Reviewerly/exHarmony huggingface dataset:
Adaptive Particle Swarm Optimization Based on Kernel Support Vector Machine for Optimal Design of Synchronous Reluctance Motor

This paper proposes an adaptive particle swarm optimization (APSO) based on a kernel support vector machine (KSVM). The proposed algorithm improves the convergence speed and exploration capability of the APSO by employing an individual adaptive parameter control based on the KSVM. To verify the algorithm's effectiveness, it was compared with both the conventional PSO and APSO based on test functions. Finally, we applied the proposed algorithm to the optimal design of a synchronous reluctance motor (Syn-RM).
Vehicle Routing for Resource Management in Time-Phased Deployment of Sensor Networks

Time-phased sensor-network deployment refers to the delivery of a set of sensors to their predetermined locations at exact times by a fleet of vehicles. Applications for such network deployments include wilderness search and rescue (WiSAR) and wildfire monitoring, where desirable resource management would imply allowing the vehicles to perform other tasks between deliveries. The goal of this paper is, thus, to formulate and solve a vehicle-routing problem (VRP) for such just-in-time time-phased sensor-network deployments. The proposed optimization method for the modified VRP outlined herein has two primary novelties: 1) the consideration of spare time as the objective function and 2) the use of a targeted local-search (LS) method. The spare-time objective function was formulated to address the uniqueness of the modified routing problem at hand. The targeted LS algorithm, on the other hand, was developed to tangibly improve the efficiency of the search for the optimal values of the chosen objective function. The proposed vehicle-route-planning method was validated via a range of simulated WiSAR scenarios, some of which are included herein. The robustness of the method to variations in problem parameters was also investigated.
Characterizing Pixel Tracking through the Lens of Disposable Email Services

Disposable email services provide temporary email addresses, which allows people to register online accounts without exposing their real email addresses. In this paper, we perform the first measurement study on disposable email services with two main goals. First, we aim to understand what disposable email services are used for, and what risks (if any) are involved in the common use cases. Second, we use the disposable email services as a public gateway to collect a large-scale email dataset for measuring email tracking. Over three months, we collected a dataset from 7 popular disposable email services which contain 2.3 million emails sent by 210K domains. We show that online accounts registered through disposable email addresses can be easily hijacked, leading to potential information leakage and financial loss. By empirically analyzing email tracking, we find that third-party tracking is highly prevalent, especially in the emails sent by popular services. We observe that trackers are using various methods to hide their tracking behavior such as falsely claiming the size of tracking images or hiding real trackers behind redirections. A few top trackers stand out in the tracking ecosystem but are not yet dominating the market.
‘Being stuck’. Analyzing text-planning activities in digitally rich upper secondary school classrooms

The aim of this article is to develop an understanding of how students use different interactional resources to manage problems that arise in their text-planning processes in digitally rich environments in Finnish and Swedish upper secondary schools. We explore both individual and collective teacher-initiated writing tasks in different subjects and during moments when text-planning seems to 'get stuck'. Theoretically, we draw on a socio-cultural understanding of the text-planning process, and use multimodal conversation analysis to examine how students display 'being stuck' during their text-planning through their embodied and verbal performances, what role smartphones and laptops play in their process of becoming 'stuck' and 'unstuck', and how different interactional resources are coordinated during the students' text-planning processes. The data consist of video-recorded face-to-face interaction, students' activities on computers and/or with a pen and paper as well as simultaneous recordings of the focus students' smartphone screens. The results demonstrate that students often resort to smartphones as resources to display, negotiate and transform problems in their text-planning process. Our results challenge common claims within the contemporary debate both in relation to digital devices as the solution to pedagogical challenges and in relation to the debate on smartphones as devices that disrupt work.
A Convergent Differentially Private k-Means Clustering Algorithm

Preserving differential privacy (DP) for the iterative clustering algorithms has been extensively studied in the interactive and the non-interactive settings. However, existing interactive differentially private clustering algorithms suffer from a non-convergence problem, i.e., these algorithms may not terminate without a predefined number of iterations. This problem severely impacts the clustering quality and the efficiency of the algorithm. To resolve this problem, we propose a novel iterative approach in the interactive settings which controls the orientation of the centroids movement over the iterations to ensure the convergence by injecting DP noise in a selected area. We prove that, in the expected case, our approach converges to the same centroids as Lloyd's algorithm in at most twice the iterations of Lloyd's algorithm. We perform experimental evaluations on real-world datasets to show that our algorithm outperforms the state-of-the-art of the interactive differentially private clustering algorithms with a guaranteed convergence and better clustering quality to meet the same DP requirement.
Improving multimodal action representation with joint motion history context

