Reviewerly/exHarmony huggingface dataset:
Higher-order concepts for the potential infinite

The lambda calculus can be seen as a language that connects mathematics with computer science. However, the models used in both disciplines have different basic properties. In computer science these models are mainly based on domain theory with its partial functions and its fixed point property. In mathematics the models use total functions and relations and allow the integration of logic. Typically, mathematical models are based on set theory with its actual infinite sets, which are incongruous with computer science. We will present a dynamic model that is based consequently on the potential infinite. It uses total functions, whereby functions and the function spaces are seen as interdependent, increasing finite entities. It moreover allows the integration of higher-order logic (to be demonstrated in a separate paper), provided the universal quantifier is interpreted in a specific, dynamic way. Such a model can serve as a common denotational semantics of the lambda calculus for both, mathematics and computer science.
Emulating quantum photon-photon interactions in waveguides by double-wire media

Arrays of atoms coupled to photons propagating in a waveguide are now actively studied due to their prospects for generation and detection of quantum light. Quantum simulators based on waveguides with long-range couplings were also predicted to manifest unusual many-body quantum states. However, quantum tomography for large arrays with N ≳ 20 atoms remains elusive since it requires independent access to every atom. Here, we present a novel concept for analog emulation of such systems by unveiling an analogy between the setup of waveguide quantum electrodynamics and the classical problem of an electromagnetic wave propagating in a wire metamaterial. Experimentally measuring near electromagnetic fields, we emulate the two-particle localization arising from polariton-polariton interactions in the quantum problem. Our results demonstrate the potential of wire metamaterials to visualize quantum light-matter coupling in a table-top experiment and may be applied to emulate other exotic quantum effects, such as quantum chaos, and self-induced topological states.
Impact of measurement error on deep neural networks for nuclear material accountancy

Continued growth in the nuclear industry is resulting in an increased burden for regulators due to the costs associated with implementing traditional safeguards. One such stakeholder, the International Atomic Energy Agency (IAEA), has published a research and development roadmap of suggested improvements that would help safeguards remain effective and efficient in the face of growing demands. These priorities include identifying, evaluating, and testing promising applications of robotics and machine learning/artificial intelligence (ML/AI) to improve safeguards. Nuclear material accountancy (NMA) is of particular interest because traditional approaches require time and resource intensive destructive assay (DA) measurements. One desirable improvement would be utilization of unattended measurements in order to reduce the burden of frequent DA measurements required at large throughput facilities, even though such unattended measurements would likely have higher uncertainty. The application of ML could improve detection of small changes, potentially due to material theft, in these higher uncertainty measurements collected at nuclear facilities to enable near real time anomaly detection. However, unique challenges arise when attempting to use deep neural networks with datasets containing errors that are characterized by models commonly associated with safeguards measurements. The purpose of this paper is to outline both traditional and ML based approaches to NMA and compare them through example. It can be shown that traditional methods for NMA often offers superior performance to the proposed ML pipeline, which consists supervised regression and unsupervised classification, when trained on datasets that contain measurement errors. ML approaches may still be competitive with traditional methods for NMA, however, special care must be taken to mitigate the impact of measurement error that disproportionately affects deep learning approaches.
HRD-related morphology discovery in breast cancer by controlling for confounding factors

Lazard et al.1 predict homologous recombination deficiency from hematoxylin and eosin-stained slides of breast cancer tissue using deep learning. By controlling for technical artifacts on a curated dataset, the model puts forward novel HRD-related morphologies in luminal breast cancers.
Evidence-based Messages Are Part of Evidence-Based Medicine: Optimizing Outreach Approaches for Cancer Screenings

Hand hygiene (HH) is the most important means of reducing hospital-acquired infections. However, compliance at health care facilities remains deficient. A process improvement study was conducted at a 191-bed, pediatric hospital in South Texas evaluating a free mobile application for HH surveillance, compared to traditional pen-and-paper methods. Using a series of Plan-Do-Study-Act (PDSA) cycles, the application was piloted on a small scale and then trialed facilitywide from June to November 2021. The number of HH audits was compared to the preceding period using percentage change analysis. The mobile application resulted in 7,388 HH observations collected, compared with 3,082 previously, representing a 140% increase. Two staff roles in the process (data entry and analysis) were eliminated, as observations were pushed directly to the infection preventionist, eliminating approximately eight hours of staff time monthly. The application enabled almost real-time updates to the HH surveillance dashboard and improved the detailedness of the data as more variables were collected during each HH observation. This is a practical alternative for innovating HH observation compared with more sophisticated and expensive HH surveillance technology.
A secure double spectrum auction scheme

