Reviewerly/exHarmony huggingface dataset:
Computer-Aided Diagnosis System for Breast Ultrasound Reports Generation and Classification Method Based on Deep Learning

Breast cancer is one of the most common malignancies that threaten women’s health. Ultrasound testing is a widespread technique employed for the early detection of tumors. However, after receiving the paper ultrasound report, most patients often have to wait for several days to receive the diagnosis results, which can increase their psychological burden and may cause treatment delay. Based on deep learning, this study designed a computer-aided diagnostic system that directly classifies benign and malignant tumors in breast ultrasound images on paper reports taken by patients, helping them obtain auxiliary diagnostic results as soon as possible. In order to segment and denoise ultrasound report images of patients, this paper proposes a breast ultrasound report generation method, which mainly includes a segmentation model, a rotating classification model and a generative model. With this method, multiple high-quality individual breast ultrasound images can be obtained from a single ultrasound report photo, improving the performance of the breast ultrasound image classification model. In order to utilize high-quality breast ultrasound images and improve classification performance, this paper proposed a breast ultrasound report classification model that includes a feature extraction module, a channel attention module and a classification module. The accuracy of the model reached 89.31%, recall rate reached 88.65%, specificity reached 89.57%, F1 score reached 89.42% and AUC reached 94.53% when input images contained noise. The method proposed in this article is more suitable for practical application scenarios and it can quickly and accurately assist patients in obtaining the benign and malignant classification results of ultrasound reports.
CP-CNN: Computational Parallelization of CNN-based Object Detectors in Heterogeneous Embedded Systems for Autonomous Driving

The success of research using convolutional neural network (CNN)-based camera sensor processing for autonomous driving has accelerated the development of autonomous driving vehicles.Since autonomous driving algorithms require high-performance computing for fast and accurate perception, a heterogeneous embedded platform consisting of a graphics processing unit (GPU) and a power-efficient dedicated deep learning accelerator (DLA) has been developed to efficiently implement deep learning algorithms in limited hardware environments.However, because the hardware utilization of these platforms remains low, performance differences such as processing speed and power efficiency between the heterogeneous platform and an embedded platform with only GPUs remain insignificant.To address this problem, this paper proposes an optimization technique that fully utilizes the available hardware resources in heterogeneous embedded platforms using parallel processing on DLA and GPU.Our proposed power-efficient network inference method improves processing speed without losing accuracy based on analyzing the problems encountered when dividing the networks between DLA and GPU for parallel processing.Moreover, the high compatibility of the proposed method is demonstrated by applying the proposed method to various CNN-based object detectors.The experimental results show that the proposed method increases the processing speed by 77.8%, 75.6%, and 55.2% and improves the power efficiency by 84%, 75.9%, and 62.3% on YOLOv3, SSD, and YOLOv5 networks, respectively, without an accuracy penalty.
Automatically adapting the number of state particles in SMC$$^2$$

Abstract Sequential Monte Carlo squared (SMC $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> ) methods can be used for parameter inference of intractable likelihood state-space models. These methods replace the likelihood with an unbiased particle filter estimate, similarly to particle Markov chain Monte Carlo (MCMC). As with particle MCMC, the efficiency of SMC $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> greatly depends on the variance of the likelihood estimator, and therefore on the number of state particles used within the particle filter. We introduce novel methods to adaptively select the number of state particles within SMC $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> using the expected squared jumping distance to trigger the adaptation, and modifying the exchange importance sampling method of Chopin et al. (J R Stat Soc: Ser B (Stat Method) 75(3):397–426, 2012) to replace the current set of state particles with the new set of state particles. The resulting algorithm is fully automatic, and can significantly improve current methods. Code for our methods is available at https://github.com/imkebotha/adaptive-exact-approximate-smc .
An Empirical Study on Multimodal Activity Clustering of Android Applications

