Reviewerly/exHarmony huggingface dataset:
Towards feature selection for digital mammogram classification

The most common cancer type amongst women is the breast cancer with a large number of cases reported each year, many of them diagnosed in an advanced phase. In this paper our scope is to create a base for a support system that helps detecting breast cancer in an early stage. After defining the region of interest (ROI) and segmenting the result image (using k-means algorithm), Gray-Level Run-Length Matrices (GLRLM) features are extracted from both the ROI and from the segmented image in four directions (horizontal, vertical, first- and second diagonals). To reduce the dimensionality of the input data composed from the GLRLM features of the ROI and its segmentation for different combination of directions (removing redundant information, selecting just the most essential ones) two methods are used: Principal Component Analysis (PCA), and genetic algorithm (GA) feature selection. For classification, two methods are used and compared, namely Decision Trees (DT) and Random Forest (RF). For experiments we used the Mammographic Image Analysis Society (MIAS) dataset to train and to test the classifiers. The best performance is obtained for GLRLM features calculated for directions 45◦, and 90◦, using PCA feature selection and RF with a 100% training accuracy and 70% test accuracy.
Strong Temporal Isolation Among Containers in OpenStack for NFV Services

In this article, the problem of temporal isolation among containerized software components running in shared cloud infrastructures is tackled, proposing an approach based on hierarchical real-time CPU scheduling. This allows for reserving a precise share of the available computing power for each container deployed in a multi-core server, so to provide it with a stable performance, independently from the load of other co-located containers. The proposed technique enables the use of reliable modeling techniques for end-to-end service chains that are effective in controlling the application-level performance. An implementation of the technique within the well-known OpenStack cloud orchestration software is presented, focusing on a use-case framed in the context of network function virtualization. The modified OpenStack is capable of leveraging the special real-time scheduling features made available in the underlying Linux operating system through a patch to the in-kernel process scheduler. The effectiveness of the technique is validated by gathering performance data from two applications running in a real test-bed with the mentioned modifications to OpenStack and the Linux kernel. A performance model is developed that tightly models the application behavior under a variety of conditions. Extensive experimentation shows that the proposed mechanism is successful in guaranteeing isolation of individual containerized activities on the platform.
Fuzzy clustering algorithms with distance metric learning and entropy regularization

Clustering has been used in various fields, such as image processing, data mining, pattern recognition, and statistical analysis. Generally, clustering algorithms consider all variables equally relevant or not correlated. Nevertheless, the pattern of data samples in the multidimensional space can be geometrically complicated, e.g., clusters may exist in different subsets of features. In this regard, new soft subspace clustering algorithms have been proposed, in which the correlation and relevance of variables are considered to improve their performance. Since regularization-based methods are robust for initializations, the approaches proposed introduce an entropy regularization term for controlling the membership degree of the objects. Such regularizations are popular due to high performance in large-scale data clustering and low computational complexity. These three-step iterative algorithms provide a fuzzy partition, a representative for each cluster, and the relevance weight of the variables or their correlation by minimizing a suitable objective function. Several experiments on synthetic and real datasets, including their application to the segmentation of noisy image textures, demonstrate the usefulness of the proposed clustering methods.
Attention guided feature pyramid network for crowd counting

Crowd counting has become a hot topic because of its wide applications in video surveillance and public security. However, one main problem of the deep learning methods for crowd counting is that the location information about the crowd is degraded irreversibly due to the spatial down-sampling of convolutional neural networks, which degrades the quality of generated density maps. To remedy the above problem, we propose an attention guided feature pyramid network (AG-FPN) for crowd counting, which can adaptively generate a high-quality density map with accurate spatial locations by combining the high- and low-level features. An attention block is added to each encoder layer to further emphasize the crowd regions and suppress the background clutters in feature extraction. Experimental results on the ShanghaiTech, UCF_CC_50, WorldExpo'10 and UCF-QNRF datasets demonstrate the superiority of the proposed method over state-of-the-art approaches.
Reduced Complexity Optimal Resource Allocation for Enhanced Video Quality in a Heterogeneous Network Environment

