Reviewerly/exHarmony huggingface dataset:
Random Number Generator Based on Hopfield Neural Network with Xorshift and Genetic Algorithms

Generating random numbers plays a fundamental role in matters of computer security. New high-security standards for data encryption have emerged with the increasing demand for secure communication, storage, and management of sensitive data on public networks. Data encryption techniques have expanded rapidly. In this paper, we present two methods of pseudorandom number generation; the first method combines the existing architectures of Hopfield neural networks and the XORShift algorithm, an the second is based on genetic algorithms. Our approaches pass the NIST and Diehard tests, which give us numbers that satisfy the statistical requirements. Compared with other architectures, the results demonstrated a robust consistency in the quality of the generated random numbers, which shows that it is a reliable method.
An Efficient Facial Verification System for Surveillance that Automatically Selects a Lightweight CNN Method and Utilizes Super-Resolution Images

In the last decade, facial recognition and verification methods have been extensively used for surveillance and security purposes. However, most of the time, recognizing and/or verifying faces is challenging due to the low facial resolution of the obtained or captured images. Likewise, low-resolution facial images contain different facial features, such as variations in pose, lighting, resolution, and camera-to-subject distance. Moreover, the methods commonly used for facial verification in images are based on deep architectures, which rely on complex deep learning models with promising verification results but come with high computational costs. On the other hand, real-world requirements for facial verification demand lightweight methods that are inspired by their counterparts but are also compact and efficient enough to be used in unrestricted scenarios, such as video surveillance or security cameras. This paper proposes a lightweight facial verification system (LFVS) that automatically selects a lightweight facial verification method based on images characteristics, such as facial rotation variations and low resolutions. Additionally, a dynamic scaling approach is proposed to upscale images to the required size. The system uses this scaling to enhance the image resolution and succeeded in improving facial verification performance. Experimental results demonstrated that the lightweight facial verification system achieved better results when using super-resolution images in low-resolution reference points, with minimal memory usage and computational complexity.
Implementation of Parallel Evolutionary Convolutional Neural Network for Classification in Human Activity and Image Recognition

Pattern recognition has been evolving to include problems posed by new sceneries containing a high number of pattern components . Processing this volume of information allows a more exact classification in wider types of applications; however, some of the difficulties of this scheme is the maintenance of numerical precision and mainly the reduction of the execution time. During the last 15 years, several Machine Learning solutions have been implemented to reduce the number of pattern components to be analyzed, such as artificial neural networks. Deep learning is an appropriate tool to accomplish this task. In this paper, a convolutional neural network is implemented for recognition and classification of human activity signals and digital images. It is achieved by automatically adjusting the parameters of the neural network through genetic algorithms using a multiprocessor and GPU platform. The results obtained show the reduction of computational costs and the possibility of better understanding of the solutions provided by Deep Learning.
T(G)V-NeRF: A Strong Baseline in Regularized Neural Radiance Fields with Few Training Views

Implicit representations such as Neural Radiance Fields (NeRFs) have become a de facto standard in the field of novel view synthesis for 3D scenes. However, their stunning results typically imply the use of dozens of training images, with their corresponding cameras well localized in the scene. This paper studies new, total variation-based regularization approaches to train NeRFs in the context of very few (less than 10) training images. It leverages the NeRF back-propagation algorithm to evaluate first-order and second-order derivatives terms on the inferred depth map to enforce smoothness on the scene underlying surfaces. Through state-of-the-art performance on standard real-images benchmarks, we show that the proposed methods, coined as TV-NeRF and TGV-NeRF, make strong baselines in novel view synthesis with few training views.
Partial key exposure attack on RSA using some private key blocks

RSA is a well-known cryptosystem in public-key cryptography and the strength of the cryptosystem depends on the hardness of factoring large integers. Several attacks have been proposed by using the partial information of the secret parameters, which can be obtained by side-channel attacks. Partial key exposure attacks exploit the information gained by a side-channel attack(s) and identify the potential of the RSA cryptosystem if an attacker knows that partial information. In this paper, we investigate the strength of RSA, if an attacker obtains some blocks of the secret exponent, and by guessing successfully a few most significant bits (MSBs) of any of the primes in RSA. Some blocks of the secret exponent can be extracted by cold boot attack and some MSBs of any of the primes can be guessed correctly. We apply LLL algorithm to attack the RSA and follow the Jochemsz and May approach to construct the lattice.
Deterministic generation of shaped single microwave photons using a parametrically driven coupler

