Reviewerly/exHarmony huggingface dataset:
Graph-BAS<sup>3</sup>Net: Boundary-Aware Semi-Supervised Segmentation Network with Bilateral Graph Convolution

Semi-supervised learning (SSL) algorithms have attracted much attentions in medical image segmentation by leveraging unlabeled data, which challenge in acquiring massive pixel-wise annotated samples. However, most of the existing SSLs neglected the geometric shape constraint in object, leading to unsatisfactory boundary and non-smooth of object. In this paper, we propose a novel boundary-aware semi-supervised medical image segmentation network, named Graph-BAS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net, which incorporates the boundary information and learns duality constraints between semantics and geometrics in the graph domain. Specifically, the proposed method consists of two components: a multi-task learning framework BAS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net and a graph-based cross-task module BGCM. The BAS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net improves the existing GAN-based SSL by adding a boundary detection task, which encodes richer features of object shape and surface. Moreover, the BGCM further explores the co-occurrence relations between the semantics segmentation and boundary detection task, so that the network learns stronger semantic and geometric correspondences from both labeled and unlabeled data. Experimental results on the LiTS dataset and COVID-19 dataset confirm that our proposed Graph-BAS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net outperforms the state-of-the-art methods in semi-supervised segmentation task.
Extension of PAR Models under Local All-Sky Conditions to Different Climatic Zones

Four models for predicting Photosynthetically Active Radiation (PAR) were obtained through MultiLinear Regression (MLR) and an Artificial Neural Network (ANN) based on 10 meteorological indices previously selected from a feature selection algorithm. One model was developed for all sky conditions and the other three for clear, partial, and overcast skies, using a sky classification based on the clearness index (kt). The experimental data were recorded in Burgos (Spain) at ten-minute intervals over 23 months between 2019 and 2021. Fits above 0.97 and Root Mean Square Error (RMSE) values below 7.5% were observed. The models developed for clear and overcast sky conditions yielded better results. Application of the models to the seven experimental ground stations that constitute the Surface Radiation Budget Network (SURFRAD) located in different Köppen climatic zones of the USA yielded fitted values higher than 0.98 and RMSE values less than 11% in all cases regardless of the sky type.
SOL: safe on-node learning in cloud platforms

Cloud platforms run many software agents on each server node. These agents manage all aspects of node operation, and in some cases frequently collect data and make decisions. Unfortunately, their behavior is typically based on pre-defined static heuristics or offline analysis; they do not leverage on-node machine learning (ML). In this paper, we first characterize the spectrum of node agents in Azure, and identify the classes of agents that are most likely to benefit from on-node ML. We then propose SOL, an extensible framework for designing ML-based agents that are safe and robust to the range of failure conditions that occur in production. SOL provides a simple API to agent developers and manages the scheduling and running of the agent-specific functions they write. We illustrate the use of SOL by implementing three ML-based agents that manage CPU cores, node power, and memory placement. Our experiments show that (1) ML substantially improves our agents, and (2) SOL ensures that agents operate safely under a variety of failure conditions. We conclude that ML-based agents show significant potential and that SOL can help build them.
Feature Selection: From the Past to the Future

Feature selection has been widely used for decades as a preprocessing step that allows for reducing the dimensionality of a problem while improving classification accuracy. The need for this kind of technique has increased dramatically in recent years with the advent of Big Data. This data explosion not only has the problem of a large number of samples, but also of big dimensionality. This chapter will analyze the paramount need for feature selection and briefly review the most popular feature selection methods and some typical applications. Moreover, as the new Big Data scenario offers new opportunities to machine learning researchers, we will discuss the new challenges that need to be faced: from the scalability of the methods to the role of feature selection in the presence of deep learning, as well as exploring its use in embedded devices. Beyond a shadow of doubt, the explosion in the number of features and computing technologies will point to a number of hot spots for feature selection researchers to launch new lines of research.
Image Enhancement and Classification of CIFAR-10 Using Convolutional Neural Networks

