Miracle-dz/UniFAR-FTUs huggingface dataset:
Real Time Anomaly Detection Using Ensembles

Finding anomalous behavior of user in networks is crucial, in analysis of such behavior to identify the real user is very complicated. Classification is one technique for identifying the anomalous behavior. The anomaly detection rate can be improved by ensemble the different classifiers. Empirically, ensembles tend to yield better results when there is a significant diversity among the models. The available models all are on synthetic data. This paper analyzes the ensemble model to identify the anomaly in real time with improved accuracy.
A Comparison of Hybrid ARMA-Elman Models with Single Models for Forecasting Interest Rates

Since ANNs model could capture the nonlinearity of time series, it performances well on forecasting time series. Hybrid or combined ANNs with ARMA models are extensively studied and used in financial time series forecasting. But we doubt the necessity to build the hybrid models to forecast time series. Do hybrid models always outperform the single ANNs models? This paper is aimed to answer it. Two kinds of hybrid ARMA-Elman models are built, one with innovations as inputs, another with innovations and original data as inputs. Using the data of benchmark interest rates of China, the empirical results indicate that the hybrid models are superior to the single ARMA model, but perform closely to the Elman recurrent neural network model. The single Elman model is enough on forecasting. But if the correct rate on forecasting change directions is concerned, the hybrid models are preferred.
A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement

Risk assessment of congestive heart failure (CHF) is essential for detection, especially helping patients make informed decisions about medications, devices, transplantation, and end-of-life care. The majority of studies have focused on disease detection between CHF patients and normal subjects using short-/long-term heart rate variability (HRV) measures but not much on quantification. We downloaded 116 nominal 24-hour RR interval records from the MIT/BIH database, including 72 normal people and 44 CHF patients. These records were analyzed under a 4-level risk assessment model: no risk (normal people, N), mild risk (patients with New York Heart Association (NYHA) class I-II, P1), moderate risk (patients with NYHA III, P2), and severe risk (patients with NYHA III-IV, P3). A novel multistage classification approach is proposed for risk assessment and rating CHF using the non-equilibrium decision-tree–based support vector machine classifier. We propose dynamic indices of HRV to capture the dynamics of 5-minute short term HRV measurements for quantifying autonomic activity changes of CHF. We extracted 54 classical measures and 126 dynamic indices and selected from these using backward elimination to detect and quantify CHF patients. Experimental results show that the multistage risk assessment model can realize CHF detection and quantification analysis with total accuracy of 96.61%. The multistage model provides a powerful predictor between predicted and actual ratings, and it could serve as a clinically meaningful outcome providing an early assessment and a prognostic marker for CHF patients.
Pre-processing power traces with a phase-sensitive detector

As cryptographic implementations are increasingly subsumed as functional blocks within larger systems on chip, it becomes more difficult to identify the power consumption signatures of cryptographic operations amongst other unrelated processing activities. In addition, at higher clock frequencies, the current decay between successive processing rounds is only partial, making it more difficult to apply existing pattern matching techniques in side-channel analysis. We show however, through the use of a phase-sensitive detector, that power traces can be pre-processed to generate a filtered output which exhibits an enhanced round pattern, enabling the identification of locations on a device where encryption operations are occurring and also assisting with the re-alignment of power traces for side-channel attacks.
Extraction of Depth Elevation Model (DEM) from High Resolution Satellite Imagery Using Shape from Shading Approach

Shading information in satellite imagery will give user an immediate appreciation for the surface topography. In this paper, an attempt has been made to reconstruct 3D shape or Depth elevation model (DEM) of satellite imagery using Shape from Shading approach. Three widely used shape from shading algorithms – Pentland‟s Linear approach, Lee and Rosenfeld‟s approach and Horn‟s approach were used to recover shape of the satellite imagery and the result were compared. Each of this approaches were modified to get the better result.
Comparing evolutionary methods for reservoir computing pre-training

Evolutionary algorithms are very efficient at finding “optimal” solutions for a variety of problems because they do not impose many limitations encountered in traditional methods. Reservoir Computing is a type of recurrent neural network that allows for the black box modeling of (nonlinear) dynamic systems. In contrast to other recurrent neural network approaches, Reservoir Computing does not train the input and internal weights of the network; only the output layer is trained. However, it is necessary to adjust parameters and topology to create a “good” reservoir for a given application. This study compares three different evolutionary methods in order to find the best reservoir applied to the task of time series forecasting. The results obtained with the methods are compared regarding the performance (prediction error) and regarding the computational complexity (time). We used three sets to compare the methods' results. The results show that it is possible to find well-adjusted networks automatically and that the weights search, without restriction of the echo state property, allows for more adequate solutions to be found for the problem with a lower computational cost.
DNA repair pathways underlie a common genetic mechanism modulating onset in polyglutamine diseases

