Reviewerly/exHarmony huggingface dataset:
Potential of kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support for predicting daily global solar radiation in humid regions

The knowledge of global solar radiation is of vital importance for the design and use of solar energy systems. This study evaluated the potential of two new powerful machine learning models, i.e., kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support, for accurately estimating daily global solar radiation in humid regions. These two models were also compared with the multilayer perceptron, M5 model tree, random forest and multivariate adaptive regression spline models, using five input combinations of daily meteorological data during 2001–2015 from four weather stations in the (sub)tropical humid regions of South China. The results showed that, when lack of complete meteorological data, machine learning models using the ratio of actual and theoretical sunshine duration, maximum and minimum temperatures obtained satisfactory daily global solar radiation estimates. Generally, the kernel-based nonlinear extension of Arps decline model offered the best prediction accuracy among the studied models, followed by the gradient boosting with categorical features support. The multilayer perceptron model exhibited the smallest average percentage increase in the root mean square error during testing over the training values, followed by the kernel-based nonlinear extension of Arps decline model. Both the kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support were successfully applied to develop general models for daily global solar radiation prediction (differences in root mean square error <5% compared with local models). The gradient boosting with categorical features support and multilayer perceptron exhibited much smaller computational time for both local and general models. Comprehensively considering the accuracy, stability and computational time, both the kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support are recommended for predicting daily global solar radiation in the humid regions of China.
A Spelling Correction Model for End-to-end Speech Recognition

Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and require only parallel audio-text pairs. Thus, the language model component of the end-to-end model is only trained on transcribed audio-text pairs, which leads to performance degradation especially on rare words. While there have been a variety of work that look at incorporating an external LM trained on text-only data into the end-to-end framework, none of them have taken into account the characteristic error distribution made by the model. In this paper, we propose a novel approach to utilizing text-only data, by training a spelling correction (SC) model to explicitly correct those errors. On the LibriSpeech dataset, we demonstrate that the proposed model results in an 18.6% relative improvement in WER over the baseline model when directly correcting top ASR hypothesis, and a 29.0% relative improvement when further rescoring an expanded n-best list using an external LM.
Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method

Cloud computing is truly transforming the way e-commerce firms do business. While there has been a sharp increase in the use of cloud computing in e-commerce, the benefits of cloud service models have yet to be explored, particularly for small-to-medium-sized businesses. A strong e-commerce offering depends on a reliable and secure online store, therefore it is important for decision makers to adopt the optimal cloud computing service model such as software-as-a-service (SaaS), platform-as-a-service (PaaS), or infrastructure-as-a-service (IaaS), which is a multi-criteria decision-making problem (MCDM). To address this MCDM problem, we propose a novel 2-tuple fuzzy linguistic multi-criteria group decision-making method based on the technique for order preference by similarity to ideal solution (TOPSIS) and rely upon a technology-organization-environment (TOE) framework to determine a set of appropriate criteria. The proposed methodology is applied to a small-to-medium-sized company to facilitate assessing the factors associated with cloud-based e-commerce and making the decision. The result analysis indicates that SaaS is the best choice for small and medium-sized e-commerce businesses considering criteria such as complexity, reliability, security and privacy, organization readiness and firm size, while the selection of PaaS or IaaS can be reinforced considering their compatibility and scalability.
Correlational Convolutional LSTM for human action recognition

In light of recent exponential growth of video data, the need for automated video processing has increased substantially. To learn the intrinsic structure of video data, many representation approaches have been proposed, focusing on learning the spatial features and time dependencies, while, motion features are hand-crafted and left out of the learning process. In this work, we present an extended version of the LSTM units named C2LSTM in which the motion data are perceived as well as the spatial features and temporal dependencies. We leverage convolution and correlation operators to credit both the spatial and motion structure of the video data. Furthermore, a deep network is designed for human action recognition using the proposed units. The network is evaluated on the two well-known benchmarks, UCF101 and HMDB51. The results confirm the potency of C2LSTM to capture motion as well as spatial features and time dependencies.
Let Me Explain

