Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Coding-Based Cooperative ARQ Scheme

In this paper we introduce a novel Automatic Repeat reQuest (ARQ) scheme for cooperative wireless networks. Our scheme adopts network coding techniques in order to enhance the total bandwidth of the network by minimizing the total number of transmissions. The performance of the proposed approach is evaluated by means of computer simulations and compared to other cooperative schemes, while an analytical solution is provided to validate the results.
Network Anomaly Detection using PSO-ANN

In this work, the continue from the last research work done [20], thus it is proposed a data mining based anomaly detection system, aiming to detect volume anomalies, using Simple Network Management Protocol (SNMP) monitoring. The method is novel in terms of combining the use of Digital Signature of Network Segment (DSNS) with the evolutionary technique called Particle Swarm Optimization (PSO)[5] and neural network training, applied in a real data set. PSO is a high efficient heuristic technique with low computational complexity, developed in 1995 by Kennedy and Eberhart [1] inspired by social behavior of bird flocking. The DSNS is a baseline that consists of different normal behavior profiles to a specific network device or segment, generated by the GBA tool (Automatic Backbone Management), using data collected from SNMP objects. The proposed anomaly detection system uses the SVM in order to clusterize the traffic collected by SNMP agents and its respective DSNS. The PSO is combined with the SVM in order to improve performance and quality of the solution in the clusterization and calculation of clusters centroids. Tests were carried out using a real network environment in the Techno India University, Kolkata. Numerical results have been shown that the obtained detection and false alarm rates are promising. It is also implemented the deterministic method proposed in order to detect anomalies on the same dataset, so that both methods could be compared.
A Variational Approach to Moir E Pattern Synthesis

Moir e phenomena occur when two or more images are nonlinearly combined to create a new \superposition image". Moir e patterns are patterns that don't exist in any of the original images but appear in the super-position image as the result of a multiplicative superposition rule. The topic of moir e pattern synthesis deals with creating images that, when superimposed, will reveal certain desired moir e patterns. Conditions ensuring that a desired moir e pattern will be present in the superposition of two images are known, however they do not specify these images uniquely. The freedom in choosing the superimposed images can be exploited to produce various degrees of visibility and ensure desired properties. Performance criteria for the images that measure when one superpo-sition is better than another are introduced. These criteria are based on the visibility of the moir e patterns to the human visual system and on the digitization which takes place when presenting the images on discrete displays. We here propose to resolve the freedom in moir e synthesis by choosing the images that optimize the chosen criteria.
Can Failure Signal Success? Evidence from Withdrawn M&A Deals

In a recent paper Jacobsen (2012) argues that the motive for withdrawing from acquisition deals can be used as an instrument to identify high quality CEOs. We employ a research design that allows us to examine whether CEOs that abandon acquisitions when the purchase price is no longer justifiable are in fact more focused on pursuing corporate investments that create superior value for shareholders. We find that CEOs that withdraw from potentially expensive deals make discernibly better acquisition decisions than the average CEO in their return to the market for corporate control, corroborating that the favourable market reaction around such withdrawals reflects a credible signal pointing to superior CEO quality in the M&A setting. We also show that this superior quality is not reflected in pre-withdrawal acquisition returns which implies that the positive information conveyed by price induced deal cancellations is novel and not previously available to the market. Overall, the results of this paper indicate that the acquisition withdrawal motive has important corporate investment and capital market implications for acquiring firms and their shareholders beyond the withdrawal announcement.
Chemical Composition and Anti-Inflammatory Effect of Ethanolic Extract of Brazilian Green Propolis on Activated J774A.1 Macrophages

The aim of this study was to investigate the chemical composition and anti-inflammatory effect of ethanolic extract of Brazilian green propolis (EEP-B) on LPS + IFN-γ or PMA stimulated J774A.1 macrophages. The identification and quantification of phenolic compounds in green propolis extract were performed using HPLC-DAD and UPLC-Q-TOF-MS methods. The cell viability was evaluated by MTT and LDH assays. The radical scavenging ability was determined using DPPH• and ABTS•+. ROS and RNS generation was analyzed by chemiluminescence. NO concentration was detected by the Griess reaction. The release of various cytokines by activated J774A.1 cells was measured in the culture supernatants using a multiplex bead array system based on xMAP technology. Artepillin C, kaempferide, and their derivatives were the main phenolics found in green propolis. At the tested concentrations, the EEP-B did not decrease the cell viability and did not cause the cytotoxicity. EEP-B exerted strong antioxidant activity and significantly inhibited the production of ROS, RNS, NO, cytokine IL-1α, IL-1β, IL-4, IL-6, IL-12p40, IL-13, TNF-α, G-CSF, GM-CSF, MCP-1, MIP-1α, MIP-1β, and RANTES in stimulated J774A.1 macrophages. Our findings provide new insights for understanding the anti-inflammatory mechanism of action of Brazilian green propolis extract and support its application in complementary and alternative medicine.
Reward in the Human Brain During Instrumental Learning With Juice and Money Overlapping Prediction Errors in Dorsal Striatum

