A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis

Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users have used for the same items, 2) items to users, based on tags they have in common with other similar users, and 3) users with common social interest, based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. These data are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the Kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.     

Economic model predictive control of nonlinear singularly perturbed systems

We focus on the development of a Lyapunov-based economic model predictive control (LEMPC) method for nonlinear singularly perturbed systems in standard form arising naturally in the modeling of two-time-scale chemical processes. A composite control structure is proposed in which, a “fast” Lyapunov-based model predictive controller (LMPC) using a quadratic cost function which penalizes the deviation of the fast states from their equilibrium slow manifold and the corresponding manipulated inputs, is used to stabilize the fast dynamics while a two-mode “slow” LEMPC design is used on the slow subsystem that addresses economic considerations as well as desired closed-loop stability properties by utilizing an economic (typically non-quadratic) cost function in its formulation and possibly dictating a time-varying process operation. Through a multirate measurement sampling scheme, fast sampling of the fast state variables is used in the fast LMPC while slow-sampling of the slow state variables is used in the slow LEMPC. Appropriate stabilizability assumptions are made and suitable constraints are imposed on the proposed control scheme to guarantee the closed-loop stability and singular perturbation theory is used to analyze the closed-loop system. The proposed control method is demonstrated through a nonlinear chemical process example.     

A biomimetic approach to inverse kinematics for a redundant robot arm

Redundant robots have received increased attention during the last decades, since they provide solutions to problems investigated for years in the robotic community, e.g. task-space tracking, obstacle avoidance etc. However, robot redundancy may arise problems of kinematic control, since robot joint motion is not uniquely determined. In this paper, a biomimetic approach is proposed for solving the problem of redundancy resolution. First, the kinematics of the human upper limb while performing random arm motion are investigated and modeled. The dependencies among the human joint angles are described using a Bayesian network. Then, an objective function, built using this model, is used in a closed-loop inverse kinematic algorithm for a redundant robot arm. Using this algorithm, the robot arm end-effector can be positioned in the three dimensional (3D) space using human-like joint configurations. Through real experiments using an anthropomorphic robot arm, it is proved that the proposed algorithm is computationally fast, while it results to human-like configurations compared to previously proposed inverse kinematics algorithms. The latter makes the proposed algorithm a strong candidate for applications where anthropomorphism is required, e.g. in humanoids or generally in cases where robotic arms interact with humans.     

Trend forecasting based on Singular Spectrum Analysis of traffic workload in a large-scale wireless LAN

Network traffic load in an IEEE802.11 infrastructure arises from the superposition of traffic accessed by wireless clients associated with access points (APs). An accurate load characterization can be beneficial in modeling network traffic and addressing a variety of problems including coverage planning, resource reservation and network monitoring for anomaly detection. This study focuses on the statistical analysis of the traffic load measured in a campus-wide IEEE802.11 infrastructure at each AP.Using the Singular Spectrum Analysis approach, we found that the time-series of traffic load at a given AP has a small intrinsic dimension. In particular, these time-series can be accurately modeled using a small number of leading (principal) components. This proved to be critical for understanding the main features of the components forming the network traffic.Statistical analysis of leading components has demonstrated that even a few first components form the main part of the information. The residual components capture the small irregular variations, which do not fit in the basic part of the network traffic and can be interpreted as a stochastic noise. Based on these properties, we also studied contributions of the various components to the overall structure of the traffic load of an AP and its variation over time.Finally, we designed and evaluated the performance of a traffic predictor for the trend component, obtained by projecting the original time-series on the set of leading components.     

Robust detection of intermittent multiplicative sensor fault

In this paper, the detection problem of intermittent multiplicative sensor fault is investigated for stochastic uncertain systems. A robust optimal filter is designed according to the criterion of minimum estimation error covariance. Then, based on this, a residual generator is constructed, and the quantitative effect of the fault on it is discussed in detail. After that we design the evaluation function and detection threshold to achieve intermittent fault detection. Our proposed strategy has a recursive form and only includes simple arithmetic operations, thus it is suitable for real‐time online applications. Finally, a simulation example is given to demonstrate the effectiveness of the proposed strategy.     