Trust in a Recommender System (RS) is crucial for its overall success. However, it remains underexplored whether users trust personal recommendation sources (i.e. other humans) more than impersonal sources (i.e. conventional RS), and, if they do, whether the perceived quality of explanation provided account for the difference. We conducted an empirical study in which we compared these two sources of recommendations and explanations. Human advisors were asked to explain movies they recommended in short texts while the RS created explanations based on item similarity. Our experiment comprised two rounds of recommending. Over both rounds the quality of explanations provided by users was assessed higher than the quality of the system's explanations. Moreover, explanation quality significantly influenced perceived recommendation quality as well as trust in the recommendation source. Consequently, we suggest that RS should provide richer explanations in order to increase their perceived recommendation quality and trustworthiness.
IoT forensic challenges and opportunities for digital traces

The increasing number of IoT devices in personal environments such as smarthomes presents opportunities and risks from a forensic perspective. These devices generate traces that can be useful for investigative and forensic purposes in any type of offense. At the same time, newer IoT devices are not supported by existing digital forensic tools and methods, making it difficult for practitioners to extract data from them without the support of a forensic advisor with specialized knowledge in this area. In addition, these traces can present evaluation challenges for forensic scientists, and can contain vulnerabilities that pose privacy risks. Security vulnerabilities of IoT devices create opportunities for extracting traces but might also be used by criminals to undermine a device. The aim of this work is to increase familiarity with traces from various IoT devices in a smarthome, and demonstrate how traces from IoT devices in a smarthome can be useful for investigative and forensic purposes. This work presents a study of IoT devices and associated smartphone applications, providing approaches to extracting and analyzing digital traces. This research led to the discovery of vulnerabilities in multiple devices, and a scenario for the DFRWS IoT forensic challenge was developed.
Comparative Study of Convolution Neural Network’s Relu and Leaky-Relu Activation Functions

Convolutional neural networks refer to a collection of feed-forward artificial neural networks. These networks have been implemented successfully on visual imagery. It uses a variety of perceptrons. These perceptrons are multilayered, that need very little preprocessing. Shift invariant or space invariant NN are alias for CNN, because of their architecture which is based on shared weights. It is also established on translation invariance features. In this paper, we have used rectified linear unit (Relu) and Leaky-Relu activation for inner CNN layer and softmax activation function for output layer to analyze its effect on MNIST dataset.
Efficient Subgraph Matching

Subgraph matching (or subgraph isomorphism) is one of the fundamental problems in graph analysis. Extensive research has been done to develop practical solutions for subgraph matching. The state-of-the-art algorithms such as \textsfCFL-Match and \textsfTurbo\textsubscriptiso convert a query graph into a spanning tree for obtaining candidates for each query vertex and obtaining a good matching order with the spanning tree. However, by using the spanning tree instead of the original query graph, it could lead to lower pruning power and a sub-optimal matching order. Another limitation is that they perform redundant computation in search without utilizing the knowledge learned from past computation. In this paper, we introduce three novel concepts to address these inherent limitations: 1) dynamic programming between a directed acyclic graph (DAG) and a graph, 2) adaptive matching order with DAG ordering, and 3) pruning by failing sets, which together lead to a much faster algorithm \textsfDAF for subgraph matching. Extensive experiments with real datasets show that \textsfDAF outperforms the fastest existing solution by up to orders of magnitude in terms of recursive calls as well as in terms of the elapsed time.
insight: A Unified Interface to Access Information from Model Objects in R

When fitting any statistical model, there are many useful pieces of information that are simultaneously calculated and stored beyond coefficient estimates and general model fit statistics.Although there exist some generic functions to obtain model information and data, many package-specific modeling functions do not provide such methods to allow users to access such valuable information.insight is an R-package (R Core Team, 2019) that fills this important gap by providing a suite of functions to support almost any model (see a list of the many models supported below in the Supported Models section).The goal of insight, then, is to offer tools that provide easy, intuitive, and consistent access to information contained in model objects.These tools aid applied researchers in virtually any field who fit, diagnose, and present statistical models by streamlining access to every aspect of model objects via consistent syntax and output.Ultimately, the development of insight is in line with the philosophy of the easystats project, which is to facilitate and streamline the process of statistical analysis, interpreting and reporting the results in the R programming language.
Semantic Stereo for Incidental Satellite Images

