Miracle-dz/UniFAR-FTUs huggingface dataset:
Screen-Replay : A Session Recording and Analysis Tool for DrScheme

Approaches to teaching “Introduction to Programming” vary considerably. However, two broad categories may be considered: product oriented vs process oriented. Whereas, in the former the final product is most significant, in the latter the process for achieving the final product is also considered very important. Process oriented programming courses strive to equip students with good programming habits. In such courses, assessment is challenging, since it requires the observation of how students develop their programs. Conventional methods and tools that assess final products are not adequate for such observation. This paper introduces a tool for non-intrusive observation of program development process. This tool is designed to support the process oriented approach of “How to Design Programs” (HtDP) and is implemented for the DrScheme environment. The design, implementation and utility of this tool is described with examples.
A non-dominated sorting hybrid algorithm for multi-objective optimization of engineering problems

Among numerous multi-objective optimization algorithms, the Elitist non-dominated sorting genetic algorithm (NSGA-II) is one of the most popular methods due to its simplicity, effectiveness and minimum involvement of the user. This article develops a multi-objective variation of the Nelder-Mead simplex method and combines it with NSGA-II in order to improve the quality and spread of the solutions. The proposed hybrid algorithm, called non-dominated sorting hybrid algorithm (NSHA), is compared with NSGA-II on several constrained and unconstrained test problems. The higher convergence rate and the wider spread of solutions obtained with NSHA make this algorithm a good candidate for engineering problems that require time-consuming simulation and analysis. To demonstrate this fact, NSHA is applied to the design of a carbon fibre bicycle stem simultaneously optimized for strength, weight and processing time.
SoPD -- A New Consensus Function for the Ensemble Clustering Problem

This paper presents a consensus function based on a new formulation for the median partition problem to address the problem of ensemble clustering. It is based on the underlying idea of minimizing the distance between pairs of objects identified as the most dissimilar among the set of all available objects. By initially finding a pairing of objects and minimizing specifically such dissimilarities a more robust heuristic is achieved to solve the problem of finding a median object, especially in cases where the objects variability is accentuated. The performance of this method is assessed in relation to other well known ensemble clustering methods.
A Dynamic Asset Pricing Model with Time-Varying Factor and Idiosyncratic Risk

This paper uses a multivariate GARCH model to account for time variation in factor loadings and idiosyncratic risk in improving the performance of the CAPM and the three-factor Fama--French model. I show how to incorporate time variation in betas and the second moments of the residuals in a very general way. Both the static and conditional CAPM substantially outperform the three-factor model in pricing industry portfolios. Using a dynamic CAPM model results in a 30% reduction in the average absolute pricing error of size/book-to-market portfolios. Ad hoc analysis shows that the market beta of a value-minus-growth portfolio decreases whenever the default premium increases as well as during economic recessions. Copyright The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org. , Oxford University Press.
Pension Politics : Consensus and Social Conflict in Ageing Societies

Introduction: Policy Change in Difficult Times 1. The Origins of Diversity in Pension Reform Processes: A Theoretical Approach 2. France: Still a 'Societe Bloquee'? 3. Belgium: Seeking to Adapt in a Crumbling Consensual World, One Small Step at a Time 4. Sweden: Do Unions Still Matter? : Committees Instil a Radical Pension Reform 5. United Kingdom: A Marriage with the Private Sector? 6. Conclusion: Comparative Tests of the Hypotheses and their Application to other Industrialized Countries
Real-time pedestrian detection using SVM and AdaBoost

This project presents an application of pedestrian detection system to reduce the number and severity of vehicle-pedestrian accident by active safety vehicle. It is important to detect pedestrian efficiently and accurately in many computer vision applications, such as intelligent transportation systems and safety driving assistant systems. In this system, we are presenting a pedestrian detection method based on images. We are using Ada-Boost algorithm and cascading methods to segment pedestrian candidates from image. To confirm whether each candidate is pedestrian or not a pedestrian. Recognizing classifier is skilled with support vector machine (SVM). We are giving input features used for SVM training are mined from both the sample gray images and edge images to the system.
A Fast and Robust Optimistic Total Order Broadcast for Online Video Games

Total order broadcast is a fundamental building block for many distributed systems. In recent online video games, players actions within a particular game instance need to follow a total order in order to ensure game consistency. The main challenge is to preserve game playable by keeping latency as low as possible. Relying on a single central server incurs high cost for the game editor and is vulnerable to denial of service attacks. For these reasons, a decentralized architecture is desirable. We present a total order broadcast protocol that is both fast and robust with no single point of failure. In its optimistic setting, it delivers message in just one step, which is optimal, and in two steps in its pessimistic setting. Our protocol can tolerate up to n/2-1 crashes where n is the number of processes in the system. When failures do occur, the latency is at worst linear in the number of consecutive crashes.
Handling Influence among Multiple Applications in a Smart Space

