Reviewerly/exHarmony huggingface dataset:
Quantitative Analysis of Opacity in Cloud Computing Systems

Federated cloud systems increase the reliability and reduce the cost of the computational support. The resulting combination of secure private clouds and less secure public clouds, together with the fact that resources need to be located within different clouds, strongly affects the information flow security of the entire system. In this paper, the clouds as well as entities of a federated cloud system are assigned security levels, and a probabilistic flow sensitive security model for a federated cloud system is proposed. Then the notion of opacity—a notion capturing the security of information flow—of a cloud computing systems is introduced, and different variants of quantitative analysis of opacity are presented. As a result, one can track the information flow in a cloud system, and analyze the impact of different resource allocation strategies by quantifying the corresponding opacity characteristics.
Bag of textual graphs (BoTG): A general graph‐based text representation model

Text representation models are the fundamental basis for information retrieval and text mining tasks. Although different text models have been proposed, they typically target specific task aspects in isolation, such as time efficiency, accuracy, or applicability for different scenarios. Here we present Bag of Textual Graphs (BoTG), a general text representation model that addresses these three requirements at the same time. The proposed textual representation is based on a graph‐based scheme that encodes term proximity and term ordering, and represents text documents into an efficient vector space that addresses all these aspects as well as provides discriminative textual patterns. Extensive experiments are conducted in two experimental scenarios—classification and retrieval—considering multiple well‐known text collections. We also compare our model against several methods from the literature. Experimental results demonstrate that our model is generic enough to handle different tasks and collections. It is also more efficient than the widely used state‐of‐the‐art methods in textual classification and retrieval tasks, with a competitive effectiveness, sometimes with gains by large margins.
Efficient level‐set segmentation model driven by the local GMM and split Bregman method

An efficient level-set model driven by the local Gaussian mixture model (GMM) and split Bregman method is proposed for image segmentation. Firstly, the two intensity fitting functions in the original local binary fitting (LBF) model are pre-estimated by using a local GMM-based intensity distribution estimator before curve evolution. The benefit of this pre-computation strategy is to avoid updating the fitting functions at each step of the curve evolution, and it also overcomes the initialisation problem of common gradient descent-based active contour models, i.e. the level-set function can be initialised as an arbitrary random matrix instead of a signed distance function in the proposed processing framework. Secondly, two processing ideas named global convex segmentation (GCS) method and split Bregman are introduced into the numerical implementation, where the role of GCS is to transform the proposed model into a simplified convex segmentation model, and the purpose of the split Bregman is to quickly output a convergent solution of the newly derived convex segmentation model in an alternate iterative format. Experimental results for synthetic and real images of different modalities with inhomogeneity or homogeneity validate the desired performances of the proposed method in terms of accuracy, robustness, and rapidity.
Fast Online 'Next Best Offers' using Deep Learning

In this paper we present iPrescribe, a scalable low-latency architecture for recommending 'next-best-offers' in an online setting. The paper presents the design of iPrescribe and compares its performance for implementations using different real-time streaming technology stacks. iPrescribe uses ensemble of deep learning and machine learning algorithms for prediction. We describe the scalable real-time streaming technology stack and optimised machine-learning implementations to achieve a 90th percentile recommendation latency of 38 milliseconds. Optimizations include a novel mechanism to deploy recurrent Long Short Term Memory (LSTM) deep learning networks efficiently.
Surface Rotation Correction and Strain Precision of Wide-Angle 2D DIC for Field Use

The paper describes how two-dimensional (2D) digital image correlation (DIC) is used on underneath surfaces of concrete bridges with a wide-angle-lens camera during load testing, and how it has potential as a stop criterion in proof loadings. A method is proposed for correction of out-of-plane deflection including rotation of the surface. The method is applied to laboratory tests, using well-defined circular speckle patterns, as well as to a field-tested bridge (on raw concrete). The proposed correction corresponds to the level of pseudo strain but is very sensitive to precise surface deflection measurements. In the laboratory tests, a strain precision of the wide-angle-lens camera is compared to a regular-lens camera. The parametric study concludes that a pattern pixel relation (PPR), in the interval from four to nine pixels per pattern circle diameter, provides the optimal precision regardless of the camera type. The field-tested bridge has less good precision compared to most parameter combinations of the laboratory tests. Nevertheless, the field strain precision has potential for improvement based on results from the laboratory tests.
Advancements for A* and RRT in 3D path planning of UAVs