The increasingly common use of incidental satellite images for stereo reconstruction versus rigidly tasked binocular or trinocular coincident collection is helping to enable timely global-scale 3D mapping; however, reliable stereo correspondence from multi-date image pairs remains very challenging due to seasonal appearance differences and scene change. Promising recent work suggests that semantic scene segmentation can provide a robust regularizing prior for resolving ambiguities in stereo correspondence and reconstruction problems. To enable research for pairwise semantic stereo and multi-view semantic 3D reconstruction with incidental satellite images, we have established a large-scale public dataset including multi-view, multi-band satellite images and ground truth geometric and semantic labels for two large cities. To demonstrate the complementary nature of the stereo and segmentation tasks, we present lightweight public baselines adapted from recent state of the art convolutional neural network models and assess their performance.
On Pre-trained Image Features and Synthetic Images for Deep Learning

Deep Learning methods usually require huge amounts of training data to perform at their full potential, and often require expensive manual labeling. Using synthetic images is therefore very attractive to train object detectors, as the labeling comes for free, and several approaches have been proposed to combine synthetic and real images for training. In this paper, we evaluate if ‘freezing’ the layers responsible for feature extraction to generic layers pre-trained on real images, and training only the remaining layers with plain OpenGL rendering may allow for training with synthetic images only. Our experiments with very recent deep architectures for object recognition (Faster-RCNN, R-FCN, Mask-RCNN) and image feature extractors (InceptionResnet and Resnet) show this simple approach performs surprisingly well.
SpOT-Light: Lightweight Private Set Intersection from Sparse OT Extension

We describe a novel approach for two-party private set intersection (PSI) with semi-honest security. Compared to existing PSI protocols, ours has a more favorable balance between communication and computation. Specifically, our protocol has the lowest monetary cost of any known PSI protocol, when run over the Internet using cloud-based computing services (taking into account current rates for CPU + data). On slow networks (e.g., 10 Mbps) our protocol is actually the fastest. Our novel underlying technique is a variant of oblivious transfer (OT) extension that we call sparse OT extension. Conceptually it can be thought of as a communication-efficient multipoint oblivious PRF evaluation. Our sparse OT technique relies heavily on manipulating high-degree polynomials over large finite fields (i.e. elements whose representation requires hundreds of bits). We introduce extensive algorithmic and engineering improvements for interpolation and multi-point evaluation of such polynomials, which we believe will be of independent interest. Finally, we present an extensive empirical comparison of state-of-the-art PSI protocols in several application scenarios and along several dimensions of measurement: running time, communication, peak memory consumption, and—arguably the most relevant metric for practice—monetary cost.
Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records

Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step in reducing the burden of diabetes and its complications. Electronic health records (EHRs) for each individual or a population have become important tools in understanding developing trends of diseases. Using EHRs to predict the onset of diabetes could improve the quality and efficiency of medical care. In this paper, we apply a wide and deep learning model that combines the strength of a generalised linear model with various features and a deep feed-forward neural network to improve the prediction of the onset of type 2 diabetes mellitus (T2DM).The proposed method was implemented by training various models into a logistic loss function using a stochastic gradient descent. We applied this model using public hospital record data provided by the Practice Fusion EHRs for the United States population. The dataset consists of de-identified electronic health records for 9948 patients, of which 1904 have been diagnosed with T2DM. Prediction of diabetes in 2012 was based on data obtained from previous years (2009-2011). The imbalance class of the model was handled by Synthetic Minority Oversampling Technique (SMOTE) for each cross-validation training fold to analyse the performance when synthetic examples for the minority class are created. We used SMOTE of 150 and 300 percent, in which 300 percent means that three new synthetic instances are created for each minority class instance. This results in the approximated diabetes:non-diabetes distributions in the training set of 1:2 and 1:1, respectively.Our final ensemble model not using SMOTE obtained an accuracy of 84.28%, area under the receiver operating characteristic curve (AUC) of 84.13%, sensitivity of 31.17% and specificity of 96.85%. Using SMOTE of 150 and 300 percent did not improve AUC (83.33% and 82.12%, respectively) but increased sensitivity (49.40% and 71.57%, respectively) with a moderate decrease in specificity (90.16% and 76.59%, respectively).Our algorithm has further optimised the prediction of diabetes onset using a novel state-of-the-art machine learning algorithm: the wide and deep learning neural network architecture.
Efficient Privacy-Preserving Machine Learning for Blockchain Network

