Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved SVD algorithm based on Slope One

Singular Value Decomposition (SVD) algorithm is a good method in Collaborative Filtering (CF) algorithm. But the traditional SVD method faces the matrix sparse problem, people solve this problem by Latent Factor Model (LMF). The data imbalances problem also troubles us. The algorithm of this paper combines Slope One and SVD algorithm to solve the data imbalances problem. First, we use Slope One algorithm to deal with the users who have few rating records. Then this kind of user will have more rating records. And we can use new data set to train the SVD model to predict the final results. The experiment results show that our algorithm is better than the traditional Slope One and SVD algorithm.
An adaptive data placement strategy in scientific workflows over cloud computing environments

Data of scientific workflow applications are tend to be distributed over many data centers to be effectively stored, retrieved, and transferred among them. The result of an experiment with those data shows diverse execution performance depending on the placement of input and intermediate data which are generated during application execution. However, initial data placement strategy would not be the best plan for long running experiments because of the dynamic change of resource condition time to time. We propose an adaptive data placement strategy considering dynamic resource change for efficient data-intensive applications. The strategy consists of two stages that group the datasets in data centers during the build- time stage and dynamically clusters every time newly generated datasets repeatedly to the most appropriate data centers during runtime stage, which is based on task dependency, intense degree of data usage, and just-in-time resource availability. Just-in-time data placement coming with task execution is more efficient than the one with initialization stage of experiments in the aspect of resource utilization. Experiments show that data movement can be effectively reduced while the workflow is running
Fuzzy sliding mode projective synchronization of HX-type hyperchaotic Liu system with disturbances

In this paper, we first give the HX-type hyperchaotic Liu system based on fuzzy inference modelling. Then for the switching system with disturbaces, we theoretically investigate the projective synchronization by using active sliding mode control. For weakening chattering phenomenon, a fuzzy system is introduced. Numerical simulations are provided to verify the effectiveness of the control methods. By comparison of the numerical results, the fuzzy sliding mode control can weaken chattering effectively.
Representing a Partially Observed Non-Rigid 3D Human Using Eigen-Texture and Eigen-Deformation

Reconstruction of the shape and motion of humans from RGB-D is a challenging problem, receiving much attention in recent years. Recent approaches for full-body reconstruction use a statistic shape model, which is built upon accurate full-body scans of people in skin-tight clothes, to complete invisible parts due to occlusion. Such a statistic model may still be fit to an RGB-D measurement with loose clothes but cannot describe its deformations, such as clothing wrinkles. Observed surfaces may be reconstructed precisely from actual measurements, while we have no cues for unobserved surfaces. For full-body reconstruction with loose clothes, we propose to use lower dimensional embeddings of texture and deformation referred to as eigen-texturing and eigen-deformation, to reproduce views of even unobserved surfaces. Provided a full-body reconstruction from a sequence of partial measurements as 3D meshes, the texture and deformation of each triangle are then embedded using eigen-decomposition. Combined with neural-network-based coefficient regression, our method synthesizes the texture and deformation from arbitrary viewpoints. We evaluate our method using simulated data and visually demonstrate how our method works on real data.
Thrombin and Plasmin Alter the Proteome of Neutrophil Extracellular Traps

Neutrophil extracellular traps (NETs) consist of a decondensed DNA scaffold decorated with neutrophil-derived proteins. The proteome of NETs, or “NETome,” has been largely elucidated in vitro. However, components such as plasma and extracellular matrix proteins may affect the NETome under physiological conditions. Here, using a reductionistic approach, we explored the effects of two proteases active during injury and wounding, human thrombin and plasmin, on the NETome. Using high-resolution mass spectrometry, we identified a total of 164 proteins, including those previously not described in NETs. The serine proteases, particularly thrombin, were also found to interact with DNA and bound to NETs in vitro. Among the most abundant proteins were those identified previously, including histones, neutrophil elastase, and antimicrobial proteins. We observed reduced histone (H2B, H3, and H4) and neutrophil elastase levels upon the addition of the two proteases. Analyses of NET-derived tryptic peptides identified subtle changes upon protease treatments. Our results provide evidence that exogenous proteases, present during wounding and inflammation, influence the NETome. Taken together, regulation of NETs and their proteins under different physiological conditions may affect their roles in infection, inflammation, and the host response.
The impact of LOS component on information disclosed to eavesdroppers in wireless channels with PHY-based secret key generation

Extracting information-theoretically secret keys from random fluctuations of wireless channels is a promising technology for protecting data in future radio systems like IoT, SCADA and 5G. However, the presence of Line-of-Sight (LoS) component can drastically degrade the key rate between legitimate parties. Moreover, knowledge of antenna location brings additional information to eavesdroppers about the radio channel. In the following paper we quantify this information and try to evaluate how it affects secret key capacity. Finally, we postulate randomizing the LOS channel, which will increase eavesdropper's equivocation.
Nonlinear disturbance observer-based sliding backstepping hovering control of a quadrotor