Evaluation of atmospheric correction to airborne hyperspectral data relying on radiative transfer concepts

Physically based atmospheric correction is one of the most important but also perilous radiometric corrections in remote-sensing imagery. The main objective of this work was to evaluate the efficiency of atmospheric correction algorithms, based not only on quantification of the changes induced but also on a step-by-step interpretation of the results, in association with the radiative transfer (RT) processes that occur in the atmosphere and on Earth. A four-level atmospheric correction scheme was applied to airborne hyperspectral visible/near-infrared (VNIR) imagery and the performance was evaluated. Each atmospheric correction level was more numerous than the previous following adjunctive assessment of the following parameters: (1) atmospheric influence, (2) the adjacency effect, (3) cast shadows, and (4) effects induced by the Earth’s surface reflectance anisotropy. Performance assessment showed that, even though a more complex atmospheric correction scheme resembles in greater detail the conditions under which the image acquisition was carried out, it is more sensitive to restrictions that arise from either the sensor’s characteristics or the algorithms and data used. Moreover, it was shown that evaluating atmospheric correction results using criteria based on RT concepts can considerably assist in the evaluation process.     

Non-linear feedback control of parabolic partial differential difference equation systems

This paper proposes a general method for the synthesis of non-linear output feedback controllers for single-input singleoutput quasi-linear parabolic partial differential difference equation (PDDE) systems, for which the eigenspectrum of the spatial differential operator can be partitioned into a finite-dimensional slow one and an infinite-dimensional stable fast complement. Initially, a non-linear model reduction scheme which is based on combination of Galerkin's method with the concept of approximate inertial manifold is employed for the derivation of differential difference equation (DDE) systems that describe the dominant dynamics of the PDDE system. Then, these DDE systems are used as the basis for the explicit construction of non-linear output feedback controllers through combination of geometric and Lyapunov techniques. The controllers guarantee stability and enforce output tracking in the closed-loop parabolic PDDE system independently of the size of the state delay, provided that the separation of the slow and fast eigenvalues of the spatial differential operator is sufficiently large and an appropriate matrix is positive definite. The methodology is successfully employed to stabilize the temperature profile of a tubular reactor with recycle at a spatially non-uniform unstable steadystate.     

Pregnancy-Related Extracellular Vesicles Revisited

Extracellular vesicles (EVs) are small vesicles ranging from 20–200 nm to 10 μm in diameter that are discharged and taken in by many different types of cells. Depending on the nature and quantity of their content—which generally includes proteins, lipids as well as microRNAs (miRNAs), messenger-RNA (mRNA), and DNA—these particles can bring about functional modifications in the receiving cells. During pregnancy, placenta and/or fetal-derived EVs have recently been isolated, eliciting interest in discovering their clinical significance. To date, various studies have associated variations in the circulating levels of maternal and fetal EVs and their contents, with complications including gestational diabetes and preeclampsia, ultimately leading to adverse pregnancy outcomes. Furthermore, EVs have also been identified as messengers and important players in viral infections during pregnancy, as well as in various congenital malformations. Their presence can be detected in the maternal blood from the first trimester and their level increases towards term, thus acting as liquid biopsies that give invaluable insight into the status of the feto-placental unit. However, their exact roles in the metabolic and vascular adaptations associated with physiological and pathological pregnancy is still under investigation. Analyzing peer-reviewed journal articles available in online databases, the purpose of this review is to synthesize current knowledge regarding the utility of quantification of pregnancy related EVs in general and placental EVs in particular as non-invasive evidence of placental dysfunction and adverse pregnancy outcomes, and to develop the current understanding of these particles and their applicability in clinical practice.     

Direct alcohol fuel cells: A novel non-platinum and alcohol inert ORR electrocatalyst

In the present investigation, a series of binary Pd_xFe_y/C electrocatalysts for the oxygen reduction reaction (ORR) was synthesized in a very short time by employing a pulse-microwave assisted polyol method. The physico-chemical properties were obtained by employing the techniques of X-ray diffraction (XRD) and transmission electron microscopy (TEM). The electrochemical activity was studied by the aid of the techniques of cyclic voltammetry and rotating disk electrode. Based on the obtained experimental results, it can be deduced that the addition of Fe increases the activity of Pd towards the reaction of oxygen reduction. More precisely, when the molar ratio between Pd and Fe was 3:1, the highest oxygen reduction activity among all the investigated Pd_xFe_y/C catalysts was obtained. It was also found that Pd₃Fe₁/C exhibited an oxygen reduction activity comparable to that of Pt/C, while showed almost no activity for alcohols oxidation. Moreover, in the simultaneous presence of both methanol and oxygen, Pd₃Fe₁/C displayed an excellent selectivity for oxygen reduction reaction.