Reviewerly/exHarmony huggingface dataset:
I Want to Ride My BICYCL : BICYCL Implements CryptographY in CLass Groups

We introduce BICYCL an open-source C++ library that implements arithmetic in the ideal class groups of imaginary quadratic fields, together with a set of cryptographic primitives based on class groups. It is available at https://gite.lirmm.fr/crypto/bicycl under GNU General Public License version 3 or any later version.BICYCL provides significant speed-ups on the implementation of the arithmetic of class groups. Concerning cryptographic applications, BICYCL is orders of magnitude faster than any previous pilot implementation of the $$\textsf{CL}$$ linearly encryption scheme, making it faster than Paillier's encryption scheme at any security level. Linearly homomorphic encryption is the core of many multi-party computation protocols, sometimes involving a huge number of encryptions and homomorphic evaluations: class group-based protocols become the best solution in terms of bandwidth and computational efficiency to rely upon.
Chinese Event Extraction Method Based on Roformer Model

Event extraction is an important research direction in the field of natural language processing. The current Chinese event extraction field still suffers from errors in the pretraining and fine-tuning stages, inability to directly handle texts with more than 512 tokens, and inaccurate event extraction due to insufficient semantic sample diversity. In this paper, we propose a Chinese event extraction method RoformerFC (Roformer model with FGM and CRF) based on the Roformer model to address the above problems. Firstly, our method utilizes the Roformer model based on rotary position embedding, which both moderates the errors in the pretraining and fine-tuning phases and allows the model to directly handle texts with more than 512 tokens; then, the adversarial networks based on FGM (fast gradient method) are realized to increase the diversity of semantic feature samples; finally, the classical CRF (conditional random fields) model is used to decode and identify the event element entity and its corresponding event role and event type. On the short text DuEE dataset, the microP, microR, and microF of our method improved 1.26%, 4.01%, and 2.68%, respectively, over the classical Chinese event extraction method BERT-CRF. On the long text JsEE dataset, the microP, microR, and microF of our method improved 2.26%, 5.03%, and 3.72%, respectively, over the classical Chinese event extraction method BERT-CRF.
Deep learning based spatio‐temporal hand gesture recognition system in complex environment

Abstract Gesture recognition nowadays has grabbed the attention of researchers as they represent human behaviour in multiple practical ways. Amongst a variety of gestures available, hand gestures play an essential role in the field of human‐computer interaction when recognised efficiently in complex and dynamic environments. In this paper, we propose a dynamic hand gesture recognition system to recognise hand gestures appearing in different indoor and outdoor environments. Hand detection and tracking uses a two‐level system resulting in the formation of gesture trajectory in challenging conditions in which existing detection and tracking algorithms could not do so. A set of 45 features is provided as input to the various classification techniques. The redundancy problem has been reduced by selecting a set of optimum features using the analysis of variance method, which ranks the list of features. An incremental feature selection technique calculates recognition accuracy by selecting features according to rankings. This system provides an accuracy of 96.32% when used with machine learning and 97.5% when used with deep learning techniques. Recognition accuracy is calculated for various environments, including an extra hand, multiple persons in the video frame, and outdoor environment. All machine‐learning classifiers are combined using classifier combination to calculate the accuracy according to the majority‐voting rule. Based on the experimental results, it has been observed that deep learning provides better results compared to machine learning.
Breast Cancer Mass Classification Using Machine Learning, Binary-Coded Genetic Algorithms and an Ensemble of Deep Transfer Learning

Abstract The diagnosis of breast cancer (BC) as early as possible is crucial for increasing the survival rate. Mammography enables finding the breast tissue changes years before they could develop into cancer symptoms. In this study, machine learning methods for BC mass pathology classification have been investigated using the radiologists’ mass annotations on the screen-film mammograms of the Breast Cancer Digital Repository (BCDR). The performances of precomputed features in the BCDR and discrete wavelet transform followed by Radon transform have been investigated by using four sequential feature selections and three genetic algorithms. Feature fusion from craniocaudal and mediolateral oblique views was shown to increase the performance of the classifier. Mass classification has been implemented by deep transfer learning (DTL) using the weights of ResNet50, NASNetLarge and Xception networks. An ensemble of DTL (EDTL) was shown to have higher classification performance than the DTL models. The proposed EDTL has area under the receiver operating curve (AUC) scores of 0.8843 and 0.9089 for mass classification on the region of interest (ROI) and ROI union datasets, respectively. The proposed EDTL has the highest BC mass classification AUC score on the BCDR to date and may be useful for other datasets.
Women Mentorship Application using PHP/MySQL