As a typical ubiquitous/pervasive computing environment, a smart space usually consists of many sensors/actuators for interacting with the physical environment together with many context-aware applications providing users with kinds of services. However, as context-aware applications deployed in a smart space share the same physical environment, they may have influences on one another. These influences, if not carefully handled, might lead to performance degradation of running applications and further affect user experience. To guarantee the user experience, mechanisms for handling such influences are needed. In this paper, we model context-aware applications in a smart space together with their pairwise influences by a special colored weighted directed acyclic graph(CWDAG)-Influence Graph, and propose an efficient genetic algorithm for finding a fairly good plan for configuring applications running in the same smart space based on the influence graph. Exhausted simulations are carried out to show the effectiveness and efficiency of the proposed algorithm. Keywords-context-aware computing; influence handling; smart space;
Polynomial time algorithms for multicast network code construction

The famous max-flow min-cut theorem states that a source node s can send information through a network (V, E) to a sink node t at a rate determined by the min-cut separating s and t. Recently, it has been shown that this rate can also be achieved for multicasting to several sinks provided that the intermediate nodes are allowed to re-encode the information they receive. We demonstrate examples of networks where the achievable rates obtained by coding at intermediate nodes are arbitrarily larger than if coding is not allowed. We give deterministic polynomial time algorithms and even faster randomized algorithms for designing linear codes for directed acyclic graphs with edges of unit capacity. We extend these algorithms to integer capacities and to codes that are tolerant to edge failures.
THE DEMAND FOR MONEY IN PAPUA NEW GUINEA: EVIDENCE FROM COINTEGRATION TESTS / LA DEMANDE DE MONNAIE EN PNG: DES RESULTATS TIRES D'UN TEST DE COINTÉGRATION

In this study an attempt is made to analyse the long run money demand function of a small, open and less developed economy, this economy being the PNG economy, using Johansen's cointegration methodology. The evidence suggests that the variables entering into the demand for narrow money equation may not form a cointegrated system unless the nominal effective exchange rate is included. In contrast, the results indicate that the presence of a long run demand function for broad money when real gross domestic product, interest rates, price level and nominal effective exchange rate are included in the system. All the estimated elasticities proved significant and were cor rectly signed. In particular, the long-run effect of change in exchange rate on money demand is positive. The results suggest that the modelling money demand as real money demand in PNG is not a correct procedure. Further, interest rate variables play an im portant role in determining the demand for money in Papua New Guinea. The evidence from this paper suggests that only M3 can be considered as a better candidate for a monetary aggregate indicator and M3 targeting is likely to be associated with dynamic instability and interest rate overkill.
Corporate venture capital as a real option in the markets for technology

Despite the fact that one of the main goals of corporate venture capital (CVC) investments in high-tech industries is to gain a window on future technologies, the relationship between CVC investments and strategies used to acquire technologies in the markets, such as licensing, has not been adequately explored. To address this gap, we build on the real option literature suggesting that CVC investments can be used as real options in the markets for technology. Accordingly, we formulate hypotheses about key drivers of the option value of CVC investments and the decision to exercise the option. Using a longitudinal dataset based on 604 dyads formed by a sample of global pharmaceutical firms and their external technology partners, we find that corporate investors' scientific capabilities, technological domains, research pipelines, and the resolution of exogenous uncertainty related to partner firms' technologies impact investors' decisions on CVC investments and ex post technology acquisition. In our research setting, the most common way to exercise the option post-CVC investment is via technology licensing.
Time Scheduling, Subcarrier and Power Allocation in Multi-Service Downlink OFDMA Systems

In this paper we develop a time Scheduling, Subcarrier and Power Allocation scheme for Multi-Service Downlink OFDMA Systems. We propose a two step solution where time scheduling is separated from subcarrier and power allocation decisions. The system is modeled as a linear dynamic system with the aim of minimizing a quadratic cost function. The proposed scheduler is a Linear-Quadratic-Regulator (LQR) which achieves fairness among users by proposing an instantaneous data rate for each user in each time slot. The data rate vector proposed by the regulator is then fed as a constraint to the subcarrier and power allocation problem. This problem is formulated as a constrained convex optimization problem and we develop algorithms for subcarrier and power allocation to achieve the data rates proposed by LQR. Simulation results show good performance and better fairness among users.
Detecting Similar Files Based on Hash and Statistical Analysis for Digital Forensic Investigation