The latest Heterogeneous Network (HetNet) environments, supported by 5th generation (5G) network solutions, include small cells deployed to increase the traditional macro-cell network performance. In HetNet environments, before data transmission starts, there is a user association (UA) process with a specific base station (BS). Additionally, during data transmission, diverse resource allocation (RA) schemes are employed. UA-RA solutions play a critical role in improving network load balancing, spectral performance, and energy efficiency. Although several studies have examined the joint UA-RA problem, there is no optimal strategy to address it with low complexity while also reducing the time overhead. We propose two different versions of simulated annealing (SA): Reduced Search Space SA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RS^{3}A$ </tex-math></inline-formula> ) and Performance-Improved Reduced Search Space SA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PIRS^{3}A$ </tex-math></inline-formula> ), algorithms for solving UA-RA problem in HetNets. First, the UA-RA problem is formulated as a multiple knapsack problem (MKP) with constraints on the maximum BS capacity and transport block size (TBS) index. Second, the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RS^{3}A$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PIRS^{3}A$ </tex-math></inline-formula> are used to solve the formulated MKP. Simulation results show that the proposed scheme <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PIRS^{3}A$ </tex-math></inline-formula> outperforms <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RS^{3}A$ </tex-math></inline-formula> and other existing schemes such as Default Simulated Annealing (DSA), and Default Genetic Algorithm (DGA) in terms of variability and DSA and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RS^{3}A$ </tex-math></inline-formula> in terms of Quality of Service (QoS) metrics, including throughput, packet loss ratio (PLR), delay and jitter. Simulation results show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PIRS^{3}A$ </tex-math></inline-formula> generates solutions that are very close to the optimal solution.
Ontology-Based Design of Inductive Modeling Tools

The task of structure designe of the software complex of tools for inductive modeling on the GMDH basis is considered. A novel is the use of the knowledge base in the form of an ontology of the subject area of inductive modeling. The application of the ontological approach to the construction of the knowledge base makes it possible to reuse information and effectively process this information when modeling complex systems of different nature using statistical data, to form queries and obtain logical conclusions. Fragments of the ontology of inductive modeling based on GMDH as an example of creating a formal description of the subject area are given. The Protégé_4.3 ontoeditor was used to construct ontological models of the inductive modeling domain.
Speckle noise removal based on structural convolutional neural networks with feature fusion for medical image

Application of convolutional neural networks (CNNs) for image additive white Gaussian noise (AWGN) removal has attracted considerable attentions with the rapid development of deep learning in recent years. However, the work of image multiplicative speckle noise removal is rarely done. Moreover, most of the existing speckle noise removal algorithms are based on traditional methods with human priori knowledge, which means that the parameters of the algorithms need to be set manually. Nowadays, deep learning methods show clear advantages on image feature extraction. Multiplicative speckle noise is very common in real life images, especially in medical images. In this paper, a novel neural network structure is proposed to recover noisy images with speckle noise. Our proposed method mainly consists of three subnetworks. One network is rough clean image estimate subnetwork. Another is subnetwork of noise estimation. The last one is an information fusion network based on U-Net and several convolutional layers. Different from the existing speckle denoising model based on the statistics of images, the proposed network model can handle speckle denoising of different noise levels with an end-to-end trainable model. Extensive experimental results on several test datasets clearly demonstrate the superior performance of our proposed network over state-of-the-arts in terms of quantitative metrics and visual quality.
Optimal deployment of face recognition solutions in a heterogeneous IoT platform for secure elderly care applications

Face recognition provides a desirable solution for authentication and surveillance in Internet of Things platforms for elderly care. However, its inclusion is challenging because of the possibly reduced interaction capabilities of users, the high variety of interaction devices, and the need of managing biometric data securely. Our approach relies on lightweight deep neural networks for secure recognition and to guide users during interaction. An automated procedure selects the appropriate inference engine, model configurations, and batch size, based on edge device characteristics. Biometric data is homomorphically encrypted to preserve privacy. An evaluation with respect to state-of-the-art alternatives shows its potential.
Text summarization based on multi-head self-attention mechanism and pointer network

Abstract Existing text summarization methods mainly rely on the mapping between manually labeled standard summaries and the original text for feature extraction, often ignoring the internal structure and semantic feature information of the original document. Therefore, the text summary extracted by the existing model has the problems of grammatical structure errors and semantic deviation from the original text. This paper attempts to enhance the model’s attention to the inherent feature information of the source text so that the model can more accurately identify the grammatical structure and semantic information of the document. Therefore, this paper proposes a model based on the multi-head self-attention mechanism and the soft attention mechanism. By introducing an improved multi-head self-attention mechanism in the model coding stage, the training model enables the correct summary syntax and semantic information to obtain higher weight, thereby making the generated summary more coherent and accurate. At the same time, the pointer network model is adopted, and the coverage mechanism is improved to solve out-of-vocabulary and repetitive problems when generating abstracts. This article uses CNN/DailyMail dataset to verify the model proposed in this article and uses the ROUGE indicator to evaluate the model. The experimental results show that the model in this article improves the quality of the generated summary compared with other models.
ABSTAT-HD: a scalable tool for profiling very large knowledge graphs