A distributed quantum computing system requires a quantum communication channel between spatially separated processing units. In superconducting circuits, such a channel can be realized by using propagating microwave photons to encode and transfer quantum information between an emitter and a receiver node. Here we experimentally demonstrate a superconducting circuit that deterministically transfers the state of a data qubit into a propagating microwave mode, with a process fidelity of 94.5%. We use a time-varying parametric drive to shape the temporal profile of the propagating mode to be time symmetric and with constant phase, so that reabsorption by the receiving processor can be implemented as a time-reversed version of the emission. We demonstrate a self-calibrating routine to correct for time-dependent shifts of the emitted frequencies due to the modulation of the parametric drive. Our work provides a reliable method to implement high-fidelity quantum state transfer and remote entanglement operations in a distributed quantum computing network.
A density-based oversampling approach for class imbalance and data overlap

In data mining classification, class imbalance is characterized that different classes have an obvious difference in the number of samples. Most classifiers typically assume a balanced class distribution or assign equal classification error costs to different classes. Therefore, directly using imbalanced class will worsen the classification performance. The oversampling algorithms can achieve the balance by synthesizing new samples, but the uncontrollable positions of the synthetic samples may aggravate the data overlap and further deteriorate the classification performance. To tackle this challenge, an improved synthetic minority oversampling technique based on kernel density estimation and neighbor density selection (KDENDS_SMOTE) is proposed in this paper. First, each sample is mapped into a high-dimensional space to avoid the choice of the window width and to overcome the nonlinear separable limitation. Kernel density estimation is then used to derive the density ratio, which serves as a measure of the degree of data overlap. Subsequently, the stability degree of the density ratio is calculated using neighbor information, and a scoring mechanism combining the density ratio and its stability degree is proposed to assess the fitness of selected samples. Furthermore, the neighbor density selection based on the above scoring mechanism can guide SMOTE to generate new samples within a safe and stable region, away from areas with data overlap. Finally, compared with six advanced oversampling methods on fifteen real-world datasets, the KDENDS_SMOTE can effectively mitigate the data overlap and improve the classification performance.
BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems

Abstract Purpose Computer‐aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI‐RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above‐mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD systems. Acquisition and Validation Methods The BUS dataset comprises 1875 anonymized images from 1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy‐proven tumors divided into 722 benign and 342 malignant cases. Besides, a senior ultrasonographer performed a BI‐RADS assessment in categories 2 to 5. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5‐ and 10‐fold cross‐validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breast lesions. Data Format and Usage Notes The BUS dataset is publicly available for academic and research purposes through an open‐access repository under the name BUS‐BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma‐Separated Value (CSV) files. Potential Applications The BUS‐BRA dataset can be used to develop and assess artificial intelligence‐based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI‐RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for image description.
Efficient History-Driven Adversarial Perturbation Distribution Learning in Low Frequency Domain

The existence of adversarial image makes us have to doubt the credibility of artificial intelligence system. Attackers can use carefully processed adversarial images to carry out a variety of attacks. Inspired by the theory of image compressed sensing, this paper proposes a new black-box attack, \(\mathcal {N}\text{-HSA}_{LF} \) . It uses covariance matrix adaptive evolution strategy (CMA-ES) to learn the distribution of adversarial perturbation in low frequency domain, reducing the dimensionality of solution space. And sep-CMA-ES is used to set the covariance matrix as a diagonal matrix, which further reduces the dimensions that need to be updated for the covariance matrix of multivariate Gaussian distribution learned in attacks, thereby reducing the computational cost of attack. And on this basis, we propose history-driven mean update and current optimal solution-guided improvement strategies to avoid the evolution of distribution to a worse direction. The experimental results show that the proposed \(\mathcal {N}\text{-HSA}_{LF} \) can achieve a higher attack success rate with fewer queries on attacking both CNN-based and transformer-based target models under L 2 -norm and L ∞ -norm constraints of perturbation. We also conduct an ablation study and the results show that the proposed improved strategies can effectively reduce the number of visits to the target model when making adversarial examples for hard examples. In addition, our attack is able to make the integrated defense strategy of GRIP-GAN and noise-embedded training ineffective to a certain extent.
Exploring the History and Culture of Main Square Los Tupes with Augmented Reality in San Diego, Cesar