[PDF] [Full Text] [Abstract] , May , 2010; 103 (5): 2506-2512. J Neurophysiol Signe Bray, Shinsuke Shimojo and John P. O'Doherty Real Rewards Human Medial Orbitofrontal Cortex Is Recruited During Experience of Imagined and [PDF] [Full Text] [Abstract] , August 23, 2010; . Cereb. Cortex Hackjin Kim, Shinsuke Shimojo and John P. O'Doherty Ventromedial Prefrontal Cortex Overlapping Responses for the Expectation of Juice and Money Rewards in Human [PDF] [Full Text] [Abstract] , March 23, 2011; 31 (12): 4466-4474. J. Neurosci. Ninad Gujar, Seung-Schik Yoo, Peter Hu and Matthew P. Walker of Positive Emotional Experiences Sleep Deprivation Amplifies Reactivity of Brain Reward Networks, Biasing the Appraisal [PDF] [Full Text] [Abstract] , April , 2011; 21 (4): 769-776. Cereb. Cortex Hackjin Kim, Shinsuke Shimojo and John P. O'Doherty Ventromedial Prefrontal Cortex Overlapping Responses for the Expectation of Juice and Money Rewards in Human [PDF] [Full Text] [Abstract] , April 27, 2011; 31 (17): 6296-6304. J. Neurosci. Ryan K. Jessup and John P. O'Doherty Behavior from the Gambler's Fallacy Human Dorsal Striatal Activity during Choice Discriminates Reinforcement Learning
Community-based approach to handle wireless connectivity context data

Connectivity management in heterogeneous wireless networks can be done combining context-aware techniques, aiming at perform optimal handovers. For instance, mobility and QoS predictors use, as input, georeferenced historical network context data. Moreover, the predictor's accuracy is strongly related to the quality of this required data set. In this work we propose a community-based solution to allow mobile users collaborate to discover wireless connectivity islands. Also, we introduce a smart way to summarize and delivery connectivity context data. It is result of a prototying effort and we focus the discussion on its feasibility in terms of storage size, power consumption and QoS metrics.
Simultaneous recovery of scene structure and blind restoration of defocused images

We present an algorithm that uses two or more defocused images of the same scene for recovery of scene structure and simultaneous restoration of sharp image. Defocusation is modeled by convolution with arbitrary known spatially-variant mask unlike the vast majority of published algorithms that used fixed cylindrical or Gaussian mask shapes. In this way it is able to deal with aberrations present in real optical systems. The mask can be given ana- lytically or by a table obtained from physical measurements or generated by a ray tracing algorithm. For simplicity, we apply additional constraint that points of the same depth produce the same mask regardless of their position in the field of view. This assumption can be never- theless relaxed at the cost of higher time or memory require- ments. Algorithm can be easily parallelized and has a potential to be used in real time applications.
Can Saliency Information Benefit Image Captioning Models?

To bridge the gap between humans and machines in image understanding and describing, we need further insight into how people describe a perceived scene. In this paper, we study the agreement between bottom-up saliency-based visual attention and object referrals in scene description constructs. We investigate the properties of human-written descriptions and machine-generated ones. We then propose a saliency-boosted image captioning model in order to investigate benefits from low-level cues in language models. We learn that (1) humans mention more salient objects earlier than less salient ones in their descriptions, (2) the better a captioning model performs, the better attention agreement it has with human descriptions, (3) the proposed saliency-boosted model, compared to its baseline form, does not improve significantly on the MS COCO database, indicating explicit bottom-up boosting does not help when the task is well learnt and tuned on a data, (4) a better generalization ability is, however, observed for the saliency-boosted model on unseen data.
Market orientation in the top 200 British charity organizations and its impact on their performance

Examines the degree to which leading UK charities have adopted the marketing concept in the last five years and how this has affected their performance. Based on Kohli and Jaworski’s (1990) work, an adaptation of the MARKOR scale was used to measure charities’ orientation towards their markets for funds and volunteers (donors). A mail questionnaire was sent to the top 200 UK charity organizations and a response rate of 29 per cent was achieved. Although charities’ orientation towards their donor market is still relatively low, it has significantly increased in the last five years. No differences in the levels of donor market orientation among different type of charities were observed. Donor market orientation was negatively related to the organizational size and the number of departments. A lag effect between donor market orientation and performance was detected. Discusses limitations and recommendations for future research.
A Framework for Developing Spreadsheet Applications in Economics

