Miracle-dz/UniFAR-FTUs huggingface dataset:
A Web-based Interactive Visual Graph Analytics Platform

This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decision making. Networks can be visualized and explored within seconds by simply drag-and-dropping a graph file into the web browser. The structure, properties, and patterns of the network are computed automatically and can be instantly explored in real-time. At the heart of GraphVis lies a multi-level interactive network visualization and analytics engine that allows for real-time graph mining and exploration across multiple levels of granularity simultaneously. Both the graph analytic and visualization techniques (at each level of granularity) are dynamic and interactive, with immediate and continuous visual feedback upon every user interaction (e.g., change of a slider for filtering). Furthermore, nodes, edges, and subgraphs are easily inserted, deleted or exported via a number of novel techniques and tools that make it extremely easy and flexible for exploring, testing hypothesis, and understanding networks in real-time over the web. A number of interactive visual graph analytic techniques are also proposed including interactive role discovery methods, community detection, as well as a number of novel block models for generating graphs with community structure. Finally, we also highlight other key aspects including filtering, querying, ranking, manipulating, exporting, partitioning, as well as tools for dynamic network analysis and visualization, interactive graph generators, and a variety of multi-level network analysis, summarization, and statistical techniques.
Using Reduced Execution Flow Graph to Identify Library Functions in Binary Code

Discontinuity and polymorphism of a library function create two challenges for library function identification, which is a key technique in reverse engineering. A new hybrid representation of dependence graph and control flow graph called Execution Flow Graph (EFG) is introduced to describe the semantics of binary code. Library function identification turns to be a subgraph isomorphism testing problem since the EFG of a library function instance is isomorphic to the sub-EFG of this library function. Subgraph isomorphism detection is time-consuming. Thus, we introduce a new representation called Reduced Execution Flow Graph (REFG) based on EFG to speed up the isomorphism testing. We have proved that EFGs are subgraph isomorphic as long as their corresponding REFGs are subgraph isomorphic. The high efficiency of the REFG approach in subgraph isomorphism detection comes from fewer nodes and edges in REFGs and new lossless filters for excluding the unmatched subgraphs before detection. Experimental results show that precisions of both the EFG and REFG approaches are higher than the state-of-the-art tool and the REFG approach sharply decreases the processing time of the EFG approach with consistent precision and recall.
Performance loss minimization in cooperative networks based on quantized channel feedback

In wireless networks, having channel state information (CSI) at some central node is essential for most resource allocation problems. We turn our attention to the uplink transmission in a cellular network where the cell users are assisted by a single relay; our goal is to maximize sum rate over the users. Understanding that assuming perfect CSI is a purely theoretical construct, we investigate the scenario where only quantized CSI (QCSI) is available at a central node. Here we design quantization levels which minimize the performance loss in terms of loss in sum rate due to quantization in the network. Next, assuming a finite capacity for the CSI feedback link, we argue that the performance loss may be further reduced through proper bit allocation, i.e., quantization of different links with different precision. We further show that for any given network, there is always savings in terms of CSI bits required as long as optimal bit allocation is deployed. Finally, we propose a lower bound on performance loss achieved through optimal bit allocation.
Long Run and Short Run Linkages between Stock Indices in Bombay Stock Exchange: A Structural Cointegration Approach

We have analyzed the short term and long term linkages between the sectoral indexes of Bombay Stock Exchange in India by using the daily data on nine sectoral indexes for the period 23rd August 2004 to 31st June 2010. After confirming the same order of integration of the study variables from the unit root test incorporating endogenously determined structural breaks, structural cointegration test has been carried out followed by VECM , Impulse response functions and variance decomposition analysis. The cointegration analysis results indicate that most of the sectoral indexes in India are cointegrated with at least one of the other indexes indicating that the sectoral indexes posses’ useful information about the movements of other indexes. This is confirmed by the Impulse response function analysis also. The comovements between the sectoral indices indicate that the Bombay stock exchange is not weak form efficient and the possibility of sectoral portfolio diversification is limited.
Does improving market access help de-industrialize lagging regions? 'Leaking by linking' in Ghana 1

