ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Detection and Localization of PMU Time Synchronization Attacks via Graph Signal Processing

Time Synchronization Attacks (TSAs) against Phasor Measurement Units (PMUs) constitute a major threat to modern smart grid applications. By compromising the time reference of a set of PMUs, an attacker can change the phase angle of their measured phasors, with potentially detrimental impact on grid operation and control. Going beyond traditional residual-based techniques in detecting TSAs, in this paper we propose the use of Graph Signal Processing (GSP) to model the power grid so as to facilitate the detection and localization of TSAs. We analytically show that modeling the state of the power system as a low-pass graph signal can significantly improve the resilience of the grid against TSAs. We propose TSA detection and localization methods based on GSP, leveraging state-of-the-art machine learning algorithms. We provide empirical evidence for the efficiency of the proposed methods based on extensive simulations on five IEEE benchmark systems. In fact, our methods can detect at least 77% more TSAs of significant impact and localize an additional 70% of the attacked PMUs compared to state-of-the-art techniques.
Metallosphaera javensis sp. nov., a novel species of thermoacidophilic archaea, isolated from a volcanic area

A novel thermoacidophilic archeaon, strain J1T (=DSM 112778T,=JCM 34702T), was isolated from a hot pool in a volcanic area of Java, Indonesia. Cells of the strain were irregular, motile cocci of 1.0-1.2 µm diameter. Aerobic, organoheterotrophic growth with casamino acids was observed at an optimum temperature of 70 °C in a range of 55-78 °C and at an optimum pH of 3 in a range of 1.5 to 5. Various organic compounds were utilized, including a greater variety of sugars than has been reported for growth of other species of the genus. Chemolithoautotrophic growth was observed with reduced sulphur compounds, including mineral sulphides. Ferric iron was reduced during anaerobic growth with elemental sulphur. Cellular lipids were calditoglycerocaldarchaeol and caldarchaeol with some derivates. The organism contained the respiratory quinone caldariellaquinone. On the basis of phylogenetic and chemotaxonomic comparison with its closest relatives, it was concluded that strain J1T represents a novel species, for which the name Metallosphaera javensis is proposed. Low DNA-DNA relatedness values (16S rRNA gene <98.4%, average nucleotide identity (ANI) <80.1%) distinguished J1T from other species of the genus Metallosphaera and the DNA G+C content of 47.3% is the highest among the known species of the genus.
Stable and sensitive sensor for alkaline phosphatase based on target-triggered wavelength tuning of fluorescent copper nanoclusters

Generally, the volatility and vulnerability of intensity signal largely reduced the reproducibility and stability of fluorescent nanosensor. Herein, we proposed a novel and stable fluorescent sensor by employing the wavelength shift of copper nanoclusters (CuNCs) as signal readout. The thymine-templated CuNCs were prepared by facile and rapid one-pot reduction method. Interestingly and importantly, it was found that the fluorescence wavelength of CuNCs could be precisely tuned by manganese ion (Mn2+). Through systematic investigation, it was further proved that the wavelength readout of CuNCs is significantly more stable than intensity readout. Since alkaline phosphatase (ALP) can catalyze the hydrolysis of phosphorylated ascorbic acid into ascorbic acid (AA) which can trigger the decomposition of MnO2 nanosheets (NS) into Mn2+, a stable sensor for sensitive ALP detection was constructed based on precise wavelength tuning of fluorescent CuNCs. The sensor exhibited good linear response to ALP over the range from 0.025 to 10 U/L with a detection limit of 0.008 U/L. Additionally, this sensor was also extended to assay two typical ALP inhibitors (Na3VO4 and EDTA) with reasonable results. Last but not least, it was confirmed that the detection results of ALP in real serum samples with this sensor were highly consistent with the data from clinical test, which was mainly attributed to the improved stability and practicability of wavelength readout-based sensor.
Recognizing Actions Through Action-Specific Person Detection

