Reviewerly/exHarmony huggingface dataset:
Educational Effect of Remote Lectures for Students Aiming to Become Radiologic Technologists: Questionnaire on Nuclear Medicine Examinations

In the latter half of 2019, coronavirus disease 2019 (COVID-19) began spreading worldwide. To prevent COVID-19 infection, all teaching at Suzuka University of Medical Sciences from April to June 2020 took place as remote lectures, not in the face-to-face format. This study analyzed postlecture questionnaire responses regarding face-to-face and remote teaching on the subject of nuclear medicine technology examinations. We examined the educational effect of using remote lectures. Methods: We conducted a questionnaire survey among students by means of a 5-point evaluation scale about satisfaction, comprehension, concentration, preparation, reviewing, and the question environment for face-to-face and remote lectures. Results: We present the results as means and SDs. Satisfaction results for face-to-face and remote lectures were 3.30 ± 0.72 and 3.36 ± 0.88, respectively. Comprehension results for face-to-face and remote lectures were 3.30 ± 0.71 and 3.30 ± 0.83, respectively. Concentration results for face-to-face and remote lectures were 3.50 ± 0.69 and 3.05 ± 0.90, respectively. The preparation results for face-to-face and remote lectures were 2.57 ± 0.88 and 2.67 ± 0.94, respectively. The reviewing results for face-to-face and remote lectures were 2.84 ± 0.85 and 3.39 ± 0.89, respectively. The question environment results for face-to-face and remote lectures lessons were 2.94 ± 0.90 and 3.43 ± 0.84, respectively. There were no significant differences between face-to-face and remote lectures in terms of satisfaction, comprehension, or preparation. There were significant differences between face-to-face and remote lectures in terms of concentration, reviewing, and the questioning environment (P < 0.001). Conclusion: This comparative analysis of the postlecture questionnaire responses for face-to-face and remote formats in nuclear medicine technology examinations showed that remote lectures have a strong educational effect. We believe that, in future, remote lectures should be considered a tool in student education.
Ms-Gwnn: Multi-Scale Graph Wavelet Neural Network for Breast Cancer Diagnosis

Breast cancer is one of the most common cancers worldwide, and early detection can significantly reduce its mortality rate. It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. And thus, it is the key to design an accurate computer-aided detection (CAD) system to capture multi-scale contextual features in a cancerous tissue. In this work, we present a novel graph convolutional neural network for histopathological image classification of breast cancer. The new method, named multi-scale graph wavelet neural network (MS-GWNN), leverages the localization property of spectral graph wavelet to perform multi-scale analysis. By aggregating features at different scales, MS-GWNN can encode the multi-scale contextual interactions in the whole pathological slide. Experimental results on two public datasets demonstrate the superiority of MS-GWNN. Moreover, ablation studies show that multi-scale analysis has a significant impact on the accuracy of cancer diagnosis.
Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach

Recently, data centers (DCs) have become an indispensable part of modern computing infrastructures. However, DCs often consume a significant amount of energy and lead to the workload unbalance with increasing service requests. Keeping focus on this point, in this paper, we propose a novel energy-aware DC management scheme. To design an efficient DC control algorithm, the main challenge is uncertainties such as uncertain energy price and unpredictable users' demands. In response to these uncertainties, we adopt the idea of cooperative game theory, and introduce a new two-phase bargaining model to get the mutual advantage. To decide the energy price, we formulate the Stackelberg bargaining game while adapting the current system situation. To balance the workloads among DCs, the migration bargaining game is developed. These two game models are tightly coupled to achieve greater and reciprocal advantages during dynamic DC operations. The main novelty of our proposed two-phase bargaining approach is to handle comprehensively contradictory requirements for the DC management. Finally, extensive experiment results validate the efficiency of our proposed algorithm by comparing with the existing state-of-the-art DC management protocols in terms of average payoff of all DCs, system throughput and fairness among DCs.
A Provenance Meta Learning Framework for Missing Data Handling Methods Selection

Missing data is a big problem in many real-world data sets and applications, which can lead to wrong or misleading results of analyses and lower quality and confidence in the results. A large number of missing data handling methods have been proposed in the research community but there exists no universally single best method which can handle all the missing data problems. To select the right method for a specific missing data handling problem, it usually depends on multiple inter-twined factors. To alleviate this methods selection problem, in this paper, we propose a Provenance Meta Learning Framework to simplify this process. We conducted an extensive literature review over 118 missing data handling method survey papers from 2000 to 2019. With this review, we analyse 9 influential factors and 12 selection criteria for missing data handling methods and further perform a detailed analysis of 6 popular missing data handling methods (4 machine learning methods, i.e., KNN Imputation (KNNI), Weighted KNN Imputation (WKNNI), K Means Imputation (KMI), and Fuzzy KMI (FKMI), and 2 ad-hoc methods, i.e., Median/Mode Imputation (MMI) and Group/Class MMI (CMMI)). We focus on missing data handling methods selection for 3 different classification techniques, i.e., C4.5, KNN, and RIPPER. In our evaluations, we adopt 25 real world data sets from KEEL and UCI data sets repositories. Our Provenance Meta Learning Framework suggests that using KNNI to handle missing values when missing data mechanism is Missing Complete At Random (MCAR), missing data pattern is uni-attribute missing data pattern, or monotone missing data pattern, missing data rate is within [1%,5%], number of class labels is 2, sample size is no more than 10'000, since it can keep classification performance better and have higher imputation accuracy and imputation exhaustiveness than all the other 5 missing data handling methods when subsequent classification methods are KNN or RIPPER.
Domain Adaptation of Binary Sensors in Smart Environments Through Activity Alignment

Activity Recognition is an active research topic focused on detecting human actions and behaviours in smart environments. In most cases, the use of data-driven models aim to relate data from sensors to an activity through a model developed by a supervised approach. In this work, we focus on the goal of domain adaptation between smart environments, which has required a novel approach to relate the concepts of domain adaptation using binary sensor and learning from daily imbalanced data. In this work, the sensor activation from a given context is translated to a different one, based on the temporal alignment from human activities. The domain adaptation of binary sensor is accomplished through a three step procedure: i) clustering of sensor activation, ii) activity based alignment of sensor data between the two environments, iii) an ensemble of classifiers used to mine a mapping function, translating sensor data between the two environments. The proposed method was evaluated over a publicly available dataset, and obtained preliminary results which were encouraging with an F1-Score of 87%.
Information Theory for Human and Social Processes

Ever since its earliest years, information theory has enjoyed both a promising and complicated relationship with the social sciences [...]
A Knowledge Search Algorithm Based on Multidimensional Semantic Similarity Analysis in Knowledge Graph Systems of Power Grid Networks

In the face of intelligent demand for information search, knowledge search is now the most promising and has been widely used in the current knowledge graph research. However, the traditional search methods based on node label only are difficult to present the semantic relationships between multidimensional nodes due to that the structure information and information in other dimensions is neglected, thus resulting in low semantic relevance and search efficiency for query results. In this paper, in order to improve the semantic relevance and quality of search results, we propose a knowledge searching algorithm based on analysis of multidimensional semantic similarity for knowledge graph systems, which combines both the ontology information and multi-hop neighborhood information together during the search process. The algorithm is designed for a knowledge graph system developed by the State Grid Anhui power distribution network, China. Several experiments are performed and the results show that the proposed algorithm outperforms the recent knowledge searching methods.
Towards Declarative Specification of Multi-Drone BVLOS Missions for UTM

Autonomous drones operate in an airspace populated by other drones and manned aviation. Unmanned traffic management (UTM) systems are necessary to safely and efficiently integrate drones into the existing airspace traffic. Existing UTM systems however cannot resolve airspace conflicts without risking altering critical elements of planned drone operations. To enable the resolution of conflicts while ensuring that drone missions still fulfill their purpose, we propose DOTS, a declarative language to specify drone missions. From this specification, DOTS generates a planning system that can dynamically replan drone flights without compromising the purpose of the mission.
A Light-Weight Approach to Software Assignment at the Edge

Containerised software running on edge infrastructures is required to be updated following agile practices to react to emerging business requirements, contextual changes, and security threats. Which version needs to be deployed on a particular device depends on multiple context properties, such as hardware/software resources, physical environment, user preferences, subscription type, etc. As fleets of edge devices are nowadays comprised of thousands of units, the amount of effort required to perform such assignment often goes beyond manual capabilities, and automating this assignment task is an important pre-requisite for application providers to implement continuous software delivery. This paper looks at this challenge as a generalised assignment problem and demonstrates how it can be solved using simple, yet efficient combinatorial optimisation techniques. The proof of concept implementation demonstrates the general viability of the approach, as well as its performance and scalability through a series of benchmarking experiments.
FPGA implementation of digital 3-D image skeletonization algorithm for shape matching applications

Image skeletonisation plays an important role in applications such as object identification, object's shape description, pattern recognition, computer vision, telemedicine, and image compression. In this paper, an architecture is developed for the extraction of a skeleton for the 3-D greyscale object. The algorithm used in this paper is an extension of the 2-D greyscale skeletonisation algorithm presented in the literature. The skeleton of a 3-D greyscale object is extracted by using the boundary peeling off method. The skeletonisation algorithms are computationally intensive and difficult to achieve requirements in real-time. Field Programmable Gate Arrays (FPGAs) are selected for implementation so the real-time requirements are met. The proposed architecture is implemented on Virtex-5 FPGA and tested for an 8 × 8 x 3 greyscale object. Resource utilisation of FPGA and timing performance are investigated and are encouraging.
Application of window-optimized 2D S-transform in the optical 3D shape measurement

