Reviewerly/exHarmony huggingface dataset:
A Survey on Medical Health Records and AI

As the population increases, so do the number of patients getting admitted in hospitals. This generates an overwhelming amount of data within the electronic health records (EHRs) that is impossible to manage manually. This is where machine learning concepts come in handy. The ML algorithms for regression-classification have become increasingly popular within the healthcare sector. Especially for cardiovascular diseases (CVD). Estimation states that by 2030, over 23 million people will die from CVD each year. But, it is estimated that 90% of CVD is preventable. The on time recognition and diagnosis of heart failure from the pre-existing medical records is a way to do so. However, the EHRs are not particularly reliable when it comes to the comparison of structured and unstructured data. This is due to the use of colloquial language and possible existence of sparse content. To tackle this issue, the proposed system uses the KTI-RNN model for recognition of unstructured data and removal of sparse content, TF-IWF model to extract the keyword set, the LDA model to extract the topic word set, and finally, the GA-BiRNN model to identify heart failure from extensive medical texts. The GA-BiRNN model is made up of a bidirectional recurrent neural network model and its output layer embedded with global attention mechanism and gating mechanism.
Designing a Random Password Generator Using Python Programming Language

Every individual who uses various online services, is concerned about security and privacy to safeguard personal information and data from hackers. The password-generating system is one of several generating systems available for the security of users' data. Because of the rise in sharing of information online, internet usage, electronic commerce transactions, and data transmission, both password security and authenticity have become grave issues. However, this shows that the password's strength (or length) is also necessary to be strong. As a result, cybersecurity experts generally advise using complex password combinations. However, due to difficult patterns, individuals frequently forget their passwords. Unlike previous random password generators, the authors are putting forth a novel approach in this paper that will produce a strong password. The Python programming language has been used to create the password-generating system. The authors have used pyperclip, tkinter, random and string libraries in the code design. According to various tests of the system carried out, the trustworthiness of the generated passwords is one hundred percent.
Your First GAN

This chapter will delve deeper into implementing generative adversarial networks (GANs). Chapter 1 presented a comprehensive overview of GANs, highlighting their versatility and promising results in various applications, including image, video, audio, and text generation. This chapter will focus on the most basic implementation of GANs, starting with the most straightforward code that generates a straight line “y = x”. This approach will provide a fundamental understanding of the components and workings of GANs and how they can be utilized for 1D GAN. By the end of this chapter, the reader will better appreciate GANs and their capabilities.
Impossible Differential Cryptanalysis and A Security Evaluation Framework for AND-RX Ciphers

In this paper, a security evaluation framework for AND-RX ciphers against impossible differential cryptanalysis is proposed. This framework is constructed based on three different methods towards finding the theoretical upper boundary, theoretical lower boundary, and practical boundary of impossible differential distinguishers (short for ID) respectively. The provable security boundary (upper boundary) can be calculated with two round-function-related matrices through a few matrix multiplications, this calculation is beyond actual input and output differences. For searching longer IDs (lower boundary), an automatic method is proposed. With this method, given the input and output difference, all the possible direct and indirect contradictions are detected. For the practical boundary, a method of approximating all the potential longest IDs with concrete differential trails is introduced. The three boundaries validate the correctness from each other. According to our result, on the one hand, the boundaries derived with well-designed ID-construction methods can already reach the practical boundary for some block ciphers and it is unlikely to be improved based on known construction methods or future unknown construction methods. On the other hand, for those ciphers whose current best result does not reach our boundary, longer IDs can be discovered with this framework. The correctness is validated by a series of applications. For the provable security boundary, four family ciphers-SIMON, Simeck, Friet-PC and SAND are investigated. For SIMON and Simeck, the lengths of current longest IDs have reached their provable security boundaries. For Friet-PC and SAND, there is a gap between the provable security boundary and current best results. With the automatic searching method, some longer IDs on Friet-PC and SAND are discovered. For Friet-PC, 128 11-round IDs are discovered, while the previous best differential distinguisher is 9-round. For SAND64, 256 11-round IDs are proposed. For SAND128, 456 14-round IDs are presented. Both results extend previous longest IDs by one round and all these newly proposed distinguishers reached corresponding provable security boundaries. For Simeck, the length of longest IDs has not been improved. However, more distinguishers of the same length are discovered. For Simeck64, the increased ratio for the quantity can reach 300%. Besides, the practical boundary of SIMON is investigated, the results indicate that for SIMON, the practical boundary is identical with the provable security boundary or the boundary derived with the automatic searching method.
CATADIOPTRIC IMAGE DENOISING: A SPATIALLY VARIANT APPROACH

