Reviewerly/exHarmony huggingface dataset:
A change management model and its application in software development projects

Change is inevitable in software projects and software engineers strive to find ways to manage changes.A complete task could be easily in a team`s agenda sometime later due to change demands.Change demands are caused by failures and/or improvements and require additional effort which in most cases have not been planned upfront and affect project progress significantly.Earned Value Management (EVM) is a powerful performance management and feedback tool for project management.EVM depicts the project progress in terms of scope, cost, and schedule and provides future predictions based on trends and patterns of the past.Even though EVM works quite well and widely used in disciplines like construction and mining, it is not the case for software discipline.Software projects require special attention and adoption for change.In this study, we present a model to measure change and subsequent rework and evolution costs to monitor software projects accurately.We have performed five case studies in five different companies to explore the usability of the proposed model.This paper depicts the proposed model and discusses the results of the case studies.
Generative models as parsimonious descriptions of sensorimotor loops

The Bayesian brain hypothesis, predictive processing, and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can (and should) be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world.
Ethereum-based Emergency Service for Smart Home System: Smart Contract Implementation

Emergency service call for public service providers has become an important role for smart home applications in order to support safety and security in the household building. Blockchain has been a promising solution with cryptography and incentive distributed mechanisms to support the verification, execution and recording of transactions between untrusted parties. In this paper, we present a Smart Home System (SHS) based on Ethereum with smart contract infrastructure for handling an emergency service sending from SHS to Home Service Providers (HSP) when there are unusual environmental conditions. Our SHS testbed consists of three domains: (1) Smart Home Sensor Manager (SM) or IoT devices to gather environmental sensor data and send an emergency call to HSP, (2) Home Service Provider (HSP) system deployed with Ethereum Virtual Machine (EVM) and smart contract, and (3) decentralize Meteor framework to interface between Ethereum and web based applications for homeowners (HO) and HSP staffs. To achieve homeowner privacy and security, we enable digital signature coupling with InterPlanetary File System (IPFS) for handling the emergency call from SM to HSP and One Time Passcode (OTP) produced by HSP for HSP staffs to verify themselves for further access control when they go to service homeowner's house. Each smart contract transaction in solidity is described. Finally, security and privacy issues for our proposed work are discussed.
A hybrid data mining heuristic to solve the point‐feature cartographic label placement problem

Abstract A clean map visualization requires the fewest possible overlaps and depends on how labels are attached to point features. In this paper, we address the cartographic label placement variant problem whose objective is to label a set of points maximizing the number of conflict‐free points. Thus, we propose a hybrid data mining heuristic to solve the point‐feature cartographic label placement problem based on a clustering search (CS) heuristic, a state‐of‐the‐art method for this problem. Although several works have investigated the combination of data mining and multistart metaheuristics, this is the first time data mining has been used to improve CS and simulated annealing based heuristics. Computational experiments showed that the proposed hybrid heuristic was able to reach better cost solutions than the original strategy, with the same time effort. The proposed heuristic also could find almost all known optimal solutions and improved most of the best results for the set of large instances reported so far in the literature.
Code similarity detection through control statement and program features

Software clone detection is an emerging research area in the field of software engineering. Software systems are subjected to continuous modifications in source code to improve the performance of the software, which may lead to code redundancy. Duplicate code/code clone is a piece of code reworked several times in software programs due to copy paste activity or reusability of existing software. Code clone is a prime subject in software evolution. Detection of software clones at the time of software evolution may improve the performance of software and reduce the maintenance cost and effort. This paper proposes metric based methods to detect code clones, as software clone is a universal problem in large scale programming environment. This paper introduces two metric based approaches to detect code clones by comparing (i) Control Statement Features (ii) Program Features like different types of statements, operators and operands. In order to demonstrate the effectiveness of the proposed approaches, extensive experiments are conducted on two datasets, C projects of Bellon's benchmark dataset and student lab programs (SLP).The methods efficiently identify similar functional clones. Proposed models only find similarity of whole programs but intelligent enough to highlight similar code segments across program files.
An easy-to-hard learning strategy for within-image co-saliency detection