Action recognition in still images is a challenging problem in computer vision. To facilitate comparative evaluation independently of person detection, the standard evaluation protocol for action recognition uses an oracle person detector to obtain perfect bounding box information at both training and test time. The assumption is that, in practice, a general person detector will provide candidate bounding boxes for action recognition. In this paper, we argue that this paradigm is suboptimal and that action class labels should already be considered during the detection stage. Motivated by the observation that body pose is strongly conditioned on action class, we show that: 1) the existing state-of-the-art generic person detectors are not adequate for proposing candidate bounding boxes for action classification; 2) due to limited training examples, the direct training of action-specific person detectors is also inadequate; and 3) using only a small number of labeled action examples, the transfer learning is able to adapt an existing detector to propose higher quality bounding boxes for subsequent action classification. To the best of our knowledge, we are the first to investigate transfer learning for the task of action-specific person detection in still images. We perform extensive experiments on two benchmark data sets: 1) Stanford-40 and 2) PASCAL VOC 2012. For the action detection task (i.e., both person localization and classification of the action performed), our approach outperforms methods based on general person detection by 5.7% mean average precision (MAP) on Stanford-40 and 2.1% MAP on PASCAL VOC 2012. Our approach also significantly outperforms the state of the art with a MAP of 45.4% on Stanford-40 and 31.4% on PASCAL VOC 2012. We also evaluate our action detection approach for the task of action classification (i.e., recognizing actions without localizing them). For this task, our approach, without using any ground-truth person localization at test time, outperforms on both data sets state-of-the-art methods, which do use person locations.
Kernelized Bayesian Matrix Factorization

We extend kernelized matrix factorization with a full-Bayesian treatment and with an ability to work with multiple side information sources expressed as different kernels. Kernels have been introduced to integrate side information about the rows and columns, which is necessary for making out-of-matrix predictions. We discuss specifically binary output matrices but extensions to real-valued matrices are straightforward. We extend the state of the art in two key aspects: (i) A full-conjugate probabilistic formulation of the kernelized matrix factorization enables an efficient variational approximation, whereas full-Bayesian treatments are not computationally feasible in the earlier approaches. (ii) Multiple side information sources are included, treated as different kernels in multiple kernel learning which additionally reveals which side sources are informative. We then show that the framework can also be used for supervised and semi-supervised multilabel classification and multi-output regression, by considering samples and outputs as the domains where matrix factorization operates. Our method outperforms alternatives in predicting drug-protein interactions on two data sets. On multilabel classification, our algorithm obtains the lowest Hamming losses on 10 out of 14 data sets compared to five state-of-the-art multilabel classification algorithms. We finally show that the proposed approach outperforms alternatives in multi-output regression experiments on a yeast cell cycle data set.
SPACE AND TIME OF LIGHTING DESIGN: THE RESULTS OF THE INTERNATIONAL RESEARCH-TO-PRACTICE CONFERENCE "LIGHTING DESIGN-2016"

The paper is an overview of the main discussion areas of the International Research-to-Practice Conference "Lighting design-2016". The theme of the year 2016 was devoted to "Light, Space, and Time". Professional design community, scientists, architects, artists, engineers, representatives of media and IT-technologies from nine countries including Russia discussed the issues related to art and science integration, urban lighting environment, technical culture and new technologies, education in the field of lighting design.
Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are extremely labor-intensive and prone to errors. As a result, a robust and automated method for analyzing atrial structures in 3-D is of high interest. We have, therefore, developed AtriaNet, a 16-layer convolutional neural network (CNN), on 154 3-D LGE-MRIs with a spatial resolution of 0.625 mm x 0.625 mm x 1.25 mm from patients with AF, to automatically segment the left atrial (LA) epicardium and endocardium. AtriaNet consists of a multi-scaled, dual-pathway architecture that captures both the local atrial tissue geometry and the global positional information of LA using 13 successive convolutions and three further convolutions for merging. By utilizing computationally efficient batch prediction, AtriaNet was able to successfully process each 3-D LGE-MRI within 1 min. Furthermore, benchmarking experiments have shown that AtriaNet has outperformed the state-of-the-art CNNs, with a DICE score of 0.940 and 0.942 for the LA epicardium and endocardium, respectively, and an inter-patient variance of <0.001. The estimated LA diameter and volume computed from the automatic segmentations were accurate to within 1.59 mm and 4.01 cm(3) of the ground truths. Our proposed CNN was tested on the largest known data set for LA segmentation, and to the best of our knowledge, it is the most robust approach that has ever been developed for segmenting LGE-MRIs. The increased accuracy of atrial reconstruction and analysis could potentially improve the understanding and treatment of AF.
Methodologies and applications for critical infrastructure protection: State-of-the-art

