Miracle-dz/UniFAR-FTUs huggingface dataset:
Forward Discount Puzzle and Official Interventions : An Empirical Note

In this paper, we explore how official interventions affect the relationship between current forward rates and future spot rates of the yen/dollar for the period between 1991 and 2004. According to our empirical investigation, Japanese interventions induced not opposite movements between the two (recognized as the forward discount puzzle), but an overreaction of future spot rates to current forward discounts, in particular, during the first half of the 1990s. Our finding demonstrates that official interventions were not responsible for the forward discount puzzle that was present for the full sample period, and suggests other factors responsible for the puzzle.
Venture Capital and IPO Lockup Expiration: An Empirical Analysis

Most initial public offerings (IOPs) feature "lockup" agreements, which bar insiders from selling the stock for a set period following the IPO, usually 180 days. We examine stock price behavior in the period surrounding lockup expiration for a sample of 2,529 firms from 1988 to 1997. We find that lockup expirations are, on average, associated with significant and negative abnormal returns, but the losses are concentrated in firms with venture capital backing. For the venture-capital-backed group, the largest losses occur for high-tech firms and firms with the greatest post-IPO stock price increases, the largest relative trading volume in the period surrounding expiration, and the highest quality underwriters.
Proactive Versus Reactive Security Investments in the Healthcare Sector

This study identifies the effects of security investments that arise from previous failures or external regulatory pressure. Building on organizational learning theory, the study focuses on the healthcare sector where legislation mandates breach disclosure and detailed data on security investments are available. Using a Cox proportional hazard model, we demonstrate that proactive security investments are associated with lower security failure rates. Coupling that result with the economics of breach disclosure, we also show that proactive investments are more cost effective in healthcare security than reactive investments. Our results further indicate that this effect is amplified at the state level, supporting the argument that security investments create positive externalities. We also find that external pressure decreases the effect of proactive investments on security performance. This implies that proactive investments, voluntarily made, have more impact than those involuntarily made. Our findings suggest that security managers and policy makers should pay attention to the strategic and regulatory factors influencing security investment decisions.
Coloring high-resolution near-infrared images using chromatic cues from low-resolution references

Even with the availability of high-resolution color imaging sensors, there are certain limitations in using the visible light band. For instance, visible light easily scatters causing haze in images. The near-infrared (NIR) band offers a viable alternative for such applications. Previous work have demonstrated the uses of NIR in combination with high-resolution colored images. However, the use of two high-resolution imaging devices tend to be expensive and inconvenient to transport. This work proposes the transfer of color from a low-resolution colored image to a higher resolution NIR image. Initial tests have demonstrated the capability of the proposed algorithm in retaining NIR details while preserving the original color intent of the scene.
A System for Intrusion Prediction in Cloud Computing

Modern critical infrastructures have to process significantly large data sets. Intrusion-detection systems and unified threat management systems have the role of keeping critical infrastructures secure against cyber-attacks. However, in the world of big data, these systems are struggling to cope with overload and often become the bottle neck in the data network. To overcome this, our research investigates the use of deploying intrusion-detection and intrusion-prediction techniques in a cloud environment. Consequently, in this paper, a survey of existing intrusion-detection systems is presented and a discussion on how their deployment can enhance current security techniques in a cloud computing environment is put forward. A novel technique for intrusion prediction system is also put forward in this paper. Predictive statistical methods are used for proving the concepts put forward. The initial results show the necessity for using evolving statistical methods in prediction solutions; and the insufficiency of 'single-technique models' for building general solutions to predict intrusions. Furthermore, as this research shows, the concept of integrating multiple methods, such as game theory concepts and risk assessment methods, facilitates the development of a more efficient prediction model.
Performance Comparison of TCP Mechanisms over Satellite Networks

The performance of TCP over satellite links has been suboptimal due to a variety of protocol algorithm and configuration issues. Connection speed over satellite networks is rather slow and, in order to preserve good link quality, packet loss is interpreted as an indication of network congestion. Appropriate back-off mechanisms are therefore, implemented to prevent any further overload. However, these back-off strategies are not helpful when packet losses are due to transmission errors, particularly if these are bursty, which reveals the need for TCP/IP enhancements when used over satellite links. High efficiency on fast satellite links may eventually require new protocol modifications, but full link utilization may be achievable using the TCP performance enhancements that have already been approved or that are currently in the standards process. This paper describes different types of TCP standards and compares their performance using the OPNET simulator to show how improvements may be achieved.
Video Based Ambient Smoke Detection By Detecting Directional Contrast Decrease

