Reviewerly/exHarmony huggingface dataset:
Lattice curved surface array based collaborative fusion for intelligent diagnosis

A new concept of lattice curved surface array is proposed in this paper to represent multi-source medical examination data for getting precise conclusions of intelligent pneumonia diagnosis through reliable collaborative fusion. To this end, the quantum phase based lattice curved surfaces of all source datasets are constructed to form a lattice curved surface array which provides the precise division and fusion space for all entity nodes. Based on the horizontal category degrees and vertical collaborative degrees between entity nodes, the constructed lattice curved surface array is segmented into different subset arrays, and the entity nodes in each subset array are fused into a single object entity node according to the weights of their included part nodes. The experimental data evaluation shows that, compared with the homogeneous fusion methods, this new concept based collaborative fusion method is impressively effective with a preciseness of 95.52% achieved in the reliable fusion results based pneumonia diagnosis.
High Resolution Image Processing and Rural Landscape Standardization Effect Simulation Based on VR Technology

The application of virtual reality technology in landscape design can better combine the actual situation and enhance the expression effect. In order to build rural landscape model, virtual reality technology is applied in this article, first of all, build the relevant area of the model, then completed a landscape design and rendering, and then through the virtual reality technology to simulate the regional landscape design renderings, and carries on the practice and adjustment, will eventually be presented the results of the simulation. It is proposed that the UAV tilt photography technology can restore the real three-dimensional scene of the mountain village, so that the three-dimensional real scene model can be used to comprehensively collect regional information. This method will reduce the difficulty for designers to obtain site information, and try to integrate the scheme directly into the rural landscape design on this basis. By using the data difference obtained before and after, we can more directly judge the rationality of the design scheme, conduct a panoramic survey and effect simulation on the design scheme questionnaire, and collect the participants' satisfaction with the design scheme and the detailed satisfaction scores of the constituent landscape elements according to the completion point of the design scheme. This paper studies the subject of high-resolution image processing based on VR technology, and applies the research results to the effect simulation of rural landscape standardization, which promotes the rapid development of rural landscape. Data will be made available on request.
HAMLET: A framework for Human-centered AutoML via Structured Argumentation

Machine Learning (ML) plays a crucial role in data analysis and data platforms (i.e., integrated sets of technologies that collectively meet end-to-end data needs). In the last decade, we have witnessed an exponential growth in both the complexity and the number of ML techniques; leveraging such techniques to solve real-case problems has become difficult for Data Scientists. Automated Machine Learning (AutoML) tools have been devised to alleviate this task, but easily became as complex as the ML techniques themselves — with Data Scientists losing again control over the process. In this paper, we design and extend HAMLET (Human-centered AutoMl via Logic and argumEnTation), a framework that helps Data Scientists to redeem their centrality. HAMLET enables Data Scientists to express ML constraints in a uniformed human- and machine-readable medium. User-defined constraints are interpreted to drive the exploration of ML pipelines (i.e., Data Pre-processing transformations shape the data so that the ML task can be performed at its best). AutoML retrieves the most performing pipeline instance, and finally, new constraints are learned and integrated through Logic and Argumentation. By doing so, HAMLET not only allows an easy exploitation of the knowledge acquired at each iteration, but also enables its continuous revision via the AutoML tool and the collaboration of both Data Scientists and domain experts.
Urban Ecological Landscape Visualization System Based on Sign Detection Algorithm

In recent years, the problems caused by traffic safety and traffic congestion are becoming more and more common, and effective measures should be taken to solve them. This paper simulates the visualization system of urban ecological landscape based on signal detection algorithm. Urban ecological landscape is a multi-disciplinary ecosystem, including natural landscape and urban human landscape. Using machine vision principle, digital image acquisition, transmission and processing. Data is transferred from the equipment FIFO transmission system and DMA channels to the computer for subsequent equipment control and visual identification and processing. Through the application of relevant technologies, the visualization system of urban ecological landscape can be built, which can be combined with the smart city system to build an all-round urban information model, help to deal with multiple problems including traffic, ecology, etc., and realize the diversified development of landscape. Data will be made available on request.
Foreground discovery in streaming videos with dynamic construction of content graphs