In modern society, rapid increase in using mass storage devices, and it makes forensic examiners find important evidence hardly in the focus of time-consuming. Examiners spend much time to search files related to the case in variety of storage devices. Recently, NIST(National Institute of Standards and Technology) has developed a new database, called NSRL(National Software Reference Library), which contains hash values of trusted operating systems and programs(1). As establishing this database service in public, NIST contribute to reduce time-consuming in searching file and detecting forgery on the devices. On the other hand, the hash value based detection technique cannot be distinguished the similarity from other files perfectly. In this paper, therefore, we present novel methods for detecting similar files considering the known fuzzy hashing and statistical analysis and developed out prototype tool, called SimFD.
On object selection in gaze controlled environments

In the past twenty years, gaze control has become a reliable alternative input method not only for handicapped users. The selection of objects, however, which is of highest importance and of highest frequency in computer control, requires explicit control not inherent in eye movements. Objects have been therefore usually selected via prolonged fixations (dwell times). Dwell times seemed to be for many years the unique reliable method for selection. In this paper, we review pros and cons of classical selection methods and novel metaphors, which are based on pies and gestures. The focus is on the effectiveness and efficiency of selections. In order to estimate the potential of current suggestions for selection, a basic empirical comparison is recommended.
Ice sheet grounding line dynamics: Steady states, stability, and hysteresis

[1] The ice sheet–ice shelf transition zone plays an important role in controlling marine ice sheet dynamics, as it determines the rate at which ice flows out of the grounded part of the ice sheet. Together with accumulation, this outflow is the main control on the mass balance of the grounded sheet. In this paper, we verify the results of a boundary layer theory for ice flux in the transition zone against numerical solutions that are able to resolve the transition zone. Very close agreement is obtained, and grid refinement in the transition zone is identified as a critical component in obtaining reliable numerical results. The boundary layer theory confirms that ice flux through the grounding line in a two-dimensional sheet-shelf system increases sharply with ice thickness at the grounding line. This result is then applied to the large-scale dynamics of a marine ice sheet. Our principal results are that (1) marine ice sheets do not exhibit neutral equilibrium but have well-defined, discrete equilibrium profiles; (2) steady grounding lines cannot be stable on reverse bed slopes; and (3) marine ice sheets with overdeepened beds can undergo hysteresis under variations in sea level, accumulation rate, basal slipperiness, and ice viscosity. This hysteretic behavior can in principle explain the retreat of the West Antarctic ice sheet following the Last Glacial Maximum and may play a role in the dynamics of Heinrich events.
Applications of Rhetorical Structure Theory

Rhetorical Structure Theory (RST) is a theory of text organization that has led to areas of application beyond discourse analysis and text generation, its original goals. In this article, we review the most important applications in several areas: discourse analysis, theoretical linguistics, psycholinguistics, and computational linguistics. We also provide a list of resources useful for work within the RST framework. The present article is a complement to our review of the theoretical aspects of the theory (Taboada and Mann, 2006).
Imaging and spectroscopy of the faint remnant G 114.3+0.3

We present the first calibrated CCD images of the faint supernova remnant G 114.3+0.3 in the emission lines of [OII], [OIII], Halpha+[NII] and [SII]. The deep low ionization CCD images reveal diffuse emission in the south and central areas of the remnant. These are correlated with areas of intense radio emission, while estimates of the [SII]/Halpha ratio suggest that the detected emission originates from shock heated gas. In the medium ionization image of [OIII] we discovered a thin filament in the south matching very well the outer radio contours. This filament is not continuous over its total extent but shows variations in the intensity, mainly in the south-west suggesting inhomogeneous interstellar clouds. Deep long-slit spectra were also taken along the [OIII] filament clearly identifying the observed emission as emission from shock heated gas. The Halpha emission is a few times 10^(-17) erg s^(-1) cm^(-2) arcsec^(-2), while the variations seen in the [OIII] flux suggest shock velocities into the interstellar clouds around or below 100 km/s. The sulfur line ratio approaches the low density limit implying electron densities less than ~500 cm^(-3).
Evolution of Task Switching Behaviors in Real Mobile Robots

In recent years, much research is focused on evolution or learning of different behaviors. Because both algorithms require much computational time, most of these approaches are conducted in simulated environments. When evolution or learning took place in real robot, a single task was considered. In this work, we evolve neural controllers for task switching behavior using e-puck robots. The e-puck robot has to move to the sound source while first reaching the lights distributed in the environment. Experimental results show a good performance of neural controllers evolved in the real hardware of the e-puck robot.
Cantilever embedded MOSFET for bio-sensing

