Reviewerly/exHarmony huggingface dataset:
Back to Direct Style: Typed and Tight

Translating programs into continuation-passing style is a well-studied tool to explicitly deal with the control structure of programs. This is useful, for example, for compilation. In a typed setting, there also is a logical interpretation of such a translation as an embedding of classical logic into intuitionistic logic. A naturally arising question is whether there is an inverse translation back to direct style. The answer to this question depends on how the continuation-passing translation is defined and on the domain of the inverse translation. In general, translating programs from continuation-passing style back to direct style requires the use of control operators to account for the use of continuations in non-trivial ways. We present two languages, one in direct style and one in continuation-passing style. Both languages are typed and equipped with an abstract machine semantics. Moreover, both languages allow for non-trivial control flow. We further present a translation to continuation-passing style and a translation back to direct style. We show that both translations are type-preserving and also preserve semantics in a very precise way giving an operational correspondence between the two languages. Moreover, we show that the compositions of the translations are well-behaved. In particular, they are syntactic one-sided inverses on the full language and full syntactic inverses when restricted to trivial control flow.
Medical knowledge-guided deep learning for mammographic breast density classification

Classification of Breast Imaging Reporting and Data System (BI-RADS) breast density categories generally reflects the amount of dense/fibroglandular tissue in the breast. Studies have consistently shown that breast with higher density has a higher risk of developing breast cancer compared to breast with lower density. In this paper, we propose a novel end-to-end method, namely, Medical Knowledge-guided Deep Learning (MKDL), for breast mammogram density classification. The principle behind MKDL lies in the fact that many breast image density classification tasks are partly or largely based on certain pre-known image features, such as image contrast and brightness. These pre-known features can be computationally represented and then leveraged as prior knowledge to facilitate more effective model learning and thus boost the classification performance. We designed specific knowledge-based transformations for breast density classification and showed that our model outperformed several state-of-the-art models.
Anatomically constrained and attention-guided deep feature fusion for joint segmentation and deformable medical image registration

The main objective of anatomically plausible results for deformable image registration is to improve model’s registration accuracy by minimizing the difference between a pair of fixed and moving images. Since many anatomical features are closely related to each other, leveraging supervision from auxiliary tasks (such as supervised anatomical segmentation) has the potential to enhance the realism of the warped images after registration. In this work, we employ a Multi-Task Learning framework to formulate registration and segmentation as a joint issue, in which we utilize anatomical constraint from auxiliary supervised segmentation to enhance the realism of the predicted images. First, we propose a Cross-Task Attention Block to fuse the high-level feature from both the registration and segmentation network. With the help of initial anatomical segmentation, the registration network can benefit from learning the task-shared feature correlation and rapidly focusing on the parts that need deformation. On the other hand, the anatomical segmentation discrepancy from ground-truth fixed annotations and predicted segmentation maps of initial warped images are integrated into the loss function to guide the convergence of the registration network. Ideally, a good deformation field should be able to minimize the loss function of registration and segmentation. The voxel-wise anatomical constraint inferred from segmentation helps the registration network to reach a global optimum for both deformable and segmentation learning. Both networks can be employed independently during the testing phase, enabling only the registration output to be predicted when the segmentation labels are unavailable. Qualitative and quantitative results indicate that our proposed methodology significantly outperforms the previous state-of-the-art approaches on inter-patient brain MRI registration and pre- and intra-operative uterus MRI registration tasks within our specific experimental setup, which leads to state-of-the-art registration quality scores of 0.755 and 0.731 (i.e., by 0.8% and 0.5% increases) DSC for both tasks, respectively.
Sign Language Detection using Convolutional Neural Network

Hand gesture is one of the methods used in sign language for non-verbal communication. It is most commonly used by deaf &amp; dumb people who have hearing or speech problems to communicate among themselves or with normal people. Various sign language systems have been developed by many makers around the world but they are neither flexible nor cost effective for the end users. Hence in this paper introduced software which presents a system prototype that is able to automatically recognize sign language to help deaf and dumb people to communicate more effectively with each other or normal people. Pattern recognition and Gesture recognition are the developing fields of research. Being a significant part in nonverbal communication, hand gestures play a key role in our daily life.
An Efficient Priority Queue Data Structure for Big Data Applications