A blockchain as a trustworthy and secure decentralized and distributed network has been emerged for many applications such as in banking, finance, insurance, healthcare and business.Recently, many communities in blockchain networks want to deploy machine learning models to get meaningful knowledge from geographically distributed large-scale data owned by each participant.To run a learning model without data centralization, distributed machine learning (DML) for blockchain networks has been studied.While several works have been proposed, privacy and security have not been sufficiently addressed, and as we show later, there are vulnerabilities in the architecture and limitations in terms of efficiency.In this paper, we propose a privacy-preserving DML model for a permissioned blockchain to resolve the privacy, security, and performance issues in a systematic way.We develop a differentially private stochastic gradient descent method and an error-based aggregation rule as core primitives.Our model can treat any type of differentially private learning algorithm where non-deterministic functions should be defined.The proposed error-based aggregation rule is effective to prevent attacks by an adversarial node that tries to deteriorate the accuracy of DML models.Our experiment results show that our proposed model provides stronger resilience against adversarial attacks than other aggregation rules under a differentially private scenario.Finally, we show that our proposed model has high usability because it has low computational complexity and low transaction latency.
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air

We study collaborative machine learning at the wireless edge, where power and bandwidth-limited devices (workers), with limited local datasets, implement distributed stochastic gradient descent (DSGD) over-the-air with the help of a remote parameter server (PS). We consider a wireless multiple access channel (MAC) from the workers to the PS for communicating the local gradient estimates. We first introduce a digital DSGD (D-DSGD) scheme, assuming that the workers operate on the boundary of the MAC capacity region at each iteration of the DSGD algorithm, and digitize their estimates within the bit budget allowed by the employed power allocation. We then introduce an analog scheme, called A-DSGD, motivated by the additive nature of the wireless MAC, where the workers send their gradient estimates over the MAC through the available channel bandwidth without employing any digital code. Numerical results show that A-DSGD converges much faster than D-DSGD. The improvement is particularly compelling at low power and low bandwidth regimes. We also observe that the performance of A-DSGD improves with the number of workers, while D-DSGD deteriorates, limiting the ability of the latter in harnessing the computation power of many edge devices.
Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles

There are many sensor fusion frameworks proposed in the literature using different sensors and fusion methods combinations and configurations. More focus has been on improving the accuracy performance; however, the implementation feasibility of these frameworks in an autonomous vehicle is less explored. Some fusion architectures can perform very well in lab conditions using powerful computational resources; however, in real-world applications, they cannot be implemented in an embedded edge computer due to their high cost and computational need. We propose a new hybrid multi-sensor fusion pipeline configuration that performs environment perception for autonomous vehicles such as road segmentation, obstacle detection, and tracking. This fusion framework uses a proposed encoder-decoder based Fully Convolutional Neural Network (FCNx) and a traditional Extended Kalman Filter (EKF) nonlinear state estimator method. It also uses a configuration of camera, LiDAR, and radar sensors that are best suited for each fusion method. The goal of this hybrid framework is to provide a cost-effective, lightweight, modular, and robust (in case of a sensor failure) fusion system solution. It uses FCNx algorithm that improve road detection accuracy compared to benchmark models while maintaining real-time efficiency that can be used in an autonomous vehicle embedded computer. Tested on over 3K road scenes, our fusion algorithm shows better performance in various environment scenarios compared to baseline benchmark networks. Moreover, the algorithm is implemented in a vehicle and tested using actual sensor data collected from a vehicle, performing real-time environment perception.
A Dilated Inception Network for Visual Saliency Prediction

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we propose an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a global probability distribution prediction task for better saliency inference instead of a pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.