Recently, researchers have started looking at Android activities to improve user interface (UI) design. Since similar activities in Android have similar functional behaviors, activity clustering is a fundamental step toward efficient Android app development. Well-grouped activities are useful not only for UI design, but also for app design, development, and testing. However, there are no studies on activity clustering yet, and no activity dataset with labels and categories. The purpose of this study is to use the Rico dataset to know (i) whether the Rico dataset can be used for activity clustering, (ii) how useful activity attributes expressed in XML are for activity clustering, and (iii) how useful fusion with activity image and attributes is for activity clustering. We generate various activity latent vectors using a CNN autoencoder for the Rico dataset. Then, we produce a sequence-to-sequence latent vector from the semantic properties of the Rico dataset. Finally, by fusing the two models, we propose an activity clustering approach using multimodal learning. Since there are no labels in the dataset, we make 2000 labeled data for evaluation. The experimental results show that the activity clustering works well by fusing the semantic activity latent vector and the seq2seq latent vector. Especially, activity attributes such as component and position information are effective for activity clustering and help to boost the performance better than real activity images or Rico. Research findings on clustering and newly created labeled data can be a starting point for various studies on Android activity.
SoLGR: Social Enhancement Group Recommendation via Light Graph Convolution Networks

With the rapid development of social networks, online and offline group activities are becoming more common and diverse. Considering the different interests of group members, the recommendation service for a group is more challenging than the common personalized recommendation. In essence, group recommendation interaction data is a typical heterogeneous graph structure. Therefore, the two challenges to this research are 1) how to learn the representation of groups, users, and items from interaction graphs and social relationship graphs, and 2) how to aggregate the representation of groups, users, and items from these graphs. In this research, we proposed a novel end-to-end Social enhancement Group Recommendation via Light graph convolution network (SoLGR) to address those challenges. Specifically, we first utilize the meta-path to explore the potential social relationship from the user’s perspective. Afterwards, SoLGR deploys a light graph convolution operation on interactive graphs and metapath-based graphs to aggerate the embedding of groups, users, and items on each graph. Finally, the representations of different layers are accumulated and used to achieve predictions for both groups and users. The experimental results show that our proposed model significantly improves the group recommendation performance on two real-world datasets.
A new type of generic, self-evolving and efficient automated deduction algorithm based on category theory

&lt;abstract&gt;&lt;p&gt;In this article, a new type of generalized, self-evolving and efficient automated statement proof algorithm based on new data structures, i.e., brackets and map graphs, and new algorithms is presented. The brackets structure provides an elegant low-knowledge representation of mathematical concepts. The map graphs offer an efficient machine-learning method which let the computer learn knowledge while proving. Additionally, the new finding is built completely on category theory. Furthermore, a prototype of the program is presented and examined for performance.&lt;/p&gt;&lt;/abstract&gt;
A study on the impact of environmental information disclosure on corporate financial performance

Based on the analysis of relevant enterprise data from 2018 to 2022, this study found that environmental information disclosure has a positive effect on enterprise financial performance, and investor sentiment plays a mediating role in environmental information disclosure on enterprise financial performance, and put forward relevant suggestions on how to improve enterprise financial performance through environmental information disclosure and investor sentiment.
Pseudorandom Binary Sequences: Quality Measures and Number-Theoretic Constructions

In this survey we summarize properties of pseudorandomness and non-randomness of some number-theoretic sequences and present results on their behaviour under the following measures of pseudorandomness: balance, linear complexity, correlation measure of order k, expansion complexity and 2-adic complexity. The number-theoretic sequences are the Legendre sequence and the two-prime generator, the Thue-Morse sequence and its sub-sequence along squares, and the prime omega sequences for integers and polynomials.
Leveraging Cross-Modal Effects to Support Squat Exercise

This study proposes a system that uses virtual reality to create the illusion of decreased training load through cross-modal effects, which are when two senses, such as sight and hearing, interact and influence one another. The system utilizes a head-mounted display and headphones for auditory and visual sensory presentation while participants engage in squatting exercises. We use a chair that adjusts seat elevation to prevent user from losing balance during exercise and a non-transmissive head-mounted display to project a 3D scene that changes based on user's movement. Then, we conducted an experimental evaluation to explore the relationship between immersive tendency characteristics and subjective fatigue level. The results indicated that the system's assistance primarily reduced subjective fatigue, with minimal impact on heart rate. Additionally, it was found that the subjects' inclination towards immersion may have contributed to an increase in subjective fatigue, as the discrepancy between the avatar's movements and the subjects' own movements became more pronounced.
Making English Voices Similar to the User’s Voice by Using Interactive Differential Evolution

