Miracle-dz/UniFAR-FTUs huggingface dataset:
Issues and Concerns About Computer-Supported Meetings: The Facilitator's Perspective

In an effort to boost meeting productivity and success, managers may employ trained group facilitators. They may also implement group support systems (GSS) for the same reason. The two approaches can be taken separately or together. In this study, in depth interviews with 37 practicing facilitators provided their perspectives on critical factors that influence meeting success and potential benefits and concerns with the use of GSS. Respondents focused on a core of communication and group process skills as critical for facilitator success. Overall, the respondents observed or anticipated more efficient and effective task performance as benefits of GSS technology. Their concerns focused on technology related issues: participant anxiety, systems inflexibility, and systems reliability. Views of facilitators with high and low levels of GSS experience are largely consistent. High experience GSS facilitators viewed technical issues as more central to meeting success, while low experience GSS facilitators focused more heavily on attributes of the group. The paper concludes by offering suggestions for identifying and training GSS facilitators and comments on key issues of importance to GSS designers, based on the facilitator's perspective.
Comparison of nearest neighbour and neural network based classifications of patient's activity

This paper presents a comparison of 1-nearest neighbour (1-NN) and neural network based classification of patient activity. The data for classification was acquired from two 6 degree-of-freedom accelerometers deployed at the wrists of a patient. Instead of calculating statistical values, we studied the use of data samples acquired from 200ms time window. The best results were achieved with the 1-nearest neighbour algorithm. The overall accuracy of the 1-NN method was nearly 100%. The learning method for neural network used was the backpropagation with momentum. According to our experiments, the results of classification were more accurate with 1-NN in comparison with the result of neural network (93.4%).
Reproductive Biology and Endocrinology Open Access the Krüppel-like Factor 9 (klf9) Network in Hec-1-a Endometrial Carcinoma Cells Suggests the Carcinogenic Potential of Dys-regulated Klf9 Expression

Background: Krüppel-like factor 9 (KLF9) is a transcriptional regulator of uterine endometrial cell proliferation, adhesion and differentiation; processes essential for pregnancy success and which are subverted during tumorigenesis. The network of endometrial genes controlled by KLF9 is largely unknown. Over-expression of KLF9 in the human endometrial cancer cell line HEC-1-A alters cell morphology, proliferative indices, and differentiation, when compared to KLF9 under-expressing HEC-1-A cells. This cell line provides a unique model for identifying KLF9 downstream gene targets and signaling pathways.
A Short Implicant of CNFs with Relatively Many Satisfying Assignments

To state our results precisely, we introduce some notation. Throughout this paper, let F be a given Boolean function over N variables, and we assume that it is given as a CNF formula with M clauses and that it has P2N satisfying assignments, where P will be referred as the sat. assignment ratio of F . Furthermore, we introduce two parameters1 δ > 0 and e > 0, and consider the following situation: (i) P ≥ 2−Nδ , and (ii) M ≤ N1+e For such a CNF formula F , we discuss the size of its implicant in terms of δ and e. As our main result, we show that if δ + e 0; that is, F has an implicant of size ≤ αN . (In this paper, for any partial assignment, by its “size” we will mean the number of variables fixed by this assignment; we say that a partial assignment is short if it fixes at most αN variables for some constant α < 1.) If a function F has a short partial assignment, then it has many satisfying assignments. Our result shows that a certain converse relation holds provided that F is expressed as a CNF formula with a relatively small number of clauses. We believe that this structural property would be of some help for designing algorithms for CNF formulas. In fact, we derive, from our analysis, a deterministic algorithm that finds a short partial assignment in O(2N β )-time2 for some β < 1 for ∗This work was started from the discussion at the workshop on Computational Complexity at the Banff International Research Station for Mathematical Innovation and Discovery (BIRS), 2013. The first author is supported in part by an NSF postdoctoral research fellowship. The second author is supported in part by the ELC project (MEXT KAKENHI Grant No. 24106008). For simplicity, throughout this paper, we assume that these parameters are constants, and whenever necessary that N is sufficiently large w.r. t. these parameters. By O(t(N)) we mean O ( t(N)(log t(N)) ) .
Incremental Switch Deployment for Hybrid Software-Defined Networks