A catadioptric camera uses a conventional camera in conjunction with a quadratic mirror for capturing an omnidirectional field of view in real-time. The resolution of catadiop- tric images, however, is non-uniform due to the mirror curvature. A widely used approach to processing catadioptric images is to apply classical methods to them directly or via a trans- formed domain. The aim of this work is to demonstrate that for the task of image denoising, an appropriate approach is to modify classical methods so that they become spatially adaptive with the non-uniform resolution of catadioptric images. To this end, we modify the famous Rudin-Osher-Fatemi (ROF) denoising model by introducing a space-variant regularizer. The proposed model comprises a spatially varying total variation term, which adjusts the edge- preservation and the noise reduction abilities in the whole image domain. We carry out an empirical evaluation of the performance of the proposed model compared with the widely used methods for processing catadioptric images. The results reveal that, despite its simplic- ity, our model improves the performance of the original method in terms of both quantitative and qualitative aspects.
OPTIMIZATION METHOD FOR INTEGRATION OF CONVOLUTIONAL AND RECURRENT NEURAL NETWORK

Abstract In recent years, convolutional neural networks have been widely used in image processing and have shown good results. Particularly useful was their ability to automatically extract image features (textures and shapes of objects). The article proposes a method that improves the accuracy and speed of recognition of an ultra-precise neural network based on image recognition of people’s faces. At first, a recurrent neural network is introduced into the convolutional neural network, thereby studying the characteristics of the image more deeply. Deep image characteristics are studied in parallel using a convolutional and recurrent neural network. In line with the idea of skipping the ResNet convolution layer, a new ShortCut3- ResNet residual module is built. A double optimization model is created to fully optimize the convolution process. A study of the influence of various parameters of a convolutional neural network on network performance is demonstrated, also analyzed using simulation experiments. As a result, the optimal parameters of the convolutional neural network are established. Ex- periments show that the method presented in this paper can study various images of people’s faces regardless of age, gender, and also improves the accuracy of feature extraction and image recognition ability.
Youdale, Roy (2020): Using Computers in the Translation of Literary Style: Challenges and Opportunities. New York/London: Routledge, 242 p.

Youdale, Roy (2020): Using Computers in the Translation of Literary Style: Challenges and Opportunities. New York/London: Routledge, 242 p.. Un article de la revue Meta (De la paratraduction) diffusée par la plateforme Érudit.
What sinister time machine?

A recent OASPA guest post reminded me of something I have been wondering about for several years now. What sinister time travel device is keeping some sections of the scholarly ecosystem from leaving the past and coming back to the present?
UKSG 2023: scholarly communication for the information community

Purpose It reports new research and innovative ideas and technology presented at the UKSG Conference held in Glasgow, Scotland. The report is to disseminate outcomes from the conference plenaries, lightning talks, breakout sessions and exhibitions. Topics like open access, global equity, artificial intelligence and scholarly communication, research archiving and preservation among others are described as presented during the conference. Design/methodology/approach This paper uses the viewpoint method in reporting a United Kingdom Serials Group (UKSG) experience of a conference held in April, 2023 in Glasgow, Scotland. Findings The scholarly community debated much that is trending on various topics and concluded that a lot needs to be done in collaborations, open access, research into new ways of dealing with contemporary issues and new technologies. Originality/value The paper presents the issues discussed at the UKSG Conference 2023 have not been presented before. Current trends are brought to the fore.
Revolutionizing Translation with AI: Unravelling Neural Machine Translation and Generative Pre-Trained Large Language Models

