Reviewerly/exHarmony huggingface dataset:
Study of a robotic system to detect water leakage and fuel debris -System proposal and feasibility study of visual odometry providing intuitive bird's eye view

Abstract To obtain the necessary information of the fuel debris and water leakages in the decommissioning task of Fukushima Daiichi nuclear power plants, an ultrasonic-based investigating method had been proposed towards the future internal investigation of the Primary Containment Vessel (PCV). In this article, we studied the assisting mechanisms and methods for the investigating method, as the rotatable winch mechanism and visual localization method. We used the rotatable winch mechanism to adjust the height and orientation of the ultrasonic sensor and localized the robot with cameras to localize the sensor, as assisting information for data combination. We studied the feasibility of the conventional visual odometry method of applying to the situation and performed localizing accuracy evaluating experiments with a mobile robotic platform prototype. Results showed that the visual odometry method could generate intuitive maps from bird’s eye view, and provided the average error rate of 35mm/1500mm, which met the required error rate for grating movement. The adjustable parameter ranges that could provide the required accuracy were also conducted.
MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition

Pre-trained language models lead Named Entity Recognition (NER) into a new era, while some more knowledge is needed to improve their performance in specific problems. In Chinese NER, character substitution is a complicated linguistic phenomenon. Some Chinese characters are quite similar for sharing the same components or having similar pronunciations. People replace characters in a named entity with similar characters to generate a new collocation but referring to the same object. It becomes even more common in the Internet age and is often used to avoid Internet censorship or just for fun. Such character substitution is not friendly to those pre-trained language models because the new collocations are occasional. As a result, it always leads to unrecognizable or recognition errors in the NER task. In this paper, we propose a new method, Multi-Feature Fusion Embedding for Chinese Named Entity Recognition (MFE-NER), to strengthen the language pattern of Chinese and handle the character substitution problem in Chinese Named Entity Recognition. MFE fuses semantic, glyph, and phonetic features together. In the glyph domain, we disassemble Chinese characters into components to denote structure features so that characters with similar structures can have close embedding space representation. Meanwhile, an improved phonetic system is also proposed in our work, making it reasonable to calculate phonetic similarity among Chinese characters. Experiments demonstrate that our method improves the overall performance of Chinese NER and especially performs well in informal language environments.
Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains

Prerequisite chain learning helps people acquire new knowledge efficiently. While people may quickly determine learning paths over concepts in a domain, finding such paths in other domains can be challenging. We introduce Domain-Adversarial Variational Graph Autoencoders (DAVGAE) to solve this cross-domain prerequisite chain learning task efficiently. Our novel model consists of a variational graph autoencoder (VGAE) and a domain discriminator. The VGAE is trained to predict concept relations through link prediction, while the domain discriminator takes both source and target domain data as input and is trained to predict domain labels. Most importantly, this method only needs simple homogeneous graphs as input, compared with the current state-of-the-art model. We evaluate our model on the LectureBankCD dataset, and results show that our model outperforms recent graph-based benchmarks while using only 1/10 of graph scale and 1/3 computation time.
Lifelong Robotic Reinforcement Learning by Retaining Experiences

Multi-task learning ideally allows robots to acquire a diverse repertoire of useful skills. However, many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times. In reality, the tasks that the robot learns arrive sequentially, depending on the user and the robot's current environment. In this work, we study a practical sequential multi-task RL problem that is motivated by the practical constraints of physical robotic systems, and derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set. In a series of simulated robotic manipulation experiments, our approach requires less than half the samples than learning each task from scratch, while avoiding impractical round-robin data collection. On a Franka Emika Panda robot arm, our approach incrementally learns ten challenging tasks, including bottle capping and block insertion.
Cloud Computing Primer

This chapter introduces fundamental cloud computing concepts, including a short history, to clarify how cloud computing became the next evolutionary step in information technology . After reading this chapter and studying the materials, you should:
Digital score of Trio no. 3 for two soprano and one alto saxophones

Musical score of 'Trio no. 3 for two soprano and one alto saxophones'. Title is partially cut off on item. Title taken from item. Quantity: 1 Electronic document(s).
EMPEDOCLE’S TEACHING

In the history of Philosophy, Empedocles first creates a philosophical system, complete a whole of learning that contains ontology, cosmology, biology, epistemology and ethics. Exceeding her predecessors Heraclitus and Parmenides, Empedocles develops an impressive consistent system of concepts that with their originality and creativity have a significant impact on various aspects of the history and development of the great ideas that are the foundation of human humanism and civilization, much more than it is commonly recognized in the previous studies of Empedocles.
On Hermitian LCD Generalized Gabidulin Codes