Software-Defined Networking (SDN) brings great opportunities to improve network performance. However, due to budget constraints and technique limitations, Internet Service Providers (ISPs) can upgrade only a limited number of conventional switches to SDN switches in real backbone networks at one time. In this paper, we propose one heuristic scheme for deploying SDN switches in hybrid SDNs. Our scheme works for two different cases: (1) maximizing the network control ability with a given upgrading budget constraint, and (2) minimizing the upgrading cost to achieve the best network control ability. We evaluate our scheme in real topologies. We evaluate our scheme in real topologies. The results show that our scheme can achieve 95% of flows controlled with only 10% upgrading cost.
MARC: Adaptive Rate Control scheme for improving the QoE of streaming services in mobile broadband networks

Recently, due to the prevalence of various mobile devices and broadband wireless networks, there has been a significant increase in interest and demand for multimedia streaming services such as the mobile IPTV. In order to deploy a mobile IPTV service in wireless broadband networks, offering users efficient wireless resource utilization and seamlessly offering Quality of Experience (QoE) are important issues. In this paper, we propose a new adaptive streaming scheme, called Mobile-aware Adaptive Rate Control (MARC), which adjusts the quality of bit-stream and transmission rate of video streaming based on the wireless channel status and network status. The proposed scheme can provide a seamless multimedia playback service in wireless broadband networks in addition to improving the quality of service (QoS) of multimedia streaming services. The proposed MARC scheme alleviates the discontinuity of multimedia playback and allocates a suitable client buffer to the wireless broadband network. The simulation results demonstrate the effectiveness of our proposed scheme.
Joint Graph Learning and Video Segmentation via Multiple Cues and Topology Calibration

Video segmentation has become an important and active research area with a large diversity of proposed approaches. Graph-based methods, enabling top performance on recent benchmarks, usually focus on either obtaining a precise similarity graph or designing efficient graph cutting strategies. However, these two components are often conducted in two separated steps, and thus the obtained similarity graph may not be the optimal one for segmentation and this may lead to suboptimal results. In this paper, we propose a novel framework, joint graph learning and video segmentation (JGLVS)}, which learns the similarity graph and video segmentation simultaneously. JGLVS learns the similarity graph by assigning adaptive neighbors for each vertex based on multiple cues (appearance, motion, boundary and spatial information). Meanwhile, the new rank constraint is imposed to the Laplacian matrix of the similarity graph, such that the connected components in the resulted similarity graph are exactly equal to the number of segmentations. Furthermore, JGLVS can automatically weigh multiple cues and calibrate the pairwise distance of superpixels based on their topology structures. Most noticeably, empirical results on the challenging dataset VSB100 show that JGLVS achieves promising performance on the benchmark dataset which outperforms the state-of-the-art by up to 11% for the BPR metric.
A globally convergent observer for velocity estimation in robotic manipulators with uncertain dynamics

We present a method for global estimation of joint velocities in robot manipulators. A non-minimal model of a robotic manipulator is used to design an adaptive observer capable of handling uncertainties in robot dynamics. Dimension of the proposed observer is shown to be at least 3n where n stands for the manipulator degrees of freedom. This number is less than the dimension of most of existing globally convergent adaptive observers. Global asymptotic convergence of system state estimates to their true values is achieved under no persis-tency of excitation condition. Smoothness of the dynamics of the proposed observer allows its easy implementation in comparison with non-smooth observers. Simulation results illustrate low noise sensitivity of the proposed observer in comparison with non-smooth observers.
Ranking user's comments by use of proposed weighting method