Augmented reality (AR) has gained popularity as a tool for exploring cultural heritage sites. This study investigates the use of AR technology to enhance the visitor experience at Plaza Principal Los Tupes in San Diego, Cesar. As an important cultural heritage site, the study aims to explore the potential of AR technology in providing visitors with an immersive and interactive experience of the site's history and culture. An AR application was developed, incorporating historical and cultural information about Plaza Principal Los Tupes, to offer visitors an interactive and educational experience. A user study was conducted to evaluate the effectiveness of the AR application in enhancing visitor experience. The results reveal that AR technology significantly enhances the visitor experience at cultural heritage sites, allowing for a deeper understanding of historical and cultural significance. The study demonstrates the potential of AR technology in increasing visitor engagement, accessibility, preservation of cultural heritage, and overall visitor satisfaction and revenue. However, challenges associated with AR technology usage are identified, emphasizing the need for further research to overcome these challenges and fully realize the potential of AR technology in the cultural heritage sector.
Teaching First Order Logic with Friendly Puzzles

Instructors in artificial intelligence are aware of the cognitive and educational value of modelling puzzles and thus developing problem-solving skills. Puzzles can be seen both as a warm-up activity to start a lecture on knowledge representation or as an instrument to teach First Order Logic. The goal of puzzles is to keep students engaged. I illustrated here how notions from FOL, either basic (implication tables, order of quantifiers, resolution, logical faults) or intermediate (interpretation models, domain closure, isomorphic models) can be introduced to students by means of friendly puzzles. As a next step to train students in more advanced knowledge representation and reasoning technicalities, I also demonstrate the use of platforms such as MineFOL for designing logical puzzles.
A System to Support Readers in Automatically Acquiring Complete Summarized Information on an Event from Different Sources

Today, most newspapers utilize social media to disseminate news. On the one hand, this results in an overload of related articles for social media users. On the other hand, since social media tends to form echo chambers around their users, different opinions and information may be hidden. Enabling users to access different information (possibly outside of their echo chambers, without the burden of reading entire articles, often containing redundant information) may be a step forward in allowing them to form their own opinions. To address this challenge, we propose a system that integrates Transformer neural models and text summarization models along with decision rules. Given a reference article already read by the user, our system first collects articles related to the same topic from a configurable number of different sources. Then, it identifies and summarizes the information that differs from the reference article and outputs the summary to the user. The core of the system is the sentence classification algorithm, which classifies sentences in the collected articles into three classes based on similarity with the reference article: sentences classified as dissimilar are summarized by using a pre-trained abstractive summarization model. We evaluated the proposed system in two steps. First, we assessed its effectiveness in identifying content differences between the reference article and the related articles by using human judgments obtained through crowdsourcing as ground truth. We obtained an average F1 score of 0.772 against average F1 scores of 0.797 and 0.676 achieved by two state-of-the-art approaches based, respectively, on model tuning and prompt tuning, which require an appropriate tuning phase and, therefore, greater computational effort. Second, we asked a sample of people to evaluate how well the summary generated by the system represents the information that is not present in the article read by the user. The results are extremely encouraging. Finally, we present a use case.
Research of Artificial Intelligence Empowering Teachers ICT Competence of Rural Area in China

The training of artificial intelligence to empower the ICT competence of rural teachers directly affects the quality of rural education and teaching. This paper is based on the practical exploration of Cloudbridge Academy’s ICT competence enhancement training for rural primary and secondary school teachers. It introduces the design concept, content design, and program practice of the online training program, and highlights the experience in the practice process, in order to inspire rural primary and secondary school teachers to carry out ICT competence enhancement training around the world.
Classification of Human- and AI-Generated Texts: Investigating Features for ChatGPT