Advancements in Unmanned Aerial Vehicles (UAVs) design, actuator and sensory systems and control are making such devices financially available to a wide spectrum of users with various demands and expectations.To mitigate with this ever increasing demand robust, efficient and application-specific path planning is important.This paper presents advancements over the A* and the smoothing algorithms presented in, 1 utilising the same test scenarios.Analysis of results in 1 showed a ripple in path length as the resolution changes for all scenarios considered and less than 0.1% path length improvements after certain amount of smoothing iterates.To attenuate the path length ripple, the A* ripple reduction algorithm was developed.Results show a reduction of more than 46% in terms of standard deviation with respect to the original A* algorithm without any increase in the mean path length for all scenarios.Secondly, the smoothing algorithm developed in 1 was improved to stop smoothing based on the rate of smoothing of previous iterates.Results show more than 10 multiple less path smoothing time maintaining a path length reduction especially for simple scenarios.These advancements further portray the discussed path planning algorithms as candidates to the realisation of online 3D UAV path planning.
Supporting automated containment checking of software behavioural models using model transformations and model checking

Models are extensively used in many areas of software engineering to represent the behaviour of software systems at different levels of abstraction. Because of the involvement of different stakeholders in constructing these models and their independent evolution, inconsistencies might occur between the models. It is thus crucial to detect these inconsistencies at early phases of the software development process, and especially as soon as refined models deviate from their abstract counterparts. In this article, we introduce a containment checking approach to verify whether a certain low-level behaviour model, typically created by refining and enhancing a high-level model, still is consistent with the specification provided in its high-level counterpart. We interpret the containment checking problem as a model checking problem, which has not received special treatment in the literature so far. Because the containment checking is based on model checking, it requires both formal consistency constraints and specifications of these models. Unfortunately, creating formal consistency constraints and specifications is currently done manually, and therefore, labour-intensive and error prone. To alleviate this issue, we define and develop a fully automated transformation of behaviour models into formal specifications and properties. The generated formal specifications and properties can directly be used by existing model checkers for detecting any discrepancy between the input models and yield corresponding counterexamples. Moreover, our approach can provide the developers more informative and comprehensive feedback regarding the inconsistency issues, and therefore, help them to efficiently identify and resolve the problems. The evaluation of various scenarios from industrial case studies demonstrates that the proposed approach efficiently translates the behaviour models into formal specifications and properties. • Containment checking of software behaviour models at different abstraction levels. • Automated transformation of models into LTL formulas and formal SMV descriptions. • Counterexample interpretation for resolving the inconsistencies between models. • Applicability and feasibility of the approach for realistically sized models. • Performance evaluation in a typical developer's working environment.
Time Efficient Segmented Technique for Dynamic Programming Based Algorithms with FPGA Implementation

Although dynamic programming (DP) is an optimization approach used to solve a complex problem fast, the time required to solve it is still not efficient and grows polynomially with the size of the input. In this contribution, we improve the computation time of the dynamic programming based algorithms by proposing a novel technique, which is called “SDP: Segmented Dynamic programming”. SDP finds the best way of splitting the compared sequences into segments and then applies the dynamic programming algorithm to each segment individually. This will reduce the computation time dramatically. SDP may be applied to any dynamic programming based algorithm to improve its computation time. As case studies, we apply the SDP technique on two different dynamic programming based algorithms; “Needleman–Wunsch (NW)”, the widely used program for optimal sequence alignment, and the LCS algorithm, which finds the “Longest Common Subsequence” between two input strings. The results show that applying the SDP technique in conjunction with the DP based algorithms improves the computation time by up to 80% in comparison to the sole DP algorithms, but with small or ignorable degradation in comparing results. This degradation is controllable and it is based on the number of split segments as an input parameter. However, we compare our results with the well-known heuristic FASTA sequence alignment algorithm, “GGSEARCH”. We show that our results are much closer to the optimal results than the “GGSEARCH” algorithm. The results are valid independent from the sequences length and their level of similarity. To show the functionality of our technique on the hardware and to verify the results, we implement it on the Xilinx Zynq-7000 FPGA.
An Effective Guided Fireworks Algorithm for Solving UCAV Path Planning Problem

The use of the unmanned aerial vehicles is rapidly growing in the ever more extensive range of applications where the military is the oldest ones. One of the fundamental problems in the unmanned combat aerial vehicles (UCAV) control is the path planning problem that refers to optimization of the flight route subject to various constraints inside the battlefield environments. Since the number of control points is high as well as the number of radars, the traditional methods could not produce acceptable results when tackling this problem. In this paper, we propose an adjustment of the recent guided fireworks algorithm from the class of swarm intelligence algorithms for locating the optimal path by unmanned combat aerial vehicle taking into consideration fuel consumption and safety degree. For experimental purposes, we compared it with eight different methods from the literature. Based on the experimental results, it can be concluded that our proposed approach is robust, exhibits better performance in almost all cases.
Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement

One of the challenges in fine-grained classification is that subcategories with significant similarity are hard to be distinguished due to the equal treatment of all subcategories in existing algorithms. In order to solve this problem, a fine-grained image classification method by combining a bilinear convolutional neural network (B-CNN) and the measurement of subcategory similarities is proposed. Firstly, an improved weakly supervised localization method is designed to obtain the bounding box of the main object, which allows the model to eliminate the influence of background noise and obtain more accurate features. Then, sample features in the training set are computed by B-CNN so that the fuzzing similarity matrix for measuring interclass similarities can be obtained. To further improve classification accuracy, the loss function is designed by weighting triplet loss and softmax loss. Extensive experiments implemented on two benchmarks datasets, Stanford Cars-196 and Caltech-UCSD Birds-200-2011 (CUB-200-2011), show that the newly proposed method outperforms in accuracy several state-of-the-art weakly supervised classification models.
QTLS

Hardware accelerators are a promising solution to optimize the Total Cost of Ownership (TCO) of cloud datacenters. This paper targets the costly Transport Layer Security (TLS) and investigates the TLS acceleration for the widely-deployed event-driven TLS servers or terminators. Our study reveals an important fact: the straight offloading of TLS-involved crypto operations suffers from the frequent long-lasting blockings in the offload I/O, leading to the underutilization of both CPU and accelerator resources.
On the Diversity of Conditional Image Synthesis With Semantic Layouts

Many image processing tasks can be formulated as translating images between two image domains such as colorization, super-resolution, and conditional image synthesis. In most of these tasks, an input image may correspond to multiple outputs. However, current existing approaches only show minor stochasticity of the outputs. In this paper, we present a novel approach to synthesize diverse realistic images corresponding to a semantic layout. We introduce a diversity loss objective that maximizes the distance between synthesized image pairs and relates the input noise to the semantic segments in the synthesized images. Thus, our approach can not only produce multiple diverse images but also allow users to manipulate the output images by adjusting the noise manually. The experimental results show that images synthesized by our approach are more diverse than that of the current existing works and equipping our diversity loss does not degrade the reality of the base networks. Moreover, our approach can be applied to unpaired datasets.
Crypto-Currency

This chapter sheds light on the future of crypto-currencies in the world as they have become a major part of trading and are now being adopted by leading investment firms as a new way of buying and selling. Despite the substantial security risk in crypto-currency trading, it has become the most traded commodity. Many new crypto-currencies are being introduced that attract investors. Banking institutions in USA and other leading countries have started to take part in investments in crypto-currency as it has revolutionized financial technology.
Assessment of Optimization Methods Used to Determine Plasticity Parameters Based on DIC and back Calculation Methods

In this paper, we analyse various minimization algorithms applied to the problem of determining elasto-plastic material parameters using an inverse analysis and digital image correlation (DIC) system. As the DIC system, ARAMIS is used, while for the finite element solution of boundary value problems, Abaqus software is applied. Different minimization algorithms, implemented in the SciPy Python library, were initially juxtaposed, compared and evaluated based on benchmark functions. Next the proper evaluation of the algorithms was performed to determine the material parameters for isotropic metal plasticity with the Huber-Mises yield criterion and isotropic or combined kinematic-isotropic plastic hardening models. For all researchers utilizing back calculation methods based on a DIC measuring system, such analysis results may be interesting. It was concluded that among the local minimization methods, derivative free optimization algorithms, especially the Powell algorithm, perform the most efficiently.
MOCA: Multi-Objective, Collaborative, and Attentive Sentiment Analysis

Document-level sentiment analysis aims to predict the overall ratings of texts (e.g., reviews) written by users for items. Most current works model this problem as a supervised learning task, i.e., classification or regression. Recent studies argue that user preferences and item characteristics also have significant influences on ratings that are modeled by learning user-item-specific text embeddings based on neural networks. However, these studies only use the explicit influence observed in the texts and fail to model the implicit influence that cannot be observed in the texts. To this end, in this paper, we propose a multi-objective, collaborative, and attentive framework called MOCA for document-level sentiment analysis. Our MOCA has three important characteristics: 1) attentive model for explicit influence; MOCA applies a bidirectional recurrent neural network with attention mechanism to learn user-item-specific text embeddings for exploiting the explicit influence the of users and items; 2) collaborative model for implicit influence; MOCA devises a new neural collaborative filtering model based on multilayer perceptron to capture the implicit influence that is implied in the highly personalized interactions between the users and items; and 3) multi-objective optimization; MOCA models this problem as both classification and regression tasks and simultaneously optimizes the two objectives to reinforce one another. The experimental results show that our MOCA significantly outperforms other state-of-the-art techniques on three real-world datasets collected from IMDB and Yelp.
Towards Designing an Automated Classification of Lymphoma subtypes using Deep Neural Networks

