Reviewerly/exHarmony huggingface dataset:
Privacy Preservation in Federated Learning, its Attacks and Defenses using SMC-GAN

When using collaborative machine learning, maintaining privacy is a major concern because different parties want to train a model on their own data without sharing it with others. In collaborative machine learning, the approaches of Secure Multiparty Computation (SMC) and Generative Adversarial Networks (GAN) can be utilized to protect privacy. While GANs can create artificial data samples that are comparable to the real data, SMC allows participants to collaboratively compute a function without disclosing their inputs. In collaborative machine learning, this work provides a method for privacy preservation utilizing SMC and GANs. The proposed approach can produce high model accuracy while maintaining privacy, according to experimental data. The method is assessed using a number of benchmark datasets and contrasted with other privacy-preserving strategies. The findings demonstrate that, in terms of model correctness and privacy preservation, the suggested approach performs better than competing techniques.
Smart Acquiring Platform in Contactless Payments using Advanced Machine Learning : Security Controls using Device Recognition, Geo Fencing and Customer on File

Acquiring platform (Merchant Bank) today faces greater Merchant Fraud cases (catering to small medium business) post pandemic as new methods of payments are evolving. In many cases, the fraud merchants, present the legitimate Know Your Customer (KYC) documents and complete onboarding like a legitimate merchant profile. Acquirers need to mitigate the merchant risk in Payments (multiple method of payments – card Present, card Not Present, contactless Payments (Tap to Pay, Apple Pay, Samsung Pay, using Near-Field Communication (NFC) and biometric based payments), in Point of Sale (PoS) and in large ecommerce portals). To address this, acquiring platform needs to build a comprehensive risk management platform with large set of data from multiple sources (device activity, merchant activity over time, analyze merchant portfolio for a specific geography and finally line of business). Acquirer needs to also analyze KYC data as well as payment data (over period of time). While doing so, risk platform needs to address both account fraud and payments fraud. It also needs to balance accuracy in fraud detection and without compromising high authorization rates. As new payment methods emerge, it is critical that risk management platform gets data from various sources in addition to just KYC and payment data. These are device /peripherals – Internet of Things (IoT) data, leveraging estate management, tracking geo location, analyzing transaction pattern (over period of twenty hours), profiling merchants and merchant segmentation. Risk management platform needs to address PoS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> needs specifically.
What Challenges Do Developers Face About Checked-in Secrets in Software Artifacts?

Throughout 2021, GitGuardian's monitoring of public GitHub repositories revealed a two-fold increase in the number of secrets (database credentials, API keys, and other credentials) exposed compared to 2020, accumulating more than six million secrets. To our knowledge, the challenges developers face to avoid checked-in secrets are not yet characterized. The goal of our paper is to aid researchers and tool developers in understanding and prioritizing opportunities for future research and tool automation for mitigating checked-in secrets through an empirical investigation of challenges and solutions related to checked-in secrets. We extract 779 questions related to checked-in secrets on Stack Exchange and apply qualitative analysis to determine the challenges and the solutions posed by others for each of the challenges. We identify 27 challenges and 13 solutions. The four most common challenges, in ranked order, are: (i) store/version of secrets during deployment; (ii) store/version of secrets in source code; (iii) ignore/hide of secrets in source code; and (iv) sanitize VCS history. The three most common solutions, in ranked order, are: (i) move secrets out of source code/version control and use template config file; (ii) secret management in deployment; and (iii) use local environment variables. Our findings indicate that the same solution has been mentioned to mitigate multiple challenges. However, our findings also identify an increasing trend in questions lacking accepted solutions substantiating the need for future research and tool automation on managing secrets.
The Application of Informationization Teaching in Physical Education Teaching in Vocational Colleges

The application of information based teaching mode in the process of physical education teaching in vocational colleges is relatively common.With the help of digital media and information tools, the quality and efficiency of physical education teaching in vocational colleges can be comprehensively improved.During this period, vocational college teachers should enrich teaching materials and achieve personalized educational guidance for students.At the same time, it is also necessary to scientifically choose and choose teaching methods and tools during the teaching process, in order to achieve teaching innovation and innovation.
Federated Learning: Advancing Collaborative Machine Learning for a Connected Future