The polyglutamine diseases, including Huntington's disease (HD) and multiple spinocerebellar ataxias (SCAs), are among the commonest hereditary neurodegenerative diseases. They are caused by expanded CAG tracts, encoding glutamine, in different genes. Longer CAG repeat tracts are associated with earlier ages at onset, but this does not account for all of the difference, and the existence of additional genetic modifying factors has been suggested in these diseases. A recent genome‐wide association study (GWAS) in HD found association between age at onset and genetic variants in DNA repair pathways, and we therefore tested whether the modifying effects of variants in DNA repair genes have wider effects in the polyglutamine diseases.
Unperturbed vs. post-transplantation hematopoiesis: both in vivo but different

Purpose of reviewHematopoietic stem cell (HSC) transplantation has yielded tremendous information on experimental properties of HSCs. Yet, it remains unclear whether transplantation reflects the physiology of hematopoiesis. A limitation is the difficulty in accessing HSC functions without isolation, in-vitro manipulation and readout for potential. New genetic fate mapping and clonal marking techniques now shed light on hematopoiesis under physiological conditions. Recent findingsTransposon-based genetic marks were introduced across the entire hematopoietic system to follow the clonal dynamics of these tags over time. A polyclonal source downstream from stem cells was found responsible for the production of at least granulocytes. In independent experiments, HSCs were genetically marked in adult mice, and the kinetics of label emergence throughout the system was followed over time. These experiments uncovered that during physiological steady-state hematopoiesis large numbers of HSCs yield differentiated progeny. Individual HSCs were active only rarely, indicating their very slow periodicity of differentiation rather than quiescence. SummaryNoninvasive genetic experiments in mice have identified a major role of stem and progenitor cells downstream from HSCs as drivers of adult hematopoiesis, and revealed that post-transplantation hematopoiesis differs quantitatively from normal steady-state hematopoiesis.
Comparison of channel protocols for low latency , low energy communication over transmission lines

The eminence of communication costs over computation costs in current systems-on-chip (SoCs) has led to a change in the assumptions and methodology of the deep submicron design process. The emergence of non-traditional global signaling techniques such as optical interconnects, wireless communication, and transmission lines adds further complexity to the design exercise. There is a pressing need for a better understanding of communication overheads, especially in long links. Previous research has been performed that mathematically models various synchronous and asynchronous pipeline protocols for long diffusive wires. The work presented in this paper adapts these throughput, latency, and energy models for fast, low power communication over on-chip transmission line interconnects. Using common parameters and metrics to characterize the channel protocols enables analogous comparisons among them. First order models are created and populated with SPICE values to collate performance metrics for global channels. The paper then compares the behavior of transmission lines and diffusive wires under these signaling protocols. Finally, the effect of wire length on channel performance is studied.
Maintaining Prosperity In An Ageing Society : the OECD study on the policy implications of ageing THE CAPACITY FOR LONG-TERM DECISION MAKING IN SEVEN OECD COUNTRIES : THE CASE OF AGEING

Through a survey of seven OECD Member countries Australia, Canada, Finland, France, Ireland, Japan and the United States the paper identifies the many challenges facing governments when they deal with long-term issues, especially one of such a cross-cutting and complex nature as ageing. The difficulties which have to be solved include data collection, budget constraints, the coordination between a great number of central government agencies and local authorities to which public management reforms have given more power and autonomy, consensus-building and continued political support for policies involving short-term negative consequences for long-term benefits. The paper examines the institutions and practices that governments are presently using to design and implement coherent policies to respond to the these challenges, and identifies actions which could facilitate the process.
IPM Traction Machine With Single Layer Non-Overlapping Concentrated Windings

In this paper, an interior permanent-magnet traction drive machine with single layer non-overlapping concentrated stator winding is analyzed. In the single layer winding design, the number of stator slots and a winding factor are specifically considered. Moreover, the effect of varying the coil pitch on the winding factor and torque pulsations is investigated. A new calculation method is proposed, whereby the constant torque and field-weakening speed characteristics of the machine drive can be predicted using torque and flux linkage functions. The measured and predicted field-weakening performance of a 150-kW direct traction drive machine is presented.
Work-Related Flow Inventory: Evidence of Validity of the Brazilian Version

