Reviewerly/exHarmony huggingface dataset:
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation

An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generalization (DG) aims to achieve this goal. However, most DG methods for segmentation require training data from multiple domains during training. We propose a novel adversarial domain generalization method for organ segmentation trained on data from a \emph{single} domain. We synthesize the new domains via learning an adversarial domain synthesizer (ADS) and presume that the synthetic domains cover a large enough area of plausible distributions so that unseen domains can be interpolated from synthetic domains. We propose a mutual information regularizer to enforce the semantic consistency between images from the synthetic domains, which can be estimated by patch-level contrastive learning. We evaluate our method for various organ segmentation for unseen modalities, scanning protocols, and scanner sites.
Introducing Construction Semantics (CxS): a frame-semantic extension of Construction Grammar and constructicography

Abstract Construction Semantics (CxS) uses FrameNet frames to capture the semantic properties of grammatical constructions. It closes a gap between Frame Semantics and usage-based Construction Grammar as well as providing a uniform format to implement frames into constructicographic work. This article provides an overview of three core features of CxS. Starting point is (i) the modeling of semantic properties associated with constructions in terms of three types of frames. This makes possible (ii) the analysis of structural parallels between constructions and frames, in particular the semantic motivation of elements of a construct by frame elements. Lastly, (iii) an inventory of semantic parameters of constructions serves as a toolkit for generalizing over semantic properties of single constructs. A driving force behind CxS is the concept of frame proximity: a network of frames which allows one to distinguish lexically evoked frames that are related or unrelated to a frame associated with a construction.
Swap-test interferometry with biased ancilla noise

The Mach--Zehnder interferometer is a powerful device for detecting small phase shifts between two light beams. Simple input states -- such as coherent states or single photons -- can reach the standard quantum limit of phase estimation while more complicated states can be used to reach Heisenberg scaling; the latter, however, require complex states at the input of the interferometer which are difficult to prepare. The quest for highly sensitive phase estimation therefore calls for interferometers with nonlinear devices which would make the preparation of these complex states more efficient. Here, we show that the Heisenberg scaling can be recovered with simple input states (including Fock and coherent states) when the linear mirrors in the interferometer are replaced with controlled-swap gates and measurements on ancilla qubits. These swap tests project the input Fock and coherent states onto NOON and entangled coherent states, respectively, leading to improved sensitivity to small phase shifts in one of the interferometer arms. We perform detailed analysis of ancilla errors, showing that biasing the ancilla towards phase flips offers a great advantage, and perform thorough numerical simulations of a possible implementation in circuit quantum electrodynamics. Our results thus present a viable approach to phase estimation approaching Heisenberg-limited sensitivity.
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition

Graph convolutional networks (GCNs) have emerged as dominant methods for skeleton-based action recognition. However, they still suffer from two problems, namely, neighborhood constraints and entangled spatiotemporal feature representations. Most studies have focused on improving the design of graph topology to solve the first problem but they have yet to fully explore the latter. In this work, we design a disentangled spatiotemporal transformer (DSTT) block to overcome the above limitations of GCNs in three steps: (i) feature disentanglement for spatiotemporal decomposition;(ii) global spatiotemporal attention for capturing correlations in the global context; and (iii) local information enhancement for utilizing more local information. Thereon, we propose a novel architecture, named Hierarchical Graph Convolutional skeleton Transformer (HGCT), to employ the complementary advantages of GCN (i.e., local topology, temporal dynamics and hierarchy) and Transformer (i.e., global context and dynamic attention). HGCT is lightweight and computationally efficient. Quantitative analysis demonstrates the superiority and good interpretability of HGCT.
Feasibility of a Neural Network with Linearly Approximated Functions on Zynq FPGA

This paper is focused on the feasibility of a neural network with linearly approximated functions on modern FPGA. An approximate multiplier and linearly approximated activation functions were used for a neural network implemented on Zynq FPGA. The neural model was created and learned in the TensorFlow framework. The model parameters were extracted from the Keras model by Python scripts, including the generation of the hardware-accurate model in C++. We proposed a novel architecture for a fully functional, layered, and configurable neural network. The Vivado design in the form of a PYNQ overlay has been tested on a PYNQ-Z2 board under the Linux operating system. The neural network design runs at the 100MHz clock frequency. The synthesis and implementation provided valuable information on the consumption of FPGA resources and the feasibility of implementation. The results can be found at the end of the paper.
A Study of the Use of Euler Totient Function in RSA Cryptosystem and the Future of RSA Cryptosystem

