Reviewerly/exHarmony huggingface dataset:
Balancing storage efficiency and data confidentiality with tunable encrypted deduplication

Conventional encrypted deduplication approaches retain the deduplication capability on duplicate chunks after encryption by always deriving the key for encryption/decryption from the chunk content, but such a deterministic nature causes information leakage due to frequency analysis. We present TED, a tunable encrypted deduplication primitive that provides a tunable mechanism for balancing the tradeoff between storage efficiency and data confidentiality. The core idea of TED is that its key derivation is based on not only the chunk content but also the number of duplicate chunk copies, such that duplicate chunks are encrypted by distinct keys in a controlled manner. In particular, TED allows users to configure a storage blowup factor, under which the information leakage quantified by an information-theoretic measure is minimized for any input workload. We implement an encrypted deduplication prototype TEDStore to realize TED in networked environments. Evaluation on real-world file system snapshots shows that TED effectively balances the trade-off between storage efficiency and data confidentiality, with small performance overhead.
Unplatformed Design: A Model for Appropriating Social Media Technologies for Coordinated Participation

Using existing social media technologies as a resource for design offers significant potential for sustainable and scalable ways of coordinating participation. We look at three exemplar projects in three distinct domains that have successfully coordinated participation through the configuration and augmentation of existing social media technologies: participatory future forecasting, participatory health research, and connectivist learning. In this paper we conceptualise social media technologies as material for design, that is, as the raw material with which coordinated participation is realized. From this we develop a model that proposes four material qualities of social media technologies, morphology, role, representation of activity and permeability, and point to how they can be productively employed in the design of coordination of participation.
Personalized Image Enhancement Using Neural Spline Color Transforms

In this work we present SpliNet, a novel CNN-based method that estimates a global color transform for the enhancement of raw images. The method is designed to improve the perceived quality of the images by reproducing the ability of an expert in the field of photo editing. The transformation applied to the input image is found by a convolutional neural network specifically trained for this purpose. More precisely, the network takes as input a raw image and produces as output one set of control points for each of the three color channels. Then, the control points are interpolated with natural cubic splines and the resulting functions are globally applied to the values of the input pixels to produce the output image. Experimental results compare favorably against recent methods in the state of the art on the MIT-Adobe FiveK dataset. Furthermore, we also propose an extension of the SpliNet in which a single neural network is used to model the style of multiple reference retouchers by embedding them into a user space. The style of new users can be reproduced without retraining the network, after a quick modeling stage in which they are positioned in the user space on the basis of their preferences on a very small set of retouched images.
Reconciling solar forecasts: Probabilistic forecast reconciliation in a nonparametric framework

No forecast is complete without understanding it probabilistically. Previously in “Reconciling solar forecasts: Geographical hierarchy” [Sol. Energy 146 (2017) 276–286], four different point forecast reconciliation techniques were demonstrated using simulated data from 318 photovoltaic systems in California. In this paper, I show how to extend those techniques to probabilistic solar forecasting. More specifically, probabilistic forecast reconciliation is performed in a nonparametric framework through block bootstrapping. As compared to the parametric framework, which requires the base forecasts to be characterized by elliptical distributions, the nonparametric framework is not limited by such assumptions. Probabilistic forecast reconciliation not only provides a description of forecast uncertainty, it could also issue optimal point forecasts based on a directive in the form of a statistical functional. In this regard, there is very little reason to favor point forecast reconciliation, or any point forecasting for that matter, in solar energy meteorology. And probabilistic forecast reconciliation, or more generally, probabilistic solar forecasting, should be made default.
Multivariable Event-Triggered Generalized Super-Twisting Controller for Safe Navigation of Nonholonomic Mobile Robot

This article presents a multivariable event-triggered generalized super-twisting sliding-mode algorithm for a nonholonomic mobile-robot safe navigation in unknown indoor environment. For robot safe navigation, vision-based variable gain composite guidance law is proposed to generate desired angular velocity. The guidance strategy uses the centroid of the depth map of the obstacles obtained from RGB-depth sensor. The propose controller follows the path generated by the guidance law. Stability analysis of the controller is done to show the fast convergence as compared to the existing controllers. To show the robustness of the controller in comparison to the existing super-twisting algorithm, a sensitivity analysis is carried out. Furthermore, for minimizing the controller effort, an event-triggered condition for the proposed controller is obtained using Lyapunov stability theory. Comparative simulation study is carried out to show the fast convergence and robustness of the proposed controller. Experiments are done for robot safe navigation in presence of disturbance.
Vadalog: A modern architecture for automated reasoning with large knowledge graphs