A new architecture for enhancement of image and image classification is developed based on the image processing techniques and Convolutional Neural Multi class Networking Algorithm. Classification and enhancement of images deals with the combination of two different concepts, image enhancement and image classification techniques by identifying useful information from the given input images. This method works on image quality assessment and improving the images for identifying the image class from the dataset. The enhanced image is classified to identify the class of input image from the CIFAR-10 dataset. CIFAR-10 dataset is used to train Convolutional neural network model with the enhanced image for classification. This dataset consists of ten classes like airplane, automobiles, cat, dog, frog, horse, ship, bird, truck in colored images. This dataset is used for training and testing of our model. CNN algorithm acts as a mid-way between the image processing and image classification in the network and it is a systematic hierarchy of analyzing the images and image operations on 32*32 images.
Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user's view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people's decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words.
Flow-Guided Video Inpainting with Scene Templates

We consider the problem of filling in missing spatiotemporal regions of a video.We provide a novel flow-based solution by introducing a generative model of images in relation to the scene (without missing regions) and mappings from the scene to images.We use the model to jointly infer the scene template, a 2D representation of the scene, and the mappings.This ensures consistency of the frame-toframe flows generated to the underlying scene, reducing geometric distortions in flow based inpainting.The template is mapped to the missing regions in the video by a new (L 2 -L 1 ) interpolation scheme, creating crisp inpaintings and reducing common blur and distortion artifacts.We show on two benchmark datasets that our approach out-performs stateof-the-art quantitatively and in user studies.
Investigating user-defined flipping gestures for dual-display phones

Flipping is a potential interaction method for dual-display phones with front and rear screens. However, little is known about users’ phone flipping behaviors. To investigate it, we iteratively conduct three user studies in this research. We first elicit 36 flipping gestures from 22 users and present a design space according to the results. We then collect users’ flipping data and subjective evaluation of all user-defined gestures through the second user study. We design a flipping detection algorithm based on the data collected and deploy it on an off-the-shelf dual-display phone. Another evaluation study shows that it can detect users’ flipping efficiently with an average accuracy of 97.78%. Moreover, users prefer many flip-based applications on dual-display phones to existing non-flipping approaches on regular single-screen phones. In conclusion, our work provides empirical support that flipping is an intuitive and promising input modality for dual-display phones and sheds light on its design implications.
DAGguise: mitigating memory timing side channels

This paper studies the mitigation of memory timing side channels, where attackers utilize contention within DRAM controllers to infer a victim's secrets. Already practical, this class of channels poses an important challenge to secure computing in shared memory environments.
Multi-Agent Voice Assistants: An Investigation of User Experience

The use of voice assistants (VAs) is spreading widely. Most common VAs consist of a single, usually female voice that responds to the user's inquiry. We designed a VA system appearing as a group of agents, each with a different voice and a specialized task domain. We conducted a quantitative user study comparing our multi-agent approach with a conventional single-agent assistant in a smart home scenario as virtual reality (VR) simulation. The results show significantly higher user experience ratings for the multi-agent concept. Based on our findings, we discuss the potentials and challenges of designing multi-party VA systems.
Toward Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search

Neural networks often encounter various stringent resource constraints while deploying on edge devices. To tackle these problems with less human efforts, automated machine learning becomes popular in finding various neural architectures that fit diverse Artificial Intelligence of Things (AIoT) scenarios. Recently, to prevent the leakage of private information while enable automated machine intelligence, there is an emerging trend to integrate federated learning and neural architecture search (NAS). Although promising as it may seem, the coupling of difficulties from both tenets makes the algorithm development quite challenging. In particular, how to efficiently search the optimal neural architecture directly from massive nonindependent and identically distributed (non-IID) data among AIoT devices in a federated manner is a hard nut to crack. In this article, to tackle this challenge, by leveraging the advances in ProxylessNAS, we propose a federated direct neural architecture search (FDNAS) framework that allows for hardware-friendly NAS from non-IID data across devices. To further adapt to both various data distributions and different type of devices with heterogeneous embedded hardware platforms, inspired by meta-learning, a cluster federated direct neural architecture search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense that each device can learn a tailored deep learning model for its particular data distribution and hardware constraint. Extensive experiments on non-IID data sets have shown the state-of-the-art accuracy–efficiency tradeoffs achieved by the proposed solution in the presence of both data and device heterogeneity.
Effect of Pro-Environmental Prenatal Education Program on Pregnant Women’s Environmental Health Awareness and Behaviors based on the Protection Motivation Theory

