Reviewerly/exHarmony huggingface dataset:
Fusing enhanced Transformer and large kernel CNN for malignant thyroid nodule segmentation

Quick and accurate diagnosis for malignant thyroid nodules via ultrasonography is a valuable but challenging task even for experienced radiologists because of the complexity and variability of ultrasound images. Many computer-aided diagnosis (CAD) methods have been proposed to assist radiologists by providing objective suggestions. However, most existing segmentation approaches lack the ability to keep precise shape information and capture global long-range dependencies. To settle the above issues, we propose a deep learning-based CAD method called Transformer fusing CNN Network (TCNet) to segment malignant thyroid nodules automatically. Our proposed TCNet contains a large kernel CNN branch and an enhanced Transformer branch. In the former branch, we devise a Large Kernel Module (LKM) to extract the precise shape features of malignant thyroid nodules in ultrasound images. While in the latter branch, we design an Enhanced Transformer Module (ETM) to establish the remote connection between thyroid nodule pixels. To integrate multiscale feature maps produced from different phases of both branches, we develop a Multiscale Fusion Module (MFM) to connect the two branches. We compare the proposed model with several current commonly used segmentation methods on the MTNS dataset and other public datasets. The experimental results demonstrate the superiority and effectiveness of our method.
Unsupervised Dense Information Retrieval with Contrastive Learning

Recently, information retrieval has seen the emergence of dense retrievers, using neural networks, as an alternative to classical sparse methods based on term-frequency. These models have obtained state-of-the-art results on datasets and tasks where large training sets are available. However, they do not transfer well to new applications with no training data, and are outperformed by unsupervised term-frequency methods such as BM25. In this work, we explore the limits of contrastive learning as a way to train unsupervised dense retrievers and show that it leads to strong performance in various retrieval settings. On the BEIR benchmark our unsupervised model outperforms BM25 on 11 out of 15 datasets for the Recall@100. When used as pre-training before fine-tuning, either on a few thousands in-domain examples or on the large MS~MARCO dataset, our contrastive model leads to improvements on the BEIR benchmark. Finally, we evaluate our approach for multi-lingual retrieval, where training data is even scarcer than for English, and show that our approach leads to strong unsupervised performance. Our model also exhibits strong cross-lingual transfer when fine-tuned on supervised English data only and evaluated on low resources language such as Swahili. We show that our unsupervised models can perform cross-lingual retrieval between different scripts, such as retrieving English documents from Arabic queries, which would not be possible with term matching methods.
How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation

Generative Pre-trained Transformer (GPT) models have shown remarkable capabilities for natural language generation, but their performance for machine translation has not been thoroughly investigated. In this paper, we present a comprehensive evaluation of GPT models for machine translation, covering various aspects such as quality of different GPT models in comparison with state-of-the-art research and commercial systems, effect of prompting strategies, robustness towards domain shifts and document-level translation. We experiment with eighteen different translation directions involving high and low resource languages, as well as non English-centric translations, and evaluate the performance of three GPT models: ChatGPT, GPT3.5 (text-davinci-003), and text-davinci-002. Our results show that GPT models achieve very competitive translation quality for high resource languages, while having limited capabilities for low resource languages. We also show that hybrid approaches, which combine GPT models with other translation systems, can further enhance the translation quality. We perform comprehensive analysis and human evaluation to further understand the characteristics of GPT translations. We hope that our paper provides valuable insights for researchers and practitioners in the field and helps to better understand the potential and limitations of GPT models for translation.
Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning

