Reviewerly/exHarmony huggingface dataset:
Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification

Recent progress in deep learning (DL) has revived the interest on DL-based computer aided detection or diagnosis (CAD) systems for breast cancer screening. Patch-based approaches are one of the main state-of-the-art techniques for 2D mammogram image classification, but they are intrinsically limited by the choice of patch size, as there is no unique patch size that is adapted to all lesion sizes. In addition, the impact of input image resolution on performance is not yet fully understood. In this work, we study the impact of patch size and image resolution on the classifier performance for 2D mammograms. To leverage the advantages of different patch sizes and resolutions, a multi patch-size classifier and a multi-resolution classifier are proposed. These new architectures perform multi-scale classification by combining different patch sizes and input image resolutions. The AUC is increased by 3% on the public CBIS-DDSM dataset and by 5% on an internal dataset. Compared with a baseline single patch size and single resolution classifier, our multi-scale classifier reaches an AUC of 0.809 and 0.722 in each dataset.
Improved Private Set Intersection for Sets with Small Entries

We introduce new protocols for private set intersection (PSI), building upon recent constructions of pseudorandom correlation generators, such as vector-OLE and ring-OLE. Our new constructions improve over the state of the art on several aspects, and perform especially well in the setting where the parties have databases with small entries. We obtain three main contributions:
Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study

The application of machine learning (ML) to analyze clinical data with the goal to predict patient outcomes has garnered increasing attention. Ensemble learning has been used in conjunction with ML to improve predictive performance. Although stacked generalization (stacking), a type of heterogeneous ensemble of ML models, has emerged in clinical data analysis, it remains unclear how to define the best model combinations for strong predictive performance. This study develops a methodology to evaluate the performance of “base” learner models and their optimized combination using “meta” learner models in stacked ensembles to accurately assess performance in the context of clinical outcomes. De-identified COVID-19 data was obtained from the University of Louisville Hospital, where a retrospective chart review was performed from March 2020 to November 2021. Three differently-sized subsets using features from the overall dataset were chosen to train and evaluate ensemble classification performance. The number of base learners chosen from several algorithm families coupled with a complementary meta learner was varied from a minimum of 2 to a maximum of 8. Predictive performance of these combinations was evaluated in terms of mortality and severe cardiac event outcomes using area-under-the-receiver-operating-characteristic (AUROC), F1, balanced accuracy, and kappa. The results highlight the potential to accurately predict clinical outcomes, such as severe cardiac events with COVID-19, from routinely acquired in-hospital patient data. Meta learners Generalized Linear Model (GLM), Multi-Layer Perceptron (MLP), and Partial Least Squares (PLS) had the highest AUROC for both outcomes, while K-Nearest Neighbors (KNN) had the lowest. Performance trended lower in the training set as the number of features increased, and exhibited less variance in both training and validation across all feature subsets as the number of base learners increased. This study offers a methodology to robustly evaluate ensemble ML performance when analyzing clinical data.
Backup Resource Allocation of Virtual Machines With Two-Stage Probabilistic Protection

In a cloud, protection through backup of virtual machines contained in physical machines (PMs) reduces damage to users due to failure of PMs, such as hardware malfunctions. However, from a resource cost perspective, it is necessary to reduce the amount of capacity for backup while limiting the probability of unsuccessful protection. Existing studies suggest the method of sharing backup capacity among primary resources, but the amount of capacity reduction required to protect is limited. This paper proposes a backup resource allocation model with two-stage probabilistic protection to minimize the total required backup capacity for multiple simultaneous failures of PMs. In probabilistic protection, backup resources are allocated so that the probability of backup failure does not exceed a given survivability parameter which represents the acceptable probability of backup failure. Probabilistic protection which achieves efficient sharing of backup capacity enables flexible allocation and reduces the required backup capacity. In order to increase the flexibility of backup capacity allocation, the proposed model extends the probabilistic protection to two stages. By dividing the protection into two stages, the weight of probability between stages can be adjusted, enabling more effective capacity sharing. Since it is uncertain which primary PMs fail, we apply robust optimization to the probabilistic protection. By using a table that takes into account the survivability parameter and the failure probability of PMs, the proposed model is formulated as a mixed integer linear programming problem. We prove the NP-hardness of considered problem. A heuristic is introduced to solve the optimization problem. The proposed model can reduce the total required backup capacity compared to the models with dedicated protection and one-stage probabilistic protection. The model can also provide protection in a range of survivability parameters that the model with one-stage probabilistic protection cannot satisfy.
NFT-based online prescription for data management in healthcare services