This article presents a framework for thinking about the different types of spreadsheet modeling applications available for teaching economics. Spreadsheet applications are categorized by the degree to which students are involved in the spreadsheet’s construction and the degree to which students are involved in the mathematics of the model. Examples of applications from each category call attention to the high degree of flexibility that instructors have in designing spreadsheet applications and suggest alternative ways that the instructor can tailor applications to fit his or her specific instructional style and setting. Although the costs associated with developing spreadsheet applications are significant, the authors believe that the costs are manageable, even for instructors who are new to spreadsheets. They conclude that the advantages imparted to students by carefully tailored spreadsheet applications are significant, and they hope to encourage the development and use of such applications.
Multi-agent rendezvous on street networks

In this paper we present an algorithm for finding a distance optimal rendezvous location with respect to both initial and target locations of the mobile agents. These agents can be humans or robots, who need to meet and split while performing a collaborative task. Our aim is to embed the meeting process within a background activity such that the agents travel through the rendezvous location while taking the shortest paths to their respective target locations. We analyze this problem in a street network scenario with two agents who are given their individual scheduled routes to complete with an underlying common goal. The agents are allowed to select any combination of the waypoints along their routes as long as they travel the shortest path and pass through the same potential rendezvous location. The total number of path combinations that the agents need to evaluate for the shortest path increases rapidly with the number of waypoints along their routes. We address this computational cost by proposing a combination of Euclidean and street network distances for a trade-off between the number of queries and a distance optimal solution.
K-Mutual Nearest Neighbour Approach for Clustering Two-Dimensional Shapes Described by Fuzzy-Symbolic Features

In this paper, a new method of representing two- dimensional shapes using fuzzy-symbolic features and a similarity measure defined over fuzzy-symbolic features useful for clustering shapes is proposed. A k-mutual nearest neighborhood approach for clustering two-dimensional shapes is presented. The proposed shape representation scheme is invariant to similarity transformations and the clustering method exploits the mutual closeness among shapes for clustering. The feasibility of the proposed methodology is demonstrated by conducting experiments on a considerably large database of shapes and also, its validity is tested by comparing with the well known clustering methodologies.
Sexual objectification decreases women’s experiential consumption (but not material consumption)

Abstract The current investigation examined our prediction that sexual objectification decreases women’s experiential consumption but not material consumption. Three experiments provided converging support for this prediction. In particular, female participants reported lower tendency to engage in experiential consumption after recalling a past experience of objectification (Studies 1 and 3) and chose a material product over an experiential one after receiving objectifying comments (Study 2). Furthermore, Study 3 found that sexual objectification reduced purchase inclination of experiential products, and this effect did not emerge for material products. These findings contribute to the literature on sexual objectification by showing the impact of sexual objectification on women’s economic decisions and behaviors.
Sparse Kernel Principal Components Analysis for Face Recognition in RGB Spaces

This paper presents a kinds of information fusion algorithm based on multi-channel color image. The color face image is first separated into three pseudo grayscale images: R, G, and B, then the partial characteristics of face is extracted by use of Gabor wavelet transform from each component to be eigenvector in series connection, which will be through dimensionality reduction by sparse kernel principal components to be recognized and classified by the nearest classifier. In order to testify the validity, we make experiment by use of XM2VTS color face dataset and the experimental result supports the proposed method.
The P-range tree: a new data structure for range searching in secondary memory

1 Introduction and motivation External 2-dimensional range searching is a fundamental problem with many applications in relational, object-oriented, spatial, and temporal databases. For example, interval intersection can be reduced to 2-sided, 2-dimensional searching and indexing class hierarchies of objects to 3-sided, 2-dimensional searching. In this paper we introduce a new data structure called the p-range tree for performing 2-dimensional range searching in secondary memory. Given n points on the plane and a disk with block size B, the p-range tree uses O(s) disk blocks of storage and answers 2-and 3-sided queries in O(logB n + t/B + ZC*(B)) disk I/O's' Here t is the number of points in the query. We also give a secondary memory data structure for answering general 2-dimensional queries with the same I/O bound. The successful realization of any data model in a large-scale database requires supporting its language features with efficient secondary storage manipulation. Consider the relational data model of [8]. While the declarative programming features (relational calculus and algebra) of the model are important, it is crucial to support these features with data structures for searching and updating that make optimal use of secondary storage. B-trees and their variants B+-trees [1,9] are examples of such data structures. They have been an unqualified success in supporting external dynamic l-dimensional range searching in relational database systems. Using P-range trees we also give data structures for range searching in main memory. For orthogo nal range searching in d-dimension the data structure occupies O(n logdS1 n) space and answers queries in O(ZL*(n) logdS2 n + t) time. The time-space product of this data structure is better than that of previously known data structures [5,25]. The general data structure problem underlying efficient secondary storage manipulation for many data models is external d-dimensional range searching. In particular, 2-dimensional range searching (in various forms) is crucial in the implementation of object-oriented, hierarchical, temporal, spatial, and constraint databases [18,19,27,35]. B-trees, that perform l-dimensional range searching well, are inefficient at handling more general problems like two and higher dimensional range searching. We also give a lower bound for range-searching in secondary memory that shows that 2-dimensional range searching is inherently harder in secondary storage than in main memory. We show that in order to be able to answer any 2-dimensional query in O(Iogi n + t/B) disk I/O's (where c is an arbitrary positive constant), a data structure must occupy sZ($(log g)/ log …
Reliance on E-Shopping Vehicles: Conceptualization, Measurement, and Cross-National Market Differences