Within-image co-saliency detection is to detect/highlight the common saliency (similar-appearance salient objects) in a single image. Ideally, it can be solved by detecting each individual salient object first and then comparing them, which is possible for some images with simple representations. However, in practice, this way is not accurate and robust for some images with complex representations. In this paper, we propose an easy-to-hard learning strategy to solve this problem. By directly localizing and comparing salient objects in simple images, superpixel confidences as co-salient objects are inferred by an easy learning method, which provide promising but also noisy supervisions for complex images. Therefore, within-image co-saliency detection in complex images can be modeled as a hard learning problem with noisy labels. A multi-scale Multiple Instance Learning (MIL) model together with a new sampling method is proposed to solve this hard learning problem with noisy labels. Experimental results show that the proposed method achieves the best performance on a public benchmark dataset and two synthetic datasets.
Nonsynaptic Error Backpropagation in Long-Term Cognitive Networks

We introduce a neural cognitive mapping technique named long-term cognitive network (LTCN) that is able to memorize long-term dependencies between a sequence of input and output vectors, especially in those scenarios that require predicting the values of multiple dependent variables at the same time. The proposed technique is an extension of a recently proposed method named short-term cognitive network that aims at preserving the expert knowledge encoded in the weight matrix while optimizing the nonlinear mappings provided by the transfer function of each neuron. A nonsynaptic, backpropagation-based learning algorithm powered by stochastic gradient descent is put forward to iteratively optimize four parameters of the generalized sigmoid transfer function associated with each neuron. Numerical simulations over 35 multivariate regression and pattern completion data sets confirm that the proposed LTCN algorithm attains statistically significant performance differences with respect to other well-known state-of-the-art methods.
Young children’s use of an augmented reality sandbox to enhance spatial thinking

Augmented reality (AR) technology is being introduced in educational settings for its ability to overlay virtual information onto physical environments. The AR Sandbox is a conventional sandbox with depth coloured representations projected onto the sand’s surface. This research investigates how 4- and 5-year-old Australian children use an AR Sandbox to enhance their spatial thinking through play. Participants were four children, their two educators and the first author as a participant-observer researcher. Data were collected over an 8-week period during 2015 and included observation, child conferencing, photography, educators’ perspectives, and the researcher’s perspectives. Vignettes are presented to demonstrate how children’s spatial thinking was enhanced through exploration and representation in the AR Sandbox. Children’s active and dynamic manipulation of the space of the AR Sandbox, along with a range of socio-cultural contexts and tools, assisted in enhancing their spatial abilities.
Research on sensor error compensation of comprehensive logging unit based on machine learning

To studies and optimizes the fault diagnosis method of comprehensive logging unit pressure sensor, and studies the temperature drift and temperature compensation theory of pressure sensor, establishes fault diagnosis model, and uses artificial intelligence algorithm based on machine learning to ap ply the temperature drift fault of piezo resistive silicon pressure sensor. The improved particle swarm optimization RBF neural network algorithm has been used to establish the temperature compensation model. By comparing with RBF neural network and particle swarm optimization algorithm, the advantages and disadvantages are compared. The results show that the improved particle swarm optimization RBF neural network algorithm is efficient. The online real-time monitoring of the mud logging sensor network fault has achieved, and the accuracy of the sensor temperature compensation has improved, thereby improving the stability and accuracy of the sensor of the comprehensive logging unit. The simulation results show that the improved particle swarm optimization RBF neural network algorithm has a good temperature compensation effect on the piezoresistive silicon pressure sensor.
A META-ANALYSIS OF SENSITIVITY TO GRAMMATICAL INFORMATION DURING SELF-PACED READING: TOWARDS A FRAMEWORK OF REFERENCE FOR READING TIME EFFECT SIZES

Abstract Despite extensive theoretical and empirical research, we do not have estimations of the magnitude of sensitivity to grammatical information during L2 online processing. This is largely due to reliance on null hypothesis significance testing (Plonsky, 2015). The current meta-analysis draws on data from one elicitation technique, self-paced reading, across 57 studies ( N = 3,052), to estimate sensitivity to L2 morphosyntax and how far L1 background moderates this. Overall, we found a reliable sensitivity to L2 morphosyntax at advanced proficiencies ( d = .20, 95% CIs .15, .25), with some evidence that this was reliably lower than for native speakers (NSs). These patterns were not generally moderated by linguistic feature or sentence region. However, effects for anomaly detection were larger among NSs than L2 learners and the effects among L2 learners appeared to show a trend toward L1 influence. Finding smaller effects than in other subdomains, we provide an initial framework of reference for L2 reading time effect sizes.
Restricted Evasion Attack: Generation of Restricted-Area Adversarial Example