Almost 1 million engineers graduate each year in India, out of which only 15-20% are women which clearly depicts that women are highly underrepresented in STEM. It's 2022 and who says women can't do the same thing men can. Women can also pursue their passions, society is so close-minded that we don't see the potential that they have in their fields. I am a woman and I know what I am capable of and so should you! Gender inequality is everyone’s issue and not just a women's issue. India is a tough place for women even before they are born. Women are not even accepted as human beings, they are treated with injustice and inequality, and they are denied and neglected even in developed countries. Right from childhood women face lots of struggles right from the time they are born, in rural areas people do not even consider educating or sending their daughters to higher studies, WHYYYYY??? Keeping this problem in mind, we have developed an application called Potenshe which is a web application designed to help women unleash their inner power to advance their skillset, rights, and opportunities for women around the globe. Our aim is to bridge the gender inequality gap and intersecting issues, connect with leaders around the world, and access opportunities in the area nearest to their location!
Lax dynamics for Cartan decomposition with applications to Hamiltonian simulation

Abstract Simulating the time evolution of a Hamiltonian system on a classical computer is hard—The computational power required to even describe a quantum system scales exponentially with the number of its constituents, let alone integrate its equations of motion. Hamiltonian simulation on a quantum machine is a possible solution to this challenge—Assuming that a quantum system composing of spin-½ particles can be manipulated at will, then it is tenable to engineer the interaction between those particles according to the one that is to be simulated, and thus predict the value of physical quantities by simply performing the appropriate measurements on the system. Establishing a linkage between the unitary operators described mathematically as a logic solution and the unitary operators recognizable as quantum circuits for execution, is therefore essential for algorithm design and circuit implementation. Most current techniques are fallible because of truncation errors or the stagnation at local solutions. This work offers an innovative avenue by tackling the Cartan decomposition with the notion of Lax dynamics. Within the integration errors that is controllable, this approach gives rise to a genuine unitary synthesis that not only is numerically feasible, but also can be utilized to gauge the quality of results produced by other means, and extend the knowledge to a wide range of applications. This paper aims at establishing the theoretic and algorithmic foundations by exploiting the geometric properties of Hamiltonian subalgebras and describing a common mechanism for deriving the Lax dynamics.
A Swarm Artificial Intelligence Approach for Effective Treatment of Chronic Conditions

Long-term conditions or chronic diseases are multifaceted and challenging. Current treatment options, for patients with long-term conditions, are mainly pharmacological, causing numerous adverse drug events and pressing for alternative management strategies such as personalized interventions. Areas of machine learning, such as deep learning, would enable researchers to develop predictive modelling algorithms, using continuous monitoring and allowing assessing the medical risk for long-term conditions and their related complications. In this paper, we claim that harmonization of data, novel machine learning algorithms, swarm-based technologies, and the involvement of the entire healthcare community will lead to acceptable and effective personalized healthcare. Our proposed approach aims to amplify the intelligence of the healthcare community. Based upon the patients’ characteristics empowers better decisions, personalised medical risk prediction and recommendations of acceptable and effective interventions. Our future work includes the validation of the SwarmAI framework by actively engaging relevant stakeholders.
Forensic Investigation Using RAM Analysis on the Hadoop Distributed File System

The usage of cloud systems is at an all-time high, and with more organizations reaching for Big Data the forensic implications must be analyzed. The Hadoop Distributed File System is widely used both as a cloud service and with organizations implementing it themselves. This paper analyzed the forensic viability of a RAM analysis method for Hadoop based investigations and compared it against targeted process data dumping through the Java heap information. The RAM analysis was done through string searching and the use of the RAM analysis tool Volatility. This work found that RAM analysis can be a valuable tool for discovering artefacts of deleted resources from a Hadoop cluster but was unable to discover further information such as the block to node mapping. The targeted process analysis managed to provide some partial information about deleted resources and produce important metadata on the current state of the file system.
Automated Fault Detection Framework for Reliable Provision of IoT Applications in Agriculture

