Reviewerly/exHarmony huggingface dataset:
A proximal framework for fuzzy subspace clustering

This paper proposes a fuzzy partitioning subspace clustering algorithm that minimizes a variant of the FCM cost function with a weighted Euclidean distance and a non-differentiable penalty term. The form of the cost function suggests to split the optimization problem, taking advantage of the framework of proximal optimization. The expression of the proximal operator for the penalty term is derived and implemented in a new algorithm, PFSCM, which combines proximal descent and alternate optimization. A discussion on the extension of this work to produce sparse estimations of the subspaces is conducted. Experimental results show the relevance of the proposed approach.
Implementing positive maps with multiple copies of an input state

Valid transformations between quantum states are necessarily described by completely positive maps, instead of just positive maps. Positive but not completely positive maps such as the transposition map cannot be implemented due to the existence of entanglement in composite quantum systems, but there are classes of states for which the positivity is guaranteed, e.g., states not correlated to other systems. this paper, we report on the concept of $N$-copy extension of maps to quantitatively analyze the difference between positive maps and completely positive maps. We consider implementations of the action of positive but not completely positive maps on uncorrelated states by allowing an extra resource of consuming multiple copies of the input state and characterize the positive maps in terms of implementability with multiple copies. We show that by consuming multiple copies, the set of implementable positive maps becomes larger, and almost all positive maps are implementable with finite copies of an input state. The number of copies of the input state required to implement a positive map quantifies the degree by which a positive map violates complete positivity. We then analyze the optimal $N$-copy implementability of a noisy version of the transposition map.
Data-Aware Scheduling Strategy for Scientific Workflow Applications in IaaS Cloud Computing

Scientific workflows benefit from the cloud computing paradigm, which offers access to virtual resources provisioned on pay-as-you-go and on-demand basis.Minimizing resources costs to meet user's budget is very important in a cloud environment.Several optimization approaches have been proposed to improve the performance and the cost of data-intensive scientific Workflow Scheduling (DiSWS) in cloud computing.However, in the literature, the majority of the DiSWS approaches focused on the use of heuristic and metaheuristic as an optimization method.Furthermore, the tasks hierarchy in data-intensive scientific workflows has not been extensively explored in the current literature.Specifically, in this paper, a data-intensive scientific workflow is represented as a hierarchy, which specifies hierarchical relations between workflow tasks, and an approach for data-intensive workflow scheduling applications is proposed.In this approach, first, the datasets and workflow tasks are modeled as a conditional probability matrix (CPM).Second, several data transformation and hierarchical clustering are applied to the CPM structure to determine the minimum number of virtual machines needed for the workflow execution.In this approach, the hierarchical clustering is done with respect to the budget imposed by the user.After data transformation and hierarchical clustering, the amount of data transmitted between clusters can be reduced, which can improve cost and makespan of the workflow by optimizing the use of virtual resources and network bandwidth.The performance and cost are analyzed using an extension of Cloudsim simulation tool and compared with existing multi-objective approaches.The results demonstrate that our approach reduces resources cost with respect to the user budgets.
D HistoSketch: Discriminative and Dynamic Similarity-Preserving Sketching of Streaming Histograms

Histogram-based similarity has been widely adopted in many machine learning tasks. However, measuring histogram similarity is a challenging task for streaming histograms, where the elements of a histogram are observed one after the other in an online manner. The ever-growing cardinality of histogram elements over the data streams makes any similarity computation inefficient in that case. To tackle this problem, we propose in this paper D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> HistoSketch, a similarity-preserving sketching method for streaming histograms to efficiently approximate their Discriminative and Dynamic similarity. D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> HistoSketch can fast and memory-efficiently maintain a set of compact and fixed-size sketches of streaming histograms to approximate the similarity between histograms. To provide high-quality similarity approximations, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> HistoSketch considers both discriminative and gradual forgetting weights for similarity measurement, and seamlessly incorporates them in the sketches. Based on both synthetic and real-world datasets, our empirical evaluation shows that our method is able to efficiently and effectively approximate the similarity between streaming histograms while outperforming state-of-the-art sketching methods. Compared to full streaming histograms with both discriminative and gradual forgetting weights in particular, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> HistoSketch is able to dramatically reduce the classification time (with a 7500x speedup) at the expense of a small loss in accuracy only (about 3.25 percent).
A Face Alignment Accelerator Based on Optimized Coarse-to-Fine Shape Searching