Combing the merits of windowed Fourier transform and wavelet transform, 2D S-transform has been a popular method in optical 3D measurement. As an improvement of 1D S-transform, 2D S-transform has better features to reduce noises and non-linearity because it can analyze the whole fringe patterns in two directions. However, the window size of traditional 2D S-transform is related to local frequency of tested objects, therefore traditional 2D S-transform can not get accurate surface information when the tested surface is complicated. To overcome the problem, a window-optimized 2D S-transform with theory analysis and mathematical derivation is proposed with two parameters to control the width of Gauss window. Simulation verifies that the proposed method can extract more accurate wrapped phase. At last, simulation and experiments are carried out to show that the proposed method can get more accurate measurement results when the tested surface is complicated.
On a Combinatorial Generation Problem of Knuth

The well-known middle levels conjecture asserts that for every integer $n\geq 1$, all binary strings of length 2(n+1) with exactly n+1 many 0s and 1s can be ordered cyclically so that any two consecutive strings differ in swapping the first bit with a complementary bit at some later position. In his book The Art of Computer Programming, Knuth raised a stronger form of this conjecture (Problem 56 in section 7.2.1.3), which requires that the sequence of positions with which the first bit is swapped in each step of such an ordering has 2n+1 blocks of the same length, and each block is obtained by adding s=1 (modulo 2n+1) to the previous block. In this work, we prove Knuth's conjecture in a more general form, allowing for arbitrary shifts $s\geq 1$ that are coprime to 2n+1. We also present an algorithm to compute this ordering, generating each new bitstring in $\mathcal{O}(n)$ time, using $\mathcal{O}(n)$ memory in total.
First among equals: co-hyperintensionality for structured propositions

Abstract Theories of structured meanings are designed to generate fine-grained meanings, but they are also liable to overgenerate structures, thus drawing structural distinctions without a semantic difference. I recommend the proliferation of very fine-grained structures, so that we are able to draw any semantic distinctions we think we might need. But, in order to contain overgeneration, I argue we should insert some degree of individuation between logical equivalence and structural identity based on structural isomorphism . The idea amounts to forming an equivalence class of different structures according to one or more formal criteria and designating a privileged element as a representative of all the elements, i.e., a first among equals . The proposed method helps us to a cluster of notions of co-hyperintensionality . As a test case, I consider a recent objection levelled against the act theory of structured propositions. I also respond to an objection against my methodology.
Explanation for building energy prediction

Explainable AI is a technology field that has recently emerged. Currently, deep learning technology shows high accuracy in many areas. However, despite its high accuracy, there are areas where technology cannot be utilized without sufficient explanation of the results. In medicine, credit rating, and criminal fields, it is difficult to apply technology in practice without sufficient explanation of the results. This paper gives an explanation of the results of the energy demand prediction model by using feature importance and attention mechanism. The model used is seq2seq model, and the dataset used the data set generated by energyPlus™ based on the actual data.
A Novel System for Guiding Unmanned Vehicles Based on Human Gesture Recognition

Unmanned vehicle driving technology is developing rapidly in recent years. Since the relevant research of unmanned vehicles does not stop at the vehicle control method or multiple vehicle collaboration, vehicle-human interaction problems are gradually being valued by researchers. In this paper, a novel system is proposed for guiding unmanned vehicles based on human gesture recognition. The human gesture instructions are using as the bridge between people and an unmanned vehicle. The Dynamic Time Warping (DTW) method is improved to identify the instructions quickly and accurately. After the vehicle receives the leader's gesture instruction, it can enter the follow (leader) mode. A continuous path can be formed according to the movement track of the leader. The pure pursuit method is used to control the unmanned vehicle so that the vehicle can follow the leader. Experiments are performed both indoors and outdoors. The results can prove the robustness and feasibility of the proposed system.
Merging of MOS of Large Image Databases for No-reference Image Visual Quality Assessment