Automatic recognition of actions can be addressed by employing data from multiple sensors, such as RGB cameras, depth sensors or inertial measurement units. Recent studies show that multimodal representations of actions are effective in providing rich information about motion patterns. In this work, we propose a novel action descriptor, called Joint Motion History Context, which is based on depth and skeleton data. It improves action representation when used with previously introduced descriptors that are based on depth, skeleton and inertial data. A feature selection method is proposed as well, which ranks features on the basis of their inter-class discriminative power, while minimizing redundancy in the selected feature subset. Decision-level fusion, based on Support Vector Machines and Multilayer Perceptron is employed to effectively combine motion pattern information from multiple feature sets. Experimental results on two publicly available datasets, FFD and UTD-MHAD, demonstrated that the proposed methods outperform state-of-the-art algorithms.
Semantic web or web of data? a diachronic study (1999 to 2017) of the publications of tim berners‐lee and the world wide web consortium

The web has been, in the last decades, the place where information retrieval achieved its maximum importance, given its ubiquity and the sheer volume of information. However, its exponential growth made the retrieval task increasingly hard, relying in its effectiveness on idiosyncratic and somewhat biased ranking algorithms. To deal with this problem, a “new” web, called the Semantic Web (SW), was proposed, bringing along concepts like “Web of Data” and “Linked Data,” although the definitions and connections among these concepts are often unclear. Based on a qualitative approach built over a literature review, a definition of SW is presented, discussing the related concepts sometimes used as synonyms. It concludes that the SW is a comprehensive and ambitious construct that includes the great purpose of making the web a global database. It also follows the specifications developed and/or associated with its operationalization and the necessary procedures for the connection of data in an open format on the web. The goals of this comprehensive SW are the union of two outcomes still tenuously connected: the virtually unlimited possibility of connections between data—the web domain—with the potentiality of the automated inference of “intelligent” systems—the semantic component.
An evaluation of effort estimation supported by change impact analysis in agile software development

Abstract In agile software development, functionality is added to the system in an incremental and iterative manner. Practitioners often rely on expert judgment to estimate the effort in this context. However, the impact of a change on the existing system can provide objective information to practitioners to arrive at an informed estimate. In this regard, we have developed a hybrid method, that utilizes change impact analysis information for improving effort estimation. We also developed an estimation model based on gradient boosted trees (GBT). In this study, we evaluate the performance and usefulness of our hybrid method with tool support and the GBT model in a live iteration at Insiders Technologies GmbH, a German software company. Additionally, the solution was also assessed for perceived usefulness and understandability in a study with graduate and post‐graduate students. The results from the industrial evaluation show that the proposed method produces more accurate estimates than only expert‐based or only model‐based estimates. Furthermore, both students and practitioners perceived the usefulness and understandability of the method positively.
Digital image correlation to analyze slip state of tire tread block in the cornering condition

Under the tire cornering condition, the slip state of the tread is extremely important for the calculation of the tire force. The cornering-slip motion of a tread rubber specimen was researched on a transparent toughened glass plate. A new equipment was developed for measuring the torque and the strain. In the absolute nodal coordinate system, the strain distribution of tread block was observed by digital image correlation method and compared with the measured torque data. The marker point track and the state switch point from stick slip to full slip were acquired by analysis of marker point displacement and strain. The effect of angular velocity on the state switch point and track shape was discussed. The results of this study indicated that the image analysis conclusions were in alignment with the torque experimental data.
Non-uniform Initialization of Inputs Groupings in Contextual Neural Networks

Contextual neural networks which are using neurons with conditional aggregation functions were found to be efficient and useful generalizations of classical multilayer perceptrons. They allow to generate neural classification models with good generalization and low activity of connections between neurons in hidden layers. The key factor to build such solutions is achieving self-consistency between continuous values of weights of neurons' connections and their mutually related non-continuous aggregation priorities. This allows to optimize neuron inputs aggregation priorities by simultaneous gradient-based optimization of connections' weights with generalized BP algorithm. But such method additionally needs initial setting of connections groupings (scan-paths) to define priorities of signals during first ω epochs of training. In earlier studies all connections were initially assigned to a single group to give neurons access to all input signals at the beginning of training. We found out that such uniform solution not always is the best one. Thus within this text we compare efficiency of training of contextual neural networks with uniform and non-uniform, random initialization of connections groupings. On this basis we also discuss the properties of analyzed training algorithm which are related to characteristics of used scan-paths initialization methods.
Aggregating risk matrices under a normative framework