The coarse-to-fine shape searching (CFSS) framework is a recently developed algorithm that achieves relatively high accuracy in face alignment by alleviating the poor initialization problem facing traditional cascaded regression approaches. However, its high computational complexity and memory access demands make it difficult for CFSS to satisfy the requirements of real-time processing. To address this issue, a fast shape searching face alignment (F-SSFA) accelerator is presented based on the optimization of the CFSS algorithm and an efficient hardware implementation. First, the learning-based low-dimensional speeded-up robust features method, based on the correlations between the SURF features and the regression targets, is introduced to distill the feature set down to the only most distinct features to reduce the computing load. Second, the partial keypoints Euclidean distance and shape affine transformation are introduced to replace feature extraction and support vector machine classification, thereby accelerating the shape searching process. Compared with CFSS, F-SSFA achieves a $5.8\times $ speedup while achieving similar accuracy. Moreover, a VLSI architecture is proposed to realize the fixed-point F-SSFA algorithm. Multiple descriptors located in adjacent regions are simultaneously generated in a single access to the corresponding image data. Therefore, repeated memory access operations are avoided. The optimal parameter configuration for hardware implementation is also exploited based on a tradeoff between accuracy and hardware performance. Simulated with TSMC 65-nm 1P8M technology within a 3.6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area, a post-layout simulation shows that 700 fps can be achieved while consuming 300 mW at 200 MHz.
Integration of Moodle and Electronic University Systems at BMSTU

The free platform Moodle was integrated with protected University Administrative Information System Electronic University (UAIS EU) of Bauman Moscow State Technical University, which serves to support the administrative work for control of educational process. The following main problems were solved: creation of unified data representation in the two systems; creation of students' and training courses' databases in Moodle based on data from UAIS EU. As result unique software was developed, new quality of service was obtained, namely different sides of University activity such as teaching, learning, and administrative control of educational process were automated and joined together; the time required for information processing and administrative decision-making was reduced; the number of errors in the systems due to the influence of a human factor was reduced. The results obtained can be used to simplify the work of teachers and enhance the performance and operational efficiency of the administrative system at any university.
A solution of the minimum-time speed planning problem based on lattice theory

For a vehicle on an assigned path, we find the minimum-time speed law that satisfies kinematic and dynamic constraints, related to maximum speed and maximum tangential and transversal acceleration. We present a necessary and sufficient condition for the feasibility of the problem and a simple operator, based on the solution of two ordinary differential equations, which computes the optimal solution. Theoretically, we show that the problem feasible set, if not empty, is a lattice, whose supremum element corresponds to the optimal solution.
Vehicle Detection in High-Resolution Images Using Superpixel Segmentation and CNN Iteration Strategy

This letter presents a study of vehicle detection in high-resolution images using superpixel segmentation and iterative convolutional neural network strategy. First, a novel superpixel segmentation integrated with multiple local information constraints method is proposed to improve the segmentation results with a low breakage rate. To make training and detection more efficient, we extract meaningful and nonredundant patches based on the centers of the segmented superpixels. For reducing the instability in detection performance because of manual or random selection of samples, a training sample iterative selection strategy based on convolutional neural network is proposed. After a compact training sample subset is obtained from the original entire training set, a representative feature set with high discrimination ability between vehicle and background is extracted from these selected samples for detection. To further avoid overfitting the training and promote the detection efficiency, data augment and a main direction estimation method are used. Comparative experimental results on Toronto data indicated the effectiveness of our proposed method.
Forensic analysis of communication records of messaging applications from physical memory

Inspection of physical memory allows digital investigators to retrieve evidence otherwise inaccessible when analyzing other storage media. In this paper, we analyze in-memory communication records produced by instant messaging and email applications, both in desktop web-based applications and native applications running in mobile devices. Our results show that, in spite of the heterogeneity of data formats specific to each application, communication records can be represented in a common application-independent format. This format can then be used as a common representation to allow for general analysis of digital artifacts across various applications. Then, we introduce RAMAS, an extensible forensic tool which aims to ease the process of analysing communication records left behind in physical memory by instant-messaging and email clients.
A weighting intersection point prediction iteration optimization algorithm used in photogrammetry for port hoisting machinery

