Reviewerly/exHarmony huggingface dataset:
Blockchain-as-a-Service (BaaS) for Battery Energy Storage Systems

Blockchain-as-a-Service is an emerging blockchain-based platform service that can potentially contribute to the advancement of contemporary power and energy systems in cyber-physical environment, such as battery energy storage systems (BESSs). This paper explores how the blockchain technology that can be applied toward a next-generation BESS by managing critical activities and tasks including security, supply chain, health monitoring, data sharing, and energy transactions. Specifically, it is expected that the blockchain technology can lead to the advancement of BESSs by offering security enhancements as compared to applications that rely on a traditional-based approach without blockchain technology.
Detecting Fraudulent Insurance Claims Using Random Forests and Synthetic Minority Oversampling Technique

There has been a significant amount of growth in the number of fraudulent activities by the policy-holders over the last couple of years. Deliberately deceiving the insurance providers by omitting facts and hiding details while claiming for insurance has led to significant loss of money and customer value. To keeps these risks under control; a proper framework is required for judiciously monitoring insurance fraud. In this paper, we demonstrate a novel approach for building a machine- learning based auto-insurance fraud detector which will predict fraudulent insurance claims from the dataset of over 15,420 car-claim records. The proposed model is built using synthetic minority oversampling technique (SMOTE) which removes the class imbalance-ness of the dataset. We use random forests classification method to classify the claim records. The data used in our experiment is taken from a publically available auto insurance datasets. The outcomes of our approach were compared with other existing models based on various performance metrics.
A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model With a Hybrid FPGA Implementation

Computing and attending to salient regions of a visual scene is an innate and necessary preprocessing step for both biological and engineered systems performing high-level visual tasks including object detection, tracking, and classification. Computational bandwidth and speed are improved by preferentially devoting computational resources to salient regions of the visual field. The human brain computes saliency effortlessly, but modeling this task in engineered systems is challenging. We first present a neuromorphic dynamic saliency model, which is bottom-up, feed-forward, and based on the notion of proto-objects with neurophysiological spatio-temporal features requiring no training. Our neuromorphic model outperforms state-of-the-art dynamic visual saliency models in predicting human eye fixations (i.e., ground truth saliency). Secondly, we present a hybrid FPGA implementation of the model for real-time applications, capable of processing 112×84 resolution frames at 18.71 Hz running at a 100 MHz clock rate - a 23.77× speedup from the software implementation. Additionally, our fixed-point model of the FPGA implementation yields comparable results to the software implementation.
Tuning

column Share on Tuning: an approach for supporting healthful adaptation Authors: m.c. schraefel University of Southampton University of SouthamptonView Profile , Eric Hekler University of California, San Diego University of California, San DiegoView Profile Authors Info & Claims InteractionsVolume 27Issue 2March - April 2020 pp 48–53https://doi.org/10.1145/3381897Published:25 February 2020Publication History 7citation1,014DownloadsMetricsTotal Citations7Total Downloads1,014Last 12 Months165Last 6 weeks14 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Using spreadsheets as learning tools for computer simulation of neural networks

The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment with-out add-ins and macros. The article considers ways of building neural network models in cloud-based spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
Bao: Learning to Steer Query Optimizers.

Query optimization remains one of the most challenging problems in data management systems. Recent efforts to apply machine learning techniques to query optimization challenges have been promising, but have shown few practical gains due to substantive training overhead, inability to adapt to changes, and poor tail performance. Motivated by these difficulties and drawing upon a long history of research in multi-armed bandits, we introduce Bao (the BAndit Optimizer). Bao takes advantage of the wisdom built into existing query optimizers by providing per-query optimization hints. Bao combines modern tree convolutional neural networks with Thompson sampling, a decades-old and well-studied reinforcement learning algorithm. As a result, Bao automatically learns from its mistakes and adapts to changes in query workloads, data, and schema. Experimentally, we demonstrate that Bao can quickly (an order of magnitude faster than previous approaches) learn strategies that improve end-to-end query execution performance, including tail latency. In cloud environments, we show that Bao can offer both reduced costs and better performance compared with a sophisticated commercial system.
Advanced Parameter-Setting-Free Harmony Search Algorithm