The main challenges which are posed in opinion mining is information retrieval of large volumes of ideas and categorize and classify them for use in related fields. The ranking can help the users to make better choices and manufacturers in order to help improve the quality. As one of the pre-processing techniques in the field of classification, weighting methods have a crucial role in ranking ideas and comments. So, we decided to offer a new weighting method to improve some other similar methods, especially Dirichlet weighting method. In this paper, the proposed method will be described in detail, and the comparison with the three weighting methods: Dirichlet, Pivoted and Okapi also described. The proposed weighting method has higher accuracy and efficiency in comparison to similar methods. In the following, user comments of online newspapers are ranked and classified by use of proposed method. The purpose is to provide more efficient and more accurate weighting method, therefor the results of ranking will be more reliable and acceptable to users.
Discovering Regional Pathological Patterns in Brain MRI

Complex pathological brain patterns generally are found in neurodegenerative diseases which can be correlated with different clinical onsets of a particular pathology. Currently, an objective method that aids to determine such signs, in terms of global and local changes, is not available in clinical practice and the whole interpretation is dependent on the radiologist's skills. In this paper, we propose a fully automatic method that analyzes the brain structure under a multidimensional frame and highlights relevant brain patterns. An association of such patterns with the disease is herein evaluated in three classification tasks, involving probable Alzheimer's disease (AD) patients, Mild Cognitive Impairment (MCI) patients and normal subjects (NC). A set of 75 brain MR images from NC subjects (25), MCI (25) and probable AD (25) patients, split into training (15 subjects) and testing (60 subjects) sets, was used to evaluate the performance of the proposed approach. Preliminary results show that the proposed method reaches a maximum classification accuracy of 80% when discriminating AD patients from NC, of 75% for classification of MCI patients from NC.
Epstein-Barr Nuclear Antigen 1 Contributes to Nasopharyngeal Carcinoma through Disruption of PML Nuclear Bodies

Latent Epstein-Barr virus (EBV) infection is strongly associated with several cancers, including nasopharyngeal carcinoma (NPC), a tumor that is endemic in several parts of the world. We have investigated the molecular basis for how EBV latent infection promotes the development of NPC. We show that the viral EBNA1 protein, previously known to be required to maintain the EBV episomes, also causes the disruption of the cellular PML (promyelocytic leukemia) nuclear bodies (or ND10s). This disruption occurs both in the context of a native latent infection and when exogenously expressed in EBV-negative NPC cells and involves loss of the PML proteins. We also show that EBNA1 is partially localized to PML nuclear bodies in NPC cells and interacts with a specific PML isoform. PML disruption by EBNA1 requires binding to the cellular ubiquitin specific protease, USP7 or HAUSP, but is independent of p53. We further observed that p53 activation, DNA repair and apoptosis, all of which depend on PML nuclear bodies, were impaired by EBNA1 expression and that cells expressing EBNA1 were more likely to survive after induction of DNA damage. The results point to an important role for EBNA1 in the development of NPC, in which EBNA1-mediated disruption of PML nuclear bodies promotes the survival of cells with DNA damage.
A new in vivo analysis model to detect sexually dimorphic rat liver cytochrome P450 gene expression dependent on growth hormone secretory patterns

Several drug-metabolizing cytochrome P450 (CYP) enzymes exhibit sexual dimorphism depending on the pituitary growth hormone (GH) secretory patterns. However, the mechanism underlying CYP sexual dimorphism remains unclear. We previously established a transgenic (Alb-DsRed2 Tg) rat that expressed red fluorescent DsRed2 protein, particularly in hepatocytes, to visualize cell differentiation and multiplication and found that hepatic DsRed2 expression exhibited sexual dimorphism that was limited to adult males. In this study, we compared the expression patterns between sexual dimorphic Cyps and DsRed2 in Tg rats after experimentally reversing the GH secretory patterns in males and females. Postnatal day 1 male and female Tg rats were gonadectomized and then testosterone propionate (0.25 mg/rat) was subcutaneously administered to ovariectomized females immediately after surgery. Cyp mRNA and DsRed2 expression levels were quantified using RT-PCR and an in vivo imaging system, respectively. GH-dependent Cyps and hepatic DsRed2 expression patterns were reversed in males and females at 9 weeks after birth and were significantly correlated (P<0.05). This suggested that DsRed2 expression in these Tg rats depended on GH secretory patterns. Based on DsRed2 fluorescence, this Tg rat model could become a tool to readily and effectively evaluate changes in GH-dependent Cyp expression.
Color Comparison in Codebook Model for Moving Objects Detection