As a mature technology, photogrammetry is widely applied in today's engineering measurement field. However, because of the limitation of the port condition, it is impossible to obtain photos that are taken at ideal angles and distances when photogrammetry is used to measure port hoisting machinery, and this leads to invalid measurements with low accuracy data. To solve this problem, a new algorithm is proposed in this work. First, the proposed method introduces redundant measurements through an intersection point prediction algorithm to improve the measurement data's accuracy. Second, a weighting algorithm based on the lens distortion model is then provided to further improve accuracy. Third, an iterative method is established from the threshold setting method based on the weighting algorithm. Thus, the quality of the final measurement could be controllable. Finally, an experiment is devised for the characteristics of the algorithm and the port condition. The results demonstrate that the method described in this paper significantly improves the accuracy of the measuring results of photogrammetry while photos used for the calculation were taken at unsatisfactory angles and distances caused by the limitation of the port condition.
Machine learning system for automated blood smear analysis

In this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images.The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones -the healthy blood cells (erythrocytes) and the pathologic ones (echinocytes).The separated blood cells are analysed in terms of their most important features by the eigenfaces method.The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes.As the result, the proposed system is able to analyse the smear blood images in a fully automatic way and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic.The system was examined in two case studies, involving the canine and human blood, and then consulted with the experienced medicine specialists.The accuracy of classification of red blood cells into erythrocytes and echinocytes reaches 96%.
STRATFram: A framework for describing and evaluating elasticity strategies for service-based business processes in the cloud

In the recent years, growing attention has been paid to the concept of Cloud Computing as a new computing paradigm for executing and handling operations/processes in an efficient and cost-effective way. Cloud Computing’s elasticity and its flexibility in service delivery have been the most important features behind this attention which encourage companies to migrate their operation/processes to the cloud to ensure the required QoS while using resources and reduce their expenses. Elasticity management has been considered as a pivotal issue among IT community that works on finding the right tradeoffs between QoS levels and operational costs by developing novel methods and mechanisms. However, controlling process elasticity and defining non-trivial elasticity strategies are challenging issues. Also, despite the growing attention paid to the cloud and its elasticity property in particular, there is still a lack of solutions that support the evaluation of elasticity strategies used to ensure the elasticity of processes at service-level. In this paper, we present a framework for describing and evaluating elasticity strategies for Service-based Business Processes (SBP), called StratFram. It is composed of a set of domain-specific languages designed to generalize the use of the framework and to facilitate the description of evaluation elements that are needed to evaluate elasticity strategies before using them in real cloud environment. Using StratFram, SBP holders are allowed to define: (i) an elasticity model with specific elasticity capabilities on which they want to define and evaluate their elasticity strategies, (ii) a SBP model for which the elasticity strategies will be defined and evaluated, (iii) a set of elasticity strategies based on the elasticity capabilities of the defined elasticity model and for the provided SBP model, and (iv) a simulation configuration which identifies simulation properties/elements. The evaluation of elasticity strategies consists in providing a set of plots that allows the analysis and the comparison of strategies. Our contributions and developments provide Cloud tenants with facilities to choose elasticity strategies that fit to their business processes and usage behaviors.
Analysis of DBSCAN and K-means algorithm for evaluating outlier on RFM model of customer behaviour

The aim of study is to discover outlier of customer data to found customer behaviour. The customer behaviour determined with RFM (Recency, Frequency and Monetary) models with K-Mean and DBSCAN algorithm as clustering customer data. There are six step in this study. The first step is determining the best number of clusters with the dunn index (DN) validation method for each algorithm. Based on the dunn index, the best cluster values were 2 clusters with DN value for DBSCAN 1.19 which were minpts and epsilon value 0.2 and 3 and DN for K-Means was 1.31. The next step was to cluster the dataset with the DBSCAN and K-Means algorithm based on the best cluster that was 2. DBSCAN algorithm had 37 outliers data and K-means algorithm had 63 outliers (cluster 1 are 26 outliers and cluster 2 are 37 outliers). This research shown that outlier in DBSCAN and K-Means in cluster 1 have similarities is 100%. But overal outliers similarities is 67%. Based the outliers shown that the behaviour of customers is a small frequency of spending but high recency and monetary.
Response Characteristics Prediction of Surge Protective Device Based on NARX Neural Network