In this paper, the nonlinear observer based hovering control is addressed for a quadrotor with system uncertainties and external disturbances. Main contributions are as follows: 1) A nonlinear observer for complex unknowns including unmodelled dynamics and external disturbances is designed for a quadrotor, and thereby contributing to more robustness pertaining to the closed-loop system; 2) The sliding backstepping based technique is then employed to realize hovering control of a quadrotor, and thereby leading to the proposed controller which achieves satisfactory control performance; 3) Exhaustive analyses ensure that tracking errors of the overall system can converge to a small neighborhood of the origin. Furthermore, a large number of simulation experiments show the effectiveness of the proposed control law.
Preliminary data processing by means of onboard computer systems of spacecraft

The article is devoted to the solution of scientific problems of developing stand-alone storage technologies as well as data processing methods oriented to the usage of small spacecraft in onboard computer systems while solving the tasks of aggregating information from technical vision system sensors, machine learning and intellectual data processing. The relevance of the project is caused by the necessity to solve the tasks requiring large amount of calculations on board, in the conditions of limited computing resources concerning memory volume and performance. The work solves the task of developing and unifying effective built-in onboard control systems and database control systems with low requirements to computer power on board the small spacecraft. Scientific significance of such problem solution is to receive new efficient technologies of control onboard systems design on the basis of open source operating systems and software. The implementation of the project will allow receiving scientific and technological results as well as creating basic technologies for innovation development of local and international markets of goods and services in the sphere of intellectual built-in systems.
Optimum control of one-dimensional structures with longitudinal periodic excitation

The damping of an one-dimensional structure's oscillations under the action of variable periodic excitation, which arises during the safety (emergency) braking of the mine carrier cable, is considered. The speed-in-action problem of an object's control described by the Mathieu equation is solved under conditions of limited longitudinal control action. A numerical method for calculating the switching times of piecewise constant control under conditions of a stable mode of the structure's oscillations is proposed. Numerical examples of damping of the oscillations of a skip hoist with different number of control stages and a different direction of the control action are presented.
Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning

Learning a generative model is a key component of model-based reinforcement learning. Though learning a good model in the tabular setting is a simple task, learning a useful model in the approximate setting is challenging. Recently Farahmand et al. (2017) proposed a value-aware (VAML) objective that captures the structure of value function during model learning. Using tools from Lipschitz continuity, we show that minimizing the VAML objective is in fact equivalent to minimizing the Wasserstein metric.
Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences

The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge. We propose a multi-armed bandit framework for matching incentives to users, whose preferences are unknown a priori and evolving dynamically in time, in a resource constrained environment. We design an algorithm that combines ideas from three distinct domains: (i) a greedy matching paradigm, (ii) the upper confidence bound algorithm (UCB) for bandits, and (iii) mixing times from the theory of Markov chains. For this algorithm, we provide theoretical bounds on the regret and demonstrate its performance via both synthetic and realistic (matching supply and demand in a bike-sharing platform) examples.
Car detection in images taken from unmanned aerial vehicles

In recent years, unmanned aerial vehicles have become a popular research platform with many application areas such as military, civil, commercial and recreational areas, thanks to their high maneuverability, vertical take-off / landing, and outdoor and indoor use. Today, small, light, and very high powerful embedding systems have been developed. Therefore, many real-time computer vision applications can be run on unmanned aerial vehicle platforms by integrating such embedding systems onto these vehicles. In this work, the problem of car detection (localization) in images taken from unmanned aerial vehicles has been studied. To this end, we collected a new aerial image dataset by using quadcopters and different type of cameras. To solve the car detection problem, the results were compared by using both the Polyhedral Conic Classifier and the You Only Look Once (YOLO) algorithm which is considered one of the fastest deep neural network methods in the literature.
Auto-Context R-CNN

