Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Factors that Influence the Diffusion of Open Data for New Service Development: An Interpretive Case Study

Information Systems research on Open Data has been primarily focused on its contribution to egovernment inquiries, government transparency, and open government. Recently, Open Data has been explored as a catalyser for service innovation as a consequence of big claims around the potential of such initiatives in terms of additional value that can be injected into the worldwide economy. Subsequently, the Open Data Services academic conversation was structured (Lindman et al. 2013a). The research project presented in this paper is an interpretive case study that was carried out to explore the factors that influence the diffusion of Open Data Services. This paper contributes to this debate by providing both a systematic literature review study that structures research efforts available to date in this topic, and an interpretive case study (Walsham, 1995) of a successful initiative that turned several city authorities’ raw open datasets into a set of valuable services. Results demonstrate that 12 factors and 56 related variables are the most relevant in the process of diffusion of open data for new service development. Furthermore, this paper demonstrates the suitability of Social Constructionism and interpretive case study research to inductively generate knowledge in this field.
Measuring the Stance of Monetary Policy

In this paper we construct a quantitative measure of the stance of mon policy in Canada using a vector autoregression (VAR)–based appro Following Blinder’s (1998) recent arguments, we base our policy sta measure on the control of inflation. This is appropriate given that the goa monetary policy in Canada is to keep inflation within a target range of 1 3 per cent. We regard the stance of monetary policy as a quantita measure of whether policy is too tight, neutral, or too loose relative to objective of keeping inflation constant. 1 If the stance is too tight (loose) inflation will eventually decrease (increase). In other words, a neu monetary policy stance is consistent with constant inflation in the med run (Blinder 1998, 33). A quantitative measure of policy stance is useful important for at least two reasons. First, knowing how tight or how loose current stance is helps the Bank of Canada determine the course of mon policy needed to keep inflation within the target range. Second, a qu
LMP 1 promotes nasal NK / T-cell lymphoma cell function by eIF 4 E via NF-κ B pathway

Nasal natural killer T-cell lymphoma (NKTL) is a highly malignant tumor that is closely associated with EpsteinBarr virus (EBV) infection. Latent membrane protein 1 (LMP1) is encoded by EBV and plays an important role in EBV-induced cell transformation. Therefore, we assessed the function of LMP1 as a stimulant of NKTL progression and the underlying mechanism. A human EBV-positive NKTL cell line (SNK-6) was transfected with pcDNA3.1-LMP1, LV-LMP1 shRNA or LV-eukaryotic translation initiation factor 4E (eIF4E)-shRNA. Then, 3-(4,5-dimethyl-2-thiazolyl)2,5-diphenyl-2H-tetrazolium bromide (MTT) assay was used to assess the proliferation of SNK-6 cells, and cell migration and invasion were analyzed by Transwell chamber assay. Flow cytometry was used to analyze the cell cycle and apoptosis. The results showed LMP1 was highly expressed in SNK-6 cells compared with control groups. Following pretreatment with LMP1 shRNA, the proliferation of SNK-6 cells was inhibited and resulted in a G0/G1 phase arrest. A reduction in invasion and migration was also observed. LMP1 silencing promoted cell apoptosis. Further mechanistic analysis suggested that LMP1 overexpression induced the expression of eIF4E, while eIF4E-shRNA dramatically attenuated the increase in cell proliferation, invasion, migration and the inhibition of apoptosis triggered by LMP-1 upregulation. Moreover, the effect of LMP1 on eIF4E expression was mediated by the NF-κB pathway. Therefore, this finding may provide a potential target against NKTL.
Visual attention is available at a task-relevant location rapidly after a saccade

Maintaining attention at a task-relevant spatial location while making eye-movements necessitates a rapid, saccade-synchronized shift of attentional modulation from the neuronal population representing the task-relevant location before the saccade to the one representing it after the saccade. Currently, the precise time at which spatial attention becomes fully allocated to the task-relevant location after the saccade remains unclear. Using a fine-grained temporal analysis of human peri-saccadic detection performance in an attention task, we show that spatial attention is fully available at the task-relevant location within 30 milliseconds after the saccade. Subjects tracked the attentional target veridically throughout our task: i.e. they almost never responded to non-target stimuli. Spatial attention and saccadic processing therefore co-ordinate well to ensure that relevant locations are attentionally enhanced soon after the beginning of each eye fixation. DOI: http://dx.doi. org/10.7554/eLife.18009. 001
Understanding the process of participating in open source communities