Federated learning is an emerging paradigm in machine learning that enables multiple devices or organizations to collaboratively train a shared model without sharing their raw data.This distributed learning approach offers significant advantages, including privacy preservation, reduced data transmission, and improved scalability.This research article provides an overview of federated learning, highlighting its advancements, challenges, and future directions.We delve into the underlying principles, discuss the key components of federated learning, and explore its applications across various domains.Additionally, we address the challenges associated with federated learning, such as communication efficiency, security, and model aggregation.Finally, we outline potential research directions and technological advancements that can further enhance federated learning and its adoption in real-world scenarios.
Computational Brain-Body Co-Evolution

This chapter investigates the synergies between the evolution and development of the nervous systems and the body plan. It starts with the evolution of an undulatory swimming behavior of an elongated animat when the motor configuration and the neural controller are subject to evolution. Then, it is shown that there is a close coupling between the function and complexity of the neural controller and the body plan when the degree of the body plan is under different constraints. Based on this, the development of the body plan is embedded in the model, thus demonstrating a close coupling between the two. Due to the differences in the evolved body plan, different neural controllers emerge, resulting in undulatory and peristaltic locomotive behaviors, respectively. Finally, the development of both the neural controller and the body plan under the regulation of a gene regulatory network is included in the model.
Penerapan Data Protection Dengan Metode Replikasi Snapmirror Relationship Pada Perusahan XYZ

The development of an IT infrastructure at this time is growing rapidly, plus a lot of data, company transactions are stored on the server and thus require external storage as a production data storage medium, such as transactions, employee payroll applications to email used by employees of PT. XYZ. However, there are obstacles such as data corruption on the server so that databases and applications stored on the server are lost and therefore a method is needed to protect data and back up data on the company, the method used is the snapmirror replication method. Where data on a server located in the DC (Data Center) is stored in external storage then the data is replicated on external storage located in the DRC (Disaster Recovery Center), which later the external storage will create a snapshot copy. And later it is hoped that with this snapmirror replication, if corrupt data occurs, it will be recovered properly.
A Speech Act Analysis of Bola Tinubu’s Election Victory Speech

Bola Tinubu of the All Progressive Party became the President-elect after Nigeria's Presidential Election which took place in March 2023.The central goal of an election victory speech such as that of Bola Tinubu is to persuade, unify and mobilize the audience.The speech analyzed in this study is the election victory speech delivered by the President-elect.Selected utterances from the speech are analyzed to find out how the speaker skillfully deployed speech acts that are germane to the psychological context of the speech, occasioned by the opponents' claim that the election was rigged.Hinging on two theoretical frameworks (Bach and Harnish's Speech Act Theory as well as Stance and Engagement theory), the study concludes that speech acts have intentional contents which are products of speaker-hearer shared knowledge, and persuasive use of language in political speeches presupposes the use of different communicative strategies: skillful selection and sequencing of speech acts, personal pronouns, emotional appeals, exploring speaker-hearer shared knowledge, establishing speaker hearer bond, among other strategies.
The Development of Objective and Quantitative Eye-Tracking-Based Method for the Diagnostics of Oculomotor Dysfunctions

Vision specialists, neurologists, and psychologists commonly rely on manual techniques to evaluate oculomotor system performance: (1) direct observation tests such as the NSUCO oculomotor test and (2) visual-verbal formats like Pierce, King-Devick, and DEM tests. Unfortunately, these tests mainly cover a rough and subjective evaluation of oculomotor performance and are limited by observer's experience or measurement obtaining. Our study aims to address these limitations by developing objective and quantitative approach to assess eye movement performance in children and adults using eye-tracking technology. We have developed a method for detailed eye movement analysis in clinical practice, which evaluates (1) saccades (reflexive, voluntary, and anti-saccades), (2) smooth pursuit eye movements (horizontal, vertical, and circular), (3) fixation stability, and (4) reading eye movement parameters, including saccades, regressions, number of fixations, fixation duration, and reading speed. Our method was tested on 378 children aged 6–13 years using Tobii Pro Fusion eye-tracker. By applying our newly developed quantitative methodology, we were able to identify the benefits and limitations of our approach. We believe our method will be particularly useful for vision specialists, and we aim to continue refining and improving it in future studies.
Evaluating denoising performances of basic filters in the detection of microcalcifications on mammogram images