Abstract Flow in work is constituted by the positive experiences and mental state experienced during the day-to-day occupational activities. This study aims to adapt and assess the psychometric properties of the Brazilian Version of the Work Related Flow Inventory (WOLF). Participants were a nationwide sample of 640 professional (74% women), aged 19 to 73 years (M = 35.9, SD = 10.5). Confirmatory factor analyses (CFA) supported the oblique three-factor structure (absorption, work enjoyment and intrinsic work motivation) as being the most reliable to the data. Multigroup CFA achieved full measurement invariance for the gender and employment status (autonomous and non-autonomous). Flow dimensions were positively related to occupational self-efficacy and job satisfaction. WOLF presented adequate psychometric properties, suggesting its usefulness in evaluating flow at work in the Brazilian context.
Compositions and fluxes of particulate organic material in the Amazon River1

Lignin, elemental, and stable carbon isotope compositions are reported for local plants and for coarse (>63 µm) and fine (<63 µm) suspended particulate materials collected along a 1,950-km reach of the lower Amazon River during four contrasting stages of the 1982–1983 hydrograph. Fluxes of chemically recognizable lignin in the two size classes generally parallel each other along the mainstem with the fine fraction usually predominating. Particulate organic matter transported in the coarse size fraction of the mainstem and its major tributaries is composed of recently formed and well preserved tree leaf debris along with some wood. Organic matter in the fine size fraction is comparatively old, degraded, and rich in immobilized nitrogen and derives primarily from soils. C-4 grasses, which are abundant in the mainstem floodplain (varzea), are not major components of either the coarse or fine particulate material in the river. Particulate organic matter in both size fractions is introduced largely from upstream sources within the Rio Solimoes and Rio Madeira drainage basins. Most of this organic matter is unreactive and is transported conservatively with mineral particles along the Amazon mainstem. However, some downstream compositional trends are seen in both size fractions which reflect the addition or exchange of highly degraded, 13C-depleted, and lignin-poor organic materials from lower basin sources.
Energy efficient algorithm with considering port-cost in hybrid hierarchical optical networks

In recent years, as the increasing effect of greenhouse and extremely consumption of worldwide energy, port-cost and energy-consumption savings are becoming a dual of new objectives of further developing an optical network. Using hybrid technology to replace Optical-Electrical-Optical (OEO) fabric with an all optical (OOO) one to save more consumed energy and using hierarchical technology to aggregate several wavelengths into wavebands to perform port-cost savings is an attractive solution for next-generation optical networks. In this paper, we jointly consider port-cost and energy-consumption in hybrid hierarchical optical network and develop efficient approaches ranging from Integer Linear Programming (ILP) models to heuristics. Simulation results of two heuristic approaches show that, the cost of Transmitting Ports (TPs) used in OOO switch is contrary to the energy consumed by OEO part of hybrid hierarchical Optical Cross Connect (OXC) node, and it is necessary to make a new design to obtain satisfactory trade-off performance.
Fast Motion Prediction for Collaborative Robotics

The efficient and safe performance of collaborative robots requires advancements in perception, control, design and algorithms, among other factors. With regard to algorithms, representing the structure of collaborative tasks and reasoning about progress toward task completion in an on-line fashion enables a robot to be a fluent and safe collaborator based on its ability to predict the next actions of a human agent. With this goal in mind, we focus on real-time target prediction of human reaching motion and present an algorithm based on time series classification. Results from on-line testing involving a tabletop task with a PR2 robot yielded 70% prediction accuracy with 400msec of observed trajectory.
Joint motion estimation and layer segmentation in transparent image sequences - Application to transparent-motion-compensated noise reduction in X-ray image sequences

This paper is concerned with the estimation of the motions and the segmentation of the spatial supports of the different layers involved in transparent X-ray image sequences. Classical motion estimation methods fail on sequences involving transparent effects since they do not explicitly model this phenomenon. We propose a method that comprises three main steps: initial block-matching for two-layer transparent motion estimation, motion clustering with 3D Hough transform, and joint transparent layer segmentation and parametric motion estimation. It is validated on synthetic and real clinical X-ray image sequences. Secondly, we derive an original transparent motion compensation method compatible with any spatio-time filtering technique. A direct transparent motion compensation method is proposed. To overcome its limitations, a novel hybrid filter is introduced which locally selects which type of motion compensation is to be carried out for optimal denoising. Convincing experiments on synthetic and real clinical images are also reported.
Dynamic resource management using virtual machine migrations

