Reviewerly/exHarmony huggingface dataset:
Explaining Two Strange Learning Curves

Learning curves illustrate how generalization performance of a learner evolves with more training data. While this is a useful tool to characterize learners, not all learning curve behavior is well understood. For instance, it is sometimes assumed that the more training data provided, the better the learner performs. However, counter-examples exist for both classical machine learning algorithms and deep neural networks, where errors do not monotonically decrease with training set size. Loog et al. [12] describe this monotonicity problem, and present several regression examples where simple empirical risk minimizers display unexpected learning curve behaviors. In this paper, we will study two of these proposed problems in detail and explain what caused the odd learning curves. For the first, we use a bias-variance decomposition to show that the monotonic increase in the learning curve is caused by an increase in the variance, which we explain by a mismatch between the model and the data generating process. For the second problem, we explain the recurring increases in the learning curve by showing only two solutions are attainable by the learner. The probability of obtaining a configuration of training objects that leads to the high risk solution typically decreases as the training set size increases. However, for particular training set sizes, additional configurations that produce the high risk solution become possible. We prove that these additional configurations increase the probability of the high risk solution and therefore explain the unusual learning curve. These examples contribute to a more complete understanding of learning curves and the possibilities and reasons behind their various behaviors.
The Effect of Participation in Mid-term Course Evaluation on End of Term Course Evaluation: Effectiveness of Participation in Mid-term Course Evaluation

The purpose of this study is to find out the effect of participating in mid-term course evaluation on end of term course evaluation. Its purpose is to provide basic data to encourage participation in mid-course evaluation. Data were collected by students attending S University in 2021 for mid-term course evaluation and end of term course evaluation. The results of the study showed that first, participation in the mid-term course evaluation had a positive effect on the end of term course evaluation. Second, it was found that participating in the mid-term course evaluation had a positive effect on the end of term course evaluation in the major curriculum, theory classes, and experiment and practice classes method. Finally, in the liberal art curriculum, participation in the mid-term course evaluation did not have an effect on the end of the course evaluation. These results suggest that it is necessary to encourage participation in mid-term course evaluation in order to provide high-quality lectures to students.
Korean Language Education Research Trends in France

The purpose of this paper is to understand the trend of Korean language education research for French learners by analyzing the content and research methodological characteristics of research on Korean language education conducted in France. As a result of analyzing 57 papers selected for the study, French Korean language education research deals with the current status and problems of Korean language education institutions in terms of content, and mainly focuses on pronunciation, grammar, and vocabulary. Teaching and learning research has a lot of elementary textbook analysis, and research on speaking areas and web-based education has been found to be insufficient. In terms of methodology, it has been found that there are mainly many qualitative and mixed studies, and the ratio of primary and secondary studies is equal, and case studies tend to be the main focus. Descriptive consideration is mostly based on textbook research and researcher's consideration, and overall, it was found that the diversity and expertise of research methods were insufficient.
A Case Study of Humanities Content Development by University Students Using the Legend of Sikjangsan Mountain in Daejeon

In this paper, we introduced the case of humanities content development by students who took &lt;Understanding and Application of Oral Stories&gt;, one of the courses in the interdisciplinary major of the College of Humanities at C University in Daejeon. It is a report of cases in which various genres of contents were developed through brainstorming and group discussions for the development of humanities contents based on the knowledge gained from studying the types of legends related to Sikjangsan Mountain, which represents the Daejeon region. In the design of this class, the instructor made the most important point in designing this class to develop brainstorming and synopsis forms for content development as well as providing students with oral literature accurate knowledge.the development and provision of a brainstorming form that can organize various questions and discussions and a form that can be expressed as a synopsis served as a catalyst for creative synopses of various genres by stimulating students' free imagination.
A Study on Issue Change through Trot Audition Program: Focusing on News Big Data

This study derived trot-related keywords, topics, and topic changes by using topic modeling to understand the media's interest in the trot audition program. This study was conducted as of April 24, 2023, and investigated various trot audition programs aired from December 06, 2018, when the article on the trot audition program &lt;Tomorrow is Miss. Trot&gt; was first reported, to April 24, 2023. As a result, it was confirmed that the popularity and scope of some trot-related content decreased, and direct communication between singers and fans decreased. On the other hand, it can be seen that topics related to new issues such as COVID-19-related performance prevention strategies, moral discussions among participants, political use of the voting system, and global spread of trot are increasing. This paper is meaningful in presenting future directions and suggestions so that trot, which has been neglected, does not remain in 'shining popularity'.
A Self-cultural Journal on the Experience of Operating a Local Children's Center Program