This study aimed to verify whether a pro-environmental prenatal education program has an effect on pregnant women's environmental health awareness and behaviors in Korea. This quasi-experimental study employed a nonequivalent control group and nonsynchronized design based on the protection motivation theory as a theoretical framework. In total, 96 pregnant women had their data collected and analyzed in Korea (40 in the experimental group; and 56 in the control group). Data collection through self-reported questionnaire was conducted between September 2017 and August 2018. The program consisted of lectures and group activities aimed at educating participants on environmental awareness and behaviors. The data were analyzed using t-test, chi square test, and ANCOVA using SPSS 24.0 program. After the intervention, the experimental group showed significantly higher sensitivity (54.78 ± 9.47 and 49.75 ± 5.42; F = 15.13, P < .001), susceptibility (26.30 ± 5.18 and 24.28 ± 4.53; F = 53.94, P < .001), response efficacy (27.40 ± 3.40 and 25.18 ± 4.23; F = 39.42, P < .001), self-efficacy (22.43 ± 4.15 and 21.35 ± 4.25; F = 41.13, P < .001), individual environmental behavior (58.59 ± 12.25 and 51.93 ± 12.64; F = 172.75, P < .001), and communal environmental behavior (18.45 ± 9.68 and 13.13 ± 8.24; F = 126.26, P < .001) than the control group. The developed pro-environmental prenatal education program contained content on the environment and pregnancy, environmental toxin, effects of endocrine disruptors, airborne pollutants, water pollutant, soil pollutant, radio-electronic exposure, and pro-environmental health behaviors during pregnancy. Pregnant women who participated in the pro-environmental prenatal education program had positive changes in environmental health perceptions and behaviors. As environmental hazards continue to increase, pregnant women should receive effective motivational education on eco-environmental protection to increase their sensitivity to environmental risk factors and to encourage active environmental health behaviors.
Discrete-modulation measurement-device-independent continuous-variable quantum key distribution with a quantum scissor: exact non-Gaussian calculation

In this paper, we study non-Gaussian discrete-modulated measurement-device-independent continuous-variable quantum key distribution protocol equipped with a proposed quantum scissor at the receiver side. Our suggested scissor truncates all multiphoton number states with four or more photons and amplifies remaining photon number states in a probabilistic way. Using exact non-Gaussian calculation, we find that quantum scissor meliorates the fidelity and entanglement between two legitimate parties Alice and Bob, at long distances. Therefore, quantum scissor enhances the continuous-variable quantum key distribution protocol range. Examination of the system for different values of the excess noise reveals that this improvement disappears at high noisy channels.
Efficient Flow-Based Scheduling for Geo-Distributed Simulation Tasks in Collaborative Edge and Cloud Environments

Edge computing is a good complement to cloud computing for deploying large-scale geo-distributed simulation applications, which are very sensitive to the communication delay among different simulation components (also called tasks in this paper) and users. We mainly focus on the efficient scheduling of simulation components in collaborative edge and cloud environments. As components should be deployed jointly with the consideration of capacity constraints of hosts, it is actually an NP-complete multi-dimensional bin packing problem. Meanwhile, dynamic changes of component and host states require the low deployment latency of scheduling algorithms. Unfortunately, most of the existing schedulers for modern clusters are queue-based, in which tasks are scheduled sequentially, thus lacking the ability to process tightly coupled tasks jointly. Other batching-based placement algorithms are usually time-consuming. This paper describes Pond, a novel flow-based scheduler with the awareness of interactions among tasks and users as well as heterogeneous multi-dimensional resources. First, characteristics of distributed simulation tasks are analysed and the scheduling problem is formulated as a min-cost max-flow (MCMF) problem over the flow network by mapping the communication overhead among tasks and users to the costs of arcs in the network. Considering the inherent defects of existing flow-based schedulers in dealing with multi-dimensional resources, a new method based on dominant resource is proposed and some problem specific heuristics are also designed. Extensive simulation experiments based on Alibaba production trace and some random synthetic parameters are conducted. Results show that Pond can reduce the average communication cost for each task significantly in a quite low deployment latency compared with some baselines.
Universality of gradient descent neural network training

