Miracle-dz/UniFAR-FTUs huggingface dataset:
Triple Play Service Simulation and Packet Scheduling Performance Evaluation

Triple play technology is nowadays considered as an indisputable trend. The differentiated service (DiffServ) architecture is preferred over "hard quality of service" (QoS) integrated service (IntServ) architecture. Moreover it applies perfectly to triple play, as it satisfies differing QoS requirements. In addition, latest developments in the area of video and audio encoding impose new challenges in content delivery systems. The combination of these three technologies is expected to raise additional QoS related issues. Problems such as full exploitation of available bandwidth and providing adequate QoS to subscribed users by meeting the requirements of all three supported services (video, voice and data) must be addressed. Within this context, the particular paper examines the performance of common packet scheduling techniques used in a DiffServ triple play architecture. The performance of priority queuing (PQ), weight fair queuing (WFQ) and weight fair low latency queuing (WFQ-LLQ) schemes is assessed, in order to find the most appropriate solution for the underlying network. The evaluation of the schedulers is based on simulations designed upon real triple play networks, while trace files are used for the simulation of video flows
Maximum Likelihood Failure Diagnosis in Finite State Machines Under Unreliable Observations

In this paper, we develop a probabilistic methodology for failure diagnosis in finite state machines based on a sequence of unreliable observations. Given prior knowledge of the input probability distribution but without actual knowledge of the applied input sequence, the core problem we consider is to choose from a pool of known, deterministic finite state machines (FSMs) the one that most likely matches the given sequence of observations. The problem becomes challenging because of sensor failures which may corrupt the observed sequence by inserting, deleting, and transposing symbols with certain probabilities (that are assumed known). We propose an efficient recursive algorithm for obtaining the most likely underlying FSM, given the possibly erroneous observed sequence. The proposed algorithm essentially allows us to perform online maximum likelihood failure diagnosis and is applicable to more general settings where one is required to choose the most likely underlying hidden Markov model (HMM) based on a sequence of observations that may get corrupted with known probabilities. The algorithm generalizes existing recursive algorithms for likelihood calculation in HMMs by allowing loops in the associated trellis diagram. We illustrate the proposed methodology using an example of diagnosis in the context of communication protocols.
Executed Movement Using EEG Signals through a Naive Bayes Classifier

Recent years have witnessed a rapid development of brain-computer interface (BCI) technology. An independent BCI is a communication system for controlling a device by human intension, e.g., a computer, a wheelchair or a neuroprosthes is, not depending on the brain's normal output pathways of peripheral nerves and muscles, but on detectable signals that represent responsive or intentional brain activities. This paper presents a comparative study of the usage of the linear discriminant analysis (LDA) and the naive Bayes (NB) classifiers on describing both right- and left-hand movement through electroencephalographic signal (EEG) acquisition. For the analysis, we considered the following input features: the energy of the segments of a band pass-filtered signal with the frequency band in sensorimotor rhythms and the components of the spectral energy obtained through the Welch method. We also used the common spatial pattern (CSP) filter, so as to increase the discriminatory activity among movement classes. By using the database generated by this experiment, we obtained hit rates up to 70%. The results are compatible with previous studies.
A framework for the design of error-aware power-efficient fixed-width Booth multipliers

In this paper, a framework of designing a low-error and power-efficient two's-complement fixed-width Booth multiplier that receives two n-bit numbers and produces an n-bit product is proposed. The design methodology of the framework involving four steps results in one better error-compensation bias. The better error-compensation bias can be mapped to a simple low-error fixed-width Booth multiplier with a little penalty in power consumption. For the benchmark of 8/spl times/8 multipliers, the simulation results show that a reduction of 82.04% average error compared to that using the direct-truncated fixed-width Booth multiplier can be obtained. Moreover, the power consumption can be reduced by 40.68% compared to that of full-precision Booth multiplier design.
Unity in Diversity: Discovering Topics from Words - Information Theoretic Co-clustering for Visual Categorization

This paper presents a novel approach to learning a codebook for visual categorization, that resolves the key issue of intra-category appearance variation found in complex real world datasets. The codebook of visual-topics (semantically equivalent descriptors) is made by grouping visual-words (syntactically equivalent descriptors) that are scattered in feature space. We analyze the joint distribution of images and visual-words using information theoretic co-clustering to discover visual-topics. Our approach is compared with the standard 'Bagof- Words' approach. The statistically significant performance improvement in all the datasets utilized (Pascal VOC 2006; VOC 2007; VOC 2010; Scene-15) establishes the efficacy of our approach.
A Statistical Model for Risk Stratification on the Basis of Left Ventricular Ejection Fraction and Heart-Rate Turbulence