With the growth of Internet-of-Things (IoT), smart agriculture has become one of the most compelling IoT applications that supports crop management and better resource utilization. In this context, the quality of data gathered by widely distributed IoT edge devices has become critical to guarantee the accuracy of decisions in data-driven applications and cost-effectiveness. The data may be inaccurate and contain errors due to adverse environmental conditions or device faults. Supporting knowledge-based systems for monitoring and analyzing collected data to ensure the reliability of IoT services is vital. However, several challenges are encountered in fault detection for IoT applications, such as mimicking normal sensor behaviour by a faulty sensor, limited time and workforce. Also, the lack of labelled datasets containing both normal and real abnormal data points has affected the set of satisfactory data analysis methods. This work aims to propose a novel fault detection framework by utilizing a systematic feature engineering technique which is able to automatically identify abnormal data points, even nontrivial ones. The feature engineering technique helps to build a more reliable anomaly detection model, shortens the training phase, and preserves the sensor against unseen anomalies. Moreover, we provide collections of labelled datasets obtained from experimental situations from various sensors with and without sensor faults to evaluate our approach. The experimental results indicate that the proposed anomaly detection approach combined with the feature engineering technique outperforms established approaches, which are applied to the raw data without any features. It can be seen that extracting meaningful features is a pivotal step for having more precise anomaly detection.
Theoretical Analysis and Software Implementation of a Quantum Encryption Proposal

This paper analyzes a quantum substitute proposal for current public-key cryptographic systems based on factorization and discrete logarithm. Both its theoretical analysis and its practical implementation are described here. The implementation has been carried out from scratch using IBM Qiskit SDK, reformulating the entire protocol to compact it within a previous library of the authors. The obtained results are conclusive about the insecurity of the proposed scheme.
Methods and models of organization and processing of data and knowledge in an intelligent decision support system for the identification of cultural values

The problem of developing an intellectual decision support system for the identification of cultural values (on the basis of works of art), the issue of choosing and justifying methods and tools for its construction is considered. Methods of research and construction of complex objects, prospects of using similar systems based on modern artificial neural networks as a tool for developing an intelligent decision support system are considered. Considerable attention is paid to the issues of methods of automatic identification and classification of works of art, as the main elements of an intelligent decision support system. Prospects and ways of further research and use of this subject area are also defined.
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems

Consider a linear stochastic filtering problem in which the probability measure specifying all randomness is only partially known. The deviation between the real and assumed probability models is constrained by a divergence bound between the respective probability measures under which the models are defined. This bound defines a so-called uncertainty set. A recursive set-valued filtering characterization is derived and is guaranteed (with probability one) to contain the true conditional posterior of the unknown, real world, filtering problem when the real world measure is within this uncertainty set. Some filtering approximations and related results are given. The set-valued characterization is related to the problem of robust model validation and model goodness-of-fit statistical hypothesis testing. It is shown how relevant terms involving the innovation sequence (re)appear in multiple settings from set-valued filtering to statistical model evaluation.
Security shortcomings in healthcare: a preliminary investigation of Data Protection Authorities’ decisions

As digital technologies are being more and more deployed to support the healthcare sector, the latter becomes increasingly vulnerable to cybersecurity and privacy risks. The past decades, significant effort has been put into advancing standardization and regulatory frameworks, aiming at protecting healthcare infrastructure and digital applications intended for use in healthcare, along with ongoing research on this field. Motivated by the ongoing research that uses digital applications in the healthcare, which is also conducted in two relevant HORIZON research projects (RETENTION and PHOENI2X), this work aims at providing insights on regulatory compliance challenges faced in this context and exploring respective shortcomings or solutions in practice. To this end, we reviewed decisions of the supervisory authorities within the USA and EU regarding data breaches in the healthcare sector, issued from 1/1/2020 to 31/12/2022, illustrating the most common areas of vulnerabilities and discussing the challenges and the lessons learned.
McEliece Cryptosystem: Reducing the Key Size with QC-LDPC codes