English skills are becoming necessary for students who would like to give a presentation at international conferences. However, it is very difficult for non-native English speakers to speak in English because of the differences between English and their native languages. There are some ways to practice speaking English such as Shadowing, and if the audio is similar to the learner’s voices inside their heads, it should be more comfortable for the learners to listen to it and repeat after the audio. We propose a method to make English voices similar to the user’s voice by using Interactive Differential Evolution. Experiments were conducted with the concrete system of IDE. 9 examinees participated in the experiments through 10 generations with 6 vectors in each generation and kept choosing the better option. After the experiment, we randomly chose 2 voices from the initial and final generation and asked the examinees to evaluate them. As a result, significantly higher evaluations were obtained in the voices from the final generation.
Finger bending estimation for gesture recognition by wrist shape motion

In recent years, the use of VR and AR has become common in various situations. Most of these controllers are still hand-held. Hand-held controllers have a problem of reducing the sense of immersion. Therefore, in this study, we focus on wrist-type controllerswhich is not hand-held controllers. Although the recognition accuracy of gestures is lower with the wrist type than with the conventional method, it is superior in terms of immersiveness and cost for gesture recognition, we propose a method to estimate the degree of finger bending by using a photo reflector to measure the wrist shape caused by finger bending, and by using the linearity obtained by comparing the finger bending with the sensor values. By using the proposed method, the finger bending can be estimated.
Concentration evaluation based on blinks of eyes

The evaluation areas of learning in Japanese high schools include “Knowledge and Skills,” “Thinking, Judgment, and Expression,” and “Proactive Attitude to Learning.” Aiming to solve the problem of teachers’ subjectivity in evaluating “Proactive Attitude to Learning,” we propose a system for evaluating “Proactive Attitude to Learning” based on the number of blinks, a physiological index, and the perceived difficulty of learning, a psychological index. The evaluation by this study system is 86% accurate with a ±1 margin of error. To indicate that the grade processing of a teacher can be substituted.
Development of Chat-Based English Learning System

The purpose of this research is to develop an English learning system that makes effective use of chat. To learn a foreign language, it is essential to expose a child to it for a long period of time and to maintain motivation for learning. Focusing on the fact that Japanese people spend much more time using chat than the average of OECD member countries, we decided to use a chat app as an English learning system. This system has an English writing function and a conversation promotion function. If a user is having trouble creating an English sentence when sending a chat, the English writing function can easily create the sentence using its translation and template functions. The conversation promotion function provides topics to two users and proposes a simple reply to a user who is having trouble finding an answer during a conversation. The results of our evaluation show that conversation can be promoted by easily creating English sentences using the topic provision and template functions.
Artistic Image Style Transfer Based on Laplacian Pyramid Network

As a very creative research direction in the field of deep learning, style transfer research has been attracted a lot of attention in the past two years, which can transform ordinary photos into artistic paintings and generate interesting pictures. Existing methods can be divided in two categories: arbitrary style transfer and single style transfer. The representative method of single style transfer and method LapStyle synthesizes stylized image in a progressive procedure by using AdaIN, an arbitrary style transfer method. Inspired by it, this paper proposes a novel arbitrary style transfer framework named LapAttN to optimize the single style transfer, which combines the advantages of LapStyle and AdaAttN. The experimental results on the COCO dataset show that the image quality of proposed method is superior to other SOTA algorithms.
Can individuals be identified using bio-signal indicators of subjects measured from wearable sensors?

In recent years, wearable sensors have been used to non-invasively obtain a variety of biometric information such as heart rate, pulse wave, acceleration, etc. What is the degree of personal identifiability of biometric information obtained from such wearable sensors? In Japan, the revised Personal Information Protection Law, which was fully enforced in 2017, clarified that personal identification codes such as DNA, facial features, and iris are included in personal information. And in recent years, consideration of human rights and privacy for technologies that monitor and track people has been called for. In this study, 14 parameters were extracted from wearable sensor data of 8 subjects over a 4-day period, and their personal identifiability was verified using machine learning. The results suggest that subjects whose biometric values are close to each other may be misidentified, and that subjects with large intra-individual variation in biometric information are significantly less discriminable.
Extraction of Eye Open/Close State by MediaPipe and its Application to Avatar Expression