For surveillance video, color information in scenes is important for detecting moving objects. This paper focuses on the selection of color space and the comparison of color distortion in codebook model which represents each background pixel with some codewords based on its color distortion and brightness bounds. Several computing methods are experimented for contrast. The results show that HSV color space achieves better effectiveness in detecting moving objects for a certain surveillance video.
Cosmic-Ray Electrons in Groups and Clusters of Galaxies: Primary and Secondary Populations from a Numerical Cosmological Simulation

We investigate the generation and distribution of high-energy electrons in the cosmic structure environment and their observational consequences by carrying out the first cosmological simulation that includes directly cosmic-ray (CR) particles. Starting from cosmological initial conditions, in addition to the gas and dark matter related quantities, we follow the evolution of CR electrons (primary and secondary) and CR ions along with a passive magnetic field. CR ions and primary electrons are injected in accordance with the thermal leakage model and accelerated in the test-particle limit of diffusive shock acceleration at shocks associated with large-scale structure formation. Secondary electrons are continuously generated through p-p inelastic collisions of the CR ions with the thermal nuclei of the intergalactic medium. The evolution of the CR electrons accounts for spatial transport, adiabatic expansion/compression, and losses due to Coulomb collisions, bremsstrahlung, synchrotron and inverse-Compton emission. The magnetic field is seeded at shocks according to the Biermann battery model, and thereafter amplified by shear flow and gas compression. We compute the emission due to the inverse-Compton scattering of the simulated primary and secondary electrons off cosmic microwave background photons and compare it with the published values of the detected radiation excesses in the hard X-ray and extreme-ultraviolet wavebands. We find that the few instances of detection of hard X-ray radiation excess could be explained in the framework of IC emission from primary electrons in clusters characterized by high accretion/merger activity. On the other hand, with the only exception of measured flux from the Coma Cluster by Bowyer, Berghoefer & Korpela, both primary and secondary CR electrons associated with the cosmic structure formation account at most for a small fraction of the radiation excess detected in the extreme-ultraviolet waveband. Next, we calculate the synchrotron emission after normalizing the magnetic field strength so that for a Coma-like cluster the volume-averaged ⟨B2⟩1/2 ≃ 3 μG. Our results indicate that the synchrotron emission from the secondary CR electrons reproduces several general properties observed in radio halos. These include the recently found P1.4 GHz versus TX relationship, the morphology and polarization of the emitting region, and, to some extent, even the spectral index. In addition, radio synchrotron emission from primary electrons turns out to be large enough to power extended regions of radio emission, resembling radio relics observed at the outskirts of clusters. Once again we find a striking resemblance between the general properties of morphology, polarization, and spectral index of our synthetic maps and those of reported in the literature.
The Swift Gamma-Ray Burst Mission