With the arrival of the 5G era, wireless communication technologies and services are rapidly exhausting the limited spectrum resources. Spectrum auctions came into being, which can effectively utilize spectrum resources. Because of the complexity of the electronic spectrum auction network environment, the security of spectrum auction can not be guaranteed. Most scholars focus on researching the security of the single-sided auctions, while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and buyers. Researchers begin to design the secure double spectrum auction mechanisms, in which two semi-honest agents are introduced to finish the spectrum auction rules. But these two agents may collude with each other or be bribed by buyers and sellers, which may create security risks, therefore, a secure double spectrum auction is proposed in this paper. Unlike traditional secure double spectrum auctions, the spectrum auction server with Software Guard Extensions (SGX) component is used in this paper, which is an Ethereum blockchain platform that performs spectrum auctions. A secure double spectrum protocol is also designed, using SGX technology and cryptographic tools such as Paillier cryptosystem, stealth address technology and one-time ring signatures to well protect the private information of spectrum auctions. In addition, the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification, and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum auctions. Finally, security analysis and performance evaluation of our protocol are discussed.
Research on risk assessment of large-span steel structure project construction phase based on variable weight-two dimensional cloud model

As an all-round emerging branch of the construction industry, large-span steel structure engineering has a long on-site construction period, complex on-site construction process, and a series of uncertain unfavorable factors. The resulting risks will directly affect the smooth progress of large-span steel structure projects. In order to improve the effective control of risks in the construction stage of long-span steel structures, a risk assessment model for the construction stage of long-span steel structures based on variable weight theory is proposed. The WSR system method is used to analyze the construction risks, and a comprehensive evaluation index system for risks in the construction stage of large-span steel structures is constructed. With the help of the variable weight comprehensive model, the two-dimensional cloud model is used to comprehensively evaluate the risks of the large-span steel structure in the construction stage from the two dimensions of risk hazard and risk probability. Finally, the two-dimensional cloud image is drawn through MATLAB and the fit degree is calculated to further determine the evaluation result. Taking a large-span steel structure stadium in Lanzhou City, Gansu Province as the research object, a comprehensive assessment of the risks of the large-span steel structure construction stage was carried out, confirm the applicability and effectiveness of the model.
Meta features for anomaly detection in AutoML

This abstract paper sheds light on automated machine learning systems used for anomaly detection. Such systems can propose optimal model for a given tasks by using meta learning as a core component. Systems presented in this paper are based on domain-specific meta features for choosing an optimal unsupervised model for anomaly detection. It also proposes further improvements by using multi-dimensional dense vectors to limit the dimensions of meta features.
Optimization of an Information System based on Intelligent Deep Retrieval Algorithm

Optimization of an archives management information system based on intelligent deep retrieval algorithm is studied in the paper. In this paper, a loss function based on cross entropy is proposed to make the distance of samples with large similarity in the Hamming space after hashing small, while the distance of samples with small similarity is large. Archive data security governance ensures the security of archive data through the use of various security technologies and strict and effective management systems, forms a new framework for the general archive data security governance of collaborative governance and precise governance. For these ideas, the core applications to the intelligent deep retrieval algorithm is studied. Through the simulation, the systematic performance is validated.
Parallelization of Dijkstra’s Algorithm for Robot Motion Planning: Is It Worth Increasing Speed Without Losing Accuracy?

This paper examines the problem of parallelizing Dijkstra’s algorithm, as an algorithm for robot motion planning was a challenge to twist the algorithm in such a way so it can be executed in parallel, rather than sequentially. In this paper, we test the validity of a research hypothesis that the speed of Dijkstra’s algorithm can be increased by parallelizing and keeping the same accuracy. The developed use-case of Dijkstra’s algorithm as a robot motion planning algorithm was tested against performance, both in execution speed and accuracy. Finally, we elaborate on the benefits of this parallelizing approach.
Generative Adversarial Networks based Face Fractalization by using GAN

Face Frontalization refers to generating the frontal view from a side faced view. There are a lot of crime scenes going on today in which a frontal face of the suscept is not perfectly visible. Even though many face recognition systems exist, it is still not possible to have a clear front view of the suspect. In order to find a clear face, the existing image should be rotated, this is where the face frontalization comes into action. This model will be able to rotate the available side face image in order to find a frontal face. To achieve this, the Generative Adversarial Networks (GAN) are used. The Generative Adversarial Network (GAN) consists of a discriminator and generator. The discriminator goes deep into the layers from the top layers to all the way leaving to the bottom most layers in order to get a deep understanding on the input image. The generator works in contrast to the discriminator and regain all the deep layers that are deconvoluted by the discriminator. Finally, both the generator and the discriminator will combinedly work to form a Generative Adversarial Network (GAN) and generate output based on the input image.
Analysis of Adaptive, Entropy and Wavelet based Coding Techniques- A Survey

Nowadays, communication is becoming an important part of everyday human activity. High-quality digital devices are available to everyone, and usage of high-resolution images and videos is increasing in the communication process. Sending plenty of data, especially in the form of photos, audio, and video, over the digital medium is a big challenge because of constraints with bandwidth and volume of storage, and it is costly and time absorbing. Due to the advancement in technology, every field is using computer technology to get more productivity or better results at less cost in less time. For example, when a hospital is taken, every day hundreds of patients’ data comes in the form of images and videos, which require large amounts of resources, i.e., storage or network bandwidth for fast processing of accessing and storage. Fast accessing, processing, and storage with the available resources is possible only when compression is used. Image compression techniques work by removing the redundant information from the digital image without diminishing the quality and making the digital image to be represented with a less number of bits which allows easy access and storage without the need for additional resources. In the last three decades, considerable progress has been achieved in the domain of image compression; researchers have presented many algorithms and standards of image compression. However, using new advanced techniques such as machine learning, deep learning, fuzzy-based, and many more, new compression techniques which are dynamic and powerful are being developed. This survey presents a broad overview of existing image coding techniques based on adaptive coding, entropy, and wavelets. A taxonomy of techniques is presented to categorize the methods into different categories. A comparative study of the various compression methods is presented. Parameters to evaluate the performance of the compression algorithm are presented. Finally, the major challenges which are identified during the current research are discussed, and the prospects of future scope are brought forward.
Blockchain as a Service, an overview on AWS and its BaaS

Cloud computing is a fast-growing type of computing service that enables businesses to expand their storage and computing needs. To provide the service, a dedicated cloud computing system is required, whose design should ensure that it can meet the needs of its clients efficiently and effectively. While highly available and scalable, the architecture of a cloud computing system should also avoid relying on expensive hardware for its operation. This paper will first cover the available services that Amazon Web Services (AWS) provides, which is considered a leader in the cloud computing market. The emergence of new technological advancements has enabled a seamless synthesis of cloud computing and blockchain technology, known as blockchain-as-a-service. The connection of these two technologies aims to increase trust, security, transparency, and traceability of shared data. This synthesis is expected to significantly increase the value generated by these two platforms. The main aim of this paper is to explain BaaS in general, particularly its characteristics and usage. This paper will focus on one of the leading cloud providers, AWS.
Implementation of Cyber Security for Enabling Data Protection Analysis and Data Protection using Robot Key Homomorphic Encryption

Cloud computing plays major role in the development of accessing clouduser’s document and sensitive information stored. It has variety of content and representation. Cyber security and attacks in the cloud is a challenging aspect. Information security attains a vital part in Cyber Security management. It involves actions intended to reduce the adverse impacts of such incidents. To access the documents stored in cloud safely and securely, access control will be introduced based on cloud users to access the user’s document in the cloud. To achieve this, it is highly required to combine security components (e.g., Access Control, Usage Control) in the security document to get automatic information. This research work has proposed a Role Key Homomorphic Encryption Algorithm (RKHEA) to monitor the cloud users, who access the services continuously. This method provides access creation of session-based key to store the singularized encryption to reduce the key size from random methods to occupy memory space. It has some terms and conditions to be followed by the cloud users and also has encryption method to secure the document content. Hence the documents are encrypted with the RKHEA algorithm based on Service Key Access (SKA). Then, the encrypted key will be created based on access control conditions. The proposed analytics result shows an enhanced control over the documents in cloud and improved security performance.
Overview of Blockchain and Cloud Computing Services Integration