The MPIP data set was used to obtain a model for mortality risk stratification of acute myocardial infarction patients. The predictors heart rate turbulence (HRT) and left-ventricular ejection fraction (LVEF) were employed. HRT was a categorical variable of three levels; LVEF was continuous and its influence on the relative risk was explained by the natural logarithm function (found using fractional polynomials). Cox - PH model with HRT and lnLVEF was constructed and used for risk stratification. The model can be used to divide the patients into two or more groups according to mortality risk. It also describes the relationship between risk and predictors by a (continuous) function, which allows the calculation of individual mortality risk.
Ultraluminous X-ray source populations in normal galaxies: a preliminary survey with Chandra

We present results of a Chandra survey of the ultraluminous X-ray sources (ULX) in 13 normal galaxies, in which we combine source detection with X-ray flux measurement. 22 ULX were detected, i.e. with L x > 1 x 10 3 9 ergs - 1 (L 1 0 ) and 39 other sources were detected with L x > 5 × 10 3 8 erg s - 1 (L 5 ). We also use radial intensity profiles to remove extended sources from the sample. The majority of sources are not extended, which for a typical distance constrains the emission region size to less than 50 pc. X-ray colour-colour diagrams and spectral fitting results were examined for indicators of the ULX nature. In the case of the brighter sources, spectral fitting generally requires two-component models. In only a few cases do colour-colour diagrams or spectral fitting provide evidence of a black hole nature. We find no evidence of a correlation with stellar mass, however, there is a strong correlation with star formation as indicated by the 60-μm flux as found in previous studies.
Design, synthesis, and characterization of graphene-nanoparticle hybrid materials for bioapplications.

Graphene is composed of single-atom thick sheets of sp2 bonded carbon atoms that are arranged in a perfect two-dimensional (2D) honeycomb lattice. Because of this structure, graphene is characterized by a number of unique and exceptional structural, optical, and electronic properties.1 Specifically, these extraordinary properties include, but are not limited to, a high planar surface area that is calculated to be 2630 m2 g−1,2 superior mechanical strength with a Young’s modulus of 1100 GPa,3 unparalleled thermal conductivity (5000 W m−1 K−1),4 remarkable electronic properties (e.g., high carrier mobility [10 000 cm2 V−1 s−1] and capacity),5 and alluring optical characteristics (e.g., high opacity [~97.7%] and the ability to quench fluorescence). 6 As such, it should come as no surprise that graphene is currently, without any doubt, the most intensively studied material for a wide range of applications that include electronic, energy, and sensing outlets.1c Moreover, because of these unique chemical and physical properties, graphene and graphene-based nanomaterials have attracted increasing interest, and, arguably, hold the greatest promise for implementation into a wide array of bioapplications.7 In the last several years, numerous studies have utilized graphene in bioapplications ranging from the delivery of chemotherapeutics for the treatment of cancer8 to biosensing applications for a host of medical conditions9 and even for the differentiation and imaging of stem cells.10 While promising and exciting, recent reports have demonstrated that the combination of graphene with nanomaterials such as nanoparticles, thereby forming graphene–nanoparticle hybrid structures, offers a number of additional unique physicochemical properties and functions that are both highly desirable and markedly advantageous for bioapplications when compared to the use of either material alone (Figure 1). 11 These graphene–nanoparticle hybrid structures are especially alluring because not only do they display the individual properties of the nanoparticles, which can already possess beneficial optical, electronic, magnetic, and structural properties that are unavailable in bulk materials, and of graphene, but they also exhibit additional advantageous and often synergistic properties that greatly augment their potential for bioapplications. Open in a separate window Figure 1 Graphene nanoparticle hybrids exist in two forms, as graphene–nanoparticle composites and graphene-encapsulated nanoparticles, and can be used for various bioapplications including biosensors, photothermal therapies, stem cell/tissue engineering, drug/gene delivery, and bioimaging. Panel (A) reprinted with permission from ref 110. Copyright 2012 Wiley. Panel (B) reprinted with permission from ref 211. Copyright 2013 Elsevier. Panel (C) reprinted with permission from ref 244. Copyright 2013 Wiley.
Evaluating the Demand for Soft Skills in Software Development