In this paper, we propose an advanced parameter-setting-free (PSF) scheme to solve the problem of setting the parameters for the harmony search (HS) algorithm. The use of the advanced PSF method solves the problems of the conventional PSF scheme that results from a large number of iterations and shows good results compared to fixing the parameters required for the HS algorithm. In addition, unlike the conventional PSF method, the advanced PSF method does not use additional memory. We expect the advanced PSF method to be applicable to various fields that use the HS algorithm because it reduces the memory utilization for operations while obtaining better results than conventional PSF schemes.
An Overview of Speech Recognition Technology

With the speech recognition research intensifying gradually in recent years, it is particularly important to grasp the research direction of this filed. This paper summarizes the theoretical algorithms in the development of speech recognition. Firstly, we introduce the specific process of speech recognition, including biometrics acquisition, preprocessing, feature extraction, biometrics pattern matching and recognition results. Besides, we emphatically introduce feature extraction and matching recognition algorithm. We also introduce the application platform, and give the specific application scenarios. Finally, we put forward the shortcomings of the research and prospect the future development trend.
Obstacle Avoidance Model for UAVs with Joint Target based on Multi-Strategies and Follow-up Vector Field

A multi-strategies obstacle avoidance method based on follow-up rotating vector field is proposed for UAV flight planning. The inter-UAVs repulsion with distance factor and the target directional gravity are used as the inter-UAVs control strategy. In the process of obstacle avoidance, the artificial potential field method is used outside the obstacle to guide the UAVs to sail to the target point. In the obstacle avoidance scope, the follow-up rotating vector field method is proposed to avoid convex polyhedral obstacles. Finally, smoothing strategy is conbined to the follow-up rotating vector field to obtain safety and smoothing path. The simulation results show that the proposed algorithm can solve the problem of "dead zone" and "jitter" in the track by combining various strategies, and realize the obstacle avoidance behavior while avoiding collision between machines.
Jittered Sampling in Linear Canonical Domain

Linear canonical transform (LCT) based sampling has been attracted much attention in signal processing community. A variety of sampling theorems associated with the LCT are therefore derived. However, the random jittered sampling in linear canonical domain still remains unresolved. Starting from the Shannon's result for the LCT, a biased interpolated estimate for bandlimited signals in linear canonical domain from jittered samples is proposed. The inverse filter technique is then applied to correct the bias, giving rise to an unbiased estimator whose interpolation noise variance approaches zero as the jitter becomes less and less pronounced. To be specific, for bandlimited stationary stochastic signals in linear canonical domain the interpolation noise variance is increased, and this result is also verified through some examples and simulations.
Using nanoresonators with robust chaos as hardware random number generators

In this paper, we investigate theoretically the potential of a nanoelectromechanical suspended beam resonator excited by two-external frequencies as a hardware random number generator. This system exhibits robust chaos, which is usually required for practical applications of chaos. Taking advantage of the robust chaotic oscillations, we consider the beam position as a possible random variable and perform tests to check its randomness. The beam position collected at fixed time intervals is used to create a set of values that is a candidate for a random sequence of numbers. To determine how close to a random sequence this set is, we perform several known statistical tests of randomness. The performance of the random sequence in the simulation of two relevant physical problems, the random walk and the Ising model, is also investigated. An excellent overall performance of the system as a random number generator is obtained.
An Optimal Stopping Approach for Iterative Training in Federated Learning

This paper studies the problem of iterative training in Federated Learning. We consider a system with a single parameter server (PS) and M client devices for training a predictive learning model with distributed data sets on the client devices. The clients communicate with the parameter server using a common wireless channel, so each time only one device can transmit. The training is an iterative process consisting of multiple rounds. At beginning of each round (also called an iteration), each client trains the model, broadcast by the parameter server at the beginning of the round, with its own data. After finishing training, the device transmits the update to the parameter server when the wireless channel is available. The server aggregates updates to obtain a new model and broadcasts it to all clients to start a new round. We consider adaptive training where the parameter server decides when to stop/restart a new round, and formulate the problem as an optimal stopping problem. While this optimal stopping problem is difficult to solve, we propose a modified optimal stopping problem. We first develop a low complexity algorithm to solve the modified problem, which also works for the original problem. Experiments on a real data set shows significant improvements compared with policies collecting a fixed number of updates in each round.
Recognition of Genetic Diseases Based on Combined Feature Extraction from 2D Face Images