This work explores the technical advancements in artificial intelligence (AI) for translation, focusing on neural machine translation (NMT) and large language models (LLMs) as primary drivers of progress. We analyze foundational deep learning techniques, the influential Transformer model, and the strengths and weaknesses of NMT and LLMs for translation. Moreover, we offer practical recommendations for translation educators and developers on how to effectively harness AIâ€™s power while addressing potential challenges. Our goal is to enhance understanding of AI-driven translation technologies and equip language professionals with the knowledge needed to make informed decisions when using these cutting-edge tools.
Deep Diffusion Models for Facies Modeling

Summary Deep diffusion models are a recently developed approach to model distributions over images. In this presentation we will explore order agnostic diffusion models and their relationship with traditional autoregressive facies modeling approaches. We show applications on geoscience relevant datasets and training images. In addition, we present an extension to handle domains larger than the original training image size. Finally, we highlight benefits of the approach relating to the probabilistic nature of the approach.
A Binary Field Elliptic Curve Crypto-processor with Architectural Scalability

다양한 응용분야의 연산 성능과 하드웨어 리소스 요구 사항을 충족하는 ECC 프로세서의 맞춤형 구현에 적합한 확장 가능형 이진체 타원곡선 암호 프로세서(scalable binary field elliptic curve crypto-processor; BF_sECCP)에 대해 기술한다. BF_sECCP는 처리요소 (processing element; PE)를 1개에서 8개까지 사용하여 32-비트 워드의 이진체 연산을 병렬로 처리하며, SEC2 표준의 12가지 이진체 타원곡선을 지원한다. 수정된 몽고메리 모듈러 곱셈 알고리듬을 적용하여 부채널 공격에 내성을 갖도록 했으며, 로페즈-다합 투영 좌표계에서 x 좌표만 사용하는 점연산을 적용해서 연산 복잡도를 최소화했다. BF_sECC 프로세서를 Zynq7 FPGA 디바이스에 구현하여 하드웨어 동작을 검증했다. 8개의 PE로 구현된 BF_sECCP는 17,350개의 LUT와 4,239개의 플립플롭이 사용되었으며, 250 MHz 클록 주파수에서 233 비트 점 스칼라 곱셈 연산에 약 203㎲가 소요되는 것으로 평가되었다. This paper describes a scalable binary field elliptic curve crypto-processor (BF_sECCP) suitable for customized implementations of ECC processors that meet the computational performance and hardware resource requirements of various applications. The BF_sECCP is implemented using one to eight processing elements (PEs) that perform binary field operations on 32-bit words in parallel, and supports twelve binary field elliptic curves defined in SEC2 standard. A modified Montgomery modular multiplication algorithm was applied to make it resistant to side-channel attacks, and point operations using only the x-coordinate in the Lopez-Dahab projective coordinate system were adopted to minimize computational complexity. The hardware operation of BF_sECCP was verified using a Zynq7 FPGA device. The BF_sECCP, implemented with eight PEs, used 17,350 LUTs and 4,239 flip flops, and it was evaluated to take approximately 203㎲ for a 233-bit point scalar multiplication at a 250 MHz clock frequency.
Aspect-Based Sentiment Words and Their Polarities Using Chi-Square Test

Most of the user-preferred products on e-commerce websites are accompanied by a massive number of product reviews and manually analyzing each review to understand the features and the user opinions associated with the products is an inconceivable task. A single domain of products can contain thousands of different products and an equally significant number of associated features, thereby making the polarity of the sentiment words in the product reviews vary widely according to the feature with which they are associated. The paper attempts to automatically calculate the feature-specific polarity of the sentiment words in a given domain using the Chi-square Test statistical measure. The results of the method are tested on two different domains. The experimental results show that the method delivers an accuracy of more than 75\% in both domains tested. The method also helps in discovering strong domain-specific polar adjectives specific to a domain that might be missing from the universal sentiment lexicon.
Adding Learning Capabilities to the LEX Algorithm for Computing Minimal Transversals

