Reviewerly/exHarmony huggingface dataset:
Extractive text summarization for biomedical transcripts using deep dense <scp>LSTM‐CNN</scp> framework

Abstract The most recent and precise biological and healthcare knowledge is critical in the current outbreak such as COVID. In today's small world, everyone needs timely and appropriate medical information to prevent contagious diseases. Extraction of important information from medical conversations and dissemination to patients and doctors may benefit in the treatments of doctor tiredness and patient amnesia. Problem Automatic text summarizing is essential for gaining great knowledge in any topic in an efficient and productive manner. The material included in health records is vital to our understanding of kind illness and its manifestations. Creating a comprehensive and standard kind of content is becoming an unavoidable and crucial problem in the medical process as a result of the massive amounts of fragmented data created in many sectors. Approach The purpose of this study is to employ NLP‐based deep learning algorithms for text summary that perform well on linguistic text summarization data, and then modify/adapt these for biomedical domain‐specific text summarization. This paper provides an approach developed in‐house for condensing ill‐punctuated or unpunctuated discussion transcripts into more intelligible summaries, which combines topic modelling and phrase selection with punctuation restorations. For autonomous synthesis of medical reports from biomedical transcripts, this research proposes using an end‐to‐end summarization technique, Deep Dense Long Short Term Memory Network (LSTM), followed by Convolutional Neural Network (CNN). Results Extensive testing, examination, and comparing have demonstrated that this summarizer works well for medical transcript summarization. The suggested approach achieved an average ROUGE score of 93.5% using a single document summary. Furthermore, by comparing new techniques to previous ones, the utility and accuracy of novel strategies would be shown. The results reveal that models trained on ordinary language provide comparable results on a biomedical testing set, with one model outperforming the linguistic test set.
Towards the transferable audio adversarial attack via ensemble methods

Abstract In recent years, deep learning (DL) models have achieved significant progress in many domains, such as autonomous driving, facial recognition, and speech recognition. However, the vulnerability of deep learning models to adversarial attacks has raised serious concerns in the community because of their insufficient robustness and generalization. Also, transferable attacks have become a prominent method for black-box attacks. In this work, we explore the potential factors that impact adversarial examples (AEs) transferability in DL-based speech recognition. We also discuss the vulnerability of different DL systems and the irregular nature of decision boundaries. Our results show a remarkable difference in the transferability of AEs between speech and images, with the data relevance being low in images but opposite in speech recognition. Motivated by dropout-based ensemble approaches, we propose random gradient ensembles and dynamic gradient-weighted ensembles, and we evaluate the impact of ensembles on the transferability of AEs. The results show that the AEs created by both approaches are valid for transfer to the black box API.
Cardiac Visualisation Along the RV-Continuum - A High-Fidelity Pilot Study

The different stages along the Reality-Virtuality Continuum (RVC) come with individual challenges and benefits for visualisation. While visualisation at these stages has been explored extensively over the past decades, a combination including all stages is yet to be investigated. In this publication we introduce an early prototype from the area of cardiac surgery planning connecting the different RVC stages and allowing the user to move between real, augmented and virtual environment within a single application. An overview of the implemented concepts and the functionality is presented and a pilot study gathering data about the potential use of such a system for operation planning was executed.
Peningkatan Pemahaman Terkait Muatan Barang Berbahaya (Dangerous Goods) dan Blind Spot di SMAN 7 Denpasar

It is important to understand about dangerous goods and the procedures for transporting dangerous goods, especially for the younger generation. This can help reducing the number of accidents in the transportation of goods to help the transport of dangerous goods. Based on this, the Bali Land Transportation Polytechnic provides insight about lind spots for dangerous goods transportation. This activity uses socialization/counseling methods regarding the load of dangerous goods on the road and blind spots. The target of this program is students of SMAN 7 Denpasar, because there is a close relationship between role of students and driving safety regarding to dangerous goods transportation. this socialization hopefully provides an understanding of dangerous goods and the role of the younger generation in transportation safety. During this socialization, participants can understand the explanation and answer the questions correctly
Using Area Under the Precision Recall Curve to Assess the Effect of Random Undersampling in the Classification of Imbalanced Medicare Big Data

In this paper, we investigate the impact of Random Undersampling (RUS) on a supervised Machine Learning task involving highly imbalanced Big Data. We present the results of experiments in Medicare Fraud detection. To the best of our knowledge, these experiments are conducted with the largest insurance claims datasets ever used for Medicare Fraud detection. We obtain two datasets from two Big Data repositories provided by the United States government’s Centers for Medicare and Medicaid Services. The larger of the two datasets contains nearly 174 million instances, with a minority to majority class ratio of approximately 0.0039. Our contribution is to show that RUS has a detrimental effect on a Medicare Fraud detection task when performed on large scale, imbalanced data. The effect of RUS is apparent in the Area Under the Precision Recall Curve (AUPRC) scores recorded from experimental outcomes. We use four popular, open-source classifiers in our experiments to confirm the negative impact of RUS on their AUPRC scores.
On the capacity of a quantum perceptron for storing biased patterns