Abstract At the end of the last century, an innovative two-key cryptosystem called RSA was created due to booming demand for secure remote communication. The core for this encryption system is a mathematical function called the Euler totient function (Euler φ function), introduced by Leonhard Euler. This article will first deduce four theorems related to Euler φ function, and then illustrate the correctness of the RSA cryptosystem using Euler’s φ function. The study then starts with well-known RSA attacks such as the Coppersmith attack, assesses some of RSA’s vulnerabilities, and measures the impact of quantum technologies on RSA. Finally, this study provides projections and recommendations for the future development of RSA based on all evaluation studies completed during the entire research program.
Developing multimedia-based learning media on the digestive system using Adobe Flash Professional CS6 application for class XI

This study aims to develop and test the feasibility of multimedia-based learning media for the concept of the human digestive system using Adobe Flash Professional CS6. This research and development (R&amp;D) use the ADDIE development model. The development carried out in this study is limited to the 7th step, including problem identification, data collection, product design, design validation, design revision, product testing, and product revision. The subjects of this study were determined by using random sampling. Data collection was carried out by non-test techniques using interview guide sheets, validation questionnaires from media and material experts, and student response questionnaires. The results showed that the feasibility test of the product developed based on the material was in the excellent category of 91%. Furthermore, the results showed that the feasibility test of the product developed based on the assessment of media experts was feasible with 87.5%. User-based due diligence through limited trial also showed decent results with 97.7%. These results indicate that the multimedia-based learning media developed using Adobe Flash Professional CS6 is feasible for mass production after the final revision.
Importance of the Spectral gap in Estimating Ground-State Energies

The circuit complexity in preparing the ground state of a Hamiltonian is studied as a function of the spectral gap---the energy difference between the ground and the first excited state---and an optimistic outlook for quantum variational methods is presented.
Fraud Detection in Medical Insurance Claim System using Machine Learning : A Review

Since the beginning of the insurance industry, there has been the problem of fraudulent insurance claims. These are a broad variety of illegal activities, the most of which are never uncovered while costing the insurance industry billions of dollars annually. It is estimated that India's insurance industry is suffering losses of around 600–Rs. 600 million each year because of India's growing economy, more awareness, and strengthened distribution networks. 800 crores in losses sustained yearly due to bogus claims. India comes up at number 10 for gross premiums collected by life insurance companies and number 15 for the total amount earned by non-life insurance companies. As a result of this, we are presenting a framework for the selection of features to be used in machine learning, which will enable the robust categorization of insurance claims. It will demonstrate how these technologies might be used to the development of a system that can prevent certain kinds of fraud in the field of healthcare. Several different studies have been carried out to demonstrate that the established approach may effectively identify instances of healthcare fraud. As a result, it may be useful in the prevention of false claims and gives greater insight into how to enhance patient management and treatment methods.
Sentiment Analysis of Twitter

Abstract: Hate Speech is a widespread problem that degrades a person or people based on their race, religion, gender or disability. This research work proposes a tool to raise awareness on the persistent hate speech in social media platforms. The primary aim of this research work is to highlight the content that promotes violence or hatred against individuals or groups based on religion, gender or ethnicity. Logistic regression is a technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems. Using this algorithm, the model trains itself from the dataset and identifies and displays the sentiment of the tweets. Also, to get the real-time analysis on tweets, Twitter API and libraries such as Tweepy and Textblob are used. The proposed model has the ability to detect the appropriate sentiment with 83.98 percent accuracy. The tool is made free and available for demo use to thepublic
Mobile Banking Financial Solution for Blockchain-Powered Agri-Food Supply Chain (ASC)