Abstract Processing large-scale and highly interconnected Knowledge Graphs (KG) is becoming crucial for many applications such as recommender systems, question answering, etc. Profiling approaches have been proposed to summarize large KGs with the aim to produce concise and meaningful representation so that they can be easily managed. However, constructing profiles and calculating several statistics such as cardinality descriptors or inferences are resource expensive. In this paper, we present ABSTAT-HD, a highly distributed profiling tool that supports users in profiling and understanding big and complex knowledge graphs. We demonstrate the impact of the new architecture of ABSTAT-HD by presenting a set of experiments that show its scalability with respect to three dimensions of the data to be processed: size, complexity and workload. The experimentation shows that our profiling framework provides informative and concise profiles, and can process and manage very large KGs.
Isolated Sign Language Recognition with Depth Cameras

In this paper, an approach to isolated sign language recognition with data provided by a depth camera is presented. In the introduced method, sequences of depth maps of dynamic sign language gestures are divided into smaller regions (cells). Then, statistical information is used to describe the cells. Since gesture executions have different lengths, the dynamic time warping (DTW) technique with the nearest neighbor (NN) rule is often employed for their comparison. However, due to time-consuming computations, the DTW limits the usability of the classifier. Therefore, in this paper, a selection of representative depth maps using statistics for cells is proposed. It is shown that such gesture representation can be successfully employed for isolated sign language recognition with the NN classifier using the city block distance. Furthermore, the NN rule with the DTW and the introduced statistics for cells provides superior gesture recognition performance.
Stitched image quality assessment based on local measurement errors and global statistical properties

Image stitching is developed to generate wide-field images or panoramic images for virtual reality applications. However, the quality assessment of stitched images with respect to various stitching algorithms has been less studied. Effective stitched image quality assessment (SIQA) is advantageous to evaluate the performance of various stitching methods and optimize the design of stitching methods. In this paper, we propose a novel SIQA method by exploiting local measurement errors and global statistical properties for feature extraction. Comprehensive image attributes including ghosting, misalignment, structural distortion, geometric error, chromatic aberrations and blur are considered either locally or globally. The extracted local and global features are aggregated into an overall quality via regression. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-the-art quality models designed for natural images and stitched images.
Fine-Tuning the Odds in Bayesian Networks

This paper proposes new analysis techniques for Bayes networks in which conditional probability tables (CPTs) may contain symbolic variables. The key idea is to exploit scalable and powerful techniques for synthesis problems in parametric Markov chains. Our techniques are applicable to arbitrarily many, possibly dependent, parameters that may occur in multiple CPTs. This lifts the severe restrictions on parameters, e.g., by restricting the number of parametrized CPTs to one or two, or by avoiding parameter dependencies between several CPTs, in existing works for parametric Bayes networks (pBNs). We describe how our techniques can be used for various pBN synthesis problems studied in the literature such as computing sensitivity functions (and values), simple and difference parameter tuning, ratio parameter tuning, and minimal change tuning. Experiments on several benchmarks show that our prototypical tool built on top of the probabilistic model checker Storm can handle several hundreds of parameters.
How do Code Smell Co-occurrences Removal Impact Internal Quality Attributes? A Developers’ Perspective