Screening patients with genetic diseases using automated facial image analysis is an urgent task. A method for recognizing genetic syndromes from a frontal image of a face has been developed and studied. The classification was made to 8 syndromes (Angelman, Apert, Cornelia de Lange, Down, Fragile X, Progeria, Treacher Collins, Williams). Various types of features were investigated: geometric and deep features. For facial points localization, 3D face reconstruction was used (using the Deep3DFaceReconstruction library). Sets of 68 and 35709 (all points of 3D reconstruction) points were investigated. The effect of reducing the dimension of the feature vector on the classification accuracy is also investigated. According to the results of 5-fold cross-validation, the best average recognition accuracy was 92.5% (combined features, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and logistic regression), which is comparable to the results in similar works.
Single Image 3D Hand Reconstruction with Mesh Convolutions.

Monocular 3D reconstruction of deformable objects, such as human body parts, has been typically approached by predicting parameters of heavyweight linear models. In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes. The prior on 3D hand shapes is learned by training an autoencoder with intrinsic graph convolutions performed in the spectral domain. The pre-trained decoder acts as a non-linear statistical deformable model. The latent parameters that reconstruct the shape and articulated pose of hands in the image are predicted using an image encoder. We show that our system reconstructs plausible meshes and operates in real-time. We evaluate the quality of the mesh reconstructions produced by the decoder on a new dataset and show latent space interpolation results. Our code, data, and models will be made publicly available.
Privacy-Preserving Pattern Matching on Encrypted Data

Pattern matching is one of the most fundamental and important paradigms in several application domains such as digital forensics, cyber threat intelligence, or genomic and medical data analysis. While it is a straightforward operation when performed on plaintext data, it becomes a challenging task when the privacy of both the analyzed data and the analysis patterns must be preserved. In this paper, we propose new provably correct, secure, and relatively efficient (compared to similar existing schemes) public and private key based constructions that allow arbitrary pattern matching over encrypted data while protecting both the data to be analyzed and the patterns to be matched. That is, except the pattern provider (resp. the data owner), all other involved parties in the proposed constructions will learn nothing about the patterns to be searched (resp. the data to be inspected). Compared to existing solutions, the constructions we propose have some interesting properties: (1) the size of the ciphertext is linear to the size of plaintext and independent of the sizes and the number of the analysis patterns; (2) the sizes of the issued trapdoors are constant on the size of the data to be analyzed; and (3) the search complexity is linear on the size of the data to be inspected and is constant on the sizes of the analysis patterns. The conducted evaluations show that our constructions drastically improve the performance of the most efficient state of the art solution.
Classification strategies in machine learning techniques predicting regime changes and durations in the Lorenz system

In this paper, we use machine learning strategies aiming to predict chaotic time series obtained from the Lorenz system. Such strategies prove to be successful in predicting the evolution of dynamical variables over a short period of time. Transitions between the regimes and their duration can be predicted with great accuracy by means of counting and classification strategies, for which we train multi-layer perceptron ensembles. Even for the longest regimes the occurrences and duration can be predicted. We also show the use of an echo state network to generate data of the time series with an accuracy of up to a few hundreds time steps. The ability of the classification technique to predict the regime duration of more than 11 oscillations corresponds to around 10 Lyapunov times.
A new patch-based LBP with adaptive weights for gender classification of human face

Recognition methods based on local binary patterns (LBP) depend on a pre-defined structure, such as pixel-to-pixel or pixel-to-patch comparison, for the feature extraction of picture descriptions without the addition of weights. This paper proceeds on the assumption that patch-based LBP should be based on a pyramid structure to enable the computation of gradients using weight parameters pre-defined by eigen theory. The proposed method, namely Adaptive-Weight Patch-based LBP (AWPLBP), seeks to determine the transformation that identifies sets on the hyper-plane capable of maximizing variance in projections. The transformation and quantization loss are used to train the AWPLBP descriptors to facilitate the efficient feature extraction of descriptions. Finally, a classifier with a support vector machine (SVM) is trained using the AWPLBP features for the gender classification of human faces. The experimental results show that the proposed AWPLBP descriptor may achieve better recognition performances than other LBP methods and is competitive with CNN-based methods in gender classification application.
Efficient Architecture Search for Deep Neural Networks