In this paper, we study Hermitian linear complementary dual (abbreviated Hermitian LCD) rank metric codes. A class of Hermitian LCD generalized Gabidulin codes are constructed by qm-self-dual bases of 𝔽q2m over 𝔽q2. Moreover, the exact number of qm-self-dual bases of 𝔽q2m over 𝔽q2 is derived. As a consequence, an upper bound and a lower bound of the number of the constructed Hermitian LCD generalized Gabidulin codes are determined.
Millennial Employees, Psychological Contract Breach, Cultural Differences and Turnover Intentions in the Construction Industry: An Integrative Literature Review

The problem of attracting and retaining millennial employees has recently attracted the focus of human resource managers and researchers; this cohort's population in 2020 has become a significant workforce worldwide. As such, the construction industry has been struggling with utilizing the millennial workforce to its full potential. High turnover rates associated with breach are among the main problems in this sector as young workers are reluctant to work in companies that break their verbal promises. This integrative literature review was part of a broader qualitative study offering insight into millennial employees' experiences and perceptions working in the construction business regarding psychological contract breach and turnover intentions. This overview of the extant research offers targeted knowledge that can support further research and help advance the experiences of psychological contracts and their inherent breaches. Addressing inequities in professional settings and formal work structures and promoting an anonymous online platform for employee feedback promises millennial workers benefits and career opportunities to reduce the negative effect of psychological contract breach and decrease turnover.
Perancangan dan Implementasi Augmented Reality Tanjak Melayu menggunakan metode MDLC

Tanjak is an accessory used by the people at the time the Malay kingdom stood. However, the traditional era began to develop into the era of digital culture and Malay heritage is fading. The purpose of this study is a form of concern if culture is simply forgotten without any attempt to reintroduce it. With today's technology, climbs can be visualized using technology that can display 3D objects in the real world. This technology is usually called augmented reality, so tanjak can be known to the wider community which can be accessed using smart devices that support augmented reality and use the operating system android. This research itself uses the method Multimedia Development Life Cycle. In the process using a variety of software such as cinema4D, Unity, adobe illustrator. The output obtained is an application augmented reality with a .apk extension that can be downloaded using the google play store
Improvisation of Energy Efficiency in Cloud Environment through Proposed Communication Protocol

The advancement and extension of data center industry is broadly extended the amount of server usability through developing business areas, end client applications from customers and execution improvement limits due to persistent expansion in the size and magnitude of data center tier level. The continuous demand of cloud services increasing consumption of energy and emission of carbon dioxide. So both the energy consumption and carbon dioxide emission reduction for environmental sustainability are critical concerns in cloud computing environment. Therefore consumption of energy and CO2 emissions reductions are essential. So for this concern, firstly, a novel communication protocol is designed and evaluated to improvise the efficiency of energy in the cloud environment. Poisson distribution and Markov chain techniques have been used to design this protocol. It is assessed by the given energy-efficient model with a QSA algorithm. To attain this goal, BCube data center network topology is used on an edge layer for a single performance-optimized data center. This model is accountable to provide the energy-efficient formulation for the decreasing of energy consumption to enhance the energy efficiency in the cloud environment. Secondly, carbon usage effectiveness is computed for the diminishing of CO2 emissions in the cloud data center communication systems.
Comparison of Fractal Dimension and Wavelet Transform Methods in Classification of Stress State from EEG Signals

Stress is a significant issue in everyday life that affects both physical and mental health.There are different approaches to stress classification.This research examines the implementation of the fractal dimension (FD) method as one of the features for stress state classification using brain signals.Consequently, the comparison between FD and wavelet transform has been conducted using electroencephalogram (EEG) signals recorded during the Stroop Colour Word Test (SCWT).The comparison results show that the FD is better in the classification of the stress state.The highest F1 score has been obtained using FD with quadratic support vector machine (SVM) in average 83.03% for the comparison between baseline session and different stress states.Besides, FD with medium Gaussian SVM has the highest F1 score, on average 83.36%, for comparison between various stress states.
INTERDEPENDENCE AND INTERVENTION: NEWS ORGANIZATIONS’ CONTRADICTORY RELATIONSHIP WITH ONLINE COMMENTS