Abstract Although different architectures of quantum perceptrons have been recently put forward, the capabilities of such quantum devices versus their classical counterparts remain debated. Here, we consider random patterns and targets independently distributed with biased probabilities and investigate the storage capacity of a continuous quantum perceptron model that admits a classical limit, thus facilitating the comparison of performances. Such a more general context extends a previous study of the quantum storage capacity where using statistical mechanics techniques in the limit of a large number of inputs, it was proved that no quantum advantages are to be expected concerning the storage properties. This outcome is due to the fuzziness inevitably introduced by the intrinsic stochasticity of quantum devices. We strengthen such an indication by showing that the possibility of indefinitely enhancing the storage capacity for highly correlated patterns, as it occurs in a classical setting, is instead prevented at the quantum level.
ABUS tumor segmentation via decouple contrastive knowledge distillation

Objective.In recent years, deep learning-based methods have become the mainstream for medical image segmentation. Accurate segmentation of automated breast ultrasound (ABUS) tumor plays an essential role in computer-aided diagnosis. Existing deep learning models typically require a large number of computations and parameters.Approach. Aiming at this problem, we propose a novel knowledge distillation method for ABUS tumor segmentation. The tumor or non-tumor regions from different cases tend to have similar representations in the feature space. Based on this, we propose to decouple features into positive (tumor) and negative (non-tumor) pairs and design a decoupled contrastive learning method. The contrastive loss is utilized to force the student network to mimic the tumor or non-tumor features of the teacher network. In addition, we designed a ranking loss function based on ranking the distance metric in the feature space to address the problem of hard-negative mining in medical image segmentation.Main results. The effectiveness of our knowledge distillation method is evaluated on the private ABUS dataset and a public hippocampus dataset. The experimental results demonstrate that our proposed method achieves state-of-the-art performance in ABUS tumor segmentation. Notably, after distilling knowledge from the teacher network (3D U-Net), the Dice similarity coefficient (DSC) of the student network (small 3D U-Net) is improved by 7%. Moreover, the DSC of the student network (3D HR-Net) reaches 0.780, which is very close to that of the teacher network, while their parameters are only 6.8% and 12.1% of 3D U-Net, respectively.Significance. This research introduces a novel knowledge distillation method for ABUS tumor segmentation, significantly reducing computational demands while achieving state-of-the-art performance. The method promises enhanced accuracy and feasibility for computer-aided diagnosis in diverse imaging scenarios.
A modified model for topic detection from a corpus and a new metric evaluating the understandability of topics

This paper presents a modified neural model for topic detection from a corpus and proposes a new metric to evaluate the detected topics. The new model builds upon the embedded topic model incorporating some modifications such as document clustering. Numerical experiments suggest that the new model performs favourably regardless of the document’s length. The new metric, which can be computed more efficiently than widely-used metrics such as topic coherence, provides variable information regarding the understandability of the detected topics.
Factors Influencing Customers’ Apparel Shopping Intention in Metaverse

The emergence of the metaverse has opened an unbelievable online virtual world offering real-life immersive experiences to consumers. This study examines customers’ fashion apparel shopping intentions in the e-commerce metaverse. We propose a conceptual framework based on behavioral reasoning theory (BRT) to investigate the reasons for the adoption (or not) of the metaverse. The survey of 1170 customers was performed, and PLS-SEM was used to analyze data. The findings suggest that perceived enjoyment, interactivity, imagination, and sense of immersion favors the adoption, whereas perceived risk, traditional barrier, image barrier, and technological anxiety deter metaverse adoption. Our results advance the literature on innovative shopping technology and metaverse fashion apparel shopping intention. We provide guidelines for metaverse marketers and managers.
ALICA: A Multi-S-Box Lightweight Cryptographic Algorithm Based on Generalized Feistel Structure