This paper addresses the scalability challenge of automatic deep neural architecture search by implementing a parameter sharing approach with regularized genetic algorithm (RGE). The key idea is to use a regularized genetic algorithm (RGE) on a pre-determined template and discover a high-performance architecture by searching for the optimal chromosome. During evolution, each model corresponding to a discovered chromosome is trained for a fixed number of epochs to minimize a canonical cross-entropy loss on a given training dataset. Meanwhile, the performance of the trained model on validation dataset is used as a fitness value to perform the evolutions. Because of parameter sharing the trained weights in each generation are carried to the next, thereby reducing the GPU hours required for maximizing the validation accuracy. On the CIFAR-10 dataset, the approach finds a novel architecture that outperforms the best human-invented deep architecture (DenseNet). The CIFAR-10 model achieved a test error of 4.22% with only 0.96M parameters which is better than DenseNet of 4.51% with 0.8M parameters. On CIFAR-100 dataset, the approach was able to compose a novel architecture that achieved 20.53% test error with 3.7M parameters which is on par with 20.50% test error of wide ResNet with 36.5M parameters.
Import test questions into Moodle LMS

The purpose of the study is to highlight the theoretical and methodological aspects of preparing the test questions of the most common types in the form of text files for further import into learning management system (LMS) Moodle. The subject of the research is the automated filling of the Moodle LMS test database. The objectives of the study: to analyze the import files of test questions, their advantages and disadvantages; to develop guidelines for the preparation of test questions of common types in the form of text files for further import into Moodle LMS. The action algorithms for importing questions and instructions for submitting question files in such formats as Aiken, GIFT, Moodle XML, “True/False” questions, “Multiple Choice” (one of many and many of many), “Matching”, with an open answer — “Numerical” or “Short answer” and “Essay” are offered in this article. The formats for submitting questions, examples of its designing and developed questions were demonstrated in view mode in Moodle LMS.
Error mitigation with Clifford quantum-circuit data

Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{\text{noisy}},X_i^{\text{exact}}\}$ via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where $X_i^{\text{noisy}}$ and $X_i^{\text{exact}}$ are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.
Self-Paced Contextual Reinforcement Learning

Generalization and adaptation of learned skills to novel situations is a core requirement for intelligent autonomous robots. Although contextual reinforcement learning provides a principled framework for learning and generalization of behaviors across related tasks, it generally relies on uninformed sampling of environments from an unknown, uncontrolled context distribution, thus missing the benefits of structured, sequential learning. We introduce a novel relative entropy reinforcement learning algorithm that gives the agent the freedom to control the intermediate task distribution, allowing for its gradual progression towards the target context distribution. Empirical evaluation shows that the proposed curriculum learning scheme drastically improves sample efficiency and enables learning in scenarios with both broad and sharp target context distributions in which classical approaches perform sub-optimally.
Adults' and Children's Mental Models for Gestural Interactions with Interactive Spherical Displays

Interactive spherical displays offer numerous opportunities for engagement and education in public settings. Prior work established that users' touch-gesture patterns on spherical displays differ from those on flatscreen tabletops, and speculated that these differences stem from dissimilarity in how users conceptualize interactions with these two form factors. We analyzed think-aloud data collected during a gesture elicitation study to understand adults' and children's (ages 7 to 11) conceptual models of interaction with spherical displays and compared them to conceptual models of interaction with tabletop displays from prior work. Our findings confirm that the form factor strongly influenced users' mental models of interaction with the sphere. For example, participants conceptualized that the spherical display would respond to gestures in a similar way as real-world spherical objects like physical globes. Our work contributes new understanding of how users draw upon the perceived affordances of the sphere as well as prior touchscreen experience during their interactions.