The number of participants in Open Source Software (OSS) communities has increased. Not only volunteers participate, but also companies and their employees. The motivation of the participants vary from extrinsic to intrinsic values. Community-managed and sponsored OSS projects try to explore these motivations to attract and keep these participants. This paper analyses three different OSS projects: MySQL, OpenOffice.org, and GNOME. Each has a different organizational structure that influences participants behavior. This study analyzes qualitative data from publicly available documents, such as project's wiki pages and project's webpages, and quantitative data from bug tracking systems and source code repositories. One of our findings is that the number of active developers does not change significantly when the total number of committers increases for the selected OSS projects.
A three-way perspective on scientific discourse annotation for knowledge extraction

This paper presents a three-way perspective on the annotation of discourse in scientific literature. We use three different schemes, each of which focusses on different aspects of discourse in scientific articles, to annotate a corpus of three full-text papers, and compare the results. One scheme seeks to identify the core components of scientific investigations at the sentence level, a second annotates meta-knowledge pertaining to bio-events and a third considers how epistemic knowledge is conveyed at the clause level. We present our analysis of the comparison, and a discussion of the contributions of each scheme.
Enhanced OLSR for Defense against Node Isolation Attack in Ad Hoc Networks

A Mobile Ad Hoc Network is constructed by a set of self-configured mobile nodes that are connected by wireless links without infrastructure. A MANET node can move freely within network communication range, and server as a router and host which can forward data packets to other hosts according to configured routing protocol. In MANETs, applications are mostly involved with sensitive and secret information. Since MANET assumes a trusted environment for routing, security is a major issue. In this paper we analyze the vulnerabilities of a pro-active routing protocol called OLSR (Optimized link state routing) against a specific type of denial-of-service (DOS) attack called node isolation attack. Analyzing the attack, we propose a mechanism called enhanced OLSR (EOLSR) protocol which is trust based technique to secure the OLSR nodes against the attack. Our technique is capable of finding whether a node is advertising correct topology information or not by verifying its Hello packets, thus detecting node isolation attacks.
On the Usage of Graph Spectra in Protein Structural Similarity

The spectrum of a matrix M is the multiset that contains all the eigenvalues of M. If M is a matrix obtained from a graph G, then the spectrum of M is also called the graph spectrum of G. If two graphs has the same spectrum, then they are cospectral (or isospectral) graphs. In this paper, we compare four spectra of matrices to examine their accuracy in protein structural comparison. These four spectra are derived from adjacency matrix, Laplacian matrix, signless Laplacian matrix, and Seidel adjacency matrix. In the analysis of protein subunits, corroborating protein database CATH, SCOP, and RCSB PDB, results suggest that when Seidel adjacency matrix is applied, it performs a preferable outcome in protein complex structures. In addition, the proposed method outperforms than typical RMSD geometric metric distance. This graph-theoretic approach offers a practical direction for protein subunit comparison.
Exact pairwise error probability of distributed space-time coding in wireless relays networks

In this paper, we analyze the pairwise error probability (PEP) of distributed space-time codes employing Alamouti scheme. We restrict our attention to the space-time code construction for Protocol III in the work of Nabar et al. (2004). In particular, we derive two exact closed-form expressions for PEP when the relay is either close to the source or destination. Using the alternative definition of Q-function, we can express these PEPs in terms of finite integral whose integrand is composed of trigonometric functions. We further show that with only one relay assisted source-destination link, system still achieves diversity order of two, assuming single-antenna terminals. We also perform Monte-Carlo simulations to verify the analysis.
Association of Adjuvant Antiviral Therapy with Risk of Cancer Progression and Deaths in Patients with Hepatitis-B-Virus-Related Hepatocellular Carcinoma following Curative Treatment: A Nationwide Cohort Study

Background Limited information about tumor status and the time at which antiviral therapy was initiated may have influenced effect estimation in previous research. The aim of this study was to investigate the effect of antiviral therapies on HBV-related HCC progression and deaths in patients receiving curative treatment based on clear clinical-pathological cancer status and the association of start time of adjuvant antiviral therapy initiation and outcomes. Methodology A nationwide inception cohort study of newly diagnosed HCC patients who suffered from viral hepatitis B and received curative HCC therapy as the first course of treatment were identified from the Taiwan Cancer Registry between January 1, 2004, and December 31, 2009. Matched Cox proportional hazards models based on propensity score matching and incorporated time-varying exposure were used to estimate adjusted hazard ratios and 95% confidence intervals (CIs). Findings Among 3,855 HCC patients with HBV, antiviral therapy was administered to 490 (12.7%) following curative treatment. Antiviral-treated patients had a higher percentage of young age, early stage, and smaller tumor size of HCC compared with untreated patients. After propensity score matching, treated patients demonstrated a higher risk of HCC progression (hazard ratio, 1.42; 95%CI, 1.20–1.69) and death from all causes (1.45; 1.15–1.82) than untreated patients. Similar results were also obtained in sub-cohort of patients who were alive with cancer-free status at least one year after receiving curative treatment and the sub-cohort of patients with liver resection. The interval length between initiation of antiviral therapy and first-line curative treatment did not show a significant association with all-cause mortality. Conclusions This study found that adjuvant antiviral therapy did not reduce the risk of HCC progression or mortality in HBV-related HCC patients after cancer status adjusting.
Improved Block Based Feature Level Image Fusion Technique Using Multiwavelet with Neural Network