Region-based convolutional neural networks (R-CNN)~\cite{fast_rcnn,faster_rcnn,mask_rcnn} have largely dominated object detection. Operators defined on RoIs (Region of Interests) play an important role in R-CNNs such as RoIPooling~\cite{fast_rcnn} and RoIAlign~\cite{mask_rcnn}. They all only utilize information inside RoIs for RoI prediction, even with their recent deformable extensions~\cite{deformable_cnn}. Although surrounding context is well-known for its importance in object detection, it has yet been integrated in R-CNNs in a flexible and effective way. Inspired by the auto-context work~\cite{auto_context} and the multi-class object layout work~\cite{nms_context}, this paper presents a generic context-mining RoI operator (i.e., \textit{RoICtxMining}) seamlessly integrated in R-CNNs, and the resulting object detection system is termed \textbf{Auto-Context R-CNN} which is trained end-to-end. The proposed RoICtxMining operator is a simple yet effective two-layer extension of the RoIPooling or RoIAlign operator. Centered at an object-RoI, it creates a $3\times 3$ layout to mine contextual information adaptively in the $8$ surrounding context regions on-the-fly. Within each of the $8$ context regions, a context-RoI is mined in term of discriminative power and its RoIPooling / RoIAlign features are concatenated with the object-RoI for final prediction. \textit{The proposed Auto-Context R-CNN is robust to occlusion and small objects, and shows promising vulnerability for adversarial attacks without being adversarially-trained. } In experiments, it is evaluated using RoIPooling as the backbone and shows competitive results on Pascal VOC, Microsoft COCO, and KITTI datasets (including $6.9\%$ mAP improvements over the R-FCN~\cite{rfcn} method on COCO \textit{test-dev} dataset and the first place on both KITTI pedestrian and cyclist detection as of this submission).
Deriving use cases from BPMN models: A proposal with computational support

An important step to software developing is the system functional requirements elicitation. Business process models can be adopted as source to extraction of functional requirements during the information system modeling. Commonly, functional requirements are documented in use case diagrams or textual use cases. This paper provides an approach to derive use case specification from BPMN models. This specification is described as UML Use Case diagram and textual natural language. This approach is supported by a tool - BP2UC (Business Process to Use Cases) - which performs the guidelines described in the approach. The proposed approach and tool showed feasibility since they provided useful results for the next steps of the software process.
Efficient heuristic algorithms for positive-influence dominating set in social networks

With the ongoing growth of social networks, individuals are obtaining a platform from which they can spread their opinions and ideas without restrictions and without noticing we are allowing those opinions to influence our sociological, political, and personal opinions. It is now much easier to broadcast an idea that has a potential to widely spread positive or negative influence across a social network. In an attempt to address influence in this context, the notion of a “positive influence dominating set” appeared recently: a positive influence dominating set in a graph, or network, is a set of nodes D such that every other node has a sufficient percentage of neighbors in D. The corresponding optimization problem seeks a positive influence dominating set of minimum size. In this paper we present and study efficient heuristic algorithms for the problem and show how different types of social networks/graphs require different heuristic methods to effectively extract positive influence dominating sets.
Genomic Comparison of Highly Virulent, Moderately Virulent, and Avirulent Strains From a Genetically Closely-Related MRSA ST239 Sub-lineage Provides Insights Into Pathogenesis

The genomic comparison of virulent (TW20), moderately virulent (CMRSA6/CMRSA3), and avirulent (M92) strains from a genetically closely-related MRSA ST239 sub-lineage revealed striking similarities in their genomes and antibiotic resistance profiles, despite differences in virulence and pathogenicity. The main differences were in the spa gene (coding for staphylococcal protein A), lpl genes (coding for lipoprotein-like membrane proteins), cta genes (genes involved in heme synthesis), and the dfrG gene (coding for a trimethoprim-resistant dihydrofolate reductase), as well as variations in the presence or content of some prophages and plasmids, which could explain the virulence differences of these strains. TW20 was positive for all genetic traits tested, compared to CMRSA6, CMRSA3, and M92. The major components differing among these strains included spa and lpl with TW20 carrying both whereas CMRSA6/CMRSA3 carry spa identical to TW20 but have a disrupted lpl. M92 is devoid of both these traits. Considering the role played by these components in innate immunity and virulence, it is predicted that since TW20 has both the components intact and functional, these traits contribute to its pathogenesis. However, CMRSA6/CMRSA3 are missing one of these components, hence their intermediately virulent nature. On the contrary, M92 is completely devoid of both the spa and lpl genes and is avirulent. Mobile genetic elements play a potential role in virulence. TW20 carries three prophages (ϕSa6, ϕSa3, and ϕSPβ-like), a pathogenicity island and two plasmids. CMRSA6, CMRSA3, and M92 contain variations in one or more of these components. The virulence associated genes in these components include staphylokinase, entertoxins, antibiotic/antiseptic/heavy metal resistance and bacterial persistence. Additionally, there are many hypothetical proteins (present with variations among strains) with unknown function in these mobile elements which could be making an important contribution in the virulence of these strains. The above mentioned repertoire of virulence components in TW20 likely contributes to its increased virulence, while the absence and/or modification of one or more of these components in CMRSA6/CMRSA3 and M92 likely affects the virulence of the strains.
Targeting Polo-like kinase 1 and TRAIL enhances apoptosis in non-small cell lung cancer