Deep neural networks (DNNs) show superior performance in image and speech recognition. However, adversarial examples created by adding a little noise to an original sample can lead to misclassification by a DNN. Conventional studies on adversarial examples have focused on ways of causing misclassification by a DNN by modulating the entire image. However, in some cases, a restricted adversarial example may be required in which only certain parts of the image are modified rather than the entire image and that results in misclassification by the DNN. For example, when the placement of a road sign has already been completed, an attack may be required that will change only a specific part of the sign, such as by placing a sticker on it, to cause misidentification of the entire image. As another example, an attack may be required that causes a DNN to misinterpret images according to a minimal modulation of the outside border of the image. In this paper, we propose a new restricted adversarial example that modifies only a restricted area to cause misclassification by a DNN while minimizing distortion from the original sample. It can also select the size of the restricted area. We used the CIFAR10 and ImageNet datasets to evaluate the performance. We measured the attack success rate and distortion of the restricted adversarial example while adjusting the size, shape, and position of the restricted area. The results show that the proposed scheme generates restricted adversarial examples with a 100% attack success rate in a restricted area of the whole image (approximately 14% for CIFAR10 and 1.07% for ImageNet) while minimizing the distortion distance.
A New Supervised t-SNE with Dissimilarity Measure for Effective Data Visualization and Classification

In this paper, a new version of the Supervised t- Stochastic Neighbor Embedding (S-tSNE) algorithm is proposed which introduces the use of a dissimilarity measure related to class information. The proposed S-tSNE can be applied in any high dimensional dataset for visualization or as a feature extraction for classification problems. In this study, the S-tSNE is applied to three datasets MNIST, Chest x-ray, and SEER Breast Cancer. The two-dimensional data generated by the S-tSNE showed better visualization and an improvement in terms of classification accuracy in comparison to the original t- Stochastic Neighbor Embedding(t-SNE) method. The results from k-nearest neighbors (k-NN) classification model which used the lower dimension space generated by the new S-tSNE method showed more than 20% improvement on average in accuracy in all the three datasets compared with the t-SNE method. In addition, the classification accuracy using the S-tSNE for feature extraction was even higher than classification accuracy obtained from the original high dimensional data.
Variant Grassmann Manifolds

In classification tasks, classifiers trained with finite examples might generalize poorly to new data with unknown variance. For this issue, data augmentation is a successful solution where numerous artificial examples are added to training sets. In this article, we focus on the data augmentation for improving the accuracy of action recognition, where action videos are modeled by linear dynamical systems and approximately represented as linear subspaces. These subspace representations lie in a non-Euclidean space, named Grassmann manifold, containing points as orthonormal matrixes. It is our concern that poor generalization may result from the variance of manifolds when data come from different sources or classes. Thus, we introduce infinitely many variant Grassmann manifolds (VGM) subject to a known distribution, then represent each action video as different Grassmann points leading to augmented representations. Furthermore, a prior based on the stability of subspace bases is introduced, so the manifold distribution can be adaptively determined, balancing discrimination and representation. Experimental results of multi-class and multi-source classification show that VGM softmax classifiers achieve lower test error rates compared to methods with a single manifold.
DEMC: A Deep Dual-Encoder Network for Denoising Monte Carlo Rendering

In this paper, we present DEMC, a deep dual-encoder network to remove Monte Carlo noise efficiently while preserving details. Denoising Monte Carlo rendering is different from natural image denoising since inexpensive by-products (feature buffers) can be extracted in the rendering stage. Most of them are noise-free and can provide sufficient details for image reconstruction. However, these feature buffers also contain redundant information. Hence, the main challenge of this topic is how to extract useful information and reconstruct clean images. To address this problem, we propose a novel network structure, dual-encoder network with a feature fusion sub-network, to fuse feature buffers firstly, then encode the fused feature buffers and a noisy image simultaneously, and finally reconstruct a clean image by a decoder network. Compared with the state-of-the-art methods, our model is more robust on a wide range of scenes, and is able to generate satisfactory results in a significantly faster way.
Type-1 and singleton fuzzy logic system trained by a fast scaled conjugate gradient methods for dealing with binary classification problems