Despite being little known and poorly documented, LEX is part of the family of typical testors-finding algorithms that generally has better performance than other much more divulged similar algorithms. The recently published relationship between typical testors and minimal hitting sets, potentially extends the usefulness and applicability of this algorithm to the hypergraphs and data mining fields. Unfortunately, the high time-complexity of both typical testor algorithms and minimal hitting sets algorithms still remains a major obstacle. Therefore, alternatives that can help overcome difficult problems are constantly being researched. In this paper we propose the inclusion of a symbolic learning behavior into the implementation of the LEX algorithm. The incorporated symbolic learning is a general strategy for optimizing the search process, and thus improves the efficiency of minimal transversals and typical testors algorithms. In addition, the performance of the resulting algorithm is assessed by using carefully designed benchmark test matrices.
Method for improving the pronunciation evaluation of foreign spoken Korean based on morphemes analysis

외국인의 한국어 학습에 대한 관심이 증대되는 가운데 오픈 API로 제공되는 발음평가 기술은 외국인 발화특성을 충분히 고려하지 않아 평가 점수의 신뢰도가 낮은 실정이다. 본 논문에서는 외국인의 한국어 발화에 대한 음성인식정보를 활용하여 원문과 발화 결과의 형태소 분석을 통해 형태학적 관계를 점수화하여 기존의 발음평가 채점 방식을 보완하는 기법을 제시한다. 원 문장과 외국인이 발음한 문장간의 유사도를 파악하기 위해 음성인식 및 형태소 분석을 기반으로 형태소 사전을 만든 후 다양한 거리측정 알고리즘을 활용해 유사도를 측정하였다. 실험 결과 제안한 방식이 기존 외국인 발화점수 측정모델과 비교했을 때 실제 한국인이 듣고 인식한 결과와 유사한 점수를 제공하는 것을 확인하였다. 형태소 분석을 기반으로 유사도를 측정한 점수를 외국인의 한국어 능력 평가 시험 점수에 적용한다면 한국인이 듣고 이해한 정도도 함께 고려된 평가가 가능하다. While foreigners" interest in learning Korean is increasing, the pronunciation evaluation technology provided through open API does not sufficiently consider the characteristics of foreign speech, so the reliability of the evaluation score is low. In this paper, we present a technique to supplement the existing pronunciation evaluation method by scoring the morphological relationship through the similarity analysis of the original text and the utterance result using voice recognition for foreigners" Korean speech. In order to determine the similarity between the original sentence and the sentence pronounced by a foreigner, a morpheme dictionary was created based on voice recognition and morpheme analysis, and then the similarity was measured using various distance measurement algorithms. As a result of the experiment, it was confirmed that the proposed method provides a score similar to the result that Koreans actually heard and recognized when compared with the existing foreigner speech evaluation model.
Chatbot based on Deep Learning for Recommending Relevant Products

Online shopping platforms are growing at an unprecedented rate around the world. These platforms are mostly based on search engines, which are still mostly knowledge base based and use matching keywords to find similar products. However, customers want a more interactive approach that is convenient and reliable for viewing related products. In this article, we propose a novel idea of ​​searching for products in an online shopping system using a chatbot model that uses Deep Learning which processes images and text to generate responses to the customer. A user can provide a picture or give a description of the product they are looking for, and they will be presented with similar products based on the images. The system of proposed recommendations is based on the recovery of images based on the content provided. The evaluation of the proposed model is divided into two parts: if it is done through images it generates 80.6% accuracy and if it is done through text it has 75% accuracy.
Geostatistical Functional Data Analysis

Journal Article Geostatistical Functional Data Analysis Get access Geostatistical Functional Data Analysis, edited by Jorge Mateu and Ramón Giraldo, 2022, John Wiley and Sons Ltd, xvi + 448 pp, ISBN 781119387848, £90.00 (hardback). Richard H Glendinning Richard H Glendinning Great Malvern, Worcestershire WR14 3EJ rhglendinning@yahoo.co.uk Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnad082, https://doi.org/10.1093/jrsssa/qnad082 Published: 05 July 2023
Digital Foundations of Evolvable Genomic Intelligence and Human Proteanism: Complexity With Novelty Production Beyond Bounded Rationality