Image fusion is defined as the process of combining two or more different images into a new single image retaining the important features from each image with extended information content. To overcome the spectral distortions in the fused image, many multiresolution based approaches have been proposed. They include different pyramid transforms and discrete scalar wavelet transforms. The multiwavelet transform (MWT) produces a non-redundant image representation, which provides better spatial and spectral localization of image formation. This paper has derived an efficient block based feature level multiwavelet transform with neural network (BFMN) model for image fusion. The proposed (BFMN) model integrates MWT with neural network, which is one of the feature extraction or detection machine learning applications. In the proposed BFMN model, the two fusion techniques, multiwavelet transform (MWT) and neural network (NN) are discussed for fusing the IRS-1D images using LISS III scanner about the locations Hyderabad, Vishakhapatnam, Mahaboobnagar and Patancheru in Andhra Pradesh, India. Also QuickBird image data and Landsat 7 image data are used to perform experiments on the proposed BFMN model. The features under study are contrast visibility, spatial frequency, energy of gradient, variance and edge information. Feed forward back propagation neural network is trained and tested for classification since the learning capability of neural network makes it feasible to customize the image fusion process. The trained neural network is then used to fuse the pair of source images. The proposed BFMN model is compared with other techniques to assess the quality of the fused image. Experimental results clearly prove that the proposed BFMN model is an efficient and feasible algorithm for image fusion.
Robust control of dielectric elastomer diaphragm actuator for replicating human pulse

Human pulse replication is an emerging technology that enables telediagnosis in traditional Chinese medicine. However, soft actuation with multi-frequency tracking capability is in great need for replicating human pulse signal. Dielectric elastomer (DE) is one type of soft actuating materials, which shows great potential in human pulse replication. In this paper, we developed a model-based robust control for a DE diaphragm actuator to track human pulse signal. The DE diaphragm actuator is designed and fabricated. An empirical model of the DE actuator is developed to build a black-box model based on the experimental data. Based on the empirical model, an H-infinity robust control is designed to reject high-frequency sensing noises and disturbances. The robust control was then implemented in real-time to track human pulse signal. A human pulse signal was measured at the City University of Hong Kong and replicated at Wichita State University in US. Experimental data has verified that the DE actuator with its robust control was capable of replicating human pulse signal for telediagnosis.
Graph Controllability Classes for the Laplacian Leader-Follower Dynamics

In this paper, we consider the problem of obtaining graph-theoretic characterizations of controllability for the Laplacian-based leader-follower dynamics. Our developments rely on the notion of graph controllability classes, namely, the classes of essentially controllable, completely uncontrollable, and conditionally controllable graphs. In addition to the topology of the underlying graph, the controllability classes rely on the specification of the control vectors; our particular focus is on the set of binary control vectors. The choice of binary control vectors is naturally adapted to the Laplacian dynamics, as it captures the case when the controller is unable to distinguish between the followers and, moreover, controllability properties are invariant under binary complements. We prove that the class of essentially controllable graphs is a strict subset of the class of asymmetric graphs and provide numerical results that suggests that the ratio of essentially controllable graphs to asymmetric graphs increases as the number of vertices increases. Although graph symmetries play an important role in graph-theoretic characterizations of controllability, we provide an explicit class of asymmetric graphs that are completely uncontrollable, namely the class of block graphs of Steiner triple systems. We prove that for graphs on four and five vertices, a repeated Laplacian eigenvalue is a necessary condition for complete uncontrollability but, however, show through explicit examples that for eight and nine vertices, a repeated eigenvalue is not necessary for complete uncontrollability. For the case of conditional controllability, we give an easily checkable necessary condition that identifies a class of binary control vectors that result in a two-dimensional controllable subspace. Several constructive examples demonstrate our results.
Towards Object-Based Saliency

Eye movements during scene comprehension can be seen as a series of decisions where and when to look. There has been substantial work towards determining likely fixation locations and the best known methods involve the computation of saliency maps which assign “interestingness” values to each coordinate of analyzed image. However these methods are usually purely bottom-up and only consider low-level visual features. In this paper we present preliminary work towards creating a computational framework based on the alternative cognitive relevance hypothesis.
Object Selection by Grouping of Straight Edge Segments in Digital Images