We have designed and developed an efficient priority queue data structure that utilizes buckets into which data elements are inserted and from which data elements are deleted.The data structure leverages hashing to determine the appropriate bucket to place a data element based on the data element's key value.This allows the data structure to access data elements that are in the queue with an O(1) time complexity.Heaps access data elements that are in the queue with an O(log n) time complexity, where n is the number of nodes on the heap.Thus, the data structure improves the performance of applications that utilize a min/max heap.Targeted areas include big data applications, data science, artificial intelligence, and parallel processing.In this paper, we present results several applications.We demonstrate that the data structure when used to replace a min/max heap improves the performance applications by reducing the execution time.The performance improvement increases as the number of data elements placed in the queue increases.Also, in addition to being designed as a double-ended priority queue (DEPQ), the data structure can be configured to be a queue (FIFO), a stack (LIFO), and a set (which doesn't allow duplicates).
Studies Advanced in Image Generation Techniques based on Deep Learning

A popular area of study in computer vision is image generation. How to improve the authenticity and diversity of generated images, and how to reduce the use of hardware resources and the size of model storage to make it more conducive to model landing are the focus and difficulty of image generation algorithms. Moreover, in practical applications, many other tasks are closely related to the image generation task, utilizing the created picture can lower the cost of data collecting, and the produced picture's quality directly influences how well some activities work. This article first introduces the definition of the generated model in supervised learning and unsupervised learning, and analyzes several reasons why learning should be generated into a model. It also introduces the development of the generated models in China and abroad, and makes a simple classification of the generated models with the previous depth degree.
Uncanny Valley Game in Virtual Environments

Nowadays, as VR technology is developing at an incredible rate, related topics such as the virtual environment, mobile equipment for VR, and metaverse are also becoming hot topics. The article will be depicting the process of researching the Uncanny Valley theory's demonstration in the virtual environment. We created a horror virtual reality game that simulates the environment in real life. In the early stage of making the game, we first discussed how to set a proper story background for the game to put in the environment we want for testing the theory. We got our inspiration from "Backroom". Based on our inspiration, we determined the art style of our game, and later, in 3D modeling parts, we designed several monsters. After finishing all the modeling and programming parts, we tested them in our group. Finally, we released it to the public with a questionnaire to test the public's response. After analyzing the collected data, most people will feel a feeling of horror with the environment we create. We can conclude that the Uncanny Valley theory might still be suitable for the virtual environment. It can show that the Uncanny Valley theory might suit more scales than we think. And for VR experiences, we can use this theory to better create a scary scene or experience, which could be used in in-game making or VR filmmaking.
Clustering model research based on composition analysis and identification of glass products

As we all know, ancient glass buried in the soil will be weathered, and a large number of chemical elements in it will exchange with the chemical elements of the soil. As a result, the proportion of chemical components inside the glass changes greatly, affecting the analysis and identification of ancient glass products. This paper selects the fresh data from weathering samples, establish a clustering model and use SPSS to cluster analysis, and classify the types of each cluster. If there is a significant difference, it shall be divided into subcategories; The K-Means algorithm is used to analyze again, and the observation results are compared with the results without disturbance.
Casualty on the Titanic based on Machine Learning Methods

The Titanic sank on April 15, 1914, with 2224 people on board, and only 32% survived. The survivors are somewhat random, but they are somewhat the same. Studying the types of people who are more likely to survive in a disaster will promote an understanding of the values and ideology of the society at the time. Therefore, this study collected data on some of the passengers on the Titanic and the survivors through Kaggle. This study predicts the survival of passengers through data cleaning, feature engineering, dimensionality reduction processing, and different models such as Random Forest, Decision Tree, KNN, and Logistic regression. In this study, it was found that Age, gender, representation of socioeconomic status, and whether they traveled alone were associated with survival rates. Among them, the decision tree and random forest models scored 98.65, which performed the best and had the highest prediction accuracy. After visualizing the data, this study concluded that survival rates were higher for females, children younger than ten years old first-class passengers, and non-solitary travelers.
Formation of “Teamwork Skills” in Future Teachers when Creating Didactic Games with Traditional and Digital Components