Recently, generative AIs like ChatGPT have become available to the wide public. These tools can for instance be used by students to generate essays or whole theses. But how does a teacher know whether a text is written by a student or an AI? In our work, we explore traditional and new features to (1) detect text generated by AI from scratch and (2) text rephrased by AI. Since we found that classification is more difficult when the AI has been instructed to create the text in a way that a human would not recognize that it was generated by an AI, we also investigate this more advanced case. For our experiments, we produced a new text corpus covering 10 school topics. Our best systems to classify basic and advanced human-generated/AI-generated texts have F1-scores of over 96%. Our best systems for classifying basic and advanced human-generated/AI-rephrased texts have F1-scores of more than 78%. The systems use a combination of perplexity, semantic, list lookup, error-based, readability, AI feedback, and text vector features. Our results show that the new features substantially help to improve the performance of many classifiers. Our best basic text rephrasing detection system even outperforms GPTZero by 183.8% relative in F1-score.
Multilingual Text Simplification and Its Performance on Social Sciences Coursebooks

Text simplification is an essential task in today’s society. It has the potential to help minorities get information, the broad masses have access to higher education, and assist in learning a new language. Through text simplification, we achieve an inclusive world with less language barriers. However, for training automatic artificial intelligence based systems for text simplification, no much high-quality text resources exist—particularly for various languages. Consequently, we investigate leveraging data from other languages to enhance the performance of text simplification systems. As simplification helps better understanding learning content, we finally apply our best German and English systems—which are based on a state-of-the-art Text-to-Text Transfer Transformer (T5) [1]—on sentences of German and English social sciences coursebooks from our university and perform a human evaluation in a survey. Furthermore, a comparison of text simplified with our best systems to text simplified with ChatGPT demonstrates that our English model significantly outperforms ChatGPT in 3 of 5 criteria evaluated, while our German model even outperforms ChatGPT in 4 of the 5 criteria.
Determination of the number of shots for Grover’s search algorithm

Abstract This paper focuses on Grover’s quantum search algorithm, which is of paramount importance as a masterpiece of Quantum Computing software. Given the inherent probabilistic nature of quantum computers, quantum programs based on Grover’s algorithm need to be run a number of times in order to generate a histogram of candidate values for solutions, which are then checked to identify the valid ones. In this paper, the distribution of the required number of shots to find all or a fraction of all the solutions to the Grover’s search problem is studied. Firstly, considering the similarity of the probability problem with the well-known coupon collector’s problem, two formulae are obtained from asymptotic results on the distribution of the required number of shots, as the number of problem solutions grows. These expressions allow to compute the number of shots required to ensure that, with probability p , all or a fraction of all the solutions are found. Secondly, the probability mass function of the required number of shots is derived, which serves as a benchmark to assess the validity of the asymptotic approximations derived previously. A comparison between the two approaches is presented and, as a result, a rule of thumb to decide under which circumstances employ one or the other is proposed.
Information Retrieval Evaluation Measures Defined on Some Axiomatic Models of Preferences

Information retrieval (IR) evaluation measures are essential for capturing the relevance of documents to topics and determining the task performance efficiency of retrieval systems. The study of IR evaluation measures through their formal properties enables a better understanding of their suitability for a specific task. Some works have modeled the effectiveness of retrieval measures with axioms, heuristics, or desirable properties, leading to order relationships on the set where they are defined. Each of these ordering structures constitutes an axiomatic model of preferences (AMP), which can be considered as an “ideal” scenario of retrieval. Based on lattice theory and on the representational theory of measurement, this work formally explores numeric, metric, and scale properties of some effectiveness measures defined on AMPs. In some of these scenarios, retrieval measures are completely determined from the scores of a subset of document rankings: join-irreducible elements. All the possible metrics and pseudometrics, defined on these structures are expressed in terms of the join-irreducible elements. The deduced scale properties of the precision, recall, F -measure, RBP , DCG , and AP confirm some recent results in the IR field.
Utilizing Machine Learning and Big Data Analysis for Risk Mitigation and Fraud Detection in Finance

With the rise of online banking systems and easy transactions, there is an increase in fraud in the banking system and in the field of finance. To reduce fraud in the transactions we can apply the systems of machine learning algorithms and big data analysis. In this research paper, we discuss various methods used in the field such as Supervised learning, Unsupervised learning, and Ensemble Methods in the field of machine learning and transaction monitoring, behavior analytics, network analytics, and pattern recognition in the field of real-time monitoring. We have used a data set from Kaggle on credit card transactions and the methods of Random Forest Classification and Support Vector Machine which comes under the supervised learning method in machine learning and discussed other results and benefits achieved from it.