Risk matrices have been proven as useful risk management tools, especially in the cases where data are not sufficient. Current usage of risk matrices in both literature and practice is related to single risk assessment. However, sometimes the decision makers care more about the overall risk consisting of several of single risks, which is in the scope of the aggregation of single risks measured by risk matrices. Unfortunately, two notions, namely, incomparability of different qualitative risk ratings and incomparability of different risk types, hinder the aggregation of risk matrices. Therefore, few literature studied on this issue and even ISO have not given a solution. In this paper, a general framework for risk matrix aggregation is proposed. The basic idea is to transform the risk matrices into other equivalents to operate the aggregation process. Based on this framework, three methods, namely, the fuzzy set method, the interval number, and the probability density function methods, are introduced. The three methods are applied to an illustrative example to show the feasibility of the general framework. Then these methods are compared from three different aspects, i.e., comprehensibility, complexity, and degree of explanation of risk matrix. In practice, when these methods are employed to aggregate risk matrices, the findings of this paper show that the interval number method is the least robust; though the probability density function method has a lower degree of explanation of risk matrix, it could be taken as a substitution as the fuzzy set method, thanks to its simplicity.
vSocket: virtual socket interface for RDMA in public clouds

RDMA has been widely adopted as a promising solution for high performance networks, but is still unavailable for a large number of socket-based applications running in public clouds due to the following reasons. There is no available virtualization technique of RDMA that can meet the cloud's requirements. Moreover, it is cost prohibitive to rewrite the socket-based applications with the Verbs API. To address the above problems, we present vSocket, a software-based RDMA virtualization framework for socket-based applications in public clouds. vSocket takes into account the demands of clouds such as security rules and network isolation, so it can be deployed in the current public clouds. Furthermore, vSocket provides native socket API so that socket-based applications can use it without any modifications. Finally, to validate the performance gains, we implemented a prototype and compared it with current virtual network solutions against 1) basic network benchmarks and 2) the Redis, a typical I/O intensive application. Experimental results show that the latency of basic benchmarks can be reduced by 88% and the throughput of Redis is improved by 4 times.
On Interpretability and Feature Representations: An Analysis of the Sentiment Neuron

We are concerned with investigating the apparent effectiveness of Radford et al.'s "Sentiment Neuron," [9] which they claim encapsulates sufficient knowledge to accurately predict sentiment in reviews. In our analysis of the Sentiment Neuron, we find that the removal of the neuron only marginally affects a classifier's ability to detect and label sentiment and may even improve performance. Moreover, the effectiveness of the Sentiment Neuron can be surpassed by simply using 100 random neurons as features to the same classifier. Using adversarial examples, we show that the generated representation containing the Sentiment Neuron (i.e., the final hidden cell state in a LSTM) is particularly sensitive to the end of a processed sequence. Accordingly, we find that caution needs to be applied when interpreting neuron-based feature representations and potential flaws should be addressed for real-world applicability.
Weighted Reservoir Sampling from Distributed Streams

We consider message-efficient continuous random sampling from a distributed stream, where the probability of inclusion of an item in the sample is proportional to a weight associated with the item. The unweighted version, where all weights are equal, is well studied, and admits tight upper and lower bounds on message complexity. For weighted sampling with replacement, there is a simple reduction to unweighted sampling with replacement. However, in many applications the stream may have only a few heavy items which may dominate a random sample when chosen with replacement. Weighted samplingwithout replacement (weighted SWOR) eludes this issue, since such heavy items can be sampled at most once. In this work, we present the first message-optimal algorithm for weighted SWOR from a distributed stream. Our algorithm also has optimal space and time complexity. As an application of our algorithm for weighted SWOR, we derive the first distributed streaming algorithms for trackingheavy hitters with residual error. Here the goal is to identify stream items that contribute significantly to the residual stream, once the heaviest items are removed. Residual heavy hitters generalize the notion of $\ell_1$ heavy hitters and are important in streams that have a skewed distribution of weights. In addition to the upper bound, we also provide a lower bound on the message complexity that is nearly tight up to a $łog(1/\eps)$ factor. Finally, we use our weighted sampling algorithm to improve the message complexity of distributed $L_1$ tracking, also known as count tracking, which is a widely studied problem in distributed streaming. We also derive a tight message lower bound, which closes the message complexity of this fundamental problem.
Using generative adversarial networks and transfer learning for breast cancer detection by convolutional neural networks

