainties in the PT workflow pose significant challenges for navigating treatment plan data and selecting the most optimal plan among alternatives. Although guidance techniques have not yet been applied to PT planning scenarios, they have successfully supported sense- and decision-making processes in other contexts. We hypothesize that augmenting PT uncertainty visualization with guidance may influence the intended users' perceived confidence and provide new insights. To this end, we follow an iterative co-design process with domain experts to develop a visualization dashboard enhanced with distinct level-of-detail uncertainty guidance mechanisms. Our approach classifies uncertainty guidance into two dimensions: degree of intrusiveness and detail-orientation. Our dashboard supports the comparison of multiple treatment plans (i.e., nominal plans with their translational variations) while accounting for multiple uncertainty factors. We subsequently evaluate the designed and developed strategies by assessing perceived confidence and effectiveness during a sense- and decision-making process. Our findings indicate that uncertainty guidance in PT planning visualization does not necessarily impact the perceived confidence of the users in the process. Nonetheless, it provides new insights and raises uncertainty awareness during treatment plan selection. This observation was particularly evident for users with longer experience in PT planning.
Movie Scene Event Extraction with Graph Attention Network Based on Argument Correlation Information

Movie scene event extraction is a practical task in media analysis, which aims at extracting structured events from unstructured movie scripts. However, although there have been many studies regarding open domain event extraction, there have only been a few studies focusing on movie scene event extraction. Specifically aimed at instances where different argument roles have the same characteristics in a movie scene, we propose the utilization of the correlation between different argument roles, which is beneficial for both movie scene trigger extraction (trigger identification and classification) and movie scene argument extraction (argument identification and classification) in event extraction. To model the correlation between different argument roles, we propose the superior role concept (SRC), a high-level role concept based upon the ordinary argument role. In this paper, we introduce a new movie scene event extraction model with two main features: (1) an attentive high-level argument role module to capture SRC information and (2) an SRC-based graph attention network (GAT) to fuse the argument role correlation information into semantic embeddings. To evaluate the performance of our model, we constructed a movie scene event extraction dataset named MovieSceneEvent and also conducted experiments on a widely used dataset to compare the results with other models. The experimental results show that our model outperforms competitive models, and the correlation information of argument roles helps to improve the performance of movie scene event extraction.
Penerapan Metode User Centered Design pada Rancangan User Interface Marketplace Pemasaran Produk Olahan Perikanan

Activities that support various activities in the fisheries sector, one of which is processed fish products, seeks to improve the economy of the community, especially fishermen in processing marine products. One of the efforts is to expand the distribution of processed seafood and penetrate the market through designing a user interface for the processed seafood distribution system. The method applied in this study is User Centered Design (UCD) with a design philosophy that places the user at the center of the system development process. The UCD approach is supported by techniques, tools, procedures, and processes that help design interactive systems that are more user centric. Based on usability testing that uses a usability scale system, the marketplace user interface model for marketing processed fish products is assessed with a yield of 86.5%. So that this marketplace user interface design mockup has been rated as user friendly which is indicated using high functionality and in accordance with the design. Keyword: User Centered Design (UCD), user interface, marketplace, processed fish.
A comparison of blockchain application and security issues from Bitcoin to Cybersecurity

Blockchain has quickly emerged as one of the trendiest Internet technologies in recent years due to the fast pace of technological advancement. With its integrated cryptography and consensus process, blockchain has redefined trust as a decentralized and distributed data management solution, ensuring security, privacy, and data integrity without the involvement of a third party. However, there are still certain technological difficulties and restrictions with blockchain. This essay has studied the present blockchain applications in cybersecurity in a methodical manner. The paper evaluates the benefits that blockchain has brought to cybersecurity and provides a summary of recent studies and blockchain applications in cybersecurity-related fields in order to address the security challenges. The paper presents four key security challenges of blockchain through in-depth research and overview of the existing work, and then conducts a more detailed investigation of each issue. The research also propos
Blockchain-Empowered Privacy-Preserving Digital Object Trading in the Metaverse

The metaverse is an advanced digital world where users can have interactive and immersive experiences. Users enter the metaverse through digital objects created by extended reality and digital twin technologies. The ownership issue regarding these digital objects can be solved by the blockchain-based nonfungible token (NFT), which is of vital importance for the economics of the metaverse. Users can utilize NFTs to engage in various social and economic activities. However, current NFT protocols expose the owner’s information to the public, which may contradict with the privacy requirement. In this article, we propose a protocol, NFTPrivate, that can realize anonymous and confidential trading of digital objects. The key idea is to utilize cryptographic commitments to hide users’ addresses. By constructing proper zero-knowledge proofs, the owner can initiate privacy-preserving yet publicly verifiable transactions. Illustrative results show that the proposed protocol has higher computation and storage overhead than traditional NFT protocols. We think this is an acceptable compromise for privacy protection.
Handover-Enabled Dynamic Computation Offloading for Vehicular Edge Computing Networks

The computation offloading technique is a promising solution that empowers computationally limited resource devices to run delay-constrained applications efficiently. Vehicular edge computing incorporates the processing capabilities into the vehicles, and thus, provides computing services for other vehicles through computation offloading. Mobility affects the communication environment and leads to critical challenges for computation offloading. In this paper, we consider an intelligent task offloading scenario for vehicular environments including smart vehicles and roadside units, which can cooperate to perform resource sharing. Intending to minimize the average offloading cost which takes into account energy consumption together with delay in transmission and processing phases, we formulate the task offloading problem as an optimization problem and implement an algorithm based on deep reinforcement learning with Double Q-learning which allows user equipments to learn the offloading cost performance by observing the environment and make steady sequences of offloading decisions despite the uncertainties of the environment. Besides, concerning the high mobility of the environment, we propose a handover-enabled computation offloading strategy that leads to a better quality of service and experience for users in beyond 5G and 6G heterogeneous networks. Simulation results demonstrate that the proposed scheme achieves low-cost performance compared to the existing offloading decision strategies in the literature.
Trust and Reputation Management for Blockchain-enabled IoT

In recent years, there has been an increasing interest in incorporating blockchain for the Internet of Things (IoT) to address the inherent issues of IoT, such as single point of failure and data silos.However, blockchain alone cannot ascertain the authenticity and veracity of the data coming from IoT devices.The append-only nature of blockchain exacerbates this issue, as it would not be possible to alter the data once recorded on-chain.Trust and Reputation Management (TRM) is an effective approach to overcome the aforementioned trust issues.However, designing TRM frameworks for blockchain-enabled IoT applications is a non-trivial task, as each application has its unique trust challenges with their unique features and requirements.In this paper, we present our experiences in designing TRM framework for various blockchain-enabled IoT applications to provide insights and highlight open research challenges for future opportunities.