One of the main insights of the New Economic Geography is that as interregional transport costs fall, agglomeration economies and interregional migration will lead to higher concentration of firms and workers in economically dense locations. In other words, improving inter-regional accessibility can increase spatial differences in production between lagging and leading regions. In this paper, we examine the importance of infrastructure connectivity, local human capital and other regional endowments in the location decisions of manufacturing firms in Ghana. We find that agglomeration economies and local education levels have a positive effect on location decisions and productivity of Ghanaian manufacturing. Proximity to markets also matters: longer travel time to the nearest city of 100,000 or more reduces the attractiveness of a location. Further, improvements in market access increases economic density in the leading Southern region, where as the North loses manufacturing activity with better connectivity to markets. We also find that the placement of transport links involves spatial efficiency-equity tradeoffs -- improving connectivity in the North reduces national productivity by 0.15 percent; in contrast improving connectivity in the South increases manufacturing productivity by 3.1 percent nationally. While economically integrating lagging regions is an important policy priority in Ghana, the government may want identify instruments that are better suited to directly improve welfare of local residents. Lessons from the WDR2009 “Reshaping Economic Geography” calling for investments in health and education in lagging areas are likely to be more useful.
A Design of a High-Speed and High-Efficiency Capsule Endoscopy System

This paper presents a high-speed and high-efficiency capsule endoscopy system. Both a transmitter and a receiver were optimized for its application through an analysis of the human body channel. ON-OFF keying modulation is utilized to achieve low power consumption of the in-body transmitter. A low drop output regulator is adopted to prevent performance degradation in the event of a voltage drop in the battery. The receiver adopts superheterodyne structure to obtain high sensitivity, considering the link budget from the previous analysis. The receiver and transmitter were fabricated using the CMOS 0.13-μm process. The output power of the transmitter is -1.6 dB·m and its efficiency is 27.7%. The minimum sensitivity of the receiver is -80 dB·m at a bit error ratio (BER) of 3 × 10 . An outer wall loop antenna is adopted for the capsule system to ensure a small size. The integrated system is evaluated using a liquid human phantom and a living pig, resulting in clean captured images.
A Collaborative Framework to support Scheduling decisions

The scheduling process involves many actors representing different manufacturing perspectives, such as management, customer-service, manufacturing, quality assurance and distribution. There is a great need to give support to those actors. In this work we propose to address the scheduling problem through a collaborative framework Our model considers the interaction between an agent based scheduling module and a group decision support module. The agent based scheduling module encapsulate several scheduling algorithms generating a set of candidate solutions, each one of them according a specific criteria. The group decision support module supports the interaction between the scheduling actors envisaging the selection of a scheduling solution.
Ultracompact Binaries as Bright X-Ray Sources in Elliptical Galaxies

Chandra observations of distant (D ~ 10 Mpc) elliptical galaxies have revealed large numbers of low-mass X-ray binaries (LMXBs) accreting at > 10-8 M☉ yr-1. The majority of these LMXBs reside in globular clusters (GCs), and it has been suggested that many of the field LMXBs also originated in GCs. We show here that ultracompact (UC) binaries with orbital periods of 8-10 minutes and He or C/O donors of 0.06-0.08 M☉ naturally provide the observed from gravitational radiation losses alone. Such systems are predicted to be formed in the dense GC environment, a hypothesis supported by the 11.4 minute binary 4U 1820-30, the brightest persistent LMXB in a Galactic GC. These binaries have short enough lifetimes (<3 × 106 yr) while bright (L > 1038 ergs s-1) that we calculate their luminosity function under a steady state approximation. This yields a luminosity function slope in agreement with that observed for 6 × 1037 ergs s-1 < L < 5 × 1038 ergs s-1, encouraging us to use the observed numbers of LMXBs per GC mass to calculate the accumulated number of UC binaries. For a constant birthrate over 8 Gyr, the number of UC binaries that have evolved through this bright phase is ~4000 in a 107 M☉ GC, consistent with dynamical interaction calculations. Perhaps most importantly, if all UC binaries become millisecond radio pulsars, then the observed normalization agrees with the inferred number of millisecond radio pulsars in 47 Tuc and Galactic GCs in general.
Expertise Mining

In this paper we present our approach for expertise mining, addressing several research problems such as expertise topic extraction, expert finding and expert profiling. We propose a hybrid solution inspired by different research fields such as expert finding, competency management, terminology extraction, keyword extraction and concept extraction. We introduce an expertise benchmarking dataset gathered by exploiting information about workshop committee members and we present Saffron, a system designed to give an overview of research areas and experts and to assist users in finding skilled individuals.
JAWS: A Framework for High-performance Web Servers

