Reviewerly/exHarmony huggingface dataset:
Stationary Stochastic Processes

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
LANGUAGE FUNCTIONS USED BY THE CARETAKERS OF AUTISTIC CHILDREN IN “MIRACLE RUN”

This study investigates language functions used by the caretakers of autistic children in ―Miracle Run‖. The objectives of the study are to find out the kinds and the effectiveness of language functions used by caretakers in the film. This study uses descriptive qualitative approach. It is categorized as discourse analysis because this study analyzes phenomena of talk in interaction. The data are collected from the utterances used by the caretakers; they are mother, doctor, teacher, and therapist when talk to the autistic children and those are analyzed by using M.A.K Halliday theory on language functions.
THE IMPROVEMENT OF STUDENTS’ HIGHER ORDER THINKING IN READING CLASS USING STUDENTS’ RESPONSES TEXTS IN THE THIRD SEMESTER OF ENGLISH EDUCATION PROGRAM PGRI UNIVERSITY OF YOGYAKARTA

The research aims to improve the level thinking qualities toward students in Reading III using students‘ responses text. The research applied descriptive qualitative method. It described about the students‘ responses text that produced by students in reading novel to improve students‘ higher order based on their own perspective. The participants of this research were nine students of the semester III, A3 class, English Education Program of PGRI University of Yogyakarta. Data were got through observation, interview, document analysis and questionaire. Qualitative data were anayzed through coding data, explanation, or data presenting and conclusion. The findings showed that using students‘ responses text in Reading III learning process that focus on novel significantly improve the students‘higher order thinking level. The first activities, students‘order thinking level still in the lower category i.e., remember and understand. The precentage is only 25%. The second activities, students‘order thinking level imrpoved up to 45%. The improvement of students‘order thinking in reading comprehension using students‘ responses also influenced students‘ motivation and feeling when they were learning. The response text helped students in improving their reading skill.
Chapter 1. Starting Points

What constitutes a state-of-the-art website? Learn the best practices for running a library website today, and what skills are most necessary for a library Web manager. Includes formal/informal requirements for usability and accessibility.
PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces

This paper provides a novel POMDP planning method, called Palm LEAf SEarch (PLEASE), which allows the selection of more than one outcome when their potential impacts are close to the highest one during its forward exploration. Compared with existing trial-based algorithms, PLEASE can save considerable time to propagate the bound improvements of beliefs in deep levels of the search tree to the root belief because of fewer backup operations. Experiments showed that PLEASE scales up SARSOP, one of the fastest algorithms, by orders of magnitude on some POMDP tasks with large observation spaces.
Stain normalization in digital pathology: Clinical multi-center evaluation of image quality

In digital pathology, the final appearance of digitized images is affected by several factors, resulting in stain color and intensity variation. Stain normalization is an innovative solution to overcome stain variability. However, the validation of color normalization tools has been assessed only from a quantitative perspective, through the computation of similarity metrics between the original and normalized images. To the best of our knowledge, no works investigate the impact of normalization on the pathologist's evaluation. The objective of this paper is to propose a multi-tissue (i.e., breast, colon, liver, lung and prostate) and multi-center qualitative analysis of a stain normalization tool with the involvement of pathologists with different years of experience. Two qualitative studies were carried out for this purpose: i) a first study focused on the analysis of the perceived image quality and absence of significant image artifacts after the normalization process; ii) a second study focused on the clinical score of the normalized image with respect to the original one. The results of the first study prove the high quality of the normalized image with a low impact artifact generation, while the second study demonstrates the superiority of the normalized image with respect to the original one in clinical practice. The normalization process can help both to reduce variability due to tissue staining procedures and facilitate the pathologist in the histological examination. The experimental results obtained in this work are encouraging and can justify the use of a stain normalization tool in clinical routine.
Energy control in a quantum oscillator using coherent control and engineered environment