For training of no-reference image visual quality metrics large specialized image databases are used. For images of the databases mean opinion scores (MOS) are experimentally obtained collecting judgments of many observers. MOS of a given image reflects an averaged human perception of visual quality of the image. Each database has its own unknown scale of MOS values depending on unique content of the database. For training of no-reference metrics based on convolutional networks usually only one selected database is used, because all MOS values on input of training loss function should be in the same scale. In this paper, a simple and effective method of merging of several large databases into one database with transforming of their MOS into one scale is proposed. Accuracy of the proposed method is analyzed. Merged MOS is used for practical training of no-reference metric. Better effectiveness of the training is shown in comparative analysis.
TCGM: An Information-Theoretic Framework for Semi-supervised Multi-modality Learning

Fusing data from multiple modalities provides more information to train machine learning systems. However, it is prohibitively expensive and time-consuming to label each modality with a large amount of data, which leads to a crucial problem of semi-supervised multi-modal learning. Existing methods suffer from either ineffective fusion across modalities or lack of theoretical guarantees under proper assumptions. In this paper, we propose a novel information-theoretic approach - namely, Total Correlation Gain Maximization (TCGM) – for semi-supervised multi-modal learning, which is endowed with promising properties: (i) it can utilize effectively the information across different modalities of unlabeled data points to facilitate training classifiers of each modality (ii) it has theoretical guarantee to identify Bayesian classifiers, i.e., the ground truth posteriors of all modalities. Specifically, by maximizing TC-induced loss (namely TC gain) over classifiers of all modalities, these classifiers can cooperatively discover the equivalent class of ground-truth classifiers; and identify the unique ones by leveraging limited percentage of labeled data. We apply our method to various tasks and achieve state-of-the-art results, including the news classification (Newsgroup dataset), emotion recognition (IEMOCAP and MOSI datasets), and disease prediction (Alzheimer's Disease Neuroimaging Initiative dataset).
A Data Protection-Oriented Design Procedure for a Federated Learning Framework

Artificial intelligence is a bellwether of today's new technological revolution. As a branch of artificial intelligence, machine learning faces two main problems in practical application: 1) data owned by most enterprises are difficult to aggregate; and, 2) big data owners pay more and more attention to data privacy and security, which leads to the problem of data island. Federated learning (FL), as a distributed FL paradigm, which can enable all parties to achieve the purpose of co-building models while ensuring data privacy and exposure, provides a possible solution to the mentioned problems of machine learning. The FL takes full advantage of participants' data and computing power and builds a more robust machine learning model without sharing the data. In an environment with strict data regulation, the FL can effectively solve the key problems, such as those related to data privacy and data rights. However, most of the existing FL-based frameworks do not pay attention to the impact of data source distribution on FL training. Therefore, this paper proposed a data-oriented FL framework called the Federated AI Engine(FAE), which can solve FL problems without leaving the data in control. The proposed framework provides a method that can be used to verify FL quickly for researchers who intend to try federal learning.
Research on sports simulation and fatigue characteristics of athletes based on machine learning

The research on the fatigue characteristics of athletes has a certain role in promoting the development of sports. In order to detect fatigue more accurately in the state of human fatigue, this article uses a method of fusing characteristic information of many physiological parameters related to fatigue to design a multi-physical parameter-based exercise fatigue recognition method with high research value and significance. Moreover, this study combines machine learning technology to construct a dynamic fatigue detection system based on BP neural network and multiple physiological parameters. In addition, this study uses samples to construct a BP neural network and achieves dynamic detection of fatigue through multiple physiological parameters. Finally, by constructing controlled trials, fatigue is predicted. The results show that the predicted output of the fatigue value is in good agreement with the expected output, and the research method has certain practical effects.
Optimal Substring Equality Queries with Applications to Sparse Text Indexing

We consider the problem of encoding a string of length n from an integer alphabet of size σ so access , substring equality , and Longest Common Extension (LCE) queries can be answered efficiently. We describe a new space-optimal data structure supporting logarithmic-time queries. Access and substring equality query times can furthermore be improved to the optimal O (1) if O (log n ) additional precomputed words are allowed in the total space. Additionally, we provide in-place algorithms for converting between the string and our data structure. Using this new string representation, we obtain the first in-place subquadratic algorithms for several string-processing problems in the restore model: The input string is rewritable and must be restored before the computation terminates. In particular, we describe the first in-place subquadratic Monte Carlo solutions to the sparse suffix sorting, sparse LCP array construction, and suffix selection problems. With the sole exception of suffix selection, our algorithms are also the first running in sublinear time for small enough sets of input suffixes. Combining these solutions, we obtain the first sublinear-time Monte Carlo algorithm for building the sparse suffix tree in compact space. We also show how to build a correct version of our data structure using small working space. This leads to the first Las Vegas in-place algorithm computing the full LCP array in O ( n log n ) time w.h.p. and to the first Las Vegas in-place algorithms solving the sparse suffix sorting and sparse LCP array construction problems in O ( n 1.5 √ log σ) time w.h.p.