We study the problem of unknown foreground discovery in image streaming scenarios, where no prior information about the dynamic scene is assumed. Contrary to existing co-segmentation principles where the entire dataset is given, in streams new information emerges as content appears and disappears continually. Any object classes to be observed in the scene are unknown, therefore no detection model can be trained for the specific class set. We also assume there is no available repository of trained features from convolutional neural nets, i.e., transfer learning is not applicable. We focus on the progressive discovery of foreground, which may or may not correspond to contextual objects of interest, depending on the camera trajectory, or, in general, the perceived motion. Without any form of supervision, we construct in a bottom up fashion dynamic graphs that capture region saliency and relative topology. Such graphs are continually updated over time, and along with occlusion information, as fundamental property of the foreground–background relationship, foreground is computed for each frame of the stream. We validate our method using indoor and outdoor scenes of varying complexity with respect to content, objects motion, camera trajectory, and occlusions.
Dynamic common correlated effects of financial inclusion on foreign direct investment: Evidence from East-Asia and Pacific countries

This research explores the dynamic common correlated effects of financial inclusion on foreign direct investment (FDI) in East Asia and Pacific (EAP) countries. Traditional econometric methodologies overlook cross-sectional dependence and heterogeneity among countries, resulting in biased estimates. As a result, we use a unique method called DCCE, which can effectively address these problems. The short run and long-run estimates reveal that financial inclusion positively and significantly influences FDI in higher-income and overall EAP countries. Furthermore, in lower-income EAP countries, financial inclusion has an insignificant correlation with FDI, which becomes significant when an interaction term for institutional performance and financial inclusion is included. Hence, governments and policy makers in EAP economies should try to increase financial inclusion in order to attain an optimal and sustainable level of FDI inflows to enhance economic growth.
KNN-Based Decision Model for Device Argumentation in Ambient Assisted Living

Aging population has increased in the past few decades with the rise in social and economic challenges. In particular, people wish to live an independent life without giving most of the burden to caretakers. This is where the Ambient Assisted Living (AAL) has gained momentum that allows elderly people to lead an independent and safe lifestyle by performing their daily activities. Such an AAL environment which consists of heterogeneous sensors and actuators helps in monitoring the daily activities carried out by elderly people throughout the day. These smart objects interact among themselves by using higher-level information generated as a result of processing the sensor values on chips to decide their own course of actions toward their goals. Thus, these smart objects which are now speaking and hearing objects (“things”) interact through argumentation. These objects could communicate and argue with each other about a particular activity in progress and to find the understanding of the present state and take decisions and act accordingly. Thus argumentation as a common basis for interactions among these smart objects, this paper focuses on implementing a KNN-based decision model for device argumentation about an activity identification and to take actions accordingly. The proposed solution shows that KNN gives an accuracy of 72.28%, Precision of 85%, Recall of 42.3% and F1-score of 38.40% for classifying ambiguous device argumentation in AAL.
Neural Networks

In this chapter we explore the concept of neurons and neuron models to solve real world challenges. We begin with a brief description of the biological neuron and investigate a model of the neuron, called the perceptron, developed by Frank RosenblattRosenblatt in 1959. We use the single perceptron model to separate objects into two categories. We extend the model to include additional perceptrons toPerceptron separate objects into multiple categories. We then investigate the single neuron and then explore the concept of backpropagation and develop a three–layer feed–forward network with backpropagation. Along the way we develop Arduino sketches of these different models.
The Perception of SPADA-DIKTI Services in Underdeveloped Area Using Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA)

A good online learning system service performance can reduce the negative influence of the learning environment, which in turn can increase the effectiveness of the learning process. SPADA-DIKTI is one of the Indonesian government&rsquo;s online learning system services. The purpose of this study is to measure the effectiveness of the service quality of the online learning system in the underdeveloped area that is referred to 3T areas (terdepan, terluar, tertinggal) in Indonesia. The Customer Satisfaction Index method is used to determine user perceptions of SPADA-DIKTI service performance, while the Important Performance Analysis is used to select attributes that need to be improved. The results showed that the CSI value was 75.39%, thus the users (student of underdeveloped area) of the online learning system services were satisfied. However, the number of Modules/Features according to the needs of the learning/teaching process) and Modules/Features according to the needs of the learning/teaching process need improvement to improve service quality.
Optimal Energy Efficient Design of Artificial Noise to Prevent Side-Channel Attacks