The development of electronic health (e-health) systems in the last decades led to the emergence of effective infrastructures for managing health records. Due to the vitality and massive amount of healthcare records, blockchain is considered one of the most important infrastructures to store and exchange health records. Access to patient’s medical histories and health record management are essential to healthcare systems. Patients’ records must be exchanged among various parts of the healthcare system and users should have access to manage their profiles and use online facilities in a secure manner. In this paper, for the first time, the non-fungible token (NFT), which is a nascent technology, is used to certify the information immutability of healthcare records in the blockchain infrastructure. The ownership of property and prevention of information disclosure is guaranteed using the encryption strategy is used in the proposed NFT technology. Furthermore, the proposed model satisfies all security criteria such as data integrity, non-repudiation, anonymity, confidentially and privacy. As a result, the proposed model outperforms the related research in the healthcare domain. The proposed model brings many facilities for both patients and physicians such as secure accessibility, data quality, patient consent, efficient record management and better availability and reliability.
Practical Robust DKG Protocols for CSIDH

A Distributed Key Generation (DKG) protocol is an essential component of threshold cryptography. DKGs enable a group of parties to generate a secret and public key pair in a distributed manner so that the secret key is protected from being exposed, even if a certain number of parties are compromised. Robustness further guarantees that the construction of the key pair is always successful, even if malicious parties try to sabotage the computation. In this paper, we construct two efficient robust DKG protocols in the CSIDH setting that work with Shamir secret sharing. Both the proposed protocols are proven to be actively secure in the quantum random oracle model and use an Information Theoretically (IT) secure Verifiable Secret Sharing (VSS) scheme that is built using bivariate polynomials. As a tool, we construct a new piecewise verifiable proof system for structured public keys, that could be of independent interest. In terms of isogeny computations, our protocols outperform the previously proposed DKG protocols CSI-RAShi and Structured CSI-RAShi. As an instance, using our DKG protocols, 4 parties can sample a PK of size 4 kB, for CSI-FiSh and CSI-SharK, respectively, 3.4 and 1.7 times faster than the current alternatives. On the other hand, since we use an IT-secure VSS, the communication cost of our schemes is generally higher, except for a few specific parameters, and the fraction of corrupted parties is limited to less than a third.
Fuzzy Logic and ANN in an Artificial Intelligent Cloud: A Comparative Study

Artificial intelligence has a remarkable effect in every field. Various tools of artificial intelligence have proved themselves in different sectors. Whether it is the banking sector, health industry, or any other sector, it is. One such sector is cloud computing. Cloud computing is Internet-enabled technology and has proved to be a boon in information technology. The aim of this research is to study the two tools of AI in the field of cloud computing. In this paper, we are targeting fuzzy logic and ANN as a tool. A comparative investigation has been made by applying these tools in cloud computing-based resource scheduling. Fuzzy logic works very well in cloud computing because it handles uncertainties efficiently. ANN, on the other hand, provides a trained expert machine for predicting future behavior. After comparing the results in the case of both tools, we get that fuzzy logic outperforms artificial neural networks in the field of resource scheduling. This research is very helpful for researchers researching scheduling cloud-based jobs by setting up their priorities based on chosen attributes.
A Real-Time Data Acquisition System for Single-Band Bathymetric LiDAR

Bathymetric light detection and ranging (LiDAR), which uses a laser with narrow pulse width and a high-repetition-rate, requires a real-time data acquisition system (RTDAQS) for quickly transmitting data with the characteristic of high sampling rates, high resolution and high accuracy. However, none of the RTDAQS in the world has reached these requirements. For this reason, this paper presents a RTDAQS with high speed SerDes data transfer technology based on the architecture of ADC+FPGA+ZYNQ. This RTDAQS consists of acquisition board and storage board. The acquisition board integrates high-speed analog-to-digital converter (ADC) acquisition module, Input/Output (I/O) control module, data transmission module, and Field Programmable Gate Array (FPGA) chip module. The storage board integrates FPGA Mezzanine Card (FMC) interface, solid state and ZYNQ chips. The corresponding software includes configuration for high-speed ADC acquisition module, I/O control module, data transmission module, and FMC interface module. The RTDAQS, which is embedded in the LiDAR with laser pulses width of 2.5ns ± 0.2ns and frequency of 500 Hz, has been verified by the experiments in indoor sink, outdoor pond, river, reservoir, Beihai Bay of the Pacific Ocean. The experimental results and comparison experiments demonstrate that the presented RTDAQS can reach a sampling rate of 2 GSPS and a sampling resolution of 14 bits and a sampling accuracy of 6 LSB with an acquisition error within 0.1 m, which meet the demands of high sampling rate with high accuracy data acquisition and quick data transmission in practice and can be applied to various devices.
TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era