The introduction of novel Datalog +/- fragments with good theoretical properties, together with the growing use of enterprise knowledge graphs motivated the development of Vadalog, a knowledge graph management system developed at the University of Oxford. It adopts Warded Datalog +/- as the core of its language for knowledge representation and reasoning, which exhibits a very good tradeoff between computational complexity of reasoning and expressive power, capturing PTIME data complexity while allowing ontological reasoning and full recursion. In this paper, we provide a detailed illustration of the Vadalog system, presenting: the essentials of the first implementation of Warded Datalog +/-; a comprehensive overview of the architecture with specific focus on runtime execution model, memory management, graph traversal strategies and join algorithms; and a detailed experimental evaluation. This paper is a substantially expanded version of the AMW 2019 paper "Datalog-based reasoning for Knowledge Graphs". To stand apart from previous works on the topic, our focus in this work shall be a comprehensive presentation of the architecture of the Vadalog system and showing how our techniques work together to provide a full-fledged KGMS. In particular, roughly half of this paper is new material created particularly for this comprehensive architectural view. This includes a new series of experiments designed to shed light on architectural choices and alternatives.
ACSE Networks and Autocorrelation Features for PRI Modulation Recognition

Pulse repetition interval (PRI) modulation recognition plays an important role in electronic warfare. Conventional recognition methods based on handcrafted features and elaborate threshold values suffer from the accuracy for multiple PRI modulations at low signal-to-noise ratio (SNR) with high percentages of missing pulses. In this letter, a method based on Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks and features in autocorrelation domain is proposed to recognize six PRI modulation modes automatically. First, features in the time domain, frequency domain and autocorrelation domain are converted to images. Then the images are input into ACSE networks which can extract and learn deep features without complex data pre-processing. Finally, a linear layer will output modulation modes directly. Via simulations, robustness of autocorrelation features is proved. The simulation results also demonstrate that the proposed recognition method can achieve higher than 91% accuracy at -12 dB under normal conditions for six modulations and higher than 95% at -4 dB under extreme conditions. Compared with the conventional SVM and three CNN methods, ACSE networks outperform at low SNRs under extreme conditions.
An Empirical Investigation of Geographically Distributed Agile Development: The Agile Enterprise Architecture is a Communication Enabler

Agile software development performance depends on active communication.Active communication is an arduous task when agile teams are geographically distributed.Agile enterprise architecture was reported to enhance such active communication and performance.There is little empirical evidence on how agile enterprise architecture can enhance communication and performance of geographically distributed software development.This paper contributes to this research gap by empirically examining the relationships between agile enterprise architecture, active communication, and performance (on-time completion, on-budget completion, software functionality, and software quality).Using a quantitative data analysis approach, the PLS results of survey responses of 160 research participants suggest that agile enterprise architecture has positive effects on active communication, on-budget completion, functionality, and quality.The results also indicate that communication efficiency has positive effects on on-time and on-budget completion; while communication effectiveness has positive effects on functionality, quality, and on-budget completion.
Rubbing against data infrastructure(s): methodological explorations on working with(in) the impossibility of exteriority

The article focuses on infrastructures as heterogeneous assemblages. Our claim is that to examine something as elusive as data infrastructure calls for an epistemological and methodological approach consistent with the fluid ontology of the object of study. Moreover, we assert that there is no position of exteriority from which to critique data infrastructures. Our question is thus primarily methodological: How might we analyse the relational and heterogeneous nature of data infrastructures in a way that both problematizes and builds on the impossibility of exteriority? We examine two dispositions of data infrastructures: first comes their provisional, dispersed and entangled character, and second, the affective attachments imbued therein. Overall, this paper, first, returns to the ontological and theoretical perspectives on what data infrastructures are; second, makes a contribution to the methodological question of how to study them; and, third, develops a means of critique that do not locate the analyst outside the object of the critique, reminding us of the immanent relationality of data infrastructures and of the necessity to take these seriously in the methodological approach. The paper works with the existing literature on data infrastructures and research methods in education policy analysis, and with our own research data and first-hand experiences.
Protein Interaction Network Reconstruction Through Ensemble Deep Learning With Attention Mechanism