This study aimed to identify the fundamental factors and core values necessary to develop and implement a program for children at local child centers, considering their needs and characteristics. To achieve this, the research questions were set as follows: “What do researchers experience in operating programs at local child centers?” and “What does the experience of operating programs at local child centers mean to researchers?” The research employed the autoethnographic research method, and data were collected through memory materials, cultural artifacts, and self-reflection materials spanning from 2011 to 2022. The collected data were analyzed using the end-point coding method. The research findings revealed four types of programs: “Program of Living as I Am,” “Replica Program of Becoming an Expert,” “Superficial Program of Wanting to Become an Expert,” and “Subtle Program of Becoming an Expert.” These research results are significant as they provide direction and important guidelines for program development and operation. Additionally, they offer insights into the role of operators when utilizing the programs.
50.1: <i>Invited Paper:</i> A Comprehensive Comparison of Eye Rotation Measurement in Eyewear Displays: Pupil or Eyeball Center?

Eyewear displays have been gradually adopted in entertainment, industrial, and medical applications. The optical performance and image quality of the eyewear displays are required to be precisely evaluated and quantified. This challenges the conventional light measuring devices since the displays are viewed in an immersed environment and at a much closer viewing distance from the display to the eye. In eyewear display, measurement within the eye box and the LMD to mimic the eyeball movement is the first step to ensure an accurate measurement. This paper explained the difference of eyeball location in LMD measurement. The comparison between the eye pupil center and eyeball rotation center which are currently included in the IEC 63145 series standards is discussed. We experimented with the setups in the measurement system and set eye rotation at the pupil and at the eyeball center for contrast ratio and Michelson contrast. The experimental results were discussed to understand the differences between eye pupil center rotation and eyeball center rotation.
P‐2.5: Research on real‐time performance of Road condition display for automatic driving

In view of the problems of multi‐scale, densely distributed and difficult small target detection in real‐time road condition detection, a new improved yolov5 real‐time target detection method was proposed. The activation function in the original spatial pyramid (SPPF) was replaced by ReLu in the backbone network to enhance the expression ability of the model. The bidirectional feature pyramid (BiFPN) is used to replace the original feature pyramid network (FPN) + pixel aggregation network (PAN) in the neck area. The bidirectional feature fusion improves the utilization of multi‐scale semantic features and strengthens the extraction of image deep features. The convolutional attention mechanism (CBAM) is introduced to further improve the feature extraction ability of the algorithm and make the algorithm pay more attention to useful information. The results of self‐made traffic condition data set show that the average accuracy of the improved yolov5 model reaches 81.3%, which is 10.9% higher than that of the original yolov5 model, showing better detection accuracy and real‐time performance. Compared with some mainstream target detection algorithms, this algorithm has certain advantages.
CONTAINER-BASED VIRTUALIZATION FOR BLOCKCHAIN TECHNOLOGY: A SURVEY

Blockchain technology has garnered interest in several scientific and engineering fields. To improve blockchain services, its execution challenges must be addressed. Container-based virtualization enables running isolated apps on a shared OS where blockchain technology can leverage this technology to run numerous nodes, smart contracts, and decentralized apps in distinct containers allowing resource isolation and allocation, faster deployment and scalability, and improved security through limited host OS and other container access. This article covers container-based virtualization for Blockchain technology, including current methodologies, prospects, and future perspectives. Initially, this study explains blockchain and containerization as well as the reason for their integration. Then reviews container virtualization services to address blockchain complexity, size, scalability, and security. Conversely, Container technology uses blockchain to protect data and enhance resource management. Next, analyzes the latest containerization and blockchain integration studies. Finally, difficulties and future directions are considered to advance this promising research.
Reconstructing Attractors from Simulated Complex Dynamic Neural Networks