A new method for finding geometric structures in digital images is proposed. An adaptive algorithm of straight line segments extraction is developed for manmade objects description in digital images. It uses an adjustment of oriented filter angle for precise extraction of line corresponding to real edge. Perceptual grouping approach is applied to these segments to obtain simple and complex structures of lines on the base of their crossings. Initial image is presented as a collection of closed structures with their locations and orientations. Applications to real aerial, satellite and radar images show a good ability to separate and select specific objects like buildings and other line-segment-rich structures.
Path Planning for Unmanned Vehicles Using Ant Colony Optimization on a Dynamic Voronoi Diagram

The objective is an unmanned vehicle is to complete a given mission, i.e., to arrive at a given destination safely within a prespecified time. As a result, the basic pathplanning problem discussed in this paper is the problem of finding a short (fuel-optimal) and collision-free path between the two states in an environment with static or dynamic obstacles [13]. For a static environment with perfect information, the traditional path-finding algorithms such as A-star [4] can provide an optimal solution. However, for a dynamic environment with uncertain factors or gradually changing topology, the conventional optimization algorithm may not be applicable. Instead, an adaptive and robust algorithm is favored. Abstract
On the Performance of Kernel Classes

We present sharp bounds on the localized Rademacher averages of the unit ball in a reproducing kernel Hilbert space in terms of the eigenvalues of the integral operator associated with the kernel. We use this result to estimate the performance of the empirical minimization algorithm when the base class is the unit ball of the reproducing kernel Hilbert space.
Evolution at two levels of gene expression in yeast

Despite the greater functional importance of protein levels, our knowledge of gene expression evolution is based almost entirely on studies of mRNA levels. In contrast, our understanding of how translational regulation evolves has lagged far behind. Here we have applied ribosome profiling—which measures both global mRNA levels and their translation rates—to two species of Saccharomyces yeast and their interspecific hybrid in order to assess the relative contributions of changes in mRNA abundance and translation to regulatory evolution. We report that both cis- and trans-acting regulatory divergence in translation are abundant, affecting at least 35% of genes. The majority of translational divergence acts to buffer changes in mRNA abundance, suggesting a widespread role for stabilizing selection acting across regulatory levels. Nevertheless, we observe evidence of lineage-specific selection acting on several yeast functional modules, including instances of reinforcing selection acting at both levels of regulation. Finally, we also uncover multiple instances of stop-codon readthrough that are conserved between species. Our analysis reveals the underappreciated complexity of post-transcriptional regulatory divergence and indicates that partitioning the search for the locus of selection into the binary categories of “coding” versus “regulatory” may overlook a significant source of selection, acting at multiple regulatory levels along the path from genotype to phenotype.
Feature-based Image Segmentation , Texture Synthesis and Hierarchical Visual Data Approximation

Features such as edges and corners play an important role in visual data processing. The proposed research employs features to facilitate image segmentation, texture synthesis and visual data approximation. Edges capture primary color/intensity changes in an image, which can be utilized to accelerate image segmentation. In example-based texture synthesis, edges and ridges/valleys can also help reduce feature discontinuities along texture patch boundaries. In visual data approximation, features need to be preserved to achieve visually satisfactory reconstruction.
Old and Young Users’ White Space Preferences for Online News Web Pages

In the future, the importance and usage rate of the Internet will increase gradually year by year. However, in the case of frequently browsed news web pages, mass news information derived from visual design elements or different layout designs may affect the preferences of users from different demographics and socio-economic backgrounds. This is due to the dissimilarities between their physical and psychological limitations. The purpose of this study is to investigate users’ white-space ratio preferences for news web pages. We tested the top ten online Chinese and English websites, 20 news web pages in total. Two statements were picked from the system usability scale (SUS) and the visual aesthetics of website inventory (VisAWI) to evaluate these samples. We took these two statements as questionnaire questions. They are the following: Q1 “I think that I would like to use this system frequently” and Q2 “Everything goes together on these web pages. ” The design of the questionnaire was based on a five-point Likert scale. The research variables include age, gender, education, occupation, white-space ratio, along with the other demographic properties. Our results indicated that there are significant differences between age, white-space ratio, education, occupation, news sources, computer usage time, and computer usage history. The group aged between 31–45 years old scored all samples higher than other groups. None prefer too high (90%) or too low (50%) white-space ratio. It is necessary to accumulate more preferences from different groups prior to the coming age of AI and machine learning.