The Swift mission, scheduled for launch in 2004, is a multiwavelength observatory for gamma-ray burst (GRB) astronomy. It is a first-of-its-kind autonomous rapid-slewing satellite for transient astronomy and pioneers the way for future rapid-reaction and multiwavelength missions. It will be far more powerful than any previous GRB mission, observing more than 100 bursts yr-1 and performing detailed X-ray and UV/optical afterglow observations spanning timescales from 1 minute to several days after the burst. The objectives are to (1) determine the origin of GRBs, (2) classify GRBs and search for new types, (3) study the interaction of the ultrarelativistic outflows of GRBs with their surrounding medium, and (4) use GRBs to study the early universe out to z > 10. The mission is being developed by a NASA-led international collaboration. It will carry three instruments: a new-generation wide-field gamma-ray (15-150 keV) detector that will detect bursts, calculate 1'-4' positions, and trigger autonomous spacecraft slews; a narrow-field X-ray telescope that will give 5'' positions and perform spectroscopy in the 0.2-10 keV band; and a narrow-field UV/optical telescope that will operate in the 170-600 nm band and provide 0. ″3 positions and optical finding charts. Redshift determinations will be made for most bursts. In addition to the primary GRB science, the mission will perform a hard X-ray survey to a sensitivity of ~1 mcrab (~2 × 10-11 ergs cm-2 s-1 in the 15-150 keV band), more than an order of magnitude better than HEAO 1 A-4. A flexible data and operations system will allow rapid follow-up observations of all types of high-energy transients, with rapid data downlink and uplink available through the NASA TDRSS system. Swift transient data will be rapidly distributed to the astronomical community, and all interested observers are encouraged to participate in follow-up measurements. A Guest Investigator program for the mission will provide funding for community involvement. Innovations from the Swift program applicable to the future include (1) a large-area gamma-ray detector using the new CdZnTe detectors, (2) an autonomous rapid-slewing spacecraft, (3) a multiwavelength payload combining optical, X-ray, and gamma-ray instruments, (4) an observing program coordinated with other ground-based and space-based observatories, and (5) immediate multiwavelength data flow to the community. The mission is currently funded for 2 yr of operations, and the spacecraft will have a lifetime to orbital decay of ~8 yr.
Understanding the World's Worst Spamming Botnet

On November 11, 2008, the primary web hosting company, McColo, for the command and control servers of Srizbi botnet was shutdown by its upstream ISPs. Subsequent reports claimed that the volume of spam dropped significantly everywhere on that very same day. In this work, we aim to understand the world’s worst spamming botnet, Srizbi, and to study the effectiveness of targeting the botnet’s com mand and control servers, i.e., McColo shutdown, from the viewpoint of Internet edge sites. We conduct an extensive measurement study that consists of e-mail delivery logs and packet traces collected at four vantage points. The total me surement period spans from July 2007 to April 2009, which includes the day of McColo shutdown. We employ passive TCP fingerprinting on the collected packet traces to identif y Srizbi bots and spam messages sent from them. The main contributions of this work are summarized as follows. We first estimate the global scale of Srizbi botnet i a probabilistic way. Next, we quantify the volume of spam sent from Srizbi and the effectiveness of the McColo shutdown from an edge site perspective. Finally, we reveal several findings that are useful in understanding the growth and evolution of spamming botnets. We detail the rise and steady growth of Srizbi botnet, as well as, the version transition o f Srizbi after the McColo shutdown.
Combination of Symbolic and Statistical Features for Symbols Recognition

In this article, we have tried to explore a new hybrid approach which well integrates the advantages of structural and statistical approaches and avoids their weaknesses. In the proposed approach, the graphic symbols are first segmented into high-level primitive like quadrilaterals. Then, a graph is built by utilizing these quadrilaterals as nodes and their spatial relationships as edges. Additional information like relative length of the quadrilaterals and their relative angles with neighbouring quadrilaterals are associated as attributes to the nodes and edges of the graph respectively. However, the observed graphs are subject to deformations due to noise and/or vectorial distortion (in case of hand-drawn images) hence differs somewhat from their ideal models by either missing or extra nodes and edges appearance. Therefore, we propose a method that computes a measure of similarity between two given graphs instead of looking for exact isomorphism. The approach is based on comparing feature vectors extracted from the graphs. The idea is to use features that can be quickly computed from a graph on the one hand, but are, on the other hand, effective in discriminating between the various graphs in the database. The nearest neighbour rule is used as a classifier due to its simplicity and good behaviour
A study to understand the impact of node density on data dissemination time in opportunistic networks

In this paper, we study the impact of node density on data dissemination time and achieved data quality in a distributed people-centric system. Our results are obtained through an extensive simulation campaign employing Random Way Point and Random Direction mobility and realistic node densities of real environments. Our simulation results show that, the impact of node density does not significantly affect the data dissemination time after a certain threshold of node density, without compromising the achieved data quality. This result is evident for both mobility models. Our study provides an insight to the parameters we need to consider while evaluating the success of any distributed people-centric system.