How to prevent side-channel attacks (SCAs) has become an important problem for securing modern cryptographic devices. One of the widely used countermeasures to against power SCAs is the injection of random noise sequence into the raw leakage traces. However, the indiscriminate injection of random noise can lead to significant increases in energy consumption. In this paper, we propose an optimal energy efficient design for artificial noise to prevent side-channel attacks. We model the side-channel as a communication channel, which allows us to use channel capacity to measure the mutual information between the secret and the leakage traces. For a given energy budget in the noise generation, we obtain the optimal design of the artificial noise injection by solving the side-channel’s channel capacity minimization problem.
Portuguese Teachers’ Conceptions of the Use of Microsoft 365 during the COVID-19 Pandemic

In 2020, education found itself involved in a whirlwind of metamorphosis, of transformations that required responses to the emergencies dictated by the pandemic. In Portugal, many schools opted for Microsoft 365 as the platform of choice for providing adequate resources for teaching and learning processes, while also ensuring remote teaching in an integrated and inclusive way. In this context, we carried out an investigation with the objectives of knowing the opinion of teachers about the use of Microsoft 365 in their classes and identify their degree of satisfaction with its use. The methodology adopted had a descriptive and exploratory nature, following a mixed paradigm. A total of 101 primary and secondary school teachers from schools in Northern Portugal participated in the study. A questionnaire and an interview were used as data collection instruments. The results showed that the most respondents revealed a high level of satisfaction with the use of Microsoft 365, and that its use was accompanied by the employment of active methodologies. Moreover, despite the lack of initial or ongoing training of teachers in the use of this technology and the students’ lack of digital competence, Microsoft 365 proved to be an adequate response to the confinement and ensured students’ learning in a safe environment.
Teachers’ efforts to increase students’ confidence in speaking Indonesian in Sarolangun through language habituation

This research is aimed at investigating teachers’ habituation pattern as an effort to enhance students’ confidence in speaking Indonesian language in Sarolangun. It implements qualitative phenomenology approach to understand why and how an incident takes place. The data are collected through observation, interview, and documentation. The subjects of the research are Indonesian students and teachers from three different senior high schools in Sarolangun, Indonesia. The results reveal that students are low skilled in using Indonesian language, and they have low confidence in speaking in Indonesian due to bullying. The senior high school teachers in Sarolangun try to improve the students’ confidence in speaking Indonesian through encouragement, punishment for the bullies, and compulsory program to use Indonesian at school. These are considered as a proper policy in boosting students’ confidence. This research concludes that in the teaching of Indonesian, the language should not be used by a certain group of people but rather it could be used by all people regardless of their social class. Furthermore, Indonesian is the language used in academic and official context. Indonesia, indeed, constitutes of various tribes and languages. The aim of Indonesian language usage is to ease people from many different ethnicities and regions to communicate to each other.
Prototype-Based Self-Adaptive Distribution Calibration for Few-Shot Image Classification

Deep learning has flourished in large-scale supervised tasks. However, in many practical conditions, rich and available labeled data are a luxury. Thus, few-shot learning (FSL) has recently received boosting interest and achieved significant progress, which can learn new classes from several labeled samples. The advanced distribution calibration approach estimates the ground-truth distribution of few-shot classes by reusing the statistics of auxiliary data. However, there is still a significant discrepancy between the estimated distributions and ground-truth distributions, and artificially set hyperparameters cannot be adapted to different application scenarios (i.e., datasets). This paper proposes a prototype-based self-adaptive distribution calibration framework for estimating ground-truth distribution accurately and self-adaptive hyperparameter optimization for different application scenarios. Specifically, the proposed method is divided into two components. The prototype-based representative mechanism is for obtaining and utilizing more global information about few-shot classes and improving classification performance. The self-adaptive hyperparameter optimization algorithm searches robust hyperparameters for the distribution calibration of different application scenarios. The ablation studies verify the effectiveness of the various components of the proposed framework. Enormous experiments are conducted on three standard benchmarks such as miniImageNet, CUB-200-2011, and CIFAR-FS. The competitive results and compelling visualizations indicate that the proposed framework achieves state-of-the-art performance.
Optimal Oracle Inequalities for Projected Fixed-Point Equations, with Applications to Policy Evaluation