Agri-food supply chain (ASC) financial flow is a very essential. Most blockchain and IoT-based ASC models advise using blockchain-based currency like Ethereum to make a seamless process. Few countries, including India, Bangladesh and others, do not allow blockchain-based crypto currency exchange. A secure and tamper-free record of financial transactions between ASC stakeholders requires alternative money flow management. Financial inclusion is the practice of providing vulnerable groups, such as low-income and weaker sectors, with cheap access to financial services and loans when needed. 62% of Bangladesh's population lives in rural areas, and most don't have bank accounts. Untapped market for commercial banks. Due to costs and legal restrictions, building bank branches in rural Bangladesh is not a viable option. Since 2011, commercial banks have tried to incorporate unbanked persons in official financial channels via Mobile Financial Services (MFS). The best solution is branchless banking. Branchless banking has enormous potential to bring financial services to poor individuals not served by typical bank branch networks; it cuts delivery costs, including costs to banks of creating and maintaining a delivery channel and to clients of accessing services, e.g., travel or queue up periods. Mobile Financial Services (MFS) is a way to provide financial services that integrates banking with mobile wireless networks to enable banking transactions. This implies depositing, withdrawing, sending, and receiving money from a mobile account. Bank agents facilitate these services by allowing mobile account holders to transact outside of bank premises. It allows millions more people to use the official financial system beyond bank branches and ATMs. Bangladesh Bank intends to produce an economically sustainable, safe MFS market. Almost everyone in Bangladesh has Bkash, Nagad, Rocket, etc. on their phone. Digital wage disbursement solutions like bKash have reduced cash handling for several garments’ companies in Bangladesh. Mobile banking creates a transection ID with all relevant information for every transfer and salary payment, comparable to blockchain's tamper-proof hash algorithm. Instead of crypto cash, we advise integrating mobile banking transection ID with blockchain smart contract. The following Figure illustrates a potential ASC model with blockchain-powered non-cryptocurrency based financial solution.
Dialogical experiences in, for, and from technologically mediated contexts in teacher education

This work proposes an analysis of pedagogical experiences developed in the context of university teacher education in dialogue with two different chronotopes: habitual face-to-face teaching modality and exceptional non-face-to-face teaching modality due to the COVID lockdown. We consider here two cases of Language and Literature teacher education courses in two universities in Argentina. Both experiences share the search for an equitable, dialogical interaction, in which there is a recovery of the students’ opinions and criteria for the progressive and collaborative elaboration of knowledge. From a qualitative perspective, we resorted to autoethnographic narratives elaborated by the responsible teaching teams of the courses. In the approach we propose, there is a dialogue among different elements of our inquiry: a dialogue between the conceptions that we assume as teachers and researchers about teaching in face-to-face and virtual environments; a dialogue between the conceptualizations and concrete teaching-decisions; between the contexts of performance and the possibilities offered by virtuality; between the pedagogical experiences and the narratives; between the records and other materials that allow us to reconstitute these experiences; and between our voices and the voices of students and graduates who give us back evaluations and sustain the continuity of the dialogue. The analysis accounts for the definition of different chronotopes in the experiences and moments addressed. In both cases, the differences observed respond to contextual factors, particularities of the courses and the previous experiences that the teaching teams have had with ICT. Beyond the above-mentioned differences, for the exceptional non-face-to-face proposals, a greater stability in the proposed sequences and in the dynamics involved is observed in the two experiences, which seeks to generate greater predictability.
Implementation of Ellipsoidal Operations in CORA 2022

Tool presentation: Ellipsoids are a broadly applied set representation for formal anal- yses, such as set-based observers, or reachability analysis. While ellipsoids are not closed under frequently used set operations like Minkowski sum or intersection, their widespread popularity can be attributed to their compact numerical representation and intuitive na- ture. As a result, there already exist toolboxes that implement many operations for el- lipsoids. That said, most are either no longer maintained, or implement only a subset of necessary ellipsoidal operations. Thus, we implement all common set operations, as well as recent research results on ellipsoids as a class in the Continuous Reachability Analyzer (CORA) - a free MATLAB toolbox for continuous reachability analysis. Previously, CORA already contained implementations for many ellipsoidal operations, which, however, were lacking in speed, accuracy, and functionality. Here, we describe the implementation of the ellipsoid class in CORA and compare it against the popular, but no longer maintained, Ellipsoidal Toolbox (ET).
Clinical Research Informatics