Virtualization is a key concept in enabling the "computing-as-a-service" vision of cloud-based solutions. Virtual machine related features such as flexible resource provisioning, and isolation and migration of machine state have improved efficiency of resource usage and dynamic resource provisioning capabilities. Live virtual machine migration transfers "state" of a virtual machine from one physical machine to another, and can mitigate overload conditions and enables uninterrupted maintenance activities. The focus of this article is to present the details of virtual machine migration techniques and their usage toward dynamic resource management in virtualized environments. We outline the components required to use virtual machine migration for dynamic resource management in the virtualized cloud environment. We present categorization and details of migration heuristics aimed at reducing server sprawl, minimizing power consumption, balancing load across physical machines, and so on. We conclude with a discussion of open research problems in the area.
Policy-based Data Integration for e-Health Monitoring Processes in a B2B Environment: Experiences from Canada

eHealth processes are data-focused, event-driven, and dynamic. They are systematically monitored for compliance with legislation, organizational guidelines and quality of care protocols. Community care, especially at home care, frequently requires the cooperation and integration of care processes across several providers and organizations. Service Oriented Architecture (SOA) through Web services and business process automation through Business Process Execution Language (BPEL) is emerging as a framework for business process integration over the Internet, but does not address all information management requirements of eHealth monitoring processes particularly with respect to policy compliance and event-based data integration. In this paper, we extend the traditional SOA framework to define a flexible policy-based approach for defining and monitoring streaming event data based on a general publish/subscribe model in a business-to-business (B2B) healthcare network. The work described here is design-oriented research where the purpose is to show the utility of the proposed framework. The approach is evaluated based on information management requirements drawn from a case study of palliative care and a prototype implementation.
Kinetic Modeling of Ethanol Batch Fermentation by Escherichia Coli FBWHR Using Hot-Water Sugar Maple Wood Extract Hydrolyzate as Substrate

A recombinant strain of Escherichia coli FBWHR was used for ethanol fermentation from hot-water sugar maple wood extract hydrolyzate in batch experiments. Kinetic studies of cell growth, sugar utilization and ethanol production were investigated at different initial total sugar concentrations of wood extract hydrolyzate. The highest ethanol concentration of 24.05 g/L was obtained using an initial total sugar concentration of 70.30 g/L. Unstructured models were developed to describe cell growth, sugar utilization and ethanol production and validated by comparing the predictions of model and experimental data. The results from this study could be expected to provide insights into the process performance, optimize the process and aid in the design of processes for large-scale production of ethanol fermentation from woody biomass.
When Open Source Meets Network Control Planes

Software-defined networking opens up new possibilities for architectures based on open source components, promising improved orchestration and agility, lower operational costs, and--most important--a wave of innovation. The Web extra at http://youtu.be/pdG2btcyyK8 is a video in which authors Christian Esteve Rothenberg, Roy Chua, and Thomas Nadeau present a slideshow and discuss how software-defined networking opens up new possibilities for architectures based on open-source components, promising improved orchestration and agility, lower operational costs, and a new wave of innovation.
DNK-WSD: A Distributed Approach for Knowledge Discovery in Peer to Peer Networks

Service oriented architectures (SOA) represent a recent proposal to obtain cooperation and interoperability in distributed computing environments. A challenge of SOA is services and resources discovery which can be realized both in centralized and distributed way. The distributed approach involves peer-to-peer (P2P) infrastructures supporting the knowledge distribution among peers. In this paper we propose DNK-WSD, a new network architecture providing an efficient distributed Web service and resource discovery mechanism based on the notification pattern. The DNK network is a peer-to-peer architecture built using the distributed network knowledge (DNK) heuristic which represents a lightweight approach to model graphs mapping logical P2P overlay networks. A case study, regarding the deployment of DNK-WSD for information sharing in real time collaborative Web applications, demonstrates the validity of DNK-WSD in terms of fast propagation of the shared data, low network load and reliability
Design of personalized recommendation system based on LBS in mobile classroom project

Collaborative filtering is one of the most successful approaches to building recommendation system. However, it still has some known disadvantages. One of them is called cold start problem caused by lack of user's historical data. Another problem appeared because mobile technology develops in high speed. The phenomenon that users change their taste according to their position quickly in mobile environment is more and more common. The changeable preference is called short-term interest. But the collaborative filtering algorithm usually ignores this short-term interest. It leads to decrease of accuracy of recommendation. In this paper, a design of personalized recommendation system based on LBS for the Mobile Classroom Project is proposed. Considering the actual conditions, this system solved the problem of cold-start using clustering method and some other solutions. Also, it focus on user's short-term interest to a certain extent. Compared with the traditional collaborative filtering, it works better.