Early detection and precise diagnosis of breast cancer (BC) plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, MRI, CTR images. The aim of this study is to propose a deep learning model (BCCNN) to detect and classify breast cancers into eight classes: benign adenosis (BA), benign fibroadenoma (BF), benign phyllodes tumor (BPT), benign tubular adenoma (BTA), malignant ductal carcinoma (MDC), malignant lobular carcinoma (MLC), malignant mucinous carcinoma (MMC), and malignant papillary carcinoma (MPC).Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy.The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively.Dataset Boosting, preprocessing and balancing played a good role in enhancing the detection and classification of breast cancer of the proposed model (BCCNN) and the fine-tuned pre-trained models' accuracies greatly. The best accuracies were attained when the 400X magnification of the MRI images due to their high images resolution.
Quantum codes, CFTs, and defects

A bstract We give a general construction relating Narain rational conformal field theories (RCFTs) and associated 3d Chern-Simons (CS) theories to quantum stabilizer codes. Starting from an abelian CS theory with a fusion group consisting of n even-order factors, we map a boundary RCFT to an n -qubit quantum code. When the relevant ’t Hooft anomalies vanish, we can orbifold our RCFTs and describe this gauging at the level of the code. Along the way, we give CFT interpretations of the code subspace and the Hilbert space of qubits while mapping error operations to CFT defect fields.
Super-resolution of brain MRI images based on denoising diffusion probabilistic model

Super-resolution of brain magnetic resonance imaging (MRI) generates high resolution brain images as opposed to low-resolution ones, thus providing more detailed anatomical information for diagnosis of neurodegenerative diseases. Although denoising diffusion probabilistic model (DDPM) has displayed remarkable performance in super-resolution of face and natural images, its application to producing high-resolution brain MRI images has not been explored. This study proposed a new deep-learning super-resolution framework for brain MRI images based on DDPM, via incorporating self-attention mechanism into DDPM. The main improvements are as follows: (a) Only one input channel is preserved. (b) The number of baseline channels is reduced from 64 to 32, to improve training speed. (c) Self-attention mechanism is added to 32 × 32, 16 × 16, and 8 × 8 resolution layers rather than only to 16 × 16 layer. (d) Feature-wise affine transformation is added to residual block. Experimental results on open T1- and diffusion-weighted brain MRI datasets show that our DDPM model outperformed the state-of-the-art super-resolution methods. In terms of learned perceptual image patch similarity (LPIPS) metric, the proposed DDPM model achieved the least distortion of generated super-resolution brain MRI images. This framework can also be conveniently used to reconstruct high-resolution MRI images of other body parts such as spinal and knee in future.
Impact of artificial intelligence on marketing

The evolution of artificial intelligence (AI) has drastically changed the dynamics of today’s business world. One of the most significant applications of AI is in the field of marketing, which assists in enhancing performance. The current research aims at finding out the impact of AI in marketing. A thorough literature research was highlighted, providing a strong knowledge of AI and its use in marketing. Second, the researcher employed a qualitative study strategy that included semi-structured interviews with marketing professionals from several Indian companies. The researcher chose a sample size of fifteen marketing experts to interview. The study's findings emphasise the elements that influence AI integration in marketing, the benefits and obstacles of AI integration in marketing, as well as your company's pre and post AI marketing strategy, ethical considerations, and use of AI in the marketing industry. The study proposes integrating AI into marketing tasks in order to improve corporate performance and, as a result, achieve profitability and competitive advantage. This study also contributes to strategic marketing research by identifying research gaps that bridge strategic AI marketing practise and research in a systematic and rigorous manner.
Fast Quantum State Discrimination with Nonlinear Positive Trace‐Preserving Channels

Abstract Models of nonlinear quantum computation based on deterministic positive trace‐preserving (PTP) channels and evolution equations are investigated. The models are defined in any finite Hilbert space, but the main results are for dimension . For every normalizable linear or nonlinear positive map ϕ on bounded linear operators X , there is an associated normalized PTP channel . Normalized PTP channels include unitary mean field theories, such as the Gross–Pitaevskii equation for interacting bosons, as well as models of linear and nonlinear dissipation. They classify into four types, yielding three distinct forms of nonlinearity whose computational power are explored. In the qubit case, these channels support Bloch ball torsion and other distortions studied previously, where it has been shown that such nonlinearity can be used to increase the separation between a pair of close qubit states, suggesting an exponential speedup for state discrimination. Building on this idea, the authors argue that this operation can be made robust to noise by using dissipation to induce a bifurcation to a novel phase where a pair of attracting fixed points create an intrinsically fault‐tolerant nonlinear state discriminator.
Probably something: a multi-layer taxonomy of non-fungible tokens