We develop and analyze a new method for manipulation of energy in a quantum harmonic oscillator using coherent, e.g., electromagnetic, field and incoherent control. Coherent control is typically implemented by shaped laser pulse or tailored electromagnetic field. Incoherent control is implemented by engineered environment, whose mean number of excitations at the frequency of the oscillator is used as a control variable. An approach to coherent and incoherent controls design based on the speed gradient algorithms in general, finite and differential forms is proposed. It is proved that the differential form is able to completely manipulate the energy of the oscillator: an arbitrary energy can be achieved starting from any initial state of the oscillator. The key instrument which allows for complete energy manipulation in this case is the use of the engineered environment. A robustified speed-gradient control algorithm in differential form is also proposed. It is shown that the proposed robustified control algorithm ensures exponential stability of the closed loop system which is preserved for sampled-data control.
DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation

Text-to-image generation aims at generating realistic images which are semantically consistent with the given text. Previous works mainly adopt the multi-stage architecture by stacking generator-discriminator pairs to engage multiple adversarial training, where the text semantics used to provide generation guidance remain static across all stages. This work argues that text features at each stage should be adaptively re-composed conditioned on the status of the historical stage (\emphi.e., historical stage's text and image features) to provide diversified and accurate semantic guidance during the coarse-to-fine generation process. We thereby propose a novel Dynamical Semantic Evolution GAN (DSE-GAN) to re-compose each stage's text features under a novel single adversarial multi-stage architecture. Specifically, we design (1) Dynamic Semantic Evolution (DSE) module, which first aggregates historical image features to summarize the generative feedback, and then dynamically selects words required to be re-composed at each stage as well as re-composed them by dynamically enhancing or suppressing different granularity subspace's semantics. (2) Single Adversarial Multi-stage Architecture (SAMA), which extends the previous structure by eliminating complicated multiple adversarial training requirements and therefore allows more stages of text-image interactions, and finally facilitates the DSE module. We conduct comprehensive experiments and show that DSE-GAN achieves 7.48% and 37.8% relative FID improvement on two widely used benchmarks, i.e., CUB-200 and MSCOCO, respectively.
Speed limits of the trace distance for open quantum system

We investigate the speed limit of the state transformation in open quantum systems described by the Lindblad type quantum master equation. We obtain universal bounds of the total entropy production described by the trace distance between the initial and final states in the interaction picture. Our bounds can be tighter than the bound of Vu and Hasegawa [Phys. Rev. Lett. 126, 010601 (2021)] which measures the distance by the eigenvalues of the initial and final states: This distance is less than or equal to the trace distance. For this reason, our results can significantly improve Vu-Hasegawa's bound. The trace distance in the Schr\"{o}dinger picture is bounded by a sum of the trace distance in the interaction picture and the trace distance for unitary dynamics described by only the Hamiltonian in the quantum master equation.
StarLight: a photonic neural network accelerator featuring a hybrid mode-wavelength division multiplexing and photonic nonvolatile memory

This paper proposes StarLight, a low-power consumption and high inference throughput photonic artificial neural network (ANN) accelerator featuring the photonic 'in-memory' computing and hybrid mode-wavelength division multiplexing (MDM-WDM) technologies. Specifically, StarLight uses nanophotonic non-volatile memory and passive microring resonators (MRs) to form a photonic dot-produce engine, achieving optical 'in-memory' multiplication operation with near-zero power consumption during the inference phase. Furthermore, we design an on-chip wavelength and mode hybrid multiplexing module and scheme to increase the computational parallelism. As a proof of concept, a 4×4×4 optical computing unit featuring 4-wavelength and 4-mode is simulated with 10 Gbps, 15 Gbps and 20 Gbps data rates. We also implemented a simulation on the Iris dataset classification and achieved an inference accuracy of 96%, which is entirely consistent with the classification accuracy on a 64-bit computer. Therefore, StarLight holds promise for realizing low energy consumption hardware accelerators to address the incoming challenges of data-intensive artificial intelligence (AI) applications.
DVS: A Drone Video Synopsis towards Storing and Analyzing Drone Surveillance Data in Smart Cities