Biosensors are used to identify analytes such as DNA molecules, proteins or micro-organisms. These devices couple a biological recognition element (the probe) with a physical transducer to generate an observable signal. The present work describes a cantilever embedded MOSFET structure for bio-sensing. The main features of the structure are piezoelectric actuation/sensing and MOSFET based readout mechanism for parallel array operation to improve the signal to noise ratio. The integration of MEMS structure with CMOS fabrication process provides the realizability of a system on chip with detection in sub-picogram range.
Hierarchical feature grouping for multiple object segmentation and tracking

In this paper, we propose a hierarchical feature grouping method for multiple object segmentation and tracking. The proposed method aims to segment and track objects in the object-level without prior knowledge about the scene and object. We firstly group the motion feature into region-level with the proposed region features which represent a homogeneous region in an object. Object-level groups are achieved by clustering the region-level groups based on foreground information and motion similarity. To find optimal object-level groups, we formulate energy minimization problem, design its objective functions and solve it using simulated annealing(SA). By this hiearchical feature grouping, the proposed method efficiently segments and tracks various kinds of objects without object detector, 3D model and geometry information. Experimental results on several video clips show that our approach robustly segments and tracks multiple object regardness of camera position and object-class.
Competing locals in an autonomous schooling system: The fracturing of the ‘social’ in social justice

This paper troubles notions of ‘social justice’ as being compromised and fractured by the autonomous school agenda. Drawing on interviews with 13 autonomous school principals in Australia, it demonstrates how the devolution of schooling simultaneously rips the seams of the ‘social’ fabric that makes collective justice possible. The stories of these principals signal a fracturing of the social cohesion that is necessary for creating a just and equal society. We aim to distinguish between individual efforts to create socially just conditions at the local level versus collective projects to create socially just conditions at the system level. We argue that, on the one hand, school autonomy affords individual principals opportunities to exercise what might be considered socially just discretion; on the other hand, this sometimes occurs at the expense of fracturing the cohesion of the greater public education system. In doing so, we challenge the extent to which social justice can be realised within a decentralised schooling system.
Making sense of growing up with a parent with psychosis: an interpretative phenomenological analysis study

ABSTRACT There is little research into the subjective experience of adults whose childhoods were spent living with a parent with psychosis. This study explored these experiences; the sense people made as a child and as an adult, and the ways their experiences shape their adult lives. Participants were encouraged to consider all aspects of their experiences, positive and negative. Five adult participants who grew up with a parent with psychosis were interviewed using a semi-structured interview approach. Four major themes were identified: Feeling uncared for; I’m different; What if people find out? and Finding my identity. Participants felt that, while some childhood experiences had been difficult, these contributed to their strengths and capabilities, such as independence and a capacity for caring for others. The findings highlight the importance of services supporting families to make sense of psychosis and supporting parents to help their children make sense of what is happening in the family. It also reinforced the importance of reducing the stigma surrounding psychosis through increasing knowledge and understanding in the general population.
Integrated Functional, Gene Expression and Genomic Analysis for the Identification of Cancer Targets

The majority of new drug approvals for cancer are based on existing therapeutic targets. One approach to the identification of novel targets is to perform high-throughput RNA interference (RNAi) cellular viability screens. We describe a novel approach combining RNAi screening in multiple cell lines with gene expression and genomic profiling to identify novel cancer targets. We performed parallel RNAi screens in multiple cancer cell lines to identify genes that are essential for viability in some cell lines but not others, suggesting that these genes constitute key drivers of cellular survival in specific cancer cells. This approach was verified by the identification of PIK3CA, silencing of which was selectively lethal to the MCF7 cell line, which harbours an activating oncogenic PIK3CA mutation. We combined our functional RNAi approach with gene expression and genomic analysis, allowing the identification of several novel kinases, including WEE1, that are essential for viability only in cell lines that have an elevated level of expression of this kinase. Furthermore, we identified a subset of breast tumours that highly express WEE1 suggesting that WEE1 could be a novel therapeutic target in breast cancer. In conclusion, this strategy represents a novel and effective strategy for the identification of functionally important therapeutic targets in cancer.
QT Interval: How to Measure It and What Is “Normal”

Abnormally long and short QT intervals have been shown to be associated with an increased risk for life-threatening ventricular arrhythmias and sudden cardiac death. In recent years, various methods for QT-interval measurement have been developed, including individual-based corrections for repolarization duration, quantitative assessment of repolarization morphology, correction for repolarization dynamicity, and analysis of repolarization variability. However, these methods require computer-processed digital-signal analysis of electronically stored ECG data and have been used most frequently in the assessment of repolarization changes in drug trials. In the present review, we will focus on methods for clinically relevant visual and manual assessment of QT-interval duration from a 12-lead ECG, which can be utilized in day-today practice for the diagnosis of long QT syndrome (LQTS) and other repolarization disorders.