Purpose This paper aims to establish a fundamental and comprehensive understanding of non-fungible tokens (NFTs) by identifying and structuring common characteristics within a taxonomy. NFTs are hyped and increasingly marketed as essential building blocks of the Metaverse. However, the dynamic evolution of the NFT space has posed challenges for those seeking to develop a deep and comprehensive understanding of NFTs, their features and their capabilities. Design/methodology/approach Utilizing common guidelines for the creation of taxonomies, the authors developed (over 3 iterations), a multi-layer taxonomy based on workshops and interviews with 11 academic and 15 industry experts. Through an evaluation of 25 NFTs, the authors demonstrate the usefulness of the taxonomy. Findings The taxonomy has 4 layers, 14 dimensions and 42 characteristics, which describe NFTs in terms of reference object, token properties, token distribution and realizable value. Originality/value The authors' framework is the first to systematically cover the emerging NFT phenomenon. This framework is concise yet extendible and presents many avenues for future research in a plethora of disciplines. The characteristics identified in the authors' taxonomy are useful for NFT- and Metaverse-related research in finance, marketing, law and information systems. Additionally, the taxonomy can serve as an information source for policymakers as they consider NFT regulation.
Development of competence in the sphere of information security to achieve sustainable development

Objective: Global information and technological changes have opened up new opportunities for information and public communication processes. The purpose of the study is to analyze the level of competence of future specialists in economics and finances in the field of information security and develop recommendations for its improvement.&#x0D; Methods: The level of information security competencies of future specialists in economics and finance has been determined, characterized by the degree of awareness of the importance of readiness to work in corporate information security systems based on an empirical study using a survey of "Finance, banking, and insurance", "Accounting and taxation", and "Economics" students (94 people total) and a subsequent pedagogical experiment with their participation.&#x0D; Results: Authors of the articles have described the main methods and directions of forming the ability of a future specialist in economics and finance to work responsibly in corporate institutions, which are associated, first of all, with the introduction of a special course "Security of financial and economic information in information systems".&#x0D; Conclusion: The lack of readiness among economics and finance specialists to work in corporate information security systems may hinder the achievement of sustainable development. To address this issue, it is important to prepare future specialists in economics and finance for professional activity in the conditions of a corporate information security system. It is possible to prevent the development of negative phenomena in the field of information security with the help of the purposeful formation of appropriate competencies in specialists in economics and finance.
Smart healthcare system using integrated and lightweight ECC with private blockchain for multimedia medical data processing

Cloud-based Healthcare 4.0 systems have research challenges with secure medical data processing, especially biomedical image processing with privacy protection. Medical records are generally text/numerical or multimedia. Multimedia data includes X-ray scans, Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, etc. Transferring biomedical multimedia data to medical authorities raises various security concerns. This paper proposes a one-of-a-kind blockchain-based secure biomedical image processing system that maintains anonymity. The integrated Healthcare 4.0 assisted multimedia image processing architecture includes an edge layer, fog computing layer, cloud storage layer, and blockchain layer. The edge layer collects and sends periodic medical information from the patient to the higher layer. The multimedia data from the edge layer is securely preserved in blockchain-assisted cloud storage through fog nodes using lightweight cryptography. Medical users then safely search such data for medical treatment or monitoring. Lightweight cryptographic procedures are proposed by employing Elliptic Curve Cryptography (ECC) with Elliptic Curve Diffie-Hellman (ECDH) and Elliptic Curve Digital Signature (ECDS) algorithm to secure biomedical image processing while maintaining privacy (ECDSA). The proposed technique is experimented with using publically available chest X-ray and CT images. The experimental results revealed that the proposed model shows higher computational efficiency (encryption and decryption time), Peak to Signal Noise Ratio (PSNR), and Meas Square Error (MSE).
Short-, Medium-, and Long-Term Prediction of Carbon Dioxide Emissions using Wavelet-Enhanced Extreme Learning Machine