There has been much success in analytic and computational neuroscience in establishing dynamic models of neuron activity. Lapique, Hodgkin Huxley, Fitzhugh Nagumo, Morris Lecar, Hindmarsh Rose amongst the most well known. Whilst it can be reasonably claimed that the neuron is the “atom”, the basic building block of the brain, it is equally clear that the neuron itself does not capture the dynamics of segments of the brain, let alone the brain itself. For that one needs to consider large complex networks of neurons where each node (neuron) has its own dynamic. Good examples of effective models in this space are to be found in the synchronization of neurons (and other oscillatory nodes) across networks and the diffusion of signals.What is proposed here is an indirect approach based upon brain waves. Neuron networks in the brain will emit signals as a consequence of spiking activity. Therefore there will be a nonlinear time series associated with a given segment of an active brain. These series can be analyzed using techniques from nonlinear time series analysis particularly singular spectrum analysis and delayed embedding. Simulated data from a Matlab routine published in a well known paper by Izhikevich (referenced below) was subjected to these techniques and an attractor was uncovered Similarly attractors were uncovered from nonlinear time series analysis applied to simulated results from a brain simulator (Brian2) The attractors differed according the the variability of the types of neurons in the simulation. Using software to simulate networks of neurons gives a great deal of flexibility both in respect to network topology and types of behavior ascribed to neurons. The paper demonstrates the feasibility of subjecting the output from neural network simulations to SSA and Delayed Embedding to recover some interesting attractors. The resulting constructed attractors could provide a basis for comparison with attractors recovered from real brain data.
Learning-Based Risk-Bounded Path Planning Under Environmental Uncertainty

Building a general and efficient path planning framework in uncertain nonconvex environments is challenging due to the safety constraints and complex configuration. Traditional avenues usually involve convexifying obstacles and presume Gaussian distribution, which are not universal. Meanwhile, the fast convergence of high-quality solutions is not guaranteed. Therefore, we develop a novel neural risk-bounded path planner to quickly find near-optimal solutions that have an acceptable collision probability in the complex environments. Firstly, we retrieve the nonconvex obstacles with arbitrary probabilistic uncertainties in the form of a deterministic point cloud map. A neural network sampler encodes it into a latent embedding and is trained with sufficient expert demonstrations, predicting states in the potential subspace. We construct a neural cost estimator to select the best informed state from those samples. Then, we recursively use the simple yet effective neural networks to march toward the start and goal bidirectionally. The collision risk of the intermediate connections is verified based on sum-of-squares optimization. Simulation results show that our approach significantly saves time and resources in finding comparable solutions over the state-of-the-art methods in the seen and unseen challenging environments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —More and more robots are deployed in unstructured environments, such as forests and subterranean caves. However, uncertainty in the environment situational awareness usually causes accidents. To quickly generate safe paths without over-conservation in uncertain complex environments, we propose a neural risk-bounded sampling-based path planner. Conventional methods consume lots of computation time and resources to generate satisfactory results. Our learning-based risk-bounded path planning framework can efficiently find paths with a guaranteed risk tolerance avoiding uncertain nonconvex static obstacles. It imitates the expert to generate informed states in a subspace that potentially contains the optimal solution. In practice, we need to formulate the observed uncertain obstacle at a grid map into the polynomial containing random variables and determine their probability distributions.
Examining Speaker and Keyword Uniqueness: Partitioning Keyword Spotting Datasets for Federated Learning with the Largest Differencing Method

Federated learning is a powerful training strategy for neural networks where several independent clients train a model without the need of sharing potentially sensitive data. However, real world client-local data is usually biased: A single client might have access to only a few lighting conditions in computer visions, patient groups in a hospital or speakers and keywords in a smart device performing keyword spotting. We help researchers to better understand and estimate the expected performance impacts by introducing a new method to partition a given dataset into an arbitrary amount of clients, each with unique properties, to simulate such conditions. We apply the method to partition the Google Speech Command dataset into clients with non-overlapping speakers and additionally unique keywords and share the script to create the novel GSC-FL dataset. The results, using convolutional neural networks, show that the performance of the final model is stable up to at least 16 clients and models trained only on local data are clearly outperformed by federated learning. However, unique speakers for each client have a negative performance impact and it increases even more with unique keywords. Our script can be applied with only minor adjustments to partition any other dataset for federated learning investigations as well.
Deep learning-based phase unwrapping for holographic tomography