It has been observed that design choices of neural networks are often crucial for their successful optimization. In this article, we therefore discuss the question if it is always possible to redesign a neural network so that it trains well with gradient descent. This yields the following universality result: If, for a given network, there is any algorithm that can find good network weights for a classification task, then there exists an extension of this network that reproduces the same forward model by mere gradient descent training. The construction is not intended for practical computations, but it provides some orientation on the possibilities of pre-trained networks in meta-learning and related approaches.
Anomaly Detection for Alzheimer’s Disease in Brain MRIs via Unsupervised Generative Adversarial Learning

Alzheimer’s disease (AD) is a neurodegenerative disease that results in cognitive decline, and even dementia, in patients. To diagnose AD, a combination of tools is typically used, with structural magnetic resonance imaging (sMRI) being one of them. sMRI images have mostly been used in supervised deep learning approaches, which requires large amounts of labeled data. To alleviate the need for labels, unsupervised deep learning could be used as an alternative. This study proposes an unsupervised model based on the deep convolutional generative adversarial network that performs anomaly detection on brain MRIs to diagnose AD. The model is able to yield an AUROC of 0.7951, a precision of 0.8228, a recall of 0.7386, and an accuracy of 74.44%.
Comparison of algorithms for simple stochastic games

Simple stochastic games are turn-based 2½-player zero-sum graph games with a reachability objective. The problem is to compute the winning probabilities as well as the optimal strategies of both players. In this paper, we compare the three known classes of algorithms – value iteration, strategy iteration and quadratic programming – both theoretically and practically. Further, we suggest several improvements for all algorithms, including the first approach based on quadratic programming that avoids transforming the stochastic game to a stopping one. Our extensive experiments show that these improvements can lead to significant speed-ups. We implemented all algorithms in PRISM-games 3.0, thereby providing the first implementation of quadratic programming for solving simple stochastic games.
Graph-Informed Neural Networks for Regressions on Graph-Structured Data

In this work, we extend the formulation of the spatial-based graph convolutional networks with a new architecture, called the graph-informed neural network (GINN). This new architecture is specifically designed for regression tasks on graph-structured data that are not suitable for the well-known graph neural networks, such as the regression of functions with the domain and codomain defined on two sets of values for the vertices of a graph. In particular, we formulate a new graph-informed (GI) layer that exploits the adjacent matrix of a given graph to define the unit connections in the neural network architecture, describing a new convolution operation for inputs associated with the vertices of the graph. We study the new GINN models with respect to two maximum-flow test problems of stochastic flow networks. GINNs show very good regression abilities and interesting potentialities. Moreover, we conclude by describing a real-world application of the GINNs to a flux regression problem in underground networks of fractures.
SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization

Nature-inspired metaheuristic algorithms remain a strong trend in optimization. Human-inspired optimization algorithms should be more intuitive and relatable. This paper proposes a novel optimization algorithm inspired by a human search party. We hypothesize the behavioral model of a search party searching for a treasure. Motivated by the search party’s behavior, we abstract the “Divide, Conquer, Assemble” (DCA) approach. The DCA approach allows us to parallelize the traditional gradient descent algorithm in a strikingly simple manner. Essentially, multiple gradient descent instances with different learning rates are run parallelly, periodically sharing information. We call it the search party gradient descent (SPGD) algorithm. Experiments performed on a diverse set of classical benchmark functions show that our algorithm is good at optimizing. We believe our algorithm’s apparent lack of complexity will equip researchers to solve problems efficiently. We compare the proposed algorithm with SciPy’s optimize library and it is found to be competent with it.
Google Earth-aided visualization and interpretation of geochemical survey data

Geochemical prospecting is an important and effective approach for discovering mineral deposits. Collection, management, visualization, interpretation, modeling, and publishing of geochemical survey data remain challenging. The main aim of this study is to illustrate the application of Google Earth in the visualization and interpretation of geochemical survey data using a dataset collected from the Daqiao district, Gansu Province, China. Google Earth, a free web-based software that provides rich set of global images along with satellite and map data, can be applied to design geochemical sampling strategies; display geochemical sampling location landscapes; collect and manage geochemical survey data; visualize and publish 0-D (point), 1-D (profile), 2-D (raster map), and 3-D (superposition of multiple maps) geochemical maps; and interpret and model geochemical patterns using various Google Earth functions and the Keyhole Markup Language of Google. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis