Reviewerly/exHarmony huggingface dataset:
Grothendieck Universes

Summary The foundation of the Mizar Mathematical Library [2], is first-order Tarski-Grothendieck set theory. However, the foundation explicitly refers only to Tarski’s Axiom A, which states that for every set X there is a Tarski universe U such that X ∈ U . In this article, we prove, using the Mizar [3] formalism, that the Grothendieck name is justified. We show the relationship between Tarski and Grothendieck universe. First we prove in Theorem (17) that every Grothendieck universe satisfies Tarski’s Axiom A. Then in Theorem (18) we prove that every Grothendieck universe that contains a given set X , even the least (with respect to inclusion) denoted by GrothendieckUniverse X , has as a subset the least (with respect to inclusion) Tarski universe that contains X , denoted by the Tarski-Class X . Since Tarski universes, as opposed to Grothendieck universes [5], might not be transitive (called epsilon-transitive in the Mizar Mathematical Library [1]) we focused our attention to demonstrate that Tarski-Class X ⊊ GrothendieckUniverse X for some X . Then we show in Theorem (19) that Tarski-Class X where X is the singleton of any infinite set is a proper subset of GrothendieckUniverse X . Finally we show that Tarski-Class X = GrothendieckUniverse X holds under the assumption that X is a transitive set. The formalisation is an extension of the formalisation used in [4].
Automated Short-Answer Grading using Semantic Similarity based on Word Embedding

Automatic short-answer grading (ASAG) is a system that aims to help speed up the assessment process without an instructor's intervention.Previous research had successfully built an ASAG system whose performance had a correlation of 0.66 and mean absolute error (MAE) starting from 0.94 with a conventionally graded set.However, this study had a weakness in the need for more than one reference answer for each question.It used a string-based equation method and keyword matching process to measure the sentences' similarity in order to produce an assessment rubric.Thus, our study aimed to build a more concise short-answer automatic scoring system using a single reference answer.The mechanism used a semantic similarity measurement approach through word embedding techniques and syntactic analysis to assess the learner's accuracy.Based on the experiment results, the semantic similarity approach showed a correlation value of 0.70 and an MAE of 0.70 when compared with the grading reference.
A Corporate Governance Approach to Cybersecurity Risk Disclosure

Abstract This article sheds light on cybersecurity risk disclosure practices, offering explanations based on the corporate governance literature. We argue that cybersecurity risk management poses particular challenges for corporations due to amplified agency problems. Cybersecurity risks are increasing in number and growing in complexity for companies worldwide. The financial sector in the Benelux region was already digitalising rapidly when, in 2020, enhanced remote-working requirements due to the COVID-19 pandemic further contributed to risk exposure. Substantiating our theoretical discussion, we present and discuss insights as to the most pressing cybersecurity risk management issues in the financial sector based on evidence from semi-structured interviews with Chief Information Security Officers/Chief Security Officers from financial sector leads in the Benelux region. We discuss contemporary factors that might induce management to dedicate more attention to cybersecurity. This apparent shift in companies’ approaches regarding cybersecurity is likely to encounter obstacles and should not be expected to be an even and linear process, given the challenges of processing and communicating information in an environment featuring high uncertainty and technical complexity as well as potentially misaligned incentives.
The Case of Aspect in Sentiment Analysis: Seeking Attention or Co-Dependency?

(1) Background: Aspect-based sentiment analysis (SA) is a natural language processing task, the aim of which is to classify the sentiment associated with a specific aspect of a written text. The performance of SA methods applied to texts related to health and well-being lags behind that of other domains. (2) Methods: In this study, we present an approach to aspect-based SA of drug reviews. Specifically, we analysed signs and symptoms, which were extracted automatically using the Unified Medical Language System. This information was then passed onto the BERT language model, which was extended by two layers to fine-tune the model for aspect-based SA. The interpretability of the model was analysed using an axiomatic attribution method. We performed a correlation analysis between the attribution scores and syntactic dependencies. (3) Results: Our fine-tuned model achieved accuracy of approximately 95% on a well-balanced test set. It outperformed our previous approach, which used syntactic information to guide the operation of a neural network and achieved an accuracy of approximately 82%. (4) Conclusions: We demonstrated that a BERT-based model of SA overcomes the negative bias associated with health-related aspects and closes the performance gap against the state-of-the-art in other domains.
Blockchain-based Secure Medical Data Management and Disease Prediction

Healthcare systems based on the Internet of Things have an increasing demand for health sensing technology. To manage the data collected and sampled by medical devices, traditional centralized data management will lead to attacks such as single point of failure, which pose a security threat. Aiming at the problems of low data trust and uncontrolled data sharing in telemedicine, we proposed blockchain-based secure medical data management and disease prediction. To securely manage healthcare data, we carefully designed three-tier architecture. Specifically, in the user sensor layer, medical sensors monitor the patient status in real-time. In the storage layer, to protect the privacy of patients, we stored their data in blocks and quantified the medical data by using information entropy technology. In addition, in the blockchain layer, we also used smart contracts for application, authorization, and access control of health data to eliminate privacy leaks caused by internal and external security risks. The information summary is recorded on the blockchain to ensure the integrity of backtracking and anti-repudiation. We designed an extensible machine learning algorithm to predict disease types using a disease prediction model algorithm based on transfer learning. Security analysis and numerical results showed that the proposed scheme can effectively manage the safety data of telemedicine and predict the patient's future condition.
Determination of Multimode Motional Quantum States in a Trapped Ion System

Trapped atomic ions are a versatile platform for studying interactions between spins and bosons by coupling the internal states of the ions to their motion. Measurement of complex motional states with multiple modes is challenging, because all motional state populations can only be measured indirectly through the spin state of ions. Here we present a general method to determine the Fock state distributions and to reconstruct the density matrix of an arbitrary multimode motional state. We experimentally verify the method using different entangled states of multiple radial modes in a five-ion chain. This method can be extended to any system with Jaynes-Cummings-type interactions.
PEDAGOGICAL TECHNOLOGY OF EDUCATIONAL E-PORTFOLIO IN THE SYSTEM OF TRAINING FUTURE HISTORY TEACHERS

The active introduction of electronic educational resources in the higher education system is now relevant in the context of global social and educational transformations. The force majeure caused in recent years by the quarantine measures of the COVID-19 pandemic and the current imposition of martial law in Ukraine complicate the effective implementation of the educational process in almost the entire country in real time. After all, the desire of educators and scientists, as well as students, to preserve an educated and strong country, the formation and development of intellectual human potential, gives impetus to the creation of an adaptive environment for distance education in Ukrainian universities. The aim of the study is to present a vision of the introduction of pedagogical technology of open educational E-portfolio of future history teachers in the system of their training in Ukrainian universities on the basis of systematic, competency and diagnostic approaches. Based on the methods of content analysis of scientific achievements of domestic and foreign scientists, a brief description of the stages of the system algorithm by the method of developing their own e-portfolio by future history teachers based on the methodology of personal development and subject-activity approaches: personal motivation; team structuring; joint planning, presentation and demonstration, expertise, proofreading and editing, competent consulting; professional use; monitoring and evaluating the effectiveness of the application. The author presents the developed criteria for indicators of control and evaluation of the results of higher pedagogical education in the historical specialty in the universities of Ukraine, the emphasis is on the peculiarities of reflection.
Research on camouflage target detection method based on improved YOLOv5

Abstract Objective: In the task of camouflage target detection, there is a problem that the target is highly integrated with the complex environment background, which is difficult to identify and leads to false detection and missed detection. A target detection algorithm CM-YOLOv5s is proposed for camouflage characteristics. Method: The algorithm uses YOLOv5s as the basic framework. First, a coordinated attention mechanism is embedded in the backbone feature extraction network, which enhances the network’s ability to extract camouflaged target features, weakens the attention to the surrounding background, and effectively improves the algorithm’s anti-background. Interference ability; secondly, the Mixup data enhancement strategy is used to simulate overlapping occlusion scenarios, which further strengthens the network model’s learning ability for complex samples. Results: The training and verification were carried out on the self-made Military Camouflage Target Dataset (MCTD), and the precision, recall, and average mAP of the improved CM-YOLOv5s algorithm reached 95.9%, 87.1%, and 94.1, respectively. %, compared with the original YOLOv5s model, the average accuracy rate is improved by 3.8 percentage points. Conclusion: The improved algorithm has better detection effect, and realizes accurate identification and rapid positioning of military camouflage targets in complex environments.
A probabilistic ensemble approach for knowledge graph embedding