TenCirChem is an open-source Python library for simulating variational quantum algorithms for quantum computational chemistry. TenCirChem shows high-performance in the simulation of unitary coupled-cluster circuits, using compact representations of quantum states and excitation operators. Additionally, TenCirChem supports noisy circuit simulation and provides algorithms for variational quantum dynamics. TenCirChem's capabilities are demonstrated through various examples, such as the calculation of the potential energy curve of H2O with a 6-31G(d) basis set using a 34-qubit quantum circuit, the examination of the impact of quantum gate errors on the variational energy of the H2 molecule, and the exploration of the Marcus inverted region for charge transfer rate based on variational quantum dynamics. Furthermore, TenCirChem is capable of running real quantum hardware experiments, making it a versatile tool for both simulation and experimentation in the field of quantum computational chemistry.
Using Deep Learning to Embed Dual Marks with Encryption Through 3D Chaotic Map

Several deep learning (DL) based data hiding techniques have been proposed in recent years. Generally, these techniques embed and extract hidden marks using deep learning networks. Although these methods are imperceptible and robust, many algorithms are being demonstrated to embed a single mark, which may incur the risk of transmission insecurity. To solve these problems, this paper proposes a robust and secure data-hiding method using deep learning for embedding dual marks in the form of images within a carrier image file. This has significant advantages in data tracing, data usage monitoring and multiple property management. Further, the proposed framework uses a chaotic system over the marked file to achieve a high level of security. Extensive experimental results demonstrate that our proposed method using deep learning is superior to the state-of-the-art techniques with significant improvement of 42.33% and 41.62% in terms of visual quality and robustness, respectively.
Apply the Search Engine Optimization (SEO) Method to determine Website Ranking on Search Engines

In today's digital age, the internet has become an essential tool for various activities, including business promotion. One effective way to promote your business online is by creating a website that offers useful information to visitors. However, simply having a website is not enough; it is crucial to ensure that your website ranks high on search engine results pages (SERPs) in order to attract more visitors. One of the most effective methods to optimize a website for search engines is through search engine optimization (SEO) techniques. These techniques aim to improve a website's visibility on SERPs by using various tactics, such as keyword research, content optimization, and link building. However, it is important to note that SEO optimization is not a one-time task but rather an ongoing process. It requires constant attention and effort to maintain high search engine rankings and ensure the continued success of your online business. By optimizing your website, you can improve its visibility on search engines, attract more visitors, and ultimately drive business growth. It is important to remain vigilant in implementing SEO techniques to maintain high search engine rankings and ensure the continued success of your online business.
Exploring the Impact of AI on The EFL Context: A Case Study of Saudi Universities

This research aims to determine whether or not it is possible to use artificial intelligence (AI) in English for speakers of other languages (ESOL) courses and review previous research pertinent to artificial intelligence in EFL/ESL instruction to present a comprehensive picture of the current degree of artificial intelligence in EFL/ESL instruction. Utilization of intelligent teaching systems, self-regulated learning, virtual reality, immersive virtual environment, and natural language processing in teaching English as a foreign language classroom. The study adopted the questionnaire as a tool for data collection then data was analyzed and discussed to reach the results. The results showed that the ethical responsibility for making the most effective use of AI in the classroom now falls on both educators and students themselves. The article also concludes that artificial intelligence (AI) positively impacts the field of English language teaching (ELT) and learning; however, it needs to be better integrated into educational settings. Teachers and students need to be more aware of the new applications and tools that have flooded the field of AI in recent years. This conclusion was reached in the context of the article.
Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification

We investigate the problem of private read-update-write (PRUW) in relation to private federated submodel learning (FSL), where a machine learning model is divided into multiple submodels based on the different types of data used to train the model. In PRUW, each user downloads the required submodel without revealing its index in the reading phase, and uploads the updates of the submodel without revealing the submodel index or the values of the updates in the writing phase. In this work, we first provide a basic communication efficient PRUW scheme, and study further means of reducing the communication cost via sparsification. Gradient sparsification is a widely used concept in learning applications, where only a selected set of parameters is downloaded and updated, which significantly reduces the communication cost. In this paper, we study how the concept of sparsification can be incorporated in private FSL with the goal of reducing the communication cost, while guaranteeing information-theoretic privacy of the updated submodel index as well as the values of the updates. To this end, we introduce two schemes: PRUW with top <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> sparsification and PRUW with random sparsification. The former communicates only the most significant parameters/updates among the servers and the users, while the latter communicates a randomly selected set of parameters/updates. The two proposed schemes introduce novel techniques such as parameter/update (noisy) permutations to handle the additional sources of information leakage in PRUW caused by sparsification. Both schemes result in significantly reduced communication costs compared to that of the basic (non-sparse) PRUW scheme.
High dynamic reflection surface 3D reconstruction with sharing phase demodulation mechanism and multi-indicators guided phase domain fusion

Accurate and complete 3D measurement of complex high dynamic range (HDR) surfaces has been challenging for structured light projection technique. The behavior of spraying a layer of diffuse reflection material, which will inevitably incur additional thickness. Existing methods based on additional facilities will increase the cost of hardware system. The algorithms-based methods are cost-effective and nondestructive, but they generally require redundant patterns for image fusion and model training, which fail to be suitable for practicing automated 3D measurement for complex HDR surfaces. In this paper, a HDR surface 3D reconstruction method based on sharing demodulation phase unwrapping mechanism and multi-indicators guided phase fusion strategy is proposed. The division of the exposure interval is optimized via the image entropy to generate an optimal exposure sequence. The combination of temporal-spatial binary (TSB) encoding fringe patterns with time-integration strategy and the variable exposure mode of digital mirror device (DMD)-based projector with a minimum projection exposure time of 233 μ s enables the proposed approach to broadly adapt complex HDR surfaces. We propose an efficient phase analysis solution called sharing mechanism that wrapped phase sequences from captured different intensity fringe images are unwrapped through sharing the same group of misaligned Gray code (MGC) decoding result. Finally, a phase sequences fusion model guided by multi-indicators, including exposure quality, phase gradient smoothness and pixel effectiveness, is established to obtain an optimum phase map for final 3D reconstruction. Comparative experiments indicate that the proposed method can completely restore the 3D topography of HDR surfaces with the images reduction of at least 65% and the measurement integrity is maintained at over 98% while preserving the measurement accuracy and excluding the outliers.
Applying dual models on optimized LSTM with U-net segmentation for breast cancer diagnosis using mammogram images

Breast cancer is the most fatal disease that widely affects women. When the cancerous lumps grow from the cells of the breast, it causes breast cancer. Self-analysis and regular medical check-ups help for detecting the disease earlier and enhance the survival rate. Hence, an automated breast cancer detection system in mammograms can assist clinicians in the patient's treatment. In medical techniques, the categorization of breast cancer becomes challenging for investigators and researchers. The advancement in deep learning approaches has established more attention to their advantages to medical imaging issues, especially for breast cancer detection. The research work plans to develop a novel hybrid model for breast cancer diagnosis with the support of optimized deep-learning architecture. The required images are gathered from the benchmark datasets. These collected datasets are used in three pre-processing approaches like “Median Filtering, Histogram Equalization, and morphological operation”, which helps to remove unwanted regions from the images. Then, the pre-processed images are applied to the Optimized U-net-based tumor segmentation phase for obtaining accurate segmented results along with the optimization of certain parameters in U-Net by employing “Adapted-Black Widow Optimization (A-BWO)”. Further, the detection is performed in two different ways that is given as model 1 and model 2. In model 1, the segmented tumors are used to extract the significant patterns with the help of the “Gray-Level Co-occurrence Matrix (GLCM) and Local Gradient pattern (LGP)”. Further, these extracted patterns are utilized in the “Dual Model accessed Optimized Long Short-Term Memory (DM-OLSTM)” for performing breast cancer detection and the detected score 1 is obtained. In model 2, the same segmented tumors are given into the different variants of CNN, such as “VGG19, Resnet150, and Inception”. The extracted deep features from three CNN-based approaches are fused to form a single set of deep features. These fused deep features are inserted into the developed DM-OLSTM for getting the detected score 2 for breast cancer diagnosis. In the final phase of the hybrid model, the score 1 and score 2 obtained from model 1 and model 2 are averaged to get the final detection output. The accuracy and F1-score of the offered DM-OLSTM model are achieved at 96 % and 95 %. Experimental analysis proves that the recommended methodology achieves better performance by analyzing with the benchmark dataset. Hence, the designed model is helpful for detecting breast cancer in real-time applications.