Developers of communication software face many challenges. Communication software contains both inherent complexities, such as fault detection and recovery, and accidental complexities, such as the continuous re-rediscovery and re-invention of key concepts and components. Meeting these challenges requires a thorough understanding of object-oriented application frameworks and patterns. This paper illustrates how we have applied frameworks and patterns for communication software to develop a high-performance Web server called JAWS. JAWS is an object-oriented framework that supports the configuration of various Web server strategies, such as a Thread Pool concurrency model with asynchronous I/O and LRU caching vs. a Thread-per-Request concurrency model with synchronous I/O and LFU caching. Because JAWS is a framework, these strategies can be customized systematically and measured both independently and collaboratively to determine the best strategy profiles. Using these profiles, JAWS can statically and dynamically adapt its behavior to deploy the most effective strategies for a given software/hardware platform and workload. JAWS’ adaptive software features make it a powerful application framework for constructing highperformance Web servers.
Multi-resolution eddy detection from ocean color and Sea Surface Temperature images

We propose a multi resolution method for automatic detection of eddies on both simulated and satellite Sea Surface Temperature (SST) or ocean color images. Our approach consists in a curvelet based analysis of image fronts along circles with specified radius values. The main interests of the proposed method are: • It is carried out on image gradient, and is thus not sensitive to the choice of binarization threshold; • With the multi-resolution aspect and the anisotropic spatial support shape of curvelet elements, we can deal with no perfectly circular eddies (curvelet width) and we also take into account the image regularity to distinguish between real eddies and noise (curvelet length). We have tested the method on several modeled and satellite images in the North Western Mediterranean sea and the results prove the relevance of the proposed approach.
Conflict in inter-organisational virtual communication

This study explores the nature of conflict in virtual communication in the course of inter-organisational collaborations. Conflict appears to exist inherently when organisations cooperate together because each company operates with different goals, norms and values, which are vital considerations for successful business collaborations. Special attention, therefore, needs to be paid to gaining a grounded understanding of conflict in the context of virtual communication in the interorganisational business collaborations of today. This research draws on fieldwork carried out over five months, using a multiple-case study approach, involving four cases of inter-organisational collaborations between a large high-tech corporation in Taiwan and its four supplier companies in Korea. In addition, participant observation was employed as the main method of data collection in this study, which allowed for this researcher to gain rich data in a direct way. The collected data included daily logs based on observations, in addition to interviews and documentation. This resulted in an extensive amount of useful information being gathered, which was analysed, categorised, interpreted, and summarised in relation to theory generalisation. In this inter-organisational research setting, it was found that three patterns of interorganisational conflict, namely: business strategic conflict, cultural conflict and organisational process conflict interact to influence participants’ communication media selection and, in turn, conflict is influenced by the selected media. Moreover, it was found that conflict is expressed in email communication in a stylised way that is significantly different from spoken conversation and it could lead to conflict escalation and the subsequent termination of business collaboration. Based on the research findings, a comprehensive framework was established to describe and explain the interrelations between conflict transformation and computer-mediated communication (CMC) in inter-organisational collaborations.
Efficient Computation of Group Optimal Sequenced Routes in Road Networks

The proliferation of location-based social networks allows people to access location-based services as a group. We address Group Optimal Sequenced Route (GOSR) queries that enable a group to plan a trip with a minimum aggregate trip distance. The trip starts from the source locations of the group members, goes via a predefined sequence of different point of interests (POIs) such as a restaurant, shopping center and movie theater, and ends at the destination locations of the group members. The aggregate trip distance can be the total or the maximum trip distance of the group members. We introduce a novel approach to efficiently compute group optimal sequenced routes in road networks. We exploit elliptical properties to refine the POI search space and develop efficient algorithms for GOSR queries. Experiments show that our approach outperforms a naive approach significantly in terms of processing time and I/Os.
Visual attention can enhance or prioritize the processing of specific stimuli over the overwhelming number of other sensory inputs by selecting spatial locations

Performance on a visual task is improved when attention is directed to relevant spatial locations or specific visual features. Spatial attention can be directed either voluntarily (endogenously) or automatically (exogenously). However, feature-based attention has only been shown to operate endogenously. Here, we show that an exogenous cue to a visual feature can lead to improved performance in visual search. Response times were measured as subjects detected or discriminated a target oval among an array of disks, each with a unique color. An uninformative colored cue was flashed at the beginning of each trial that sometimes matched the location and/or color of the target oval. Subjects detected or discriminated the target faster when the color of the cue matched the color of the target, regardless of the cue’s location relative to the target. Our results suggest evidence for a previously unknown exogenous cuing mechanism for feature-based attention.
Understanding Risk-taking Behavior in Bullies, Victims, and Bully Victims Using Cognitive- and Emotion-Focused Approaches