Protein interactions play an essential role in studying living systems and life phenomena. A considerable amount of literature has been published on analyzing and predicting protein interactions, such as support vector machine method, homology-based method and similarity-based method, each has its pros and cons. Most existing methods for predicting protein interactions require prior domain knowledge, making it difficult to effectively extract protein features. Single method is dissatisfactory in predicting protein interactions, declaring the need for a comprehensive method that combines the advantages of various methods. On this basis, a deep ensemble learning method called EnAmDNN (Ensemble Deep Neural Networks with Attention Mechanism) is proposed to predict protein interactions which is an appropriate candidate for comprehensive learning, combining multiple models, and considering the advantages of various methods. Particularly, it encode protein sequences by the local descriptor, auto covariance, conjoint triad, pseudo amino acid composition and combine the vector representation of each protein in the protein interaction network. Then it takes advantage of the multi-layer convolutional neural networks to automatically extract protein features and construct an attention mechanism to analyze deep-seated relationships between proteins. We set up four different structures of deep learning models. In the ensemble learning model, second layer data sets are generated with five-fold cross validation from basic learners, then predict the protein interaction network by combining 16 models. Results on five independent PPI data sets demonstrate that EnAmDNN achieves superior prediction performance than other comparing methods.
Creative Methodologies in Teaching English for Engineering Students

The present paper is an argument in favor of a consistent application of creative educational methods in the process of teaching English for Specific Purposes (ESP). The theoretical part explores the role and function of creativity in the context of the changing paradigm regarding the overall scope of the educational system in general and of the specialized language learning in particular, highlighting a series of factors pertaining to creative methodologies in ESP. This changing paradigm is triggered by the application of Industry 4.0 principles not only in the industrial sector, but also in the educational domains which form the professionals who will be acting in the Industry 4.0 context. The study addresses problems faced by language instructors in achieving success in the acquisition of foreign language skills and competences by ESP students given the specifics of the language teaching process. It aims to demonstrate the advantages of applying creative and innovative methods in teaching ESP and to illustrate the conditions and factors which determine the best choice of methodologies, materials and activities involved in teaching English in technical academic environments. The research highlights the importance of the language instructor’s choice of teaching strategies, materials and techniques in achieving overall language success. The application of creative methods in teaching ESP is one of the factors contributing to increased student motivation, which is one of the essential preconditions of the successful practice of language skills in a professional language context.
Swimming Stroke Phase Segmentation Based on Wearable Motion Capture Technique

Wearable motion capture technique is widely used in kinematic analysis, which contributes to understanding motion patterns and provides quantitative data on human postures. Swimming stroke phase plays an important role in spatial-temporal swimming parameters. As a sporting pattern that involves all limbs, the swimming phase is more complicated than gait phase and makes the swimming phase segmentation a new issue of pattern recognition. This article focuses on the swimming phase segmentation as pattern classification. By analyzing the human posture data given by motion capture system, swimming phase could be described qualitatively and used to obtain posture features. The swimming phase of the four competitive swimming styles is studied in this article and classified accurately. In the tenfold cross-validation, the mean values of accuracy, sensitivity, and specificity could reach 98.22%, 95.65%, and 98.67%, respectively, under the 2.5-ms timing tolerance. In terms of leave-one-subject-out cross-validation, performance metrics perform best under a relatively small timing tolerance. The results of the experiment show that the study could well-address the issue of swimming phase segmentation and provide spatial-temporal parameters for further swimming motion analysis.
Secure message classification services through identity-based signcryption with equality test towards the Internet of vehicles

To provide a classification function that has the ability to promote the management of signcrypted messages transmitted by the numerous vehicles in the IoV system, in this paper, we construct an identity-based signcryption with equality test scheme (IBSC-ET) for the first time. Our scheme not only ensures the integrity, confidentiality as well as authentication of messages, but also enables the cloud server to execute the equality test between two ciphertexts signcrypted by the same or different public keys to decide whether the same plaintext is contained, which is counted as the essential factor contributing to the classification. Besides, the identity-based mechanism greatly improves the efficiency since the certificate management problem is avoided. Furthermore, the security of the introduced scheme can be proved in the random oracle model under the Computational Diffie-Hellman Assumption (CDHA) as well as the Bilinear Diffie-Hellman Assumption (BDHA). In the end, the feasibility and efficiency of our proposed are demonstrated through the performance analysis.
Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)