In accordance with the requirements of the professional standard, the teacher of the digital school must have skills related to information and communication technologies, communicative competence, and teamwork skills.The formation of relevant qualities in the conditions of higher education is complicated by a number of different problems.The purpose of the study is to study the features of the formation of teamwork skills in future teachers when developing didactic games with traditional and digital components.Research methodology.Gamification is used both as a technology for acquiring new knowledge and as a technology for team building.The team activity method is used to design didactic games.The experiment involved 60 undergraduates of the Vyatka State University of the training program "Psychological and Pedagogical Education".To assess team effectiveness Laura Stack's methodology was chosen (five levels of team building).Research results.The students of the experimental group are involved in team work on educational games: they study basic mechanics in didactic games; master interactive services; develop their own project, including both non-computer and digital elements.In conclusion problematic questions are formulated, the answers to which make it possible to determine the directions of the work of the didactic game design team: discussion of the goal of the game and the game goal, the choice of basic mechanics and formed competences, the rules and resources, etc.
Problems of Student Communication in Online Learning

Despite the lifting of epidemiological restrictions, online learning has not lost its relevance.There are noted the development of hybrid forms of education, the integration of traditional and remote work formats.In this regard, the relevance is the study of the communication process, which becomes a predicate of many dysfunctions in the education system.It is the communication between students and the teacher that is subject to the greatest transformations in the conditions of online learning.The purpose of the author's research is to analyze the experience of online learning for students of Russian universities, to identify the problems of communication between participants in the educational process in the new conditions.The authors used a set of general scientific research methods and analytical procedures (document analysis, comparative analysis, analysis of arbitrary contingency tables using the x2 criterion, etc.).A questionnaire survey of students of Russian universities was chosen as the key research method.Empirical data are presented by the results of two measurements: N1 = 1553 (2020) and N2 = 1107 (2021).The results of the study showed that in the conditions of online learning, students for the most part highlight such negative trends as "deterioration of communication skills" and "decrease in skills for live communication with a teacher".The personal experience of students during the pandemic led to a decrease in "technological optimism" and increased the importance of personal interaction with the teacher.Respondents who rated the quality of teacher feedback excellently feel less of such online learning problems as "routine" and "heavy workloads".The authors established the relationship between the choice of the form of education (online or traditional) and the assessment of feedback between students and the teacher.In the course of the study, an inverse relationship was established between the involvement of students in the educational process and the opportunity to ask a question (or make a comment during the lesson).Students with a high level of involvement in
Bidirectional Object-Oriented Programming: Towards Programmatic and Direct Manipulation of Objects

Many bidirectional programming languages, which are mainly functional and relational, have been designed to support writing programs that run in both forward and backward directions. Nevertheless, there is little study on the bidirectionalization of object-oriented languages that are more popular in practice. This paper presents the first bidirectional object-oriented language that supports programmatic and direct manipulation of objects. Specifically, we carefully extend a core object-oriented language, which has a standard forward evaluation semantics, with backward updating semantics for class inheritance hierarchies and references. We formally prove that the bidirectional evaluation semantics satisfies the round-tripping properties if the output is altered consistently. To validate the utility of our approach, we have developed a tool called BiOOP for generating HTML documents through bidirectional GUI design. We evaluate the expressiveness and effectiveness of BiOOP for HTML webpage development by reproducing ten classic object-oriented applications from a Java Swing tutorial and one large project from GitHub. The experimental results show the response time of direct manipulation programming on object-oriented programs that produce HTML webpages is acceptable for developers.
Lightweight multi-person motion capture system in the wild

Markerless multi-person 3D motion capture techniques find applications in various domains such as digital film and animation, 3D games, virtual-real interaction, and human gait analysis.However, most research papers tend to focus on algorithm innovation rather than building an effective motion capture system.In this paper, we present a lightweight motion capture system that uses only sparse multiview cameras for multiperson interactive scenarios.Our algorithm achieves accurate 3D human pose estimation by fully leveraging the complementary information from multiple views and filtering unexpected 2D human joint detections.We set up a lightweight hardware system that enables real-time acquisition, transfer, and effective processing of multiview data.Furthermore, we introduce a modularized parallel processing scheme that facilitates dataflow control.Finally, we achieve a scalable, maintainable, and efficient framework for hardware and software configurations.The experimental results demonstrate the accuracy, effectiveness, and efficiency of our algorithms and the proposed lightweight motion capture system.
Analysis of COVID-19 Disease With Careless Infective Using SEITRS Model