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) can selectively induce apoptosis in cancer cells without causing damage to normal cells. However, some tumors are resistant to TRAIL monotherapy and clinical studies assessing targeted agents towards the TRAIL receptor have failed to show robust therapeutic activity. Evidence has shown that standard anti-mitotic drugs can induce synergistic apoptosis upon combination with TRAIL via cell cycle arrest. Polo like kinase-1 (PLK1) plays a critical role in different stages of cell cycle progression and mitosis. A number of investigations have demonstrated that PLK1 inhibition causes cell cycle arrest and mitotic catastrophe in non-small cell lung cancer (NSCLC), and we thus postulated that PLK1 inhibition could enhance TRAIL-induced apoptosis. We demonstrate that the combination of a TRAIL receptor agonist and a PLK1 inhibitor synergistically reduces cell viability, and strongly increases apoptosis in NSCLC cellular models. Consistent with our in vitro observations, this drug combination also significantly reduces tumor growth in vivo. Our data additionally reveal that G2/M cell cycle arrest and downregulation of Mcl-1 and signal transducer and activator of transcription 3 (STAT3) activity following PLK1 inhibition may contribute to the sensitization of TRAIL-induced apoptosis in NSCLC. Together, these data support the further exploration of combined TRAIL and PLK1 inhibition in the treatment of NSCLC.
Retinal blood vessels segmentation based on multi-classifier fusion

In this paper, we propose a supervised learning method based on multiple features and multi-classifier fusion to segment retinal blood vessels. We treat vessel segmentation as a pixel-wise classification problem, so feature extraction is a key step. Four types features, including contrast enhanced intensity, B-COSFIRE filter response, line strength and fused gray-voting result with the 2D-Gabor filter result, are extracted. For classifier decision, we fuse results of decision tree and AdaBoost algorithm classifier, because we find that both classifiers get different even complementary results in experimental process. Finally, the used method is evaluated in public DRIVE database, our approach achieves average classification accuracy of 95.91% and sensitivity is high 82.15 %.
Approximate implementation of FIR filters on FPGA

In signal processing applications, accuracy of the results can be traded-off for reducing area and power consumption. In this work, finite impulse response filters are designed approximately on FPGA. A frequency response error, which can be adjusted depending on the application, is defined to determine approximation level. Coefficient sensitivity is numerically analysed for this error. With the result obtained from this analysis, a multiple constant multiplication optimization method is executed, with the aim of reducing transposed filter structure areas. The proposed sensitivity analysis and the modified multiple constant multiplication optimization are used in collaboration, on lowpass and bandpass filter realizations. When filter characteristic error tolerances of 0.01 and 0.001 are introduced, an average area reduction of 534% and 3–28% are obtained, respectively.
On the use of genetic algorithms for synthesis of signal temporal logic formulas

In this work, we studied use of genetic algorithms to synthesize Signal Temporal Logic formulas from a labeled data set. In the synthesis problem, the goal is to generate the best STL formula representing the labeled signals in the set. To use genetic algorithms in this domain, the basic genetic algorithm processes are defined for STL formulas. The developed synthesis method produces a formula from the data set efficiently in a fully automated way. In particular, the proposed approach does not require an expert to write a template formulas. The findings of the paper are illustrated on a sample data set.
Modified El Gamal algorithm for multiple senders and single receiver encryption

This paper presents a modification of the ElGamal Encryption Algorithm which allows multiple senders to encrypt a plaintext message using multiple private keys intended for a single recipient for decryption. This provides a solution to situations requiring an extra level of security through multiple monitoring and multiple controls. The additional private keys used by the process also improve the complexity of the algorithm which limits the chance of breaking the algorithm using brute force or systematic attack.
FPGA implementation of symbol timing synchronization for OFDM systems

In this work, after some of the commonly used symbol timing synchronization algorithms used in orthogonal frequency division multiplexing systems are examined, a two-step (coarse and fine) symbol time offset (STO) correction algorithm based on Schmidl & Cox [1] approach is investigated in Simulink model. Subsequently, relying on this Simulink model, the STO algorithm is implemented on ZYNQ ZC706 XC7Z045 FPGA working fully parallel at a clock rate of 200 MHz. The implementation results are compared with the other related studies in the literature.
O C ] 9 J ul 2 01 8 On Sparse Reflexive Generalized Inverses

We study sparse generalized inverses H of a rank-r real matrix A. We give a “block construction” for reflexive generalized inverses having at most r nonzeros. When r = 1, we demonstrate how minimizing the (vector) 1-norm of H among generalized inverses can be achieved by a particular reflexive generalized inverse following our block construction. When r = 2 and A is equivalent to a nonnegative matrix by signing rows and columns, we again demonstrate how minimizing the (vector) 1-norm of H among generalized inverses can be achieved by a particular reflexive generalized inverse following our block construction. Finally, for general r, we demonstrate how to efficiently find a reflexive generalized inverse following our block construction that is within approximately a factor of r of the (vector) 1-norm of the generalized inverse having minimum (vector) 1-norm.