This work introduces the type-1 and singleton fuzzy logic system trained by scaled conjugate gradient method and its usage for binary classification problems. Aiming to improve the performance of the training procedure, we propose the multiplication of the Hessian matrix by the directional vector using the so-called differential operator R{·}, which results in another proposal for training the aforementioned fuzzy logic system. In order to evaluate the proposals, performance analyses based on well-known data sets provided by UCI Machine Learning Repository and Knowledge Extraction based on Evolutionary Learning Repository together with well-established metrics are detailed. The numerical results show that the proposals achieve improvements in comparison with others gradient based training methods applied to the type-1 fuzzy logic system present in the literature. These improvements regard to the fast convergence speed under the constraint over the number of epochs during the training phase.
Understanding Security Requirements for Industrial Control System Supply Chains

We address the need for security requirements to take into account risks arising from complex supply chains underpinning cyber-physical infrastructures such as industrial control systems (ICS). We present SEISMiC (SEcurity Industrial control SysteM supply Chains), a framework that takes into account the whole spectrum of security risks - from technical aspects through to human and organizational issues - across an ICS supply chain. We demonstrate the effectiveness of SEISMiC through a supply chain risk assessment of Natanz, Iran's nuclear facility that was the subject of the Stuxnet attack.
Derivative-free Huber–Kalman smoothing based on alternating minimization

We consider robust smoothing for nonlinear state space models in which both the process and measurement noises have heavy tails. To improve the robustness of Kalman smoothing, we formulate the robust smoothing problem by replacing the quadratic loss in the conventional Gaussian Kalman smoother by Huber's cost function. However, the Huber based smoother cannot be directly implemented within the Kalman smoothing framework, which has well recognized benefits in computational efficiency and stability. To address this issue, we introduce an auxiliary parameter to construct a surrogate function for the Huber cost function, leading to a reformulation of the robust Kalman smoothing problem. The reformulated robust smoothing is solved by an alternating minimization method, which iterates between a simple auxiliary parameter update step and a modified conventional Kalman smoothing step. Simulation results show that the proposed method achieves a performance improvement over several conventional and existing robust smoothers with a slight increase of computational time.
Infrared image enhancement model based on gravitational force and lateral inhibition networks

Problems such as low contrast, noise, and edge blur are often encountered in color infrared images produced by infrared cameras. To solve these problems, we propose a new image enhancement algorithm based on the gravitational force and lateral inhibition network. First, information on total gravitational force for each dimension of the color infrared image was obtained. These two-dimensional three gray level images obtained using three-dimensional color properties help to define noise, edge and region within each dimension. Secondly, these three gray level images were subjected to a dual threshold value. A mean filter was used to reduce noise, while the lateral inhibition network was used for resolution and edge detection, and the regional gravity factor was used for contrast control. Finally, each dimension was combined again and a color enhanced image was produced. This study sets out to develop a method of enhancement images for infrared image analysis in cooling systems. The images used in the study are made up of a compressor, a condenser, and an evaporator belonging to the cooling system. The implementation of our method is simple and easy to understand and yields more accurate results. The experimental results show that the proposed method can eliminate noise, blur, and low contrast, and can also improve the details of infrared images better than other methods.
A wavelet broad learning adaptive filter for forecasting and cancelling the physiological tremor in teleoperation

Physiological tremor forecasting is one of the most important issues in tele-operation that can improve the operational precision greatly. In the tele-operation, signals are three-dimensional and nonstationary time series. In this paper, a wavelet broad learning adaptive filter (WBLAF) is proposed to forecast and cancel the physiological tremor in tele-operation. Firstly, the variant architecture of original broad learning system (BLS) is proposed to extract the features of physiological tremor and the coupling information between dimensions and the novel WBLAF maps each dimensional data as the feature nodes respectively by the wavelet function. Secondly, in order to make the system can use the advantage of wavelet to extract the time-frequency features of input, a novel self-paced wavelet auto-encoder (SPWAE) is proposed to train the weights of feature mapping. Moreover, the ridge regression learning algorithm and the incremental learning of the proposed filter are applied for online learning. Finally, we designed three parts of experiments to show its effectiveness and feasibility. The first numerical experiment shows the convergence of the novel SPWAE and the second numerical experiment verifies the ability of WBLAF to predict non-stationary time series signals. In the end, semi-physical simulation experiment is implementation. As shown in the results, the novel method can effectively predict and filter out the physiological tremor in tele-operation.