A. The COVID-19 pandemic has had a significant impact on the global population, with millions of cases and deaths reported worldwide.In this study, we use mathematical models to analyze the spread of the disease, with a focus on careless infective models.We develop and analyze mathematical models to understand the transmission dynamics of COVID-19 , taking into account the impact of human behavior, such as the spread of the disease by individuals who are unaware that they are infected.Our results provide insights into the role of careless infective individuals in the spread of the disease and suggest the need for targeted interventions to reduce the impact of COVID-19 The results of this study contribute to a better understanding of the spread of COVID-19 and inform public health measures to control its transmission.
authAR - Automatic Authoring of Picking and Layout Optimization Instructions

Authoring of AR instructions is typically a time-consuming manual process that requires a user to use a desktop GUI interface or AR device for placing and animating 3D content. This is problematic because manual authoring does not scale to the myriad of product variations of many manufacturing companies. However, automatic authoring is possible, since companies already store data about their products in the form of a bill of materials and bill of processes. In our paper, we present authAR, a novel desktop GUI based tool that combines bill of materials with geometric data of a factory workspace to automatically author AR picking instructions on racks and boxes. In addition, authAR uses this data to propose spatial layouts of racks and boxes according to a customizable optimization algorithm. We conducted a preliminary expert review with four manufacturing workers. Our study showed that authAR has many potentials and was applicable to a real use case at the manufacturing company, Velux. The study contributes new knowledge to the field of AR authoring by including the views of domain experts and using real production data. Suggestions for improvements and new features as well as plans for a more thorough study are presented as future work.
Depth assisted portrait video background blurring

Video conferencing usage dramatically increased during the pandemic and is expected to remain high in hybrid work. One of the key aspects of video experience is background blur or background replacement, which relies on good quality portrait segmentation in each frame. Software and hardware manufacturers have worked together to utilize depth sensor to improve the process. Existing solutions have incorporated depth map into post processing to generate a more natural blurring effect. In this paper, we propose to collect background features with the help of depth map to improve the segmentation result from the RGB image. Our results show significant improvements over methods using RGB based networks and runs faster than model-based background feature collection models.
e-Healthcare data security using blockchain technology

The current medical services suffer from critical difficulties like detailed information, detectability, unchanging nature, review, information provenance, universal access, trust, protection, and security. Additionally, an enormous piece of the existing medical services framework utilized for overseeing data is brought together, which might suffer from a single point of failure in cataclysmic events. This limitation must be overcome using advanced decentralized, secure technologies like blockchain. Blockchain is a decentralized technology that can alter, reshape, and change how information is being taken care of in the medical sector. This chapter talks about utilizing blockchain to share medical data securely. This chapter presents the critical blockchain elements and attributes from a security perspective. It also examines the benefits of blockchain innovation alongside potential open doors for the medical sector to preserve privacy and security. At last, this chapter presents ongoing tasks and contextual investigations to show the reasonableness of blockchain innovation for different healthcare applications and areas. Furthermore, this proposed work distinguishes and talks about significant open examination challenges upsetting the fruitful reception of blockchain in the healthcare area.
Managing health insurance using blockchain technology

Health insurance plays a significant role in ensuring quality healthcare. In response to the escalating costs of the medical industry, the demand for health insurance is soaring. Additionally, those with health insurance are more likely to receive preventative care than those without health insurance. However, from granting health insurance to delivering services to insured individuals, the health insurance industry faces numerous obstacles. Fraudulent actions, false claims, a lack of transparency and data privacy, reliance on human effort and dishonesty from consumers, healthcare professionals, or even the insurer party itself, are the most common and important hurdles towards success. Given these constraints, this chapter briefly covers the most immediate concerns in the health insurance industry and provides insight into how blockchain technology integration can contribute to resolving these issues. This chapter finishes by highlighting existing limitations as well as potential future directions.