Fire-related incidents account for extensive loss of life and material damage. Quick and reliable detection of occurring fires has high real world implications. Whereas a major research focus lies on the detection of outdoor fires, indoor camera-based fire detection is still an open issue. Cameras in combination with computer vision helps to detect flames and smoke more quickly than conventional fire detectors. In this work, we present a computer vision-based smoke detection algorithm based on contrast changes and a multi-step classification. This work accelerates computer vision-based fire detection considerably in comparison with classical indoor-fire detection. Keywords—Contrast analysis, early fire detection, video smoke detection,
Towards a Socio-cultural Compatibility of MIR Systems

Future MIR systems will be of great use and pleasure for potential users. If researchers have a clear picture about their “customers” in mind they can aim at building and evaluating their systems exactly inside the different socio-cultural environments of such music listeners. Since music is in most cases embedded into a socio-cultural process we propose especially to evaluate MIR applications outside the lab during daily activities. For this purpose we designed a mobile music recommendation system relying on a trimodal music similarity metric, which allows for subjective on-the-fly adjustments of recommendations. It offers online access to large-scale metadata repositories as well as an audio database containing 1000 songs. We did first smallscale evaluations of this approach and came to interesting results regarding the perception of song similarity concerning the relations between sound, cultural issues and lyrics. Our paper will also give insights to the three different underlying approaches for song similarity computation (sound, cultural issues, lyrics), focusing in detail on a novel clustering of album reviews as found at online music retailers.
Advanced Technology and Entrepreneurship Opportunities

Abstract In numerous countries, many entrepreneurial opportunities have been created as a result of the transformation from a planned economy to the market-driven system. This process continues to receive attention within both the business and academic communities. According to Ivy (1996), newly created small-scale enterprises run by local entrepreneurs are making major contributions to private sector development in these countries. Ivy further states that the increasing number of small-and medium-sized enterprises (SMEs) and the role they play in economic regeneration are of vital importance to economic stability in many nations. Countries in Central and Eastern Europe have made radical changes, and many new firms have emerged. This has initiated and promoted a new way of doing business for millions of people from many different cultures. New ideas, entrepreneurship, and market-oriented methods are being quickly implemented. The transformations in Poland, Hungary, and the Czech Republic have outperformed other countries in Eastern Europe and the Commonwealth of Independent States (CIS). Poland has taken the lead in economic growth and development in the region. Its gross domestic product (GDP) has been steadily increasing over the past five years and it is on schedule to become a full member of the European Union (EU). This economic market oriented progress can be viewed as a showcase in the global economy. As such, Poland's success is worthy of closer inspection. This paper attempts to identify the factors, conditions, and opportunities that exist for entrepreneurs who are exploring new ventures generated from this remarkable transformation.
Sample Selection Bias in Measuring Online Advertising Effectiveness

A goal of measuring the effectiveness of online advertising is to predict the impact of advertising on the users perception of the advertised brand. Historically these models have been parameterized using survey data only and the bias inherent in such surveys is discussed in isolation. In this paper we examine the impact of selection/survey bias on the estimation of Ad effectiveness for two products using a censored bivariate Probit model. We find that survey completion reveals substantive differences in brand response. Put another way, a significant correlation exists between the unobserved heterogeneity in the survey model and the error term in response model. For one product, we conclude that factors that make it more likely to complete the survey make it less likely to recall the ad. Consideration of selection bias has the following impacts: standard deviation of predicted outcome widens to reflect a more diverse/general population; the impact of variables such as product ownership and gender are reduced; and, most importantly, recall of general population is estimated to be greater than measured recall of self selected participants. For the other product, we conclude that factors that make it more likely to complete the survey make it more likely to consider purchasing the product. Consideration of selection bias has the following impacts: the observed purchase consideration on self selected sample overestimates the population purchase consideration; and while the treatment is effective in influencing purchase consideration among general population its impact is less than the measured impact on self selected population.
The planar two point algorithm