Knowledge graph embedding (KGE) is a technique for embedding entities and relations of knowledge graphs (KGs) into continuous vector spaces while maintaining the inherent structure of KGs, in this way link prediction can be facilitated by scoring candidate triples. Existing KGE models only capture specific features of a KG, however, link prediction capacity heavily relies on comprehensive features of a KG. In this work, we propose an ensemble approach for KGE by incorporating the features captured by different KGE models. As a key step, we propose a probabilistic scoring index for characterizing the link prediction performance of a given KGE model. Based on this we established a theoretical framework to calculate the optimal parameters for the ensemble model and predict its corresponding link prediction rate. In particular, we proved that our ensemble approach can always improve link prediction performance under some assumptions. The Upper Confidence Bound Algorithm is then used to adjust the parameters. Experimental results on two widely used KGs show that the proposed ensemble approach achieves the state-of-the-art link prediction rate.
Big Data Analytics for Mobility Prediction and Its Classification

Kern forecasting is the detection of which customers may leave a service or cancel a subscription for a service. This is an important forecast for many businesses because gaining new customers will cost more than retaining existing customers. The Retail Banking Seer Forecast is an AI-based model that helps customers assess the likelihood of your bank being blocked. Odor is a good indicator of growth. By comparing and analyzing these two metrics that monitor lost customers, and growth rates, new customers, Kern rates accurately tell you how much your business is growing over time. If growth is greater than recession, you can say that your business is growing. With increasing skill requirements and requirements for quality of experience, mobility forecasting has become widely used for mobile communication and has become one of the key processors that use historical transport information to predict the future locations of traffic users. Predictive maintenance refers to the use of data-driven, efficient maintenance methods, via designed to analyze the Status and maintenance of equipment When Predict what needs to be done. Forecasting maintenance is a type of maintenance that directly monitors the health, condition and performance of an asset in real time. Predictive maintenance is aimed at minimizing costly and unexpected breakdowns and gives the manufacturer the opportunity to plan maintenance around their own production schedule. Some examples of the use of forecast maintenance and forecast maintenance sensors include vibration analysis, oil analysis, Includes thermal imaging and equipment monitoring. This approach guarantees cost of savings in routine or time-based preventive maintenance, due to the tasks are only done when guaranteed. Predictive maintenance techniques to determine the condition of equipment in service Designed to help and evaluate when maintenance should be done. Big Data Analytics is structured, semi-structured, Is the use of advanced analytical techniques against very large, diverse datasets. and an unstructured data of various sizes ranging from different sources to terabytes to zeta bytes. Choosing a career in the field of big data and Analytics can be an exciting career endeavor, and it can be the type of role you are trying to find. Machine learning (ML) is a prediction that considers large-scale multidimensional data from a variety of sources allows you to create models. Several studies have been conducted on the use of ML algorithms to predict road traffic. Traffic forecast is floating car data and traffic flow, average traffic speed and based on historical traffic data such as traffic events The task is to predict real-time traffic information.
Bangla Handwritten Characters Recognition Using Convolutional Neural Network

In the Bangla language, there are 50 complex-shaped characters and working with this huge amount of characters with an appropriate set of features is a tough problem to recognize handwritten characters. Moreover, ambiguity and precision errors are common in handwritten words. Furthermore, among a large number of complex-shaped letters, some are quite similar in shape, making handwritten Bangla characters difficult to recognize. In this work, we proposed a convolutional neural network-based approach for recognizing the handwritten Bangla alphabet. In character recognition, the convolutional neural network (CNN) outperforms most of the other models. However, to guarantee a satisfactory performance, CNNs usually need a great number of samples. Bangla handwriting recognition has been a hot topic for several years, but due to the similarity of many Bangla characters, it's difficult to achieve good results. By training and testing on Bangla character datasets, the model gets a 90.22% validation accuracy for Bangalekha isolated dataset and 93.22% validation accuracy for the Ekush dataset.
Statistical limits for entanglement swapping with semiconductor entangled photon sources

Semiconductor quantum dots are promising building blocks for quantum communication applications. Although deterministic, efficient, and coherent emission of entangled photons has been realized, implementing a practical quantum repeater remains outstanding. Here we explore the statistical limits for entanglement swapping with sources of polarization-entangled photons from the commonly used biexciton-exciton cascade. We stress the necessity of tuning the exciton fine structure, and explain why the often observed time evolution of photonic entanglement in quantum dots is not applicable for large quantum networks. We identify the critical, statistically distributed device parameters for entanglement swapping based on two sources. A numerical model for benchmarking the consequences of device fabrication, dynamic tuning techniques, and statistical effects is developed, in order to bring the realization of semiconductor-based quantum networks one step closer to reality.
Ternary logic design in topological quantum computing