With the development of science and technology, IoT devices have already become ubiquitous in the public eye. Through the perception layer, the collected data are displayed or transmitted to the server backend for analysis. Due to the increasing integration of IoT devices into people’s daily lives, privacy issues, such as data leaks, have received more attention. Most sensor nodes, such as temperature and pressure sensors in marine environments, have low computing power, storage capacity, and significant underlying heterogeneity, making it challenging to implement a standardized data security protection solution. Data security in the nodes is seriously challenged as a result. It is of great significance to design a lightweight block cipher for the Internet of Things (IoT) environment, which can ensure the security of node information. A new lightweight block cipher algorithm called ALICA is proposed in this paper, which is well-suited for the low computing power and heterogeneous device environment at the lower layers. A generalized Feistel structure and a linear structure with XOR and shift operations are used to achieve ease of software implementation. Two different S-boxes are used to create the nonlinear structure of the password, thereby enhancing the robust security of the cipher. In addition, the cipher adopts a design approach that prioritizes software efficiency while also considering hardware implementation efficiency. This makes it more suitable for low computing power, storage capacity, and resource-limited IoT sensor nodes at the lower layers.
Unveiling Blockchain's Power: Revolutionizing Networking with Trust, Security, and Transparent Data Traceability

Blockchain technology has emerged as a groundbreaking innovation with transformative potential across various industries. Initially recognized as the underlying technology for cryptocurrencies, its applications have expanded exponentially, particularly in the realm of networking. This paper explores the profound impact of blockchain on networking, emphasizing its pivotal role in fostering trust, enhancing security, and enabling transparent data traceability. By analyzing the core principles of blockchain and its integration into networking paradigms, this study elucidates how this technology revolutionizes traditional networking models, offering a decentralized, immutable, and efficient framework for data management and communication.
The Corpus of Stamp Seals from the Southern Levant Database

The Corpus of Stamp Seals from the Southern Levant (CSSL) database comprises some twelve thousand items from controlled excavations in Israel, Palestine, and Jordan, transforming and bringing to completion Othmar Keel’s pioneering vision to publish all seals and sealings from the region. The CSSL platform not only represents a newly available digital research tool for southern Levantine studies and related fields, but also aims at exploring innovative approaches to advance the digital study of glyptic more broadly. As the SNSF-funded Sinergia project Stamp Seals from the Southern Levant (SSSL) completes its goal, we ask: What comes next? This article provides an overview of the CSSL database; situates this new resource alongside other contemporary digital tools for the study of glyptic; and briefly reflects on the possibilities for more collaboration and integration across digital humanities projects.
Dynamic bandwidth allocation algorithm based on extreme learning machine in passive optical networks

10 gigabit passive optical network (XGPON) is considered to be an efficient and energy-saving solution for bandwidth resource allocation in mobile fronthaul (MFH), as it significantly reduces delay and jitter caused by signal transmission in the MFH network. In the upstream direction of passive optical networks, optical network units (ONUs) report their load conditions to the optical line terminal (OLT), and based on these conditions, the OLT schedules and allocates network resources using dynamic bandwidth allocation (DBA) algorithm. However, DBA based on the request/grant mechanism fails to meet the low delay requirements of 5G networks. Therefore, we propose a machine learning-based DBA algorithm that utilizes extreme learning machine (ELM) to predict the future load conditions of ONUs. ELM is widely popular in machine learning due to its fast learning speed and excellent generalization performance. It achieves this by randomly initializing the weights and biases of hidden layer neurons and then solving the output weights directly using linear equations. According to simulation results, the proposed ELM-DBA algorithm exhibits outstanding performance in terms of delay and jitter. Specifically, the algorithm achieves low delay of 125 μs, meeting the 3GPP requirements for delay below 250 μs, and the jitter remains below 80 μs consistently. Furthermore, ELM demonstrates exceptional learning speed, with training speed improved by at least 600 times compared to gate recurrent unit and long short term memory.
Detecting Wireless Signal Noise in Mobile Radio Communications Using Spatiotemporal AnoGAN-Based Approaches

With the development of radio modulation technologies for communication and wireless applications, several studies have been conducted to reduce and eliminate noise during signal transmission. Although the influence of noise can be effectively addressed, it has become a popular research topic in mobile communications. Moreover, in recent telecommunication systems, owing to their complexity and comprehensive protocols, which require a large number of mathematical and engineering approaches, predicting and classifying noise is difficult. Thus, to effectively address these challenges, we propose a spatiotemporal AnoGAN to detect the noise that can occur during radio modulation. In our approach, we assemble a set of AnoGANs based on convolutional neural networks (CNNs) and long short-term memory (LSTM) to enable the system to learn the time-series features of the radio modulation signal and shape expressed in complex planes. The proposed spatiotemporal AnoGAN can discriminate the interference caused by noise without any annotation of anomalies using a generator and discriminator. The proposed spatiotemporal AnoGAN achieves a 91.4% recall in digitally modulated signals that were previously difficult to identify. Through an empirical analysis of the proposed method, we observed that the spatiotemporal AnoGAN accurately identified abnormal interference signals.
Unified Solutions to Optimal Fuzzy Observer-Based Fault Detection for Discrete-Time Nonlinear Systems