Cancer diagnosis and treatment is a field where AI has the potential to provide tremendous scope for targeted large scale interventions. NITI Aayog, Government of India, in its discussion paper, mentions that there are over 1 million new cases of cancer in India every year, a number which is likely to rise given the annual rise in population. However, the number of pathologists experienced in oncology are few, nearly as much as a thousandth of the patients suffering from cancer, which leads to delay in cancer diagnosis and treatment. Machine learning, when used with image digitization techniques that have progressed during the last decade, can help bridge this divide. Traditionally, the initial screening and diagnosis of cancer is done using morphological analysis, which can be automated using machine learning techniques. In our work, we attempt to illustrate the potential of Deep Learning, a sub domain of Machine Learning, in contributing to the automation of cancer diagnosis, by proposing a method to classify the subtypes of lymphoma, a cancer of the lymph nodes. We implement our method on an NIA curated dataset consisting of three labeled subtypes of lymphoma namely chronic lymphocytic leukemia, follicular lymphoma and mantle cell lymphoma. The method proposed in this work, which yields an accuracy of 97.33% on the NIA curated dataset shows that deep learning has the potential to aid an automated diagnosis for lymphoma.
Heightened intensity: Reflecting on player experiences in <i>Wayfinder Live</i>

Location-based games use smartphones and other location-aware devices to incorporate their players’ actions in everyday, physical spaces – the streets and public spaces of the city – into the virtual world of the game. Scholars and designers of these games often claim that they reconfigure their players’ relationship with the people and environment around them. They argue these games either engage and immerse players more deeply in the spaces of the game or distance and detach them from the physical environment through the screen interface. To date, however, relatively few detailed empirical studies of these games have been undertaken to test out and critique these claims. This article presents a study of the 2017 iteration of the location-based augmented reality game Wayfinder Live, in which players use their phones to search for and scan urban codes hidden across Melbourne’s laneways, alleys, and public spaces. Players of the game were interviewed and invited to reflect on their experience. This article relates these experiences to the design and development of the game, particularly to five play design principles that characterize its approach to haptic play in urban space. We begin by outlining these principles and the motivations behind them. Then, drawing on an analysis of the player interviews, we evaluate the impact of the game on their perception of the city.
Layer‐wise hint‐based training for knowledge transfer in a teacher‐student framework

We devise a layer-wise hint training method to improve the existing hint-based knowledge distillation (KD) training approach, which is employed for knowledge transfer in a teacher-student framework using a residual network (ResNet). To achieve this objective, the proposed method first iteratively trains the student ResNet and incrementally employs hint-based information extracted from the pretrained teacher ResNet containing several hint and guided layers. Next, typical softening factor-based KD training is performed using the previously estimated hint-based information. We compare the recognition accuracy of the proposed approach with that of KD training without hints, hint-based KD training, and ResNet-based layer-wise pretraining using reliable datasets, including CIFAR-10, CIFAR-100, and MNIST. When using the selected multiple hint-based information items and their layer-wise transfer in the proposed method, the trained student ResNet more accurately reflects the pretrained teacher ResNet's rich information than the baseline training methods, for all the benchmark datasets we consider in this study.
A Proposal for Integrating DevOps into Software Engineering Curricula

The “2017 State of DevOps Report” asserts that 27% of its respondents work on devops teams, an increase of almost 23% from 2016 and almost 69% from the 2015. Devops practices are intended to improve an organization’s software development throughput by reducing the cycle time needed for a software change to reach its users. Although the skills needed for effective devops are in demand, it is challenging to integrate it into a academic curriculum for several reasons. First, software development curricula often only take students through the delivery stage of software development and do not spend meaningful time on operational activities, making it difficult to recruit faculty with the requisite IT operations experience. Second, many of the applications and their environments that can most benefit from devops are extremely complex, making it difficult to provide an appropriate learning environment. Third, many requirements for successful devops are not technical but instead emphasize the human and organizational aspects of our craft. Fourth, for many students, the problems addressed by devops are abstract. In this paper we look at these challenges in more detail and review one proposal for integrating devops into existing curricula in light of current devops maturity models, disciplines, and industry trends.