The global outbreak of COVID-19 that started in 2019 made face-to-face communication challenging. As one alternative, attention was shifted to the metaverse, virtual reality (VR) space on the Internet. Particularly, avatar communication using avatars garnered attention. Avatar communication aids diverse nonverbal information to be given to avatars and is presumed to be applied in different fields including education. Nevertheless, many existing metaverse systems have considerable environmental constraints to function. Additionally, the complex environment required to include nonverbal information makes it challenging for many people to use. The final objective of this study is to construct a metaverse system that can easily add nonverbal information. As an intermediate step, we executed the extraction of open/closed eyes by applying MediaPipe. The outcomes demonstrated a 86.70% increment in the percentage of correct responses in the case of wearing glasses compared to the prior investigation. Additionally, the avatar was able to move following the extracted data. Still, because of the small number of experimental situations, it is necessary to confirm whether the system is general-purpose or not. It is also necessary to integrate the system into a metaverse system to validate the actual operation.
The traditional philosophy of violins to design individual character

This study analyses the luthier's philosophy on traditional violin making through a literature review and online survey to investigate the sources of the affective value produced by the violin. Violins have existed for more than five centuries without changing their traditional artisan philosophy and design, without being taken in by industrial advances such as alternative materials and greater efficiency that have accompanied many technological innovations since the industrial revolution. The luthier's philosophy of artisanship, which has been passed down is considered key to these violins, known for their acoustic performance and appearance over time due to the use of natural materials and traditional techniques by skilled artisans. The results of this study suggest that these traditional violins have a traditional philosophy that affirms the individual character of the musical instrument and is conscious of its growth after it is worked.
Optical character recognition quality affects subjective user perception of historical newspaper clippings

Purpose This study aims to identify user perception of different qualities of optical character recognition (OCR) in texts. The purpose of this paper is to study the effect of different quality OCR on users' subjective perception through an interactive information retrieval task with a collection of one digitized historical Finnish newspaper. Design/methodology/approach This study is based on the simulated work task model used in interactive information retrieval. Thirty-two users made searches to an article collection of Finnish newspaper Uusi Suometar 1869–1918 which consists of ca. 1.45 million autosegmented articles. The article search database had two versions of each article with different quality OCR. Each user performed six pre-formulated and six self-formulated short queries and evaluated subjectively the top 10 results using a graded relevance scale of 0–3. Users were not informed about the OCR quality differences of the otherwise identical articles. Findings The main result of the study is that improved OCR quality affects subjective user perception of historical newspaper articles positively: higher relevance scores are given to better-quality texts. Originality/value To the best of the authors’ knowledge, this simulated interactive work task experiment is the first one showing empirically that users' subjective relevance assessments are affected by a change in the quality of an optically read text.
Medical Knowledge Extraction from Graph-Based Modeling of Electronic Health Records

The variety and dimensionality of health-related data cannot be addressed by the human perception to arrive at useful knowledge or conclusions for proposing individualized treatment, diagnosis, or prognosis for a disease. Treating this wealth of heterogeneous data in a tabular manner deprives us of the knowledge that is hidden in interactions between the different types of data. In this paper, the potentials of graph-based data modeling and management are explored. Entities such as patients, encounters, observations, and immunizations are structured as graph elements with meaningful connections and are, consequently, encoded to form graph embeddings. The graph embeddings contain information about the graph structure in the vicinity of the node. This vicinity contains multiple low-level graph embeddings that are further encoded into a single high-level vector for utilization in downstream tasks by applying higher-order statistics on Gaussian Mixture Models With reference to the Covid-19 pandemic, we make use of synthetic data for predicting the risk of a patient’s fatality with a focus to prevent hospital overpopulation. Initial results demonstrate that utilizing networks of health data entities for the generation of compact medical representations has a positive impact on the performance of machine learning tasks. Since the generated Electronic Health Record vectors are label independent, they can be utilized for any classification or clustering task words.
Enhancing Medication Event Classification with Syntax Parsing and Adversarial Learning

In this paper, we introduce a method for extracting detailed information from raw medical notes that could help medical providers more easily understand a patient’s medication history and make more informed medical decisions. Our system uses NLP techniques for finding the names of medications and details about the changes to their disposition in unstructured clinical notes. The system was created to extract data from the Contextualized Medication Event Dataset in three subtasks. Our system utilizes a solution based on a large language model enriched with adversarial examples for the medication extraction and event classification tasks. To extract more detailed contextual information about the medication changes, we were motivated by aspect-based sentiment analysis and used the local context focus mechanism to highlight the relevant parts of the context and extended it with information from dependency syntax. Both adversarial learning and the syntax-enhanced local focus mechanism improved the results of our system.