Post-quantum cryptography is a growing area since Shor showed that a quantum computer with enough qubits could be used to break the most widely used public-key cryptographic protocols today, such as RSA or those based on the discrete logarithm problem. For this reason, it has become urgent to design cryptosystems that are robust against quantum computer attacks. One of them is the code-based McEliece cryptosystem, which was originally proposed using Goppa codes in 1978. The improved version of the original McEliece cryptosystem, called Classic McEliece, made it as far as the fourth round of the NIST Post-Quantum Cryptography standardization process launched by the National Institute of Technology to update the standards and include post-quantum cryptography in digital signatures, encryption and key exchange. In this work we describe and analyze two variants of the original cryptosystem designed to overcome its main drawbacks, such as its large key size and weakness against known attacks. In addition, both the recent attack that allows the recovery of the private key with limited complexity and the ways in which this attack can be prevented by changing the shape of some constituent arrays in these two new variants are discussed.
Determination for minimum symbol-pair and RT weights via torsional degrees of repeated-root cyclic codes

There are various metrics for researching error-correcting codes. Especially, high-density data storage system gives the existence of inconsistency for the reading and writing process. The symbol-pair metric is motivated for outputs that have overlapping pairs of symbols in a certain channel. The Rosenbloom–Tsfasman (RT) metric is introduced since there exists a problem that is related to transmission over several parallel communication channels with some channels not available for the transmission. In this paper, we determine the minimum symbol-pair weight and RT weight of repeated-root cyclic codes over $$\mathfrak R=\mathbb {F}_{p^m}[u]/\langle u^4\rangle $$ of length $$n=p^k$$ . For the determination, we explicitly present third torsional degree for all different types of cyclic codes over $$\mathfrak R$$ of length n.
DIGITAL LITERACY AND TECHNOLOGIES FOR EDUCATION: PRINCIPLES AND TOOLS

The article considers the relevance of digital literacy and the use of modern learning technologies in educational institutions. Concepts such as digital literacy, digital consumption and digital competence, which allow to determine the role and status of modern universities in the formation of new concepts of higher education, are analyzed; the prerequisites of the decisive factors of the complex implementation of digital literacy in the process of formation of university education are analyzed. The labor market is analyzed in the context of the requirements of the digital economy. Examples of active use of digital technologies in the educational process are given. The teacher's digital literacy is analyzed as a system of basic knowledge, skills and attitudes in the field of everyday use of digital technologies. The proven value of digital technologies in distance learning organizations of educational institutions in quarantine conditions.
FilmovCinema: A Cosine Similarity-Driven Movie Suggestion Engine

Recommendation-based systems are utilized for various purposes such as suggesting web pages, books, restaurants, TV shows, movies, and so on. One of the primary objectives of movie recommendation systems is to provide movie suggestions to users based on their interests, saving them time in scouring the internet for suitable movies from a vast pool of options. Content-based recommendation systems analyze item descriptions and predict movies that a user may prefer, based on the features present in previously selected movies. These systems may utilize one or more features to recommend movies, such as the movie genre, director, or actors. In this paper, we propose a recommendation system that incorporates the features of cast, keywords, crew, and genres. We integrate a column outlining all four features that make it a powerful piece of a movie recommendation system top of Form.
Bike Showroom Management System

The bike showroom management system is a piece of software created to make running a bike showroom more efficient. This system includes modules for managing employee details, stock information, quotations, and an automatic mailing system in addition to bill calculation. Specific departments are only permitted access to various modules, and only the administrator has access to all of them. Users can compute bills, print them, and view previous bills in the bill calculation module. Users can add new staff details, see all staff details, and delete staff details as necessary using the staff details module. Through the automation of operations and the real-time delivery of correct data, this system seeks to increase the effectiveness and efficiency of managing bike showrooms.
SYNTACTIC ANALYSIS OF ARITHMETIC EXPRESSIONS FOR OPTIMIZING THE OPERATION OF PROGRAMS

Our algorithm combines the Reverse Polish Notation (RPN) and expression tree algorithms it is a novel method for efficiently evaluating arithmetic expressions. The algorithm combines the benefits of Reverse Polish Notation (RPN) and Expression Trees to provide a practical and efficient solution for evaluating expressions in both infix and postfix notation. The algorithm first converts the infix notation to RPN, which is then used to construct an expression tree. The expression tree is evaluated using a recursive function that traverses the tree and performs the required operations. The use of an expression tree allows for the optimization of sub-expressions, further improving the efficiency of the algorithm. The Combined RPN and Expression Tree Algorithm offers several advantages over other algorithms for evaluating arithmetic expressions. It is more efficient than some recursive algorithms while being more practical than stack-based algorithms. Furthermore, the use of an expression tree allows for the optimization of sub-expressions, further improving efficiency.