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021.Using PubMed, we did a bibliographic search using a combination of MeSH descriptors and free-text terms on CRI, followed by a double-blind review in order to select a list of candidate best papers to be peer-reviewed by external reviewers. After peer-review ranking, three section editors met for a consensus meeting and the editorial team was organized to finally conclude on the selected three best papers.Among the 1,096 papers (published in 2021) returned by the search and in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes an operational and scalable framework for generating EHR datasets based on a detailed clinical model with an application in the domain of the COVID-19 pandemics. The authors of the second best paper present a secure and scalable platform for the preprocessing of biomedical data for deep data-driven health management applied for the detection of pre-symptomatic COVID-19 cases and for biological characterization of insulin-resistance heterogeneity. The third best paper provides a contribution to the integration of care and research activities with the REDCap Clinical Data and Interoperability sServices (CDIS) module improving the accuracy and efficiency of data collection.The COVID-19 pandemic is still significantly stimulating research efforts in the CRI field to improve the process deeply and widely for conducting real-world studies as well as for optimizing clinical trials, the duration and cost of which are constantly increasing. The current health crisis highlights the need for healthcare institutions to continue the development and deployment of Big Data spaces, to strengthen their expertise in data science and to implement efficient data quality evaluation and improvement programs.
A Survey of Few-Shot Learning Research Based on Deep Neural Network

With the successful development of deep learning techniques in recent years, deep neural networks have achieved excellent results in both computer vision and natural language processing by relying on large-scale datasets but still face significant challenges in solving the problem of learning from few-shot. Inspired by the ability of humans to learn to recognize objects as a way to simulate the cognitive process of learning from a small sample size, few-shot learning is a hot topic of research in deep neural networks today. It is also a significant and challenging problem. This paper first introduces the research background and definition of few-shot learning, introduces the relevant models, and summarizes and analyzes the common approaches to the problem of few-shot learning based on deep neural networks at the present stage, which are divided into four types: data augmentation, model fine-tuning, metric learning and meta-learning. Finally, popular datasets for few-shot learning are described, the paper is concluded and future research directions are discussed.
Formalization of implicit knowledge based on educational competencies and background knowledge

The article is devoted to the discussion of the process of creating uniform approaches to the description of various com- ponents of corporate knowledge in a multidisciplinary information environment of the enterprise. A method for describing corporate knowledge based on the educational-competence-based approach using a separate assessment of educational competencies and background knowledge of employees is proposed. An example of using the proposed approach to solving the personnel management problems is given. As a methodological basis, the division of competencies of the company's employees according to the sources of its occurrence is used. The research is carried out based on an onto- logical approach and methods of studying texts created in natural languages. The paper shows that the employee's competencies can be uniformly determined based on a frequency analysis of documents texts that are more or less related to the employee. The novelty of the work lies in the creation of a uniform approach to the description of corporate knowledge of employees, which is based on the division of employee competencies into those obtained in the process of education, co-author activity, production experience, as well as contextual background knowledge. The possibility of creating software solutions for describing the employees' competencies, which are an integral part of corporate knowledge, is shown. The components of the methodology proposed in the article have passed the testing stage and are used to automate the functioning of enterprise services related to the management of personnel activities.
A Sensitivity Analysis of the MSMARCO Passage Collection

The recent MSMARCO passage retrieval collection has allowed researchers to develop highly tuned retrieval systems. One aspect of this data set that makes it distinctive compared to traditional corpora is that most of the topics only have a single answer passage marked relevant. Here we carry out a "what if" sensitivity study, asking whether a set of systems would still have the same relative performance if more passages per topic were deemed to be "relevant", exploring several mechanisms for identifying sets of passages to be so categorized. Our results show that, in general, while run scores can vary markedly if additional plausible passages are presumed to be relevant, the derived system ordering is relatively insensitive to additional relevance, providing support for the methodology that was used at the time the MSMARCO passage collection was created.
Automatic driving path planning based on A-Star algorithm

Path planning is one of the important contents of autonomous driving system research, the core of which is to accurately and rapidly search the optimal path between the starting point and the end point. A-Star algorithm is A path - finding algorithm which uses heuristic information to improve search efficiency. In this paper, the path planning of automatic driving is realized based on the A-Star algorithm, and the lateral control strategy is replaced by the traditional pure tracking method with Stanley track tracking method to optimize the control scheme, further meeting the requirements of real-time, high efficiency and accuracy of the decision control system. The automatic driving system was built in PreScan and Simulink co-simulation environment to explore the efficiency of the path planning algorithm and the control ability of the trajectory tracking algorithm. The error between the path trajectory and the target ideal path under the control of the algorithm is compared and analyzed in the simulation environment to prove the feasibility and effectiveness of the algorithm. Finally, the algorithm is deployed on the autonomous driving vehicle platform to verify that it can meet the requirements of autonomous driving of small unmanned vehicles.