Online news comments are intended to cultivate an interdependent relationship between news organizations and their audiences. However, uncivil online comments have become a persistent problem that requires constant intervention through moderation. In this paper, to better understand these interventions, we analyze interviews of eleven managers of online comments of large Finnish news organizations. By exploring the views of journalistic managers of moderation, this study contributes new insights to the discussion about online content moderation, as previous research has focused more on social media platforms and moderators. Our results suggest that the managers have a complex relationship with comments. They would like to see more engaging comments but were also frustrated with the continuous need to moderate the comments. The managers also expressed concern that uncivil comments keep more constructive commenters from participating, thus harming the audience relationship. Organizations tend to outsource moderation to third parties or automated moderation, as moderation is often seen as time-consuming and outside of the core work of journalists. However, the managers were not satisfied with outsourced or automated moderation, mainly because of a lack in contextual knowledge, as also noted in previous research. Reflecting previous literature, our results suggest that some aspects of uncivil commenting may require alternative approaches to moderation altogether. For example, some managers suggested replacing comments with other means of interaction. To address the inherent contradictions in online news comment moderation, we advocate a view that focuses on cultivating and rewarding civil comments instead of deleting and punishing for uncivil comments.
한국어 온라인 담화통합 쓰기 전략 사용 양상 연구

This study aims to analyze aspects of using online discourse synthesis writing strategies for Korean learners for academic purposes. To this end, after presenting a writing task to two study participants, data collection methods such as computer screen recording, recall report, and post-interview were used to reveal the pattern of writing strategy use. As a result, Korean learners for academic purposes were shown to use six strategic types of ‘information retrieval strategy’, ‘information search and selection strategy’, ‘comprehension expansion strategy’, ‘meaning construction strategy’, ‘self-check strategy’, and ‘technology utilization strategy’. Also it has shown that Korean learners used a total of 16 detailed strategies. Using this result, this study proposed helpful directions for enhancing online discourse synthesis writing strategies. (Korea University)
BAM: A Balanced Attention Mechanism for Single Image Super Resolution

Recovering texture information from the aliasing regions has always been a major challenge for Single Image Super Resolution (SISR) task. These regions are often submerged in noise so that we have to restore texture details while suppressing noise. To address this issue, we propose a Balanced Attention Mechanism (BAM), which consists of Avgpool Channel Attention Module (ACAM) and Maxpool Spatial Attention Module (MSAM) in parallel. ACAM is designed to suppress extreme noise in the large scale feature maps while MSAM preserves high-frequency texture details. Thanks to the parallel structure, these two modules not only conduct self-optimization, but also mutual optimization to obtain the balance of noise reduction and high-frequency texture restoration during the back propagation process, and the parallel structure makes the inference faster. To verify the effectiveness and robustness of BAM, we applied it to 10 SOTA SISR networks. The results demonstrate that BAM can efficiently improve the networks performance, and for those originally with attention mechanism, the substitution with BAM further reduces the amount of parameters and increases the inference speed. Moreover, we present a dataset with rich texture aliasing regions in real scenes, named realSR7. Experiments prove that BAM achieves better super-resolution results on the aliasing area.
DEVELOPMENT OF A STEGANOGRAPHIC METHOD FOR DIGITAL IMAGES BASED ON FOURIER TRANSFORM

In the modern Internet space in conditions of continuous exchange of information the need to protect it from the possibility of unauthorized use by third parties is increasing. This can be ensured by using steganographic methods that allow organizing a covert communication channel. In the article a new steganographic method for digital images based on the fast Fourier transform developed. As additional information you can use both text messages and images translated into a binary sequence. For embedding of additional information 2×2 blocks are used for which the fast Fourier transform is calculated. Message embedding is done by modifying the absolute difference between two coefficients of block’s Fourier transform. Up to four bits of a binary sequence can be embedded in each block. The results of computational experiments aimed at assessing the effectiveness of the proposed method have shown that high quality of stegos is provided (average PSNR values are 58-60 dB) compared to modern analogues while ensuring high capacity of the covert communication channel (0.75 and 1 bit/pixel). The average values of the extraction accuracy index of additional information NCC are from 0.87 to 0.97 depending on the selected color component and the number of embedded bits. The accuracy of message extraction depends on the characteristics of the container - detection errors occur when there are a large number of blocks with brightness values close to 0 or 255. However, this problem can be eliminated by preliminary analysis of the container. The developed method showed high resistance to attack by the noise "Salt and pepper" (d=0.01) at appreciable distortions of the filled container, as well as the imposition of a Gaussian and a multiplicative noise at imperceptible distortions of stegos.
Multivariate Missing Value Imputation using Weighted Cluster Softmax Technique