This research evaluates the denoising abilities of some image-processing filters used in facilitating the early detection of microcalcifications in breast tissues. The mean, median and Gaussian filters were employed to denoise mammogram images of microcalcification breast phantoms of various densities. The performances of the filters were assessed by evaluating the mean squared error (MSE), peak signal-to-noise ratio (PSNR), and signal-to-noise ratio (SNR). All experiments were carried out on MATLAB R2020a platform. The results revealed that the Gaussian filter recorded optimal performance in denoising images with all 3 types of added noises compared to the mean and median filters. The PSNR value of the heterogeneous phantom (PVAL/H) was superior to those of the less dense (PVAL/E), dense (PVAL), and extremely dense (PVAL/G) phantoms for all the tested filters. The results of this work agree with the high contrast recorded by the original image of PVAL/H.
Speech recognition of south China languages based on federated learning and mathematical construction

&lt;abstract&gt; &lt;p&gt;As speech recognition technology continues to advance in sophistication and computer processing power, more and more recognition technologies are being integrated into a variety of software platforms, enabling intelligent speech processing. We create a comprehensive processing platform for multilingual resources used in business and security fields based on speech recognition and distributed processing technology. Based on the federated learning model, this study develops speech recognition and its mathematical model for languages in South China. It also creates a speech dataset for dialects in South China, which at present includes three dialects of Mandarin and Cantonese, Chaoshan and Hakka that are widely spoken in the Guangdong region. Additionally, it uses two data enhancement techniques—audio enhancement and spectrogram enhancement—for speech signal characteristics in order to address the issue of unequal label distribution in the dataset. With a macro-average F-value of 91.54% and when compared to earlier work in the field, experimental results show that this structure is combined with hyperbolic tangent activation function and spatial domain attention to propose a dialect classification model based on hybrid domain attention.&lt;/p&gt; &lt;/abstract&gt;
VOICE BASED VIRTUAL ASSISTANT

The goal of this project is to create a voice-based smart virtual assistant that makes use of cutting-edge machine learning, speech recognition, and other technologies. Through personalized responses and recommendations, the main goal is to improve user experience and streamline tasks. Aside from setting reminders and booking appointments, the virtual assistant will also be able to make calls, send messages, and operate smart home appliances. It will also retrieve current data on the weather, news, sports scores, and other topics. The assistant will offer a seamless and intuitive interface across several languages and maintain contextual information from past talks with the help of context awareness and multilingual assistance. The virtual assistant will gradually enhance its effectiveness and gain a deeper understanding of user preferences by continuously learning from user interactions. An intelligent, hands-free virtual assistant that revolutionizes user interaction and task management is the goal of this project. Keywords— AI, Voice Assistant,
A Comparative Analysis of Quantum-based Approaches for Scalable and Efficient Data mining in Cloud Environments

The vast amount of data generated by various applications necessitates the need for advanced computing capabilities to process, analyze and extract insights from it. Quantum computing, with its ability to perform complex operations in parallel, holds immense promise for data mining in cloud environments. This article examines cutting-edge methods for using quantum computing for data mining. The paper analyzes several key quantum algorithms, including Grover's search algorithm, quantum principal component analysis (QPCA), and quantum support vector machines (QSVM). It delves into the details of these algorithms, exploring their principles, applications, and potential benefits in various domains. We also done the comparative analysis of various algorithms and discussed about the difficulties of using quantum computing for data mining, such as the requirement for specialized knowledge, scalability issues, and hardware constraints. Overall, this work demonstrates the ability of quantum computing for scalable and effective data mining in cloud systems and proposes future research avenues for investigating the use of quantum computing for data mining.
Computing the quantumguesswork: a quadratic assignment problem

The quantum guesswork quantifies the minimum number of queries needed to guess the state of a quantum ensemble if one is allowed to query only one state at a time. Previous approaches to the computation of the guesswork were based on standard semi-definite programming techniques and therefore lead to approximated results. In contrast, we show that computing the quantum guesswork of qubit ensembles with uniform probability distribution corresponds to solving a quadratic assignment problem and we provide an algorithm that, upon the input of any qubit ensemble over a discrete ring, after finitely many steps outputs the exact closed-form expression of its guesswork. While in general the complexity of our guesswork-computing algorithm is factorial in the number of states, our main result consists of showing a more-than-quadratic speedup for symmetric ensembles, a scenario corresponding to the three-dimensional analog of the maximization version of the turbine-balancing problem. To find such symmetries, we provide an algorithm that, upon the input of any point set over a discrete ring, after finitely many steps outputs its exact symmetries. The complexity of our symmetries-finding algorithm is polynomial in the number of points. As examples, we compute the guesswork of regular and quasi-regular sets of qubit states.
Dynamic Hybrid-Hypergraph Model Based AI for Systems of Biological Systems