Vision-based localization, mapping and navigation is often performed by searching for corresponding image points and estimating the epipolar geometry. It is known that the possible relative poses of a camera mounted on a mobile robot that moves over a planar ground floor, has two degrees of freedom. This report provides insight in the problem of estimating the exact planar robot pose difference using only two image point correspondences. We describe an algorithm which uses this minimal set of correspondences termed the Two-point algorithm. It is shown that sometimes two non-degenerate correspondences do not define a unique relative robot pose, but lead to two possible real solutions. The algorithm is especially useful as hypothesis generator for the well known RANSAC (RANdom SAmple Consensus) method. The algorithm is evaluated using both simulated data and data acquired by a mobile robot equipped with an omnidirectional camera. The improvement over existing methods is analogous to the improvement of the well known Five-point algorithm over other algorithms for general non-planar camera motions.
Integration of machine learning approach in item bank test system

Item test bank system plays very important role in auto generating test or exam paper in assessments in schools and universities. A quite number of researchers have proposed some algorithms in generating test paper based on some well-defined attributes such as time, question type, knowledge point, difficulty level and others. It has always been the aim of these researchers to generate high quality test paper with appropriate level of difficulty in test questions. As a result of this, Bloom taxonomy has been adopted to ensure difficulty level of test questions is appropriate. However, there is no evidence that current test items or questions in the item test bank system are classified in accordance to BT using machine learning approach. Manual classifying is tedious and laborious work and inconsistency in classifying items can take place due to different judgement from instructors. A better approach is to use machine learning namely question classifier such as Support Vector Machine to automate the classification of the test items. Despite some research work has been done on using classifiers to classify questions, there is no evidence that this type of work has been integrated into item bank test system. In view of this, this study proposes a change in existing framework of item test bank system by integrating the facility to automate classifying items in accordance to Bloom taxonomy. With all this in place, the automation of classifying questions or test items in accordance to BT with a reasonable accuracy can be achieved.
Performance Comparison of Atm Lan with Fddi and Fast Ethernet

Simulation model of a reference network is developed to compare the performance of three high speed Local Area Networks | Fast Ethernet, FDDI and ATM. TCP/IP based application throughput is considered as the performance criteria in comparing the three LANs. Our comparison is based on a hot-spot traac model; many stations send bursty data traac to a single destination. We nd that for such traac scenario, FDDI performs the best. ATM performance , without any congestion control, is orders of magnitude lower than both Fast Ethernet and FDDI. We show that performance of ATM can be signiicantly increased by providing larger buuer at the hot-spot output port of the ATM switch.
Mass Absorption Coefficient of Tungsten and Tantalum, 1450 eV to 2350 eV: Experiment, Theory, and Application

The mass absorption coefficients of tungsten and tantalum were measured with soft x-ray photons from 1450 eV to 2350 eV using an undulator source. This region includes the M3, M4, and M5 absorption edges. X-ray absorption fine structure was calculated within a real-space multiple scattering formalism; the predicted structure was observed for tungsten and to a lesser degree tantalum as well. Separately, the effects of dynamic screening were observed as shown by an atomic calculation within the relativistic time-dependent local-density approximation. Dynamic screening effects influence the spectra at the 25 % level and are observed for both tungsten and tantalum. We applied these results to characterize spatially-resolved spectra of a tungsten integrated circuit interconnect obtained using a scanning transmission x-ray microscope. The results indicate tungsten fiducial markers were deposited into silica trenches with a depths of 50 % and 60 % of the markers’ heights.
The interaction effects of aerobic exercise training and vitamin D supplementation on plasma lipid profiles and insulin resistance in ovariectomized rats

Purpose The purpose of this study was to determine the interaction effects of aerobic exercise training and vitamin D supplementation on indices of obesity and plasma lipid profiles in ovariectomized (OVX) rats. Methods Forty female Wistar rats were divided into 5 groups: aerobic training (3 days/week for 8 weeks; AT; n = 8), aerobic training and vitamin D supplementation (OVX + AT + Vit D; n = 8), vitamin D supplementation (OVX + Vit D; n = 8), ovariectomized control (OVX + C, n = 8) and SHAM (n = 8). After blood sampling, visceral fat was taken from the abdominal cavity and weighed immediately. Data was statistically analyzed by One-way ANOVA and Repeated measure ANOVA tests with a 0.05 significance level. Results Body weight, visceral fat, BMI and food intake decreased significantly in OVX + AT + Vit D (P < 0.001); whereas these variables increased significantly in OVX + C (P < 0.001) and SHAM (P < 0.023) groups. At the end of two-months of follow-up, we observed significant differences in TC, TG, HDL-C, LDL-C, glucose, insulin, and HOMA-IR in all groups. Conclusion It seems that aerobic training with vitamin D, due to the involvement of muscle mass and exposure to dynamic pressure on the bones and muscles, increased energy expenditure, stimulated insulin exudation and glucose homeostasis, decreased insulin resistance and improved the lipid profile in ovariectomized rats.
Multi-resolution modeling of power converter using waveform reconstruction