Nowadays, the adoption of cloud computing has accelerated. Businesses that accepted the cloud in the past few years have increased their usage and now are moving more toward multi - cloud strategies. On the other hand, Blockchain technology increases the trust, security, transparency, and traceability of shared data. Therefore, consolidation of these well-proven technologies promises a great improvement of systems and increased customer satisfaction and security. In this paper, we analyze the advantages of cloud systems and Blockchain technology when they are used separately and the benefits of their merge in one system.
Laplacian-Driven Softmax Probability for Noisy Pixel Detection in Image Reconstruction

The paper presents a modified tool for detection of noise in images when the pixel intensity of noise is within the pixel range. The detection is based on a combination of an edge-detection tool, i.e. the Laplacian filter, and the Softmax function. Although considered as an edge detection technique in the digital image processing, the Laplacian can be efficiently used in the determining noisy pixels. The decision–making threshold is then found using the Softmax function, which is based on the probability of the pixel intensity, which decides if a pixel should be declared as corrupted. The reconstruction is then performed using a gradient-based algorithm. The method is tested and verified through examples.
Text Recognition from Images using a Deep Learning Model

Identifying text in an image or video is a process called text recognition. Both documents and other real-time visuals, including arbitrary photos, use this method. For this, numerous hardware elements and algorithms were created. This study tries to determine which of the two algorithms has the best feature extraction method. A dataset of arbitrary real-time photos with text is collected for this use using an ICDAR dataset. Every image in the collection is preprocessed using one of two techniques. They are image scaling and picture enhancement. The preprocessed photos are then asked for text localization. The cropped image is then examined with character-level grouping. Two feature extraction algorithms are used to extract the key features. The extraction techniques are the HOG or the Histogram of Oriented Gradients method and the LBP or the Local Binary Pattern recognition technique. A deep learning model is developed using the convolutional neural network method. The model then recognizes the term. For each methodology, all the stages are repeated twice to identify the best extraction techniques. The optimum feature extraction method is then determined by putting the models to the test. The results of the models are based on three factors. They are accuracy, precision, and the F1 score. In the end, it is found that the LBP algorithm is better than the HOG algorithm in all three parameters.
Hadoop Map Reduce Techniques: Simplified Data Processing on Large Clusters with Data Mining

Data mining applications have become outdated and outmoded in recent years. The use of incremental processing to refresh mining results is a promising method. It makes use of previously saved states to save time and energy on re-computation. In this research, we offer a novel increment processing addition to the Map Reduce, the most extensively used methodology for mining the big data by using the Naive Bayes, the J48, and the Random Forest algorithms. Map reduction is a programming model for simultaneous processing and generation of massive amounts of data. We examine Map Reduce employing Naive Bayes, J48, and Random Forest algorithms with a variety of processing features for efficient mining that also saves energy. The Naive Bayes algorithm generates more energy and fewer maps. Priority-based scheduling is a task that allocates schedules based on the jobs’ requirements and utilization. As a result of decreasing the maps, the system’s workload is reduced, and energy efficiency is improved. The experimental comparison of the several algorithm techniques (Naive Bayes, J48, and Random Forest) have applied in this article and found that the Random forest is performed better than remaining two algorithms i.e. 92%.
Real-time Emotion Recognition System based on the Facial Expressions using Random Forest Algorithm

Facial expressions are one of the fundamental ways that people convey their feelings. Perhaps the most remarkable, consistent people can quickly express their feelings and intentions by making facial expressions. There has been significant progress in artificial intelligence and machine learning which are now successfully used in various fields. Additionally, pattern categorization and design recognition problems have significantly impacted machine learning computations. These works have used over 80,000 datasets from Kaggle and other websites to train a model to identify people’s emotions as a website called Real-time Emotion Recognition System Based On The Facial Expressions using Random Forest Algorithm. This recommendation predicates based on the Random Forest technique using OpenCV for Python. The System accomplishes the execution of a Convolutional Brain network for preparing a dataset and accomplishing high-order precision, and decreasing the immaterial pixels (pixels outside looks) with the assistance of the Haar Outpouring library present in the OpenCV module. The proposed innovation can distinguish the persistent feeling of the articles in the video outline. The proposed framework was contrasted with other best-at-school 3D look acknowledgment improvements in the wording of general exactness, getting a presentation of 88% against 84%, separately. The outcomes point out there to the present framework can precisely perceive looks progressively.