This research paper focuses on using Hybrid Hypergraphs modeling to generate a dynamic model for systems of systems ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SoSs$</tex> ) in the context of medical applications. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SoSs$</tex> are complex systems composed of multiple interacting components operating in a dynamic environment. The objective of this study is to create a dynamic graphical formalism that can represent and control the entirety of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SoS$</tex> using hybrid hypergraphs. We model the physical aspect of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SoS$</tex> by hypergraph nodes, and the managerial (multi-level) and communication aspects are modeled by hybrid hyperedges. The generated model has a temporal dimension that considers the dynamic characteristics of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SoSs$</tex> and takes into account the goals and parameters of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SoSs$</tex> as features vectors for each subsystem. In this paper, we apply the dynamic hybrid hypergraph model to classify a medical <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SoS$</tex> , which includes a robot performing brachytherapy on a bio-inspired phantom prostate, multiple markers for tracking lesions, and the skin as an entry point for the robot needle. We use the phantom prostate to mimic the movements of a real prostate during brachytherapy procedures and track the markers to investigate the impact of various factors, such as inflation or needle insertion, on the movement of the markers and the surrounding tissues. By analyzing this data, we aim to improve the accuracy and effectiveness of brachytherapy treatments. We demonstrate these concepts through the case study of a medical application, showcasing the use of Artificial Intelligence (AI) techniques for multiple tasks, including classification, prediction, optimization, and control in the context of the dynamic hybrid hypergraph model. The study presents a novel approach to modeling and understanding the complex interactions and dynamic behavior of systems of systems in various fields, particularly in medical applications.
A Comparative Study Between Paired And Unpaired Image Quality Assessment In Low-Dose CT Denoising

The current deep learning approaches for low-dose CT denoising can be divided into paired and unpaired methods. The former involves the use of well-paired datasets, whilst the latter relaxes this constraint. The large availability of unpaired datasets has raised the interest in deepening unpaired denoising strategies that, in turn, need for robust evaluation techniques going beyond the qualitative evaluation. To this end, we can use quantitative image quality assessment scores that we divided into two categories, i.e., paired and unpaired measures. However, the interpretation of unpaired metrics is not straightforward, also because the consistency with paired metrics has not been fully investigated. To cope with this limitation, in this work we consider 15 paired and unpaired scores, which we applied to assess the performance of low-dose CT denoising. We perform an in-depth statistical analysis that not only studies the correlation between paired and unpaired metrics but also within each category. This brings out useful guidelines that can help researchers and practitioners select the right measure for their applications.
k-PFed: Communication-efficient Personalized Federated Clustering

Federated Learning (FL) has attracted much attention in recent years as a promising framework of privacy-preserving and scalability. However, the framework is highly vulnerable to data heterogeneity, which widely exists in real-world applications. In this paper, we designed a communication-efficient personalized federated clustering algorithm k-PFed, which overcomes the data heterogeneity and various network scenarios in the FL system. By initializing local datasets by pre-clustering, clients in k-PFed are able to generate a personalized model locally. In this manner, the local and the global model in k-PFed can be trained independently. Meanwhile, the k-PFed designed three working modes for offline, P2P, and cloud-collaborative networks. Experiments on homogeneous and heterogeneous data show that the clustering accuracy is significantly better than other federated clustering algorithms, and the decrease caused by data heterogeneity on k-PFed is significantly lower than that of other algorithms.
Efficient ML Lifecycle Transferring for Large-Scale and High-Dimensional Data via Core Set-Based Dataset Similarity

An end-to-end machine learning (ML) lifecycle consists of many iterative processes, from data preparation and ML model design to model training and then deploying the trained model for inference. When building an end-to-end lifecycle for an ML problem, many ML pipelines must be designed and executed that produce a huge number of lifecycle versions. Therefore, this paper introduces VeML, a Version management system dedicated to end-to-end ML Lifecycle. Our system tackles several crucial problems that other systems have not solved. First, we address the high cost of building an ML lifecycle, especially for large-scale and high-dimensional dataset. We solve this problem by proposing to transfer the lifecycle of similar datasets managed in our system to the new training data. We design an algorithm based on the core set to compute similarity for large-scale, high-dimensional data efficiently. Another critical issue is the model accuracy degradation by the difference between training data and testing data during the ML lifetime, which leads to lifecycle rebuild. Our system helps to detect this mismatch without getting labeled data from testing data and rebuild the ML lifecycle for a new data version. To demonstrate our contributions, we conduct experiments on real-world, large-scale datasets of driving images and spatiotemporal sensor data and show promising results.