Missing Data is a common phenomenon in many research areas and can have significant impact on the conclusions that can be drawn from such data. Existing missing data imputation techniques replace the missing entries by some plausible values either using deterministic or random methods. In this work, we propose a new approach, weighted cluster softmax technique, which is a generalization of K-Means clustering for handling multivariate missing value imputation. We have considered multiple open source data set from UCI libraries to test the algorithm and it is observed that our proposed method performs better than existing simple mean imputation and K-Means clustering based method.
A framework for traceble storage and curation of measurement data

We outline a generic framework for capturing metadata from complex scientific experiments and linking these to data at the point of measurement. To digitally record metadata that may not be recorded in a machine-actionable way, we develop a simple and customisable webform based on a controlled dictionary. We implement our workflow for the CRUK Rosetta Grand Challenge Project which first combines metadata from multiple sources, including external sample or laboratory management systems, into a standard Extensible Markup Language schema. It then links the combined metadata with measurement data, and uploads the annotated data to a searchable database enabling automatic curation. This framework provides a workflow for establishing a metrologically FAIR (findable, accessible, interoperable, and reusable) traceable database. This unbroken chain of traceable measurements will improve confidence, repeatability, reuse, and longevity of data. Our standardised metadata is used to generate automatic reports of experimental methods for efficient error-free preparation of manuscripts.
Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks

In this paper, we investigate a two-layer fully connected neural network of the form $f(X)=\frac{1}{\sqrt{d_1}}\boldsymbol{a}^\top \sigma\left(WX\right)$, where $X\in\mathbb{R}^{d_0\times n}$ is a deterministic data matrix, $W\in\mathbb{R}^{d_1\times d_0}$ and $\boldsymbol{a}\in\mathbb{R}^{d_1}$ are random Gaussian weights, and $\sigma$ is a nonlinear activation function. We study the limiting spectral distributions of two empirical kernel matrices associated with $f(X)$: the empirical conjugate kernel (CK) and neural tangent kernel (NTK), beyond the linear-width regime ($d_1\asymp n$). We focus on the $\textit{ultra-wide regime}$, where the width $d_1$ of the first layer is much larger than the sample size $n$. Under appropriate assumptions on $X$ and $\sigma$, a deformed semicircle law emerges as $d_1/n\to\infty$ and $n\to\infty$. We first prove this limiting law for generalized sample covariance matrices with some dependency. To specify it for our neural network model, we provide a nonlinear Hanson-Wright inequality that is suitable for neural networks with random weights and Lipschitz activation functions. We also demonstrate non-asymptotic concentrations of the empirical CK and NTK around their limiting kernels in the spectral norm, along with lower bounds on their smallest eigenvalues. As an application, we show that random feature regression induced by the empirical kernel achieves the same asymptotic performance as its limiting kernel regression under the ultra-wide regime. This allows us to calculate the asymptotic training and test errors for random feature regression using the corresponding kernel regression.
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data

The transition to green energy grids depends on detailed wind and solar forecasts to optimize the siting and scheduling of renewable energy generation. Operational forecasts from numerical weather prediction models, however, only have a spatial resolution of 10 to 20-km, which leads to sub-optimal usage and development of renewable energy farms. Weather scientists have been developing super-resolution methods to increase the resolution, but often rely on simple interpolation techniques or computationally expensive differential equation-based models. Recently, machine learning-based models, specifically the physics-informed resolution-enhancing generative adversarial network (PhIREGAN), have outperformed traditional downscaling methods. We provide a thorough and extensible benchmark of leading deep learning-based super-resolution techniques, including the enhanced super-resolution generative adversarial network (ESRGAN) and an enhanced deep super-resolution (EDSR) network, on wind and solar data. We accompany the benchmark with a novel public, processed, and machine learning-ready dataset for benchmarking super-resolution methods on wind and solar data.
Online Instruction for Curriculum-Related Classes

During 2020-2021, the University of Michigan Library Instruction Assessment Task Force investigated curriculum-related library sessions conducted online during Fall 2020. This final report documents the feedback analysis from Fall 2020 course instructors and library instructors of curriculum-related classes.