Linear fixed-point equations in Hilbert spaces arise in a variety of settings, including reinforcement learning, and computational methods for solving differential and integral equations. We study methods that use a collection of random observations to compute approximate solutions by searching over a known low-dimensional subspace of the Hilbert space. First, we prove an instance-dependent upper bound on the mean-squared error for a linear stochastic approximation scheme that exploits Polyak–Ruppert averaging. This bound consists of two terms: an approximation error term with an instance-dependent approximation factor and a statistical error term that captures the instance-specific complexity of the noise when projected onto the low-dimensional subspace. Using information-theoretic methods, we also establish lower bounds showing that both of these terms cannot be improved, again in an instance-dependent sense. A concrete consequence of our characterization is that the optimal approximation factor in this problem can be much larger than a universal constant. We show how our results precisely characterize the error of a class of temporal difference learning methods for the policy evaluation problem with linear function approximation, establishing their optimality. Funding: This work was partially supported by grants from the Office of Naval Research [Grant DOD-ONRN00014-18-1-2640] and the National Science Foundation (NSF) [NSF-IIS Grant 1909365, NSF-DMS Grant 2015454, and NSF-CCF Grant 1955450] to M. J. Wainwright. Part of this work was performed when A. Pananjady was visiting the Simons Institute for the Theory of Computing, where he was supported by a Swiss Re Research Fellowship. Supplemental Material: The online supplementary file is available at https://doi.org/10.1287/moor.2022.1341 .
VW-SC3D: A Sparse 3D CNN-Based Spatial–Temporal Network with View Weighting for Skeleton-Based Action Recognition

In recent years, human action recognition has received increasing attention as a significant function of human–machine interaction. The human skeleton is one of the most effective representations of human actions because it is highly compact and informative. Many recent skeleton-based action recognition methods are based on graph convolutional networks (GCNs) as they preserve the topology of the human skeleton while extracting features. Although many of these methods give impressive results, there are some limitations in robustness, interoperability, and scalability. Furthermore, most of these methods ignore the underlying information of view direction and rely on the model to learn how to adjust the view from training data. In this work, we propose VW-SC3D, a spatial–temporal model with view weighting for skeleton-based action recognition. In brief, our model uses a sparse 3D CNN to extract spatial features for each frame and uses a transformer encoder to obtain temporal information within the frames. Compared to GCN-based methods, our method performs better in extracting spatial–temporal features and is more adaptive to different types of 3D skeleton data. The sparse 3D CNN makes our model more computationally efficient and more flexible. In addition, a learnable view weighting module enhances the robustness of the proposed model against viewpoint changes. A test on two different types of datasets shows a competitive result with SOTA methods, and the performance is even better in view-changing situations.
Network Reading Circulation and Personalized Guidance of Reader Service in the Information Age

With the development of modern digital communication technology, especially network communication technology, people’s information needs are becoming more and more diversified and complex. Reading is changing. The development of network media promotes the development of network reading. At the same time, the advantages and disadvantages of network media also determine the characteristics of network reading, which requires the library to provide an innovative service model based on the personalized needs of readers. Based on the research on online reading circulation and personalized guidance of reader service in the information age, the future development trend of online reading must be a curved development model, and form an interactive relationship with traditional reading.
Solar Speckle Image Deblurring With Deep Prior Constraint Based on Regularization

The solar speckle image has the characteristics with single features, more noise, and blurred local details. Most of the existing deep learning deblurring methods for solar speckle images have some problems, such as high-frequency loss, artifact generation, and dependence on the paired image. In this paper, a deep prior deblurring method fusing the regularization model and prior constraint network is proposed. Firstly, the traditional handcrafted regularization priors are added to the network parameterized blind deconvolution model. The image gradient prior and blur kernel initial parameters are respectively used to the network parameterization process of two variables in the blind deconvolution model, which are the latent clean image variables and blur kernel variables. After that, the solar speckle image deep prior deblurring model is established. Secondly, the blur kernel generation network input is estimated by using the atmospheric point spread function (PSF) to improve the model convergence speed. Thirdly, a latent clean image generation network including joint gradient branching and Feature Pyramid Network (FPN) structure is designed to enhance image local edge details. Finally, a joint loss function including pixel loss, image prior loss, and mean squared error (MSE) loss is introduced to guide the model for alternate training. It can obtain the best parameter values of latent clean image and blur kernel, and achieve the solar speckle image high-resolution reconstruction. The experimental results show that the proposed method can eliminate the dependence on the reference image, and the reconstructed image has less noise and more obvious high-frequency details, faster network convergence, and two evaluation indicators of Peak Signal Noise Ratio (PSNR) and Structural Similarity (SSIM) are significantly improved.