An analysis of 500 advertisements for IT positions focuses on the soft skills mentioned in the ads, revealing which soft skills are in high demand for software development and which ones are neglected despite their importance. Our survey indicates that soft skills are in demand in the software industry, but only to a limited extent. This highlights the lack of understanding of the role that soft skills play in an employee's professional ability and performance.
Opportunistic forwarding of network-coded multicast traffic in wireless sensor networks

Wireless sensor networks (WSN) typically rely on application-specific tree-based communication topologies between source and sink nodes to transmit application specific messages. In this paper, the OCSSIM communication scheme is proposed to realize multicast communication for multiple applications with coinciding transmissions between any number of source and sink nodes. With often changing sink and source roles, WSNs are prone to perpetual changing topologies, causing overhead for continuous routing updates. Instead of applying an additional routing scheme, OCSSIM exploits overhearing in wireless communication and uses opportunistic multipath-forwarding of network coded messages between neighbor nodes. Therefore, mechanisms are introduced to prioritize messages, reassemble coded messages on intermediate nodes and acknowledge transmissions implicitly.
Team Health, an Assessment Approach to Engage First Year Students in Cross-cultural and Cross-discipline Teams towards More Effective Team-Working

Specialists who work in a globalised environment, need to work in teams, if they are to be continuously effective. The challenge for IT educators is to design and implement inter-cultural teamwork practices into their curriculum. Investigating this challenge, this case study describes Team Health, an assessment approach designed to skill students to be more effective in team working in cross-cultural and cross-discipline teams. The educational context is teamwork practice within a first year introductory web design course. Framed by Saunders\'s virtual team lifecycle model (relationship building and team processes) and Hofstede\'s cultural dimensions (communication and working cross-culturally), the assessment approach utilises reflective and iterative strategies to support team working. At three points in the semester, students complete a survey on these four concepts, identify team strengths and weaknesses from the results of the surveys and work towards addressing one team weakness. The final assessment activity requires students to reflect on team working for the semester. Key attributes for effective team working are identified from the three surveys and the final reflective summaries. This paper compares course outcomes such as team cohesion and student grades to the previous course offering and shows that with the introduction of Team Health, the more complex student cohorts under this study achieve equally well. It is concluded that the guided reflective practices underpinning Team Health can prepare students for first year approaches to teamwork, and thereby provide starting points for working in future global teams where members are both culturally diverse and from different discipline areas.
Bioconjugation of recombinant tissue plasminogen activator to magnetic nanocarriers for targeted thrombolysis

Low-toxicity magnetic nanocarriers (MNCs) composed of a shell of poly [aniline-co-N-(1-one-butyric acid) aniline] over a Fe3O4 magnetic nanoparticle core were developed to carry recombinant tissue plasminogen activator (rtPA) in MNC-rtPA for targeted thrombolysis. With an average diameter of 14.8 nm, the MNCs exerted superparamagnetic properties. Up to 276 μg of active rtPA was immobilized per mg of MNCs, and the stability of the immobilized rtPA was greatly improved during storage at 4°C and 25°C. In vitro thrombolysis testing with a tubing system demonstrated that magnet-guided MNC-rtPA showed significantly improved thrombolysis compared with free rtPA and reduced the clot lysis time from 39.2 ± 3.2 minutes to 10.8 ± 4.2 minutes. In addition, magnet-guided MNC-rtPA at 20% of the regular rtPA dose restored blood flow within 15–25 minutes of treatment in a rat embolism model without triggering hematological toxicity. In conclusion, this improved system is based on magnetic targeting accelerated thrombolysis and is potentially amenable to therapeutic applications in thromboembolic diseases.
Metadata Partitioning for Large-Scale Distributed Storage Systems

With the emergence of large-scale storage systems that separate metadata management from fileread/write operations, and with requests targetting metadata account for over 80\% of the total number of I/O requests, metadata management has become an interesting research problem on its own. When designing a metadata server cluster, the partitioning of the metadata among the servers is of critical importance for maintaining efficient metadata operations and balanced load distribution across the cluster. We propose a dynamic programming method combined with binary search to solve the partitioning problem. With theoretical analysis and extensive experiments, we show that our algorithm finds the partitioning that minimizes load imbalance among servers and maximize efficiency of metadata operations.
H-cluster: A Novel Efficient Algorithm for Data Clustering in Sensor Networks