This work provides an update of the state-of-the-art on energy security relating to critical infrastructure protection. For this purpose, this survey is based upon the conceptual view of OECD countries, and specifically in accordance with EU Directive 114/08/EC on the identification and designation of European critical infrastructures, and on the 2009 US National Infrastructure Protection Plan. The review discusses the different definitions of energy security, critical infrastructure and key resources, and shows some of the experie'nces in countries considered as international reference on the subject, including some information-sharing issues. In addition, the paper carries out a complete review of current methodologies, software applications and modelling techniques around critical infrastructure protection in accordance with their functionality in a risk management framework. The study of threats and vulnerabilities in critical infrastructure systems shows two important trends in methodologies and modelling. A first trend relates to the identification of methods, techniques, tools and diagrams to describe the current state of infrastructure. The other trend accomplishes a dynamic behaviour of the infrastructure systems by means of simulation techniques including systems dynamics, Monte Carlo simulation, multi-agent systems, etc. (C) 2011 Elsevier Ltd. All rights reserved.
Diffusion 19F-NMR of Nanofluorides: In Situ Quantification of Colloidal Diameters and Protein Corona Formation in Solution

The NMR-detectability of elements of organic ligands that stabilize colloidal inorganic nanocrystals (NCs) allow the study of their diffusion characteristics in solutions. Nevertheless, these measurements are sensitive to dynamic ligand exchange and often lead to overestimation of diffusion coefficients of dispersed colloids. Here, we present an approach for the quantitative assessment of the diffusion properties of colloidal NCs based on the NMR signals of the elements of their inorganic cores. Benefiting from the robust 19F-NMR signals of the fluorides in the core of colloidal CaF2 and SrF2, we show the immunity of 19F-diffusion NMR to dynamic ligand exchange and, thus, the ability to quantify, with high accuracy, the colloidal diameters of different types of nanofluorides in situ. With the demonstrated ability to characterize the formation of protein corona at the surface of nanofluorides, we envision that this study can be extended to additional formulations and applications.
The "Expo" and the Post-"Expo": The Role of Public Art in Urban Regeneration Processes in the Late 20th Century

In 1998, the Lisbon Universal Exhibition-Expo'98-led to an urban regeneration process on Lisbon's waterfront. Following the example of other cities, this event was a pretext for rethinking and replacing a depressed area and for reconnecting it with the Tagus river through the creation of a set of new spaces for common use along the water. It was promoted as a public art program, which can be considered quite innovative in the Portuguese context. In view of this framework, this article aims to debate the relationships between public art and the dynamics of urban regeneration at the end of the 20th century. For that, it will analyse: (1) Expo'98's public art program, comparing its initial assumptions with the final results; and (2) the impact of this program, through the identification of the placement of public art before (1974-1998) and after (1999-2009) the event. Although most of the implemented works did not (intentionally) explore aspects of space integration nor issues of public space appropriation, Expo'98's public art program originated a monumentalisation of Lisbon's eastern riverfront, later extended to other waterfront areas. At the same time, it played an important role in the way of understanding the city and public space that decisively influenced subsequent policies and projects. It is concluded that public art had a significant role in urban processes in the late 20th century, which is quite evident in a discourse of urban art as space qualifier and as a means of economic and social development.
Traffic grooming and clique partitioning-based spectrum assignment in elastic optical networks

We adopt a fragmentation reducing policy for spectrum assignment and incorporate it with multicast traffic grooming in EON. To reduce fragmentation, the spectrum is partitioned based on the clique partitioning approach and spectral slots are assigned to traffic demands depending on which partition they belong. Simulation results predict that the proposed approach has better spectrum slot utilization compared to the state-of-the-art non-partitioning approach and the proposed approach reduces fragmentation, and also has less blocking ratio compared to the state-of-the-art partitioning approach.
Affine invariant features from the trace transform

The trace transform is a generalization of the Radon transform that allows one to construct image features that are invariant to a chosen group of image transformations. In this paper, we propose a methodology and appropriate functionals that can be computed from the image function and which can be used to calculate features invariant to the group of affine transforms. We demonstrate the usefulness of the constructed image descriptors in retrieving images from an image database and compare it with relevant state-of-the-art object retrieval methods.
A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasets

Most investigations into near-memory hardware accelerators for deep neural networks have primarily focused on inference, while the potential of accelerating training has received relatively little attention so far. Based on an in-depth analysis of the key computational patterns in state-of-the-art gradient-based training methods, we propose an efficient near-memory acceleration engine called NTX that can be used to train state-of-the-art deep convolutional neural networks at scale. Our main contributions are: (i) a loose coupling of RISC-V cores and NTX co-processors reducing offloading overhead by 7 x over previously published results; (ii) an optimized IEEE 754 compliant data path for fast high-precision convolutions and gradient propagation; (iii) evaluation of near-memory computing with NTX embedded into residual area on the Logic Base die of a Hybrid Memory Cube; and (iv) a scaling analysis to meshes of HMCs in a data center scenario. We demonstrate a 2.7 x energy efficiency improvement of NTX over contemporary GPUs at 4.4 x less silicon area, and a compute performance of 1.2 Tflop/s for training large state-of-the-art networks with full floating-point precision. At the data center scale, a mesh of NTX achieves above 95 percent parallel and energy efficiency, while providing 2.1 x energy savings or 3.1 x performance improvement over a GPU-based system.
MMA: a multi-view and multi-modality benchmark dataset for human action recognition

Human action recognition is an active research topic in both computer vision and machine learning communities, which has broad applications including surveillance, biometrics and human computer interaction. In the past decades, although some famous action datasets have been released, there still exist limitations, including the limited action categories and samples, camera views and variety of scenarios. Moreover, most of them are designed for a subset of the learning problems, such as single-view learning problem, cross-view learning problem and multi-task learning problem. In this paper, we introduce a multi-view, multi-modality benchmark dataset for human action recognition (abbreviated to MMA). MMA consists of 7080 action samples from 25 action categories, including 15 single-subject actions and 10 double-subject interactive actions in three views of two different scenarios. Further, we systematically benchmark the state-of-the-art approaches on MMA with respective to all three learning problems by different temporal-spatial feature representations. Experimental results demonstrate that MMA is challenging on all three learning problems due to significant intra-class variations, occlusion issues, views and scene variations, and multiple similar action categories. Meanwhile, we provide the baseline for the evaluation of existing state-of-the-art algorithms.
Risk assessment in livestock supply chain using the MCDM method: a case of emerging economy

The purpose of the study is to identify and assess the risks related to the livestock supply chain. The major risk related to the livestock supply chain are identified through the comprehensive literature review and finalized with the help of the expert's feedback. Initially, seventeen major livestock supply chain risks are finalized, and these risks are categorized into four major dimensions. Further, analytical hierarchical process (AHP) is used to prioritize these identified major risks based on their severity. Finally, sensitivity analysis is conducted to check the robustness of the risk priorities. The result shows that "input supply risk" is the most significant risk dimension followed by "production risk," "post-harvest risk," and "marketing & price risk." The finding also suggests that "poor quality and under supply of feed and fodder," "lack of proper waste disposal," and "absence of certification for the quality of animals" are the major risks among all seventeen risks. The highest priority risks are input supply risks which require the attention of the livestock supply chain partners. The proposed research framework is used to identify and analyze the livestock supply chain risks. The findings of this research might be beneficial for the farmers and other livestock supply chain stakeholders in developing policies/plans/strategies to control the risk in their livestock supply chain.
A joint loss function for deep face recognition

Convolutional neural networks (CNNs) have been widely used in computer vision community, and significantly improving the state-of-the-art. How to train an intra-class variant and inter-class discriminative feature is a central topic in face recognition. This paper proposes to learn an effective feature from face images by a joint loss function which combines the hard sample triplet (HST) and the absolute constraint triplet (ACT) loss, under the criteria that a maximum intra-class distance should be smaller than any inter-class distance. With the joint supervision of HST and ACT loss, CNNs is enable to learn discriminative features to improve face recognition performance. Experiments on labeled faces in the wild, IARPA Janus Benchmark (IJB-A) and YouTube Faces datasets achieve a comparable or superior performance to the state-of-the-arts.
Dimensioning V2N Services in 5G Networks Through Forecast-Based Scaling

With the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as remote driving, cooperative awareness, and hazard warning) will have to operate in an ever-changing and dynamic environment. Anticipating the dynamics of traffic flows on the roads is critical for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By predicting future events (such as traffic jams) and demands, vehicular services can take proactive actions to minimize Service Level Agreement (SLA) violations and reduce the risk of accidents. In this paper, we compare several techniques, including both traditional time-series and recent Machine Learning (ML)-based approaches, to forecast the traffic flow at different road segments in the city of Torino (Italy). Using the most accurate forecasting technique, we propose n-max algorithm as a forecast-based scaling algorithm for vertical scaling of edge resources, comparing its benefits against state-of-the-art solutions for three distinct Vehicle-to-Network (V2N) services. Results show that the proposed scaling algorithm outperforms the state-of-the-art, reducing Service Level Objective (SLO) violations for remote driving and hazard warning services.
Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system.