Despite prolific innovations and diversity in economic and biological systems, the theoretical impasse on novelty production has led to a longstanding reliance on randomness or statistical white noise error terms. Extant Decision Sciences and Game Theory, respectively, conflate rationality with optimal choice from a prespecified action set and rule out Nash equilibria with strategic innovation or ‘surprises’. In contrast, the Wolfram-Chomsky schema implies that only digital software systems incorporating Gödel Incompleteness can produce novelty. Advances in gene science and neuroscience show how this relates to genomic intelligence which reaches its apogee in general purpose highly protean human intelligence. Key developments with Adaptive Immune System (AIS) and the Mirror Neuron System (MNS), latterly mostly in primate brains, involve distinctive Gödelian features for eukaryote intelligence of self-reference (Self-Ref) and offline virtual self-representation (Self-Rep) for complex self-other interaction with prodigious open-ended capacity for anticipative malware detection and novelty production within a unique self-referential blockchain distributed ledger. This initially developed in the AIS, which from the get-go accounts for somatic hypermutations for novel anti-body production and in humans as unbounded proteanism for novel extended phenotypes in the form of artifacts outside of ourselves. Thus, models of bounded rationality, extant Decision Sciences and Complexity Economics that overlook human proteanism for novelty production may have no basis in the evolution of human intelligence and complexity. Clearly, radical rethinking is needed to navigate the burgeoning digital world.
Comparative Analysis of the Bacterial Foraging Algorithm and Differential Evolution in Global Optimization Problems

There are bio-inspired metaheuristics in nature rarely used in areas where there is not domain or knowledge of computational algorithms, to mention some, medicine, finance and administration. TS-MBFOA, a bacteria-based algorithm and the Differential Evolution Algorithm (DEA), are metaheuristic algorithms proposed for the optimization of complex problems mathematically modeled as linear or non-linear problems. In this paper, these algorithms are implemented to analyze their performance in the search for better solutions in constrained optimization problems. Tests were conducted on four optimization problems known in the literature as benchmark problems. Both algorithms were run in 30 independent executions for each problem with the same number of generations and evaluations. Although the parameters of each algorithm are different, the number of evaluations was selected for a fair comparison. Results are similar for both algorithms, however, DEA obtains better results for the problem with the larger number of constraints. Additionally, DEA generates solutions in less time than TS-MBFOA. The nonparametric Wilcoxon Signed Rank Test indicates significant differences in only 3 problems. The convergence graph of both algorithms for each problem shows that after 50 generations, both algorithms are close to the best known solution in the state of the art.
Dictionary Learning-Based Denoising for Portfolio Selection

In the finance industry, real-world data are affected by noise, which comes from several external sources. This makes it challenging to select optimal portfolios for profitable investment strategies. Therefore, noise removal (or denoising) has become important for investors to create accurate investment models that guarantee better returns. In this paper, we propose a novel dictionary learning-based denoising approach for financial time series. The transform matrix in dictionary learning is built by training the noisy data with a K-singular value decomposition (K-SVD) algorithm. We evaluated the effectiveness of the proposed method using the 30 Fama French portfolio (FF30) as sample data. Furthermore, the out-of-sample performance of the denoising approach is tested under a minimum-variance framework. Empirical results prove that the proposed dictionary learning-based denoising method outperforms the other benchmarks in terms of portfolio selection.
Econochemistry

This research purposes, a new area of research in economics, it is called Econochemistry. Econochemistry is using models and experiments from chemistry in economics. This new research field in economics can help to understand complex and dynamic economic phenomena from a Chemistry point of view. Chemistry offers so much to economics and finance. Hence, we try it to present a simple example about how we can use Chemistry as a powerful analytical and experimental tool to review or simulate unknown economic phenomena from a holistic approach. Therefore, we present the General Economic Bonding Structures Model (GEBS-Model) as a good example to apply in economics. In this case, we focused this model on the Macroeconomics research field.