Code smells are poor code structures that might harm the software quality and evolution. However, previous studies has shown that only individual occurrences of smells may not be enough to assess the real impact that these smells can bring on systems. In this context, the co-occurrences of code smells, i.e., occurrences of more than one code smell in the same class or same method, can be better indicators of design problems for software quality. Despite its importance as an indicator of design problems, we have little known about the impact of removing the co-occurrence of smells via software refactoring on internal quality attributes, such as coupling, cohesion, complexity, and inheritance. It is even less clear on what is the developers' perspective on the co-occurrences removal. We aim at addressing this gap through a qualitative study with 14 developers. To this end, we analyze the refactorings employed by developers during the removal of 60 code smells co-occurrences, during 3 months in 5 closed-source projects. We observe (i) impact of code smells co-occurrences on internal quality attributes, (ii) which are the most harmful co-occurrences from the developers' perspective, (iii) developers' perceptions during the removal of code smells co-occurrence via refactoring activities; and (iv) what are the main difficulties faced by developers during the removal of code smells co-occurrences in practice. Our findings indicate that: (i) the refactoring of some types of code smells co-occurrences (e.g., Dispersed Coupling–God Class) indicated improvement for the quality attributes; (ii) refactoring code smells co-occurrences according to the developers is difficult mainly due to the understanding of the code and complexity refactoring methods; and (iii) developers still have insecurities regarding the identification and refactoring of code smells and their co-occurrences.
Design, Implementation and Testing of a Network-Based Earthquake Early Warning System in Greece

In this study we implemented and tested the Earthquake Early Warning system PRESTo (PRobabilistic and Evolutionary early warning System, Satriano et al., 2011 ) on the Greek Ionian islands of Lefkada, Zakynthos and Kefalonia. PRESTo is a free and open source platform for regional Earthquake Early Warning developed at the University of Naples Federico II, which is currently under experimentation in Southern Italy, in the area covered by the Irpinia Seismic Network. The three Ionian islands selected for this study are located on the North-Western part of the Hellenic trench. Here the seismicity rate and the seismic hazard, coupled with the vulnerability of existing critical infrastructures, make this region among the highest seismic risk areas in Europe, where the application of Earthquake Early Warning systems may become a useful strategy to mitigate the potential damage caused by earthquakes. Here we studied the feasibility of implementing an Earthquake Early Warning system on an existing seismic network, which was not specifically made for earthquake early warning purposes, and evaluated the performance of the system, using a data set of real-earthquake recordings. We first describe the technical details of the implementation of PRESTo in the area of interest, including the preliminary parameter configuration and the empirical scaling relationship calibration. Then we evaluated the performance of the system through the off-line analysis of a database of real earthquake records belonging to the most recent M &amp;gt; 4.0 earthquakes occurred in the area. We evaluated the performance in terms of source parameter estimation (location, magnitude), accuracy of ground shaking prediction and lead-time analysis. Finally, we show the preliminary results of the real-time application of PRESTo, performed during the period 01–31 July 2019.
Efficient and Less Centralized Federated Learning

With the rapid growth in mobile computing, massive amounts of data and computing resources are now located at the edge. To this end, Federated learning (FL) is becoming a widely adopted distributed machine learning (ML) paradigm, which aims to harness this expanding skewed data locally in order to develop rich and informative models. In centralized FL, a collection of devices collaboratively solve a ML task under the coordination of a central server. However, existing FL frameworks make an over-simplistic assumption about network connectivity and ignore the communication bandwidth of the different links in the network. In this paper, we present and study a novel FL algorithm, in which devices mostly collaborate with other devices in a pairwise manner. Our nonparametric approach is able to exploit network topology to reduce communication bottlenecks. We evaluate our approach on various FL benchmarks and demonstrate that our method achieves 10\(\times \) better communication efficiency and around 8% increase in accuracy compared to the centralized approach.
PV-EncoNet: Fast Object Detection Based on Colored Point Cloud

Object detection is the most critical and foundational sensing module for the autonomous movement platform. However, most of the existing deep learning solutions are based on GPU servers, which limits their actual deployment. We present an efficient multi-sensor fusion based object detection model that can be deployed on the off-the-shelf edge computing device for the vehicle platform. To achieve real-time target detection, the model eliminates a large number of invalid point clouds through ground filtering algorithm, and then adds texture information (fused from camera image) through point cloud coloring to enhance features. The proposed PV-EncoNet efficiently encodes both the spatial and texture features of each colored point through point-wise and voxel-wise encoding, and then predicts the position, heading and class of the objects. The final model can achieve about 17.92 and 24.25 Frame per Second (FPS) on two different edge computing platforms, and the detection accuracy is comparable with the state-of-the-art models on the KITTI public dataset (i.e., 88.54% for cars, 71.94% for pedestrians and 73.04% for cyclists). The robustness and generalization ability of the PV-EncoNet for the 3D colored point cloud detection task is also verified by deploying it on the local vehicle platform and testing it on real road conditions.