This paper focuses on the problem of data clustering in wireless sensor networks (WSNs). The data time window is a landmark window, from the time WSN starts working up to the current time. The objective is to group sensory data generated by sensor nodes deployed in a two-dimensional physical space by the similarity of sensory data in the multi-dimensional sensory data space. To perform in-network data clustering efficiently, we propose HilbertMap, a novel dimensionality reduction technique based on the Hilbert Curves, to map a multi-dimensional data space to a two-dimensional physical space. Through this mapping, the communications for clustering mostly occur between geographically nearby sensor nodes. We have conducted simulation experiments on both real-world and synthetic datasets. Our results show that HilbertMap improves the communication efficiency while maintaining a good clustering quality.
Genome Sequence of Arthrobacter sp. YC-RL1, an Aromatic Compound-Degrading Bacterium

ABSTRACT We report the 4.04-Mb draft genome sequence of Arthrobacter sp. YC-RL1, an aromatic compound-degrading bacterium. YC-RL1 could degrade a wide range of aromatic compounds, including naphthaline, 1,2,3,4-tetrachlorobenzene, fluorene, 4-nitrophenol, bisphenol A, biphenyl, and p-xylene. The genome sequence of YC-RL1 will promote the investigation of the biodegradation of aromatic compounds.
Synergistic Effects of Age on Patterns of White and Gray Matter Volume across Childhood and Adolescence1,2,3

Abstract The human brain develops with a nonlinear contraction of gray matter across late childhood and adolescence with a concomitant increase in white matter volume. Across the adult population, properties of cortical gray matter covary within networks that may represent organizational units for development and degeneration. Although gray matter covariance may be strongest within structurally connected networks, the relationship to volume changes in white matter remains poorly characterized. In the present study we examined age-related trends in white and gray matter volume using T1-weighted MR images from 360 human participants from the NIH MRI study of Normal Brain Development. Images were processed through a voxel-based morphometry pipeline. Linear effects of age on white and gray matter volume were modeled within four age bins, spanning 4-18 years, each including 90 participants (45 male). White and gray matter age-slope maps were separately entered into k-means clustering to identify regions with similar age-related variability across the four age bins. Four white matter clusters were identified, each with a dominant direction of underlying fibers: anterior–posterior, left–right, and two clusters with superior–inferior directions. Corresponding, spatially proximal, gray matter clusters encompassed largely cerebellar, fronto-insular, posterior, and sensorimotor regions, respectively. Pairs of gray and white matter clusters followed parallel slope trajectories, with white matter changes generally positive from 8 years onward (indicating volume increases) and gray matter negative (decreases). As developmental disorders likely target networks rather than individual regions, characterizing typical coordination of white and gray matter development can provide a normative benchmark for understanding atypical development.
Deploying FIPA-Compliant Systems on Handheld Devices

LEAP is a runtime environment for deploying agents on a network of Java-enabled devices. It complies with FIPA international standards for multiagent systems. The Lightweight Extensive Agent Platform project is the first attempt to implement a FIPA agent platform that runs seamlessly on both mobile and fixed devices over both wireless and wired networks.
Further Advances in Forecasting Day-Ahead Electricity Prices Using Time Series Models

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York: New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.
Generating registration-free cancelable fingerprint templates based on Minutia Cylinder-Code representation

Cancelable biometrics has become an indispensable technique during biometric authentication. There have been several prior attempts to construct cancelable fingerprint templates. However, existing schemes have not provided a satisfying trade off between performance and security. In this paper, we proposed two novel approaches to construct registration-free cancelable fingerprint templates based on Minutia Cylinder Code, which is the state of the art local representation for fingerprint. And experiments show that the two approaches provide good results in terms of three criteria: performance, unlinkability and irreversibility.
Falling D0-Branes in 2D Superstring Theory

In N=1, 2D superstring theory in the linear dilaton background, there exists falling D0-branes that are described by time-dependent boundary states. These falling D0-brane boundary states can be obtained by adapting the FZZT boundary states of N=2 Super Liouville Field Theory (SLFT) to the case of the N=1, 2D superstring. In particular, we find that there are four stable, falling D0-branes (two branes and two anti-branes) in the Type 0A projection and two unstable ones in the Type 0B projection, leaving us with a puzzle for the matrix model dual of the theory.
Compound color recognition via image analysis on high-throughput compound libraries

A new machine vision method is presented to detect micro tubes with colored solutions or undissolved compounds in solvents on screening plates used in drug discovery. The method presented herein takes an image of a 96 tubes micro tube rack as input. After applying edge detection on the input image, the circles that characterize the borders of the micro tubes are detected via shape matching. A gradient search is used to extract the colors from the micro tubes. Automatic calibration is applied to adapt to changing light conditions. Experiments with more than two thousand micro tube racks (approx. 170,000 compounds) compiled over three years showed that the method is fast and robust.