ABSTRACT This study introduces the construct of “vehicle purchase reliance” (VPR) which integrates e-vehicle-specific awareness, familiarity, browsing, and purchasing into a single conceptual dimension. In a sample of 1186 Millennial university students, 863 Chinese and 323 US, the VPR Index was applied to a broad range of 23 e-shopping vehicles. Its distinction from its four components was empirically validated. In a cross-national comparison, Chinese and US samples were found to differ in VPR on 19 of the 23 e-vehicles. As hypothesized, US shoppers gravitated toward e-vehicles that are individualistic/high vendor trust dependent and Chinese consumers to e-vehicles that are collectivistic/low vendor trust dependent.
Solving the person-based multitask shift generation problem with breaks

Good rosters have many benefits for an organization, such as lower costs, more effective utilization of resources and fairer workloads. This paper introduces the person-based multitask shift generation problem with breaks. The problem is to construct a set of shifts such that the staff demand at each timeslot is covered as well as possible. A set of real-world instances derived from the actual problems solved for various companies is presented, along with our results. This research has contributed to better systems for our industry partners.
Workload-aware Power Management of Cluster Systems

Along with the increased awareness of energy cost, power management becomes a big issue for clusters. In this study, we investigate the workload aware power management techniques for cluster systems and propose a new power management policy. The considered power management techniques are dynamic workload consolidation and usage of dynamic power range enabled by low power states on servers. The power management policy has been implemented on OpenPBS with Maui, which reduces about ten percent power consumption and optimizes workload distribution to lower the impact on performance.
Visual Embedding: A Model for Visualization

The authors propose visual embedding as a model for automatically generating and evaluating visualizations. A visual embedding is a function from data points to a space of visual primitives that measurably preserves structures in the data (domain) within the mapped perceptual space (range). The authors demonstrate its use with three examples: coloring of neural tracts, scatterplots with icons, and evaluation of alternative diffusion tensor glyphs. They discuss several techniques for generating visual-embedding functions, including probabilistic graphical models for embedding in discrete visual spaces. They also describe two complementary approaches--crowdsourcing and visual product spaces--for building visual spaces with associated perceptual--distance measures. In addition, they recommend several research directions for further developing the visual-embedding model.
Surface orientation and curvature from differential texture distortion

A unified differential geometric framework for estimation of local surface shape and orientation from projective texture distortion is proposed, based on a differential version of the texture stationarity assumption introduced by Malik and Rosenholtz. This framework allows the information content of the gradient of any texture descriptor defined in a local coordinate frame to be characterized in a very compact form. The analysis encompasses both full affine texture descriptors and the classical "texture gradients". For estimation of local surface orientation and curvature from uncertain observations of affine texture distortion, the proposed framework allows the dimensionality of the search space to be reduced from five to one. <<ETX>>
A generic quantitative relationship between quality of experience and quality of service

Quality of experience ties together user perception, experience, and expectations to application and network performance, typically expressed by quality of service parameters. Quantitative relationships between QoE and QoS are required in order to be able to build effective QoE control mechanisms onto measurable QoS parameters. Against this background, this article proposes a generic formula in which QoE and QoS parameters are connected through an exponential relationship, called IQX hypothesis. The formula relates changes of QoE with respect to QoS to the current level of QoE, is simple to match, and its limit behaviors are straightforward to interpret. It validates the IQX hypothesis for streaming services, where QoE in terms of Mean Opinion Scores is expressed as functions of loss and reordering ratio, the latter of which is caused by jitter. For web surfing as the second application area, matchings provided by the IQX hypothesis are shown to outperform previously published logarithmic functions. We conclude that the IQX hypothesis is a strong candidate to be taken into account when deriving relationships between QoE and QoS parameters.