The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic neural architecture, which is commonly used for input-output modeling of nonlinear dynamical systems. Due to the lack of accurate mathematical model for the performance analysis on surge protective devices (SPDs), with the input of fast rising time electromagnetic pulse (FREMP), the NARX neural network is employed to predict the response characteristics of SPD in this paper. In order to verify the feasibility of this method, SPD test system is set up according to IEC 61000-4-24. The results show that the response curve estimated by the proposed model is in good agreement with experimental results, especially the waveform parameters such as response time, pulse peak, and residual voltage. The good forecasting performance of the network suggests that the NARX model used in this paper has good generalization ability. Moreover, with less measured data it can predict the response of the SPD under different voltage levels that were not yet measured.
How software size influence productivity and project duration

&lt;p&gt;To make a perfect project plan, the software size of the order from the customer is the most important factor. The biggest challenge for the project manager is to estimate the project end date in the beginning of the project i.e. in project planning phase with realistic accuracy. Apart from other major inputs to estimate the project end date, expected team capability (productivity) and estimated software size are the major inputs that may influence the project end date. Software size is one of the most significant independent metric available in the planning phase and project manager has to estimate the other metrics based on the initial estimated software size. There is no direct relationship available between software size and project duration or software size and team productivity, however, there are industry data published by Quantitative Software Management and ISBSG that shows how these metrics influence each other. In this paper, using the data published by ISBSG and Quantitative Software Management, we try to statistically establish how productivity and project duration are influenced by software size. We have done linear regression analysis by generating the secondary data based on the data published by ISBSG and Quantitative Software Management. Linear regression equation validated with the actual project data and experimental results suggest that that productivity is significantly dependent on software size, however, project duration does not significantly depend on software size but may also be dependent on other metrics like team size, apart from software size.&lt;/p&gt;
On Completing Sparse Knowledge Base with Transitive Relation Embedding

Multi-relation embedding is a popular approach to knowledge base completion that learns embedding representations of entities and relations to compute the plausibility of missing triplet. The effectiveness of embedding approach depends on the sparsity of KB and falls for infrequent entities that only appeared a few times. This paper addresses this issue by proposing a new model exploiting the entity-independent transitive relation patterns, namely Transitive Relation Embedding (TRE). The TRE model alleviates the sparsity problem for predicting on infrequent entities while enjoys the generalisation power of embedding. Experiments on three public datasets against seven baselines showed the merits of TRE in terms of knowledge base completion accuracy as well as computational complexity.
Similarity Preserving Deep Asymmetric Quantization for Image Retrieval

Quantization has been widely adopted for large-scale multimedia retrieval due to its effectiveness of coding highdimensional data. Deep quantization models have been demonstrated to achieve the state-of-the-art retrieval accuracy. However, training the deep models given a large-scale database is highly time-consuming as a large amount of parameters are involved. Existing deep quantization methods often sample only a subset from the database for training, which may end up with unsatisfactory retrieval performance as a large portion of label information is discarded. To alleviate this problem, we propose a novel model called Similarity Preserving Deep Asymmetric Quantization (SPDAQ) which can directly learn the compact binary codes and quantization codebooks for all the items in the database efficiently. To do that, SPDAQ makes use of an image subset as well as the label information of all the database items so the image subset items and the database items are mapped to two different but correlated distributions, where the label similarity can be well preserved. An efficient optimization algorithm is proposed for the learning. Extensive experiments conducted on four widely-used benchmark datasets demonstrate the superiority of our proposed SPDAQ model.
Robust Online Matching with User Arrival Distribution Drift

Recently, online matching problems have attracted much attention due to its emerging applications in internet advertising. Most existing online matching methods have adopted either adversarial or stochastic user arrival assumption, while on both of them significant limitation exists. The adversarial model does not exploit existing knowledge of the user sequence, and thus can be pessimistic in practice. On other hands, the stochastic model assumes that users are drawn from a stationary distribution, which may not be true in real applications. In this paper, we consider a novel user arrival model where users are drawn from drifting distribution, which is a hybrid case between the adversarial and stochastic model, and propose a new approach RDLA to deal with such assumption. Instead of maximizing empirical total revenues on the revealed users, RDLA leverages distributionally robust optimization techniques to learn dual variables via a worst-case consideration over an ambiguity set on the underlying user distribution. Experiments on a real-world dataset exhibit the superiority of our approach.