The nature of phase retrieval algorithms in holographic tomography (HT) is the reason that, in order to achieve the refractive index (RI) reconstruction, phase unwrapping (PU) has to be carried out beforehand. Conventional PU algorithms present shortcomings regarding the speed, noise robustness, reliability, and automation possibilities. These problems can be tackled by implementation of deep learning (DL) and convolutional neural networks in the PU process. The work presents a two-step pipeline consisting of denoising and unwrapping models. Both steps are realised under the umbrella of a U-Net architecture; however, unwrapping, which is carried out via semantic segmentation, is aided by Attention Gates and Residual Blocks (RB) introduced to the model. Through the experiments, the proposed pipeline is capable of denoising and unwrapping highly irregular and complex phase images captured in HT. What is more, the pipeline is implemented into a tomographic reconstruction algorithm. The quality of the final RI distribution is evaluated against the reconstruction with the conventional State-of-the-Art solution. To our knowledge, this is the first work, that puts forward phase unwrapping via segmentation with an Attention U-Net with RBs, assisted by a pre-processing denoising step. Moreover, this is the first solution, based on DL, that is trained entirely on experimental phase images captured in HT, and whose performance is evaluated on the distribution of RI of the measured specimen.
Realistic Predictors for Regression and Semantic Segmentation

Computer vision and image processing algorithms work well under strong assumptions. Computer vision algorithms are not expected to do well on all kinds of inputs. For instance, excessively noisy images may not fetch optimal results for most computer vision algorithms. Unexpected outputs from the computer vision module can have negative downstream consequences for other modules in the pipeline. To mitigate such consequences, we use a predictor framework that was simultaneously trained with a Hardness Predictor network. This framework guarantees improved performance over those images with lower "hardness" values. The proposed predictor framework, when applied to the input data, would result in a relatively lower variance estimator when the size of the training set is large, both in the domain of semantic segmentation as well as regression analysis.
Construction and Application of Knowledge Graph for Food Therapy

As healthcare popularity increases, more people use food therapy for nourishment and healing. However, without scientific guidance, it's difficult to select appropriate foods for specific needs. To address the issue, we extract knowledge from TCMSP and professional books and fuse the data from different sources. Next, the Food Therapy Knowledge Graph (FTKG) is constructed. Finally, a food therapy system is developed that integrates the concept of TCMSP and FTKG, which uses the efficient knowledge retrieval and knowledge reasoning ability of the knowledge graph. It provides scientific food therapy solutions by analyzing symptoms and substituting traditional Chinese medicine with food, s address individual health needs.
Interactive AI Material Generation and Editing in NVIDIA Omniverse

We present an AI-based tool for interactive material generation within the NVIDIA Omniverse environment. Our approach leverages a State-of-the-art Latent Diffusion model with some notable modifications to adapt it to the task of material generation. Specifically, we employ circular-padded convolution layers in place of standard convolution layers. This unique adaptation ensures the production of seamless tiling textures, as the circular padding facilitates seamless blending at image edges. Moreover, we extend the capabilities of our model by training additional decoders to generate various material properties such as surface normals, roughness, and ambient occlusions. Each decoder utilizes the same latent tensor generated by the de-noising UNet to produce a specific material channel. Furthermore, to enhance real-time performance and user interactivity, we optimize our model using NVIDIA TensorRT, resulting in improved inference speed for an efficient and responsive tool.
Conference Report on 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022) [Conference Reports]

On behalf of the organizing committee, we are delighted to deliver this conference report for the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022), which was held in Singapore from 4th to 7th December 2022. IEEE SSCI is an established flagship annual international series of symposia on computational intelligence (CI) sponsored by the IEEE Computational Intelligence Society (CIS) to promote and stimulate discussions on the latest theory, algorithms, applications, and emerging topics on computational intelligence. After two years of virtual conferences due to the global pandemic, IEEE SSCI returned as an in-person meeting with online elements in 2022.
Multi-contrast Multi-scale vision Transformers (MMT) for MRI Contrast Synthesis

Complementary information from multi-modal MRI is widely used in clinical practice for disease diagnosis. Due to scan time limitations, image corruptions, and different acquisition protocols, one or more contrasts may be missing or unusable. Recently developed CNN models for contrast synthesis are unable to capture the intricate dependencies between input contrasts and are not dynamic to the varying number of inputs. This work proposes a novel Multi-contrast and Multi-scale vision Transformer (MMT) that can take any number and combination of input sequences and synthesize the missing contrasts.
Incorporating Histological Grading for Brain Tumor Segmentation

Automatic tumor segmentation on MRI is crucial for patient management of brain tumors. However, previous automatic MRI segmentation methods based on deep learning are limited by bottlenecks of feature extraction. In this paper, we seek to investigate the influence of incorporating prior histology grading on the performance of tumor segmentation. We propose to employ histological grade labels to strengthen the feature extraction and tumor localization in the encoder, which can boost the DSC at exceeding 2% in different U-Net baselines. Our results could support the usefulness of incorporating clinical prior knowledge to improve the segmentation performance without introducing extra parameters.