The commercialization and advancement of unmanned aerial vehicles (UAVs) have increased in the past decades for surveillance. UAVs use gimbal cameras and LIDAR technology for monitoring as they are resource-constrained devices that are composed of limited storage, battery power, and computing capacity. Thus, the UAV’s surveillance camera and LIDAR data must be analyzed, extracted, and stored efficiently. Video synopsis is an efficient methodology that deals with shifting foreground objects in time and domain space, thus creating a condensed video for analysis and storage. However, traditional video synopsis methodologies are not applicable for making an abnormal behavior synopsis (e.g., creating a synopsis only of the abnormal person carrying a revolver). To mitigate this problem, we proposed an early fusion-based video synopsis. There is a drastic difference between the proposed and the existing synopsis methods as it has several pressing characteristics. Initially, we fused the 2D camera and 3D LIDAR point cloud data; Secondly, we performed abnormal object detection using a customized detector on the merged data and finally extracted only the meaningful data for creating a synopsis. We demonstrated satisfactory results while fusing, constructing the synopsis, and detecting the abnormal object; we achieved an mAP of 85.97%.
Digital Volume Correlation for large deformations of soft tissues: Pipeline and proof of concept for the application to breast ex vivo deformations

Being able to reposition tumors from prone imaging to supine surgery stances is key for bypassing current invasive marking used for conservative breast surgery. This study aims to demonstrate the feasibility of using Digital Volume Correlation (DVC) to measure the deformation of a female quarter thorax between two different body positioning when subjected to gravity. A segmented multipart mesh (bones, cartilage and tissue) was constructed and a three-step FE-based DVC procedure with heterogeneous elastic regularization was implemented. With the proposed framework, the large displacement field of a hard/soft breast sample was recovered with low registration residuals and small error between the measured and manually determined deformations of phase interfaces. The present study showed the capacity of FE-based DVC to faithfully capture large deformations of hard/soft tissues.
Digital Experience in Art and Identity: The Metaverse calls

The rapid global rise of immersive digital experience at exhibitions and museums sheds light on the intense need for deep engagement with art and the emotions that it projects and the stories it tells. With social isolation imposed by COVID, during more than two years and counting, human digital identity is evolving as digital experience moves to the heart of daily life, at home and at work, being often interchangeable. This digital paradigm shift in human activity leaving a lacuna in face-to-face engagement is increasingly being filled by digital art which we see can deliver immersive and emotional digital experiences. With the metaverse on the horizon and fast gaining ground in developing its platform to support virtual life in 3D as part of Internet 3.0, especially VR, AR, MR, AI, and NFTs, we wonder how this emerging phenomenon will change life as we know it, as the metaverse seems positioned at the epicentre of a definitive shift to digital experience during COVID and far beyond. Already, investors are buying up the metaverse landscape and creating the tools humans will need to freely participate. This portends an exciting future with new opportunities for digital artists, technology innovators, and entrepreneurs to create virtual multidimensional and multimedia spaces for people to engage their digital identity in digital experiences. Time to create your avatar - let the games begin.
The Differential Spectrum of the Power Mapping <i>x</i> <sup> <i>p<sup>n</sup> </i> </sup> <sup>−3</sup>

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> be a positive integer and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> a prime. The power mapping <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x^{p^{n}-3}$ </tex-math></inline-formula> over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}}_{p^{n}}$ </tex-math></inline-formula> has desirable differential properties, and its differential spectra for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p=2,\,3$ </tex-math></inline-formula> have been determined. In this paper, for any odd prime <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> , by investigating certain quadratic character sums and some equations over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}}_{p^{n}}$ </tex-math></inline-formula> , we determine the differential spectrum of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x^{p^{n}-3}$ </tex-math></inline-formula> with a unified approach. The obtained result shows that for any given odd prime <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> , the differential spectrum can be expressed explicitly in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . Compared with previous results, a special elliptic curve over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}}_{p}$ </tex-math></inline-formula> plays an important role in our computation for the general case <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p \ge 5$ </tex-math></inline-formula> .
Multi-criteria decision making for determining best teaching method using fuzzy analytical hierarchy process

During the outbreak of COVID-19, information technology played a critical role in promoting education all around the world. Online teaching boosts students' learning processes and has a good impact on their learning during the epidemic. Big data technology transforms traditional teaching approaches and learning processes by providing a rich learning resource for diverse teaching elements and improving teachers' teaching techniques. Due to the COVID-19 epidemic, online education spread quickly, and traditional instruction was abruptly switched to online mode, posing a number of issues for students and management. Choosing a decent teaching technique is not an easy option, and it is even more difficult when it comes to selecting the approach. We used the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) method to evaluate four instructional methods based on seven criteria to solve this challenge. Fuzzy AHP is a powerful, simple, and direct way for determining which approach is the most efficient and effective. To simplify the selection process and address the issue of uncertainty, the Fuzzy AHP technique employs the geometric mean method. The Fuzzy AHP approach was found to be efficient and successful in the decision-making process in this study.
A Multi-Purpose Shallow Convolutional Neural Network for Chart Images