Bullying and risky behavior are two common problems among adolescents and can strongly affect a youth’s overall functioning when both coexist. Some studies suggest that bullying in adolescence may promote risky behavior as a coping strategy to deal with victimization related stress. Other studies consider bullying as an outcome of high-risk behavior. Despite the association between the two is well-established, no study has examined the risk-taking patterns among bullying groups (i.e., bully, victim, and bully victim). This study attempted to elucidate the potential relationships between bullying and risk-taking by addressing the two models: a cognitive-focused model and an emotion-focused model of risk taking, and to clarify how adolescents’ characteristics in risk taking associate with bullying outcomes. Method: 136 Chinese adolescents (Mean Age = 14.5, M = 65, F = 71) were recruited and grouped according to bullying identity: Bully (n = 27), Victim (n = 20), Bully victim (n = 37) and Control (n = 52). Cognitive Appraisal of Risky Events (CARE) questionnaire was used to measure participants’ expectancies about the risks, benefits and involvement associated with risky activities. Cambridge Gambling Task (CGT) was administered to capture the emotion-laden process in risk taking. Results: Cognitively, Bully was associated with an overestimation of risk while Victim was associated with an underestimation of risk and overrated benefit. Bully victim exhibited a unique pattern with an overestimation of benefit and risk. All study groups projected higher involvement in risky behavior. Behaviorally, both Bully and Bully victim were associated with high risk modulation whereas Victim was associated with impulsive decision-making. Interestingly, compared with bully, bully victim had significantly higher bullying scores, suggesting a wider range and more frequent bullying activities. In conclusion, Bully maybe a group of adolescents that is vigilant in situational deliberation and risk modulation while Victims with high impulsivity, are more likely to place themselves in risky situations. Bully victims presented the combined pattern of the two pure groups and associated with the highest risk-taking propensity. Better picture of risk taking pattern associated with different groups was illustrated, allowing better matching for future prevention and intervention program for distinct bullying individuals.
Weighted least squares method for the approximation of directional derivatives

Using the facet model we design a family of filters for the approximation of partial derivatives of the digital image surface. Prior information (e.g., local dominant orientation) is incorporated in a two-dimensional weight function. A weighted least squares estimation of the facet parameters is applied in order to design the proposed filters. Exemplary application of the proposed filters to fingerprint image segmentation is also presented.
Speech Recognition Experiments with Audiobooks

Under real-life conditions several factors may be present that make the automatic recognition of speech difficult. The most obvious examples are background noise, peculiarities of the speaker's voice, sloppy articulation and strong emotional load. These all pose difficult problems for robust speech recognition, but it is not exactly clear how much each contributes to the difficulty of the task. In this paper we examine the abilities of our best recognition technologies under near-ideal conditions. The optimal conditions will be simulated by working with the sound material of an audiobook, in which most of the disturbing factors mentioned above are absent. Firstly pure phone recognition experiments will be performed, where neural net-based technologies will also be tried as well as the conventional Hidden Markov Models. Then we move on to large vocabulary recognition, where morph-based language models are applied to improve the performance of the standard word-based technology. The tests clearly justify our assertion that audiobooks pose a much easier recognition task than real-life databases. In both types of tasks we report the lowest error rates we have achieved so far in Hungarian continuous speech recognition.
Learning-based power management for multi-core processors via idle period manipulation

Learning-based dynamic power management (DPM) techniques, being able to adapt to varying system conditions and workloads, have attracted lots of research attention recently. To the best of our knowledge, however, none of the existing learning-based DPM solutions are dedicated to power reduction in multi-core processors, although they can be utilized by treating each processor core as a standalone entity and conducting DPM for them separately. In this work, by including task allocation into our learning-based DPM framework for multi-core processors, we are able to manipulate idle periods on processor cores to achieve a better tradeoff between power consumption and system performance. Experimental results show that the proposed solution significantly outperforms existing DPM techniques.
Preemptive RAID Scheduling

Emerging video surveillance, large-scale sensor networks, and storage-bound Web applications require large, high-performance, and reliable storage systems with high data-throughput as well as short response times for interactive requests. These conflicting requirements call for quality of service (QoS) support. These storage systems are often implemented using Redundant Arrays of Independent Disks (RAID). In this paper we investigate the effectiveness of preemptive diskscheduling algorithms to achieve better QoS. We present an architecture for QoS-aware RAID systems based on Semi-preemptible IO [5]. We showhenand how to preempt IOs to improve the overall QoS of the RAID. Using our simulator for preemptible RAID systems, we evaluate the benefits and estimate the overhead of the proposed approach.