This paper is concerned with the optimal fault detection issues for discrete-time nonlinear systems with the aid of Takagi-Sugeno (T-S) fuzzy dynamic modelling technique. To this end, in the first part of this paper, the nonlinear system is formulated in the time-varying fuzzy manner, and based on it, a unified fault detection approach is developed by solving a multiobjective optimization problem. In this sense, the optimal tradeoff between fault detectability and robustness against unknown inputs is ensured by solving the Riccati equation. Meanwhile, a fuzzy fault detection approach is studied in the second part of this paper based on piecewise-fuzzy Lyapunov functions, which is realized by solving linear matrix inequalities. Two examples are given at the end of this paper to demonstrate the proposed approaches
Vehicle detection based on improved YOLOv5s using coordinate attention and decoupled head

Vehicle detection is a fundamental problem in object detection and plays a significant role in intelligent transportation and smart driving. To enhance the accuracy of vehicle detection and the robustness of the model in detecting occluded vehicles, we propose an improved vehicle detection method for you only look once v5s (YOLOv5s). First, we introduce the coordinate attention module into the backbone of the model. This module guides the model to improve its attention toward the location information of vehicles and channel features under occlusion conditions. Second, the feature fusion component of the model is improved by incorporating bidirectional scale connections and weighted feature fusion. Finally, the prediction head of YOLOv5s is decoupled and the regression and classification tasks are assigned to two separate branches. Experimental results show that our proposed method has 2% and 2.5% higher average precision than YOLOv5s for the common objects in context vehicle dataset and University at Albany Detection and Tracking, respectively.
A blind recovery technique with integer wavelet transforms in image watermarking

The development of internet technology has simplified the sharing and modification of digital image information. The aim of this study is to propose a new blind recovery technique based on integer wavelets transform (BRIWT) by utilizing their image content. The LSB adjustment technique on the integer wavelet transform is used to embed recovery data into the two least significant bits (LSB) of the image content. Authentication bits are embedded into the current locations of the LSB of the image content, while the recovery information is embedded into different block locations based on the proposed block mapping. The embedded recovery data is securely placed at random locations within the two LSBs using a secret key. A three-layer embedding of authentication bits is used to validate the integrity of the image contents, achieving high precision and accuracy. Tamper localization accuracy is employed to identify recovery bits from the image content. This research also investigates the image inpainting method to enhance recovery from tampered images. The proposed image inpainting is performed by identifying non-tampered pixels in the surrounding tamper localization. The results demonstrate that the proposed scheme can produce highly watermarked images with imperceptibility, with an average SSIM value of 0.9978 and a PSNR value of 46.20 dB. The proposed scheme significantly improves the accuracy of tamper localization, with a precision of 0.9943 and an accuracy of 0.9971. The proposed recovery technique using integer wavelet transforms achieves high-quality blind recovery with an SSIM value of 0.9934 under a tampering rate of 10%. The findings of this study reveal that the proposed scheme improves the quality of blind recovery by 14.2 % under a tampering rate of 80 %.
Blockchain in the age of industrial revolution: A systematic literature review using bibliometric analysis

Abstract The emergence of blockchain technology has spurred research and industrial advances, presenting potential benefits and practical solutions for various industries and their communities. Blockchain technology enables the efficient distribution, sharing, and secure storage of transactional data, leading to increased transparency and productivity in supply chains. While blockchain is seen as a disruptive technology, it is essential to explore its applicability to various industries. To this end, this study conducts a bibliometric analysis of the extant blockchain literature to better understand its potential applications. The study presents a systematic literature review that highlights the practical application of blockchain technology transfer in various industries, such as pharmaceuticals, banking and financial services, and media and entertainment.
Prediction of Water Quality Using SoftMax-ELM Optimized Using Adaptive Crow-Search Algorithm

Water is a predominant source in the survival and development of all human lives. On top of all, predicting water quality is a significant one since water is essential in regulating our human body. In recent days, the advent of machine learning techniques has been supporting a lot in water quality prediction. Accordingly, Adaptive Crow Search Optimized SoftMax-Extreme Learning Machine (AdCSO-sELM) is proposed to improve the ELM performance by making the flight length adaptively with respect to the iterations. Here, the research novelty lies in making the CSOA parameters as a dynamic one which in turn provides promising ELM performance. Finally, the proposed AdCSO-sELM provides a superior accuracy of 96.54% for classifying water potability using the Kaggle dataset.