Despite the significant advances in recent years, Generative Adversarial Networks (GANs) are still notoriously hard to train. In this paper, we propose three novel curriculum learning strategies for training GANs. All strategies are first based on ranking the training images by their difficulty scores, which are estimated by a state-of-the-art image difficulty predictor. Our first strategy is to divide images into gradually more difficult batches. Our second strategy introduces a novel curriculum loss function for the discriminator that takes into account the difficulty scores of the real images. Our third strategy is based on sampling from an evolving distribution, which favors the easier images during the initial training stages and gradually converges to a uniform distribution, in which samples are equally likely, regardless of difficulty. We compare our curriculum learning strategies with the classic training procedure on two tasks: image generation and image translation. Our experiments indicate that all strategies provide faster convergence and superior results. For example, our best curriculum learning strategy applied on spectrally normalized GANs (SNGANs) fooled human annotators in thinking that generated CIFAR-like images are real in 25.0% of the presented cases, while the SNGANs trained using the classic procedure fooled the annotators in only 18.4% cases. Similarly, in image translation, the human annotators preferred the images produced by the Cycle-consistent GAN (CycleGAN) trained using curriculum learning in 40.5% cases and those produced by CycleGAN based on classic training in only 19.8% cases, 39.7% cases being labeled as ties.
Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation

The Biogeography-Based Optimization algorithm and its variants have been used widely for optimization problems. To get better performance, a novel Biogeography-Based Optimization algorithm with Hybrid migration and global-best Gaussian mutation is proposed in this paper. Firstly, a linearly dynamic random heuristic crossover strategy and an exponentially dynamic random differential mutation one are presented to form a hybrid migration operator, and the former is used to get stronger local search ability and the latter strengthen the global search ability. Secondly, a new global-best Gaussian mutation operator is put forward to balance exploration and exploitation better. Finally, a random opposition learning strategy is merged to avoid getting stuck in local optima. The experiments on the classical benchmark functions and the complexity functions from CEC-2013 and CEC-2017 test sets, and the Wilcoxon, Bonferroni-Holm and Friedman statistical tests are used to evaluate our algorithm. The results show that our algorithm obtains better performance and faster running speed compared with quite a few state-of-the-art competitive algorithms. In addition, experimental results on Minimum Spanning Tree and K-means clustering optimization show that our algorithm can cope with these two problems better than the comparison algorithms.
Crisscross differential evolution algorithm for constrained hydrothermal scheduling

This paper proposes a novel chaotic-crisscross differential evolution (CCDE) algorithm to realize an optimal generation schedule of multi-chain short-term hydrothermal system over 24 hours' time-horizon in a multi-objective framework, considering conflicting economic-environmental aspects of thermal units. The equality constraints of active power balance and the amount of available water are independently handled using variable elimination method. However, the statistical uncertainties called residues arise due to infringements of equality constraints while adjusting the violated dependent variables within their boundaries. These residues are fuzzy quantified within their prescribed bounds, and are embedded as objectives to be optimized. An interactive unified fuzzy satisfying function is aimed to solve the conflict of three objectives. The global solution accuracy and convergence rate of stochastic algorithms are significantly affected by parameter-tuning, exploration and exploitation strategies. The proposed algorithm integrates dual crisscross mechanism orthogonally with chaotically tuned DE to balance exploration and exploitation. Information collected about non-dominated solutions from search space is processed using opposition-based learning for better accuracy of global solution in three-dimensional objective function hyperspace. The numerical results show improvement in unified satisfying objective function and convergence performance metrics over the existing methods. The competence of the proposed algorithm is confirmed through illustrations on benchmark functions and is substantiated through statistical significance tests.
Improving the performance of lightweight CNNs for binary classification using quadratic mutual information regularization

In this paper, we propose regularized lightweight deep convolutional neural network models, capable of effectively operating in real-time on-drone for high-resolution video input. Furthermore, we study the impact of hinge loss against the cross entropy loss on the classification performance, mainly in binary classification problems. Finally, we propose a novel regularization method motivated by the Quadratic Mutual Information, in order to improve the generalization ability of the utilized models. Extensive experiments on various binary classification problems involved in autonomous systems are performed, indicating the effectiveness of the proposed models. The experimental evaluation on four datasets indicates that hinge loss is the optimal choice for binary classification problems, considering lightweight deep models. Finally, the effectiveness of the proposed regularizer in enhancing the generalization ability of the proposed models is also validated.