Computer simulation of switching power converters is complicated by the discontinuous (switching) nature of the converter waveforms. When switching details of the waveform are of interest, detailed simulations requiring extremely small time steps are needed. On the other hand, so-called averaged models allow capture of low-frequency converter dynamics (of interest for example when closing the feedback loop) by computing averaged continuous waveforms. The possibility of using relatively large time steps makes averaged models computationally efficient. A real-time hierarchical approach that combines the advantages of the two methods is proposed. An averaged model and a detailed model are used alternately in successive time intervals. The final state of one model at the end of a time interval is used to calculate the initial conditions for the other model running in the following time interval. The method is illustrated with a buck converter. The combined use of two models with a hierarchical or multilevel approach is shown to provide a powerful simulation tool for analysis and design. The detailed model is used 10% of the time and the averaged model 90% of the time. Results indicate that the detailed behavior is accurately simulated when the detailed model runs and the overall simulation time cost is lowered by running the averaged model 90% of the time. This approach provides a way to simulate the detailed behavior of a specific part in a rather large switching circuit network.
Matrix bidiagonalization on the Trident processor

This paper discusses the implementation and evaluation of the reduction of a dense matrix to bidiagonal form on the Trident processor. The standard Golub and Kahan Householder bidiagonalization algorithm, which is rich in matrix-vector operations, and the LAPACK subroutine /spl I.bar/GEBRD, which is rich in a mixture of vector, matrix-vector, and matrix operations, are simulated on the Trident processor. We show how to use the Trident parallel execution units, ring, and communication registers to effectively perform vector, matrix-vector, and matrix operations needed for bidiagonalizing a matrix. The number of clock cycles per FLOP is used as a metric to evaluate the performance of the Trident processor. Our results show that increasing the number of the Trident lanes proportionally decreases the number of cycles needed per FLOP. On a 32 K/spl times/32 K matrix and 128 Trident lanes, the speedup of using matrix-vector operations on the standard Golub and Kahan algorithm is around 1.5 times over using vector operations. However, using matrix operations on the GEBRD subroutine gives speedup around 3 times over vector operations, and 2 times over using matrix-vector operations on the standard Golub and Kahan algorithm.
Using process ontology together with process editor to facilitate tool integration

Modern software and system collaborative process involves various teams in different development phases thus need efficient solutions for tools integration. In Model-Driven Development, transformation technique is used to allow exchanging models created by different tools. However, in a process, transformation are often defined manually for a tool-incompatible point and rarely reusable. To facilitate the automatic generation of transformation rules for tool integration, we propose to use process ontology together with process editor when modelling process. The idea is using ontology to stock process assets from various sources so that the relations between similar elements in different technical spaces can be established automatically. The process editor enriches the ontology by process elements captured from modelling activities. Then the integrated ontology helps the editor detect tool integration points and complete the process model as well as generate the mappings between concerned process elements.
Experimental Methods in Market Research: From Information to Insight

Experimental methods have a relatively low penetration into market research practice, despite their many inherent strengths. We review the strengths and weaknesses of four major experimental and quasi-experimental designs for market research applications. We then describe three case studies of the use of experimental logic in field-based research studies. Two examine the impact of customer profitability measurement on customer management strategies; the third studies the effect on customer satisfaction and other variables of introducing desk-based account managers into a field sales organisation. We argue for increased take-up of such experimental and quasi-experimental methods if the market research community is to tackle the twin challenges of multiple sources of data and the need to evaluate what happens within the firm as well as within its resellers and retailers and customers.
Using Instructive Data Mining Methods to Revise the Impact of Virtual Classroom in E-Learning

In the past few years, Saudi universities have boarded to utilize e-learning tools and technologies to expand and look up their educational services. After a few years of carrying out e-learning programs, a discussion took place within the directors, supervisors, executives and managers or decision makers of the e-learning associations regarding which activities are the most impact on the learning development for online students. This research is expected to inspect the impact of a number of e-learning actions on the students’ learning development. The results show that involvement in virtual classroom sessions has the most considerable impact on the students’ final scoring or grade. This paper presents the procedure of applying data mining methods to the web observing records of students’ behaviors in a practical learning environment. The main idea is to rank the learning activities supported on their importance in order to develop students’ performance by focusing on the most important ones.