Charts are often used for the graphical representation of tabular data. Due to their vast expansion in various fields, it is necessary to develop computer algorithms that can easily retrieve and process information from chart images in a helpful way. Convolutional neural networks (CNNs) have succeeded in various image processing and classification tasks. Nevertheless, the success of training neural networks in terms of result accuracy and computational requirements requires careful construction of the network layers’ and networks’ parameters. We propose a novel Shallow Convolutional Neural Network (SCNN) architecture for chart-type classification and image generation. We validate the proposed novel network by using it in three different models. The first use case is a traditional SCNN classifier where the model achieves average classification accuracy of 97.14%. The second use case consists of two previously introduced SCNN-based models in parallel, with the same configuration, shared weights, and parameters mirrored and updated in both models. The model achieves average classification accuracy of 100%. The third proposed use case consists of two distinct models, a generator and a discriminator, which are both trained simultaneously using an adversarial process. The generated chart images are plausible to the originals. Extensive experimental analysis end evaluation is provided for the classification task of seven chart classes. The results show that the proposed SCNN is a powerful tool for chart image classification and generation, comparable with Deep Convolutional Neural Networks (DCNNs) but with higher efficiency, reduced computational time, and space complexity.
Contrastive Knowledge Graph Error Detection

Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are unknown and diverse, while ground-truth labels are rare or even unavailable. A traditional solution is to construct logical rules to verify triples, but it is not generalizable since different KGs have distinct rules with domain knowledge involved. Recent studies focus on designing tailored detectors or ranking triples based on KG embedding loss. However, they all rely on negative samples for training, which are generated by randomly replacing the head or tail entity of existing triples. Such a negative sampling strategy is not enough for prototyping practical KG errors, e.g., (Bruce_Lee, place_of_birth, China), in which the three elements are often relevant, although mismatched. We desire a more effective unsupervised learning mechanism tailored for KG error detection. To this end, we propose a novel framework - ContrAstive knowledge Graph Error Detection (CAGED). It introduces contrastive learning into KG learning and provides a novel way of modeling KG. Instead of following the traditional setting, i.e., considering entities as nodes and relations as semantic edges, CAGED augments a KG into different hyper-views, by regarding each relational triple as a node. After joint training with KG embedding and contrastive learning loss, CAGED assesses the trustworthiness of each triple based on two learning signals, i.e., the consistency of triple representations across multi-views and the self-consistency within the triple. Extensive experiments on three real-world KGs show that CAGED outperforms state-of-the-art methods in KG error detection. Our codes and datasets are available at https://github.com/Qing145/CAGED.git.
Blockchain-Driven Optimal Strategies for Supply Chain Finance Based on a Tripartite Game Model

Applying blockchain to supply chain financing is an effective way to solve the problems of financing difficulties, high financing costs, and slow financing for small and medium-sized enterprises (SMZEs). Using evolutionary game theory, this study constructs a tripartite game model and analyzes the influence of blockchain technology on the evolutionary stability strategies for financial institutions (FIs), core enterprises (CEs), and SMZEs, in which the default losses of CEs and SMZEs are assumed to be dynamic. The results of this study are as follows: (1) When CEs and SMZESs’ default losses are lower than some critical value, they tend to break their promises. (2) When accounts receivable are greater than some critical value, CEs cannot repay on time because they can make a relatively large profits from delayed repayment, whereas SMZEs can be constrained to be trustworthy. Finally, the results using numerical simulation show that both relatively large default losses and enough large, trustworthy income sources can make CEs and SMZEs tend to keep their promises; in turn, CEs would be non-paying and the SMZEs tend to be trustworthy for relatively large accounts receivable. The results provide theoretical support for realizing healthy and sustainable development for supply chain finance.