In the U.S., breast cancer is diagnosed in about 12% of women during their lifetime and it is the second leading reason for women’s death. Since early diagnosis could improve treatment outcomes and longer survival times for breast cancer patients, it is significant to develop breast cancer detection techniques. The Convolutional Neural Network (CNN) can extract features from images automatically and then perform classification. To train the CNN from scratch, however, requires a large number of labeled images, which is infeasible for some kinds of medical image data such as mammographic tumor images. In this paper, we proposed two solutions to the lack of training images. 1)To generate synthetic mammographic images for training by the Generative Adversarial Network (GAN). Adding GAN generated images made to train CNN from scratch successful and adding more GAN images improved CNN’s validation accuracy to at most (best) 98.85%. 2)To apply transfer learning in CNN. We used the pre-trained VGG-16 model to extract features from input mammograms and used these features to train a Neural Network (NN)-classifier. The stable average validation accuracy converged at about 91.48% for classifying abnormal vs. normal cases in the DDSM database. Then, we combined the two deep-learning based technologies together. That is to apply GAN for image augmentation and transfer learning in CNN for breast cancer detection. To the training set including real and GAN augmented images, although transfer learning model did not perform better than the CNN, the speed of training transfer learning model was about 10 times faster than CNN training. Adding GAN images can help training avoid over-fitting and image augmentation by GAN is necessary to train CNN classifiers from scratch. On the other hand, transfer learning is necessary to be applied for training on pure real images. To apply GAN to augment training images for training CNN classifier obtained the best classification performance.
Automated detection of lineaments express geological linear features of a tropical region using topographic fabric grain algorithm and the SRTM DEM

The availability of the large volume of remote sensing data has allowed for the developing of several automated algorithms for detecting linear geological features and more reliable analysis. However, most of the algorithms focus on edge detection and tone change on a satellite image, which represents all geological and non-geological features. In this study, a topographic fabric algorithm, which calculates the slope and aspect at each point in a DEM, is applied for automatically geological linear features mapping in Bau Goldfield, Malaysia using the new version of the Shuttle Radar Topographic Mission (SRTM) DEM. A series of topographic fabric input parameters was tested using different combinations of input values in order to decide the optimal parameters that provided the suitable detection parameters, best fit and the highest accuracy. Comparison with the geological map demonstrated that the tested parameters made the algorithm able to automatically detect geological structures.
Public Opinion Analysis of the Transportation Policy Using Social Media Data: A Case Study on the Delhi Odd–Even Policy

Twitter, a microblogging service, has become a popular platform for people to express their views and opinions on different issues. A sentiment analysis of the tweets can help in understanding the public opinion on different government decisions. This paper used Twitter data to extract the sentiments of people during the Phase 1 and Phase 2 of the odd–even policy implemented by the Delhi government to curb the air pollution and improve traffic flow. In this study, we used four different lexicon-based approaches: Bing, Afinn, National Research Council emotion lexicon, and Deep Recursive Neural Network-based Natural Language Processing software (CoreNLP) to extract sentiments from tweets and thereby assess overall public opinions. The daily trend obtained for each phase was normalized with the number of tweets and then compared using the Granger causality test. The causality test results showed that the trends obtained during the two phases were significantly different from each other. In particular, public sentiments were found to mostly turn negative during the later stage of the Phase 2 which indicates fading away of the public enthusiasm and positiveness towards the policy during the later stages of the policy implementation.
Hyperplane-based nonnegative matrix factorization with label information

As one commonly used dimensionality reduction method, nonnegative matrix factorization (NMF), whose goal is to learn parts-based representations, has been widely studied and applied to various areas. However, classical NMF does not utilize any label information. In this paper, we propose a novel semi-supervised NMF algorithm named the hyperplane-based nonnegative matrix factorization (HNMF). This HNMF constructs a hyperplane for each cluster such that the labelled points are close to the corresponding hyperplane and far away from the other ones. Then, the discriminative abilities of representation vectors are greatly enhanced. Clustering experiments completed on five publicly available databases demonstrate the effectiveness of this proposed HNMF compared to the state-of-the-art methods.
Analysis of Control System Models with Conventional LQR and Fuzzy LQR Controller

Linear quadratic LQR and fuzzy FLC+LQR controllers are analyzed. It is shown that under the same conditions, the fuzzy FLC+LQR controller has a much higher speed than the LQR controller.