A quantum computer can perform exponentially faster than its classical counterpart. It works on the principle of superposition. But due to the decoherence effect, the superposition of a quantum state gets destroyed by the interaction with the environment. It is a real challenge to completely isolate a quantum system to make it free of decoherence. This problem can be circumvented by the use of topological quantum phases of matter. These phases have quasiparticles excitations called anyons. The anyons are charge-flux composites and show exotic fractional statistics. When the order of exchange matters, then the anyons are called non-Abelian anyons. Majorana fermions in topological superconductors and quasiparticles in some quantum Hall states are non-Abelian anyons. Such topological phases of matter have a ground state degeneracy. The fusion of two or more non-Abelian anyons can result in a superposition of several anyons. The topological quantum gates are implemented by braiding and fusion of the non-Abelian anyons. The fault-tolerance is achieved through the topological degrees of freedom of anyons. Such degrees of freedom are non-local, hence inaccessible to the local perturbations. In this paper, the Hilbert space for a topological qubit is discussed. The Ising and Fibonacci anyonic models for binary gates are briefly given. Ternary logic gates are more compact than their binary counterparts and naturally arise in a type of anyonic model called the metaplectic anyons. The mathematical model, for the fusion and braiding matrices of metaplectic anyons, is the quantum deformation of the recoupling theory. We proposed that the existing quantum ternary arithmetic gates can be realized by braiding and topological charge measurement of the metaplectic anyons.
Prediction of Treatment Response in Triple Negative Breast Cancer From Whole Slide Images

The automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets. In this article, we propose a modification of the standard nested cross-validation procedure for hyperparameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer.
Automatic Generation of Product-Image Sequence in E-commerce

Product images are essential for providing desirable user experience in an e-commerce platform. For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images. Furthermore, there are the numerous and complicated image rules that a product image needs to comply in order to be generated/selected. To address these challenges, in this paper, we present a new learning framework in order to achieve Automatic Generation of Product-Image Sequence (AGPIS) in e-commerce. To this end, we propose a Multi-modality Unified Image-sequence Classifier (MUIsC), which is able to simultaneously detect all categories of rule violations through learning. MUIsC leverages textual review feedback as the additional training target and utilizes product textual description to provide extra semantic information. %Without using prior knowledge or manually-crafted task, a single MUIsC model is able to learn the holistic knowledge of image reviewing and detect all categories of rule violations simultaneously. Based on offline evaluations, we show that the proposed MUIsC significantly outperforms various baselines. Besides MUIsC, we also integrate some other important modules in the proposed framework, such as primary image selection, non-compliant content detection, and image deduplication. With all these modules, our framework works effectively and efficiently in JD.com recommendation platform. By Dec 2021, our AGPIS framework has generated high-standard images for about 1.5 million products and achieves 13.6% in reject rate. Code of this work is available at https://github.com/efan3000/muisc.
Blockchain for Smart Cities: The Future of City Management

Modern smart cities already form a large network of interconnected technologies, and this network is expected to grow rapidly, i.e., billions of IoT devices will form the typical smart city in the coming years. In 2016, India launched the Smart Cities mission, which aims to develop cities using smart solutions that provide basic infrastructure and provide citizens with a dignified quality of life, a clean and sustainable environment. These cities include smart street lights, smart waste management, smart water management, etc. As geographic networks for connecting IT devices leading to an extraordinary collection of data that needs to be protected from attacks and losses. Blockchain is the platform on which this kind of mass data, derived from smart cities, can be stored and accessible by a verified person. This chapter discusses the general architecture of smart cities using blockchain technology, applications, opportunities, and the future scope of blockchain technology in implementing smart cities.
Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus

The use of attributed quotes is the most direct and least filtered pathway of information propagation in news. Consequently, quotes play a central role in the conception, reception, and analysis of news stories. Since quotes provide a more direct window into a speaker's mind than regular reporting, they are a valuable resource for journalists and researchers alike. While substantial research efforts have been devoted to methods for the automated extraction of quotes from news and their attribution to speakers, few comprehensive corpora of attributed quotes from contemporary sources are available to the public. Here, we present an adaptive web interface for searching Quotebank, a massive collection of quotes from the news, which we make available at https://quotebank.dlab.tools.