Carbon dioxide (CO2) is the main greenhouse gas responsible for global warming. Early prediction of CO2 is critical for developing strategies to mitigate the effects of climate change. A sophisticated version of the extreme learning machine (ELM), the wavelet enhanced extreme learning machine (W-EELM), is used to predict CO2 on different time scales (weekly, monthly, and yearly). Data were collected from the Mauna Loa Observatory station in Hawaii, which is ideal for global air sampling. Instead of the traditional method (singular value decomposition), a complete orthogonal decomposition (COD) was used to accurately calculate the weights of the ELM output layers. Another contribution of this study is the removal of noise from the input signal using the wavelet transform technique. The results of the W-EELM model are compared with the results of the classical ELM. Various statistical metrics are used to evaluate the models, and the comparative figures confirm the superiority of the applied models over the ELM model. The proposed W-EELM model proves to be a robust and applicable computer-based technology for modeling CO2concentrations, which contributes to the fundamental knowledge of the environmental engineering perspective. Doi: 10.28991/CEJ-2023-09-04-04 Full Text: PDF
Artificial Intelligence Accelerated Transformation in The Healthcare Industry

The healthcare industry was a pioneer in the deployment of artificial intelligence (AI) technology. Due to the nature of the services and the vulnerability of a sizable portion of end users, there has been a significant amount of research and discussion on the concept of artificial intelligence. A mixed-method approach has been used to pinpoint the components of moral AI in the healthcare sector and look into how it affects value creation and market performance. Since AI technology is still developing in India, analysis is conducted in an Indian context. The understanding of how various AI components supported healthcare organisations and deliver better patient-centered care and evidence-based medicine was aided by these in-depth studies and analyses of the patient perspective.
Pedagogical techniques of Earth remote sensing data application into modern school practice

The article dwells upon the Earth remote sensing data as one of the basic directions of Geo-Information Science, a unique source of information on processes and phenomena occurring in almost all spheres of the Earth geographic shell (atmosphere, hydrosphere, lithosphere, etc.).The authors argue that the use of aerospace images by means of the information and communication technologies involvement in the learning process allows not only to increase the information context value of learning, but also contributes to the formation of students’ cognitive interest in such disciplines as geography, biology, history, physics, computer science, etc.It has been grounded that remote sensing data form students’ spatial, temporal and qualitative concepts, sensory support for the perception, knowledge and explanation of the specifics of objects and phenomena of geographical reality, which, in its turn, provides an increase in the level of educational achievements.The techniques of aerospace images application into the modern school practice have been analyzed and illustrated in the examples: from using them as visual aids, to realization of practical and research orientation of training on the basis of remote sensing data.Particular attention is paid to the practical component of the Earth remote sensing implementation into the modern school practice with the help of information and communication technologies.
A survey of mapping algorithms in the long-reads era

It has been over a decade since the first publication of a method dedicated entirely to mapping long-reads. The distinctive characteristics of long reads resulted in methods moving from the seed-and-extend framework used for short reads to a seed-and-chain framework due to the seed abundance in each read. The main novelties are based on alternative seed constructs or chaining formulations. Dozens of tools now exist, whose heuristics have evolved considerably. We provide an overview of the methods used in long-read mappers. Since they are driven by implementation-specific parameters, we develop an original visualization tool to understand the parameter settings ( http://bcazaux.polytech-lille.net/Minimap2/ ).
Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems

Abstract Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.