Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Grammatical Relations to Compare Parsers

We use the grammatical relations (GRs) described in Carroll et al. (1998) to compare a number of parsing algorithms. A first ranking of the parsers is provided by comparing the extracted GRs to a gold standard GR annotation of 500 Susanne sentences: this required an implementation of GR extraction software for Penn Treebank style parsers. In addition, we perform an experiment using the extracted GRs as input to the Lappin and Leass (1994) anaphora resolution algorithm. This produces a second ranking of the parsers, and we investigate the number of errors that are caused by the incorrect 'GRs.
IVOrpheus - A Proposal for Interaction by Voice Commands in Three-Dimensional Environments of Information Visualization

IVOrpheus is an information visualization tool for three-dimensional data that allows user's interaction by voice commands, mouse and keyboard input. The visualization technique used was the scatterplot 3D, which was implemented using Jmathplot API, and the speech recognition in Brazilian Portuguese was performed by Coruja software. IVOrpheus was developed in Java following the architectural pattern MVC, design patterns and open technologies. In voice interaction, some usability guidelines have been set in interface building process, making it more intuitive and contributing to lower the user cognitive effort. In addition, initial usability tests with users were performed to evaluate the application interface with and without interaction by voice. The tasks with and without voice interaction have shown similar results of time and completeness. The speech recognizer achieved a word error rate of approximately 17%.
Assertion-Based Hardware Debugging

Today’s increasing design complexity requires innovation in both debug automation, and user interaction. We present here a novel query-based method that uses assertions as queries to assist in the domain of hardware debugging. Our approach is generally applicable to hardware debug, and is independent of the particulars of the input design or the assertion description language. We show how assertions drive the debugging process in order to diagnose the cause of the faulty behavior through the unique techniques of trace slicing, and trace dicing. We also present here a new visualization and user interaction technique specifically designed for complex systems with numerous component interactions and abundant control flows. The visualization approach leverages design and assertion spans to present the debugging information to the user in a refined detail of abstraction and permit adequate interaction. An experimental debugging system incorporating assertion-based debug guidance has been implemented. Experimental results for productivity enhancements from a case study will be presented.
Evaluation of Cellular Toxicity of Three Denture Base Acrylic Resins

Objective This study aimed to evaluate the cellular toxicity of two newly-released acrylic resins (Futura Gen and GC Reline Hard) in comparison with the conventional heat-cure resin (Meliodent). Materials and Methods: Sample discs from each acrylic resin were placed in 24-well culture plates along with L929 mouse fibroblast cell line. A mixture of the RPMI 1640 medium, antibiotics and 10% FBS was added to the plates and the specimens were incubated in a CO2 incubator. The amount of light absorption by each plate was determined after 1 hour, 24 hours and 1 week by the MTT assay and ELISA. The cytotoxic effect of the resins was compared among groups using the two-way ANOVA and further paired comparison was performed using the post-hoc Tukey’s test. Results: After 1 hour, Futura Gen displayed a significantly lower level of light absorption in comparison with Meliodent (P=0.03). After 24 hours, GC Reline Hard rendered a significantly lower level of light absorption compared to Meliodent (P=0.02). After one-week of incubation, the mean absorption rates for GC Reline Hard, Meliodent and Futura Gen were relatively similar (P>0.05). The lowest and highest level of cytotoxicity among all resins were observed after one week and 1 hour of immersion in water, respectively. Conclusion: All the tested resins induced some degree of cytotoxicity. Cytotoxicity of Futura Gen, GC Reline Hard and Meliodent resins failed to show any significant reduction from 24 hours to one week. Thus, it is recommended to immerse the dentures in water for 24 hours prior to delivery to the patient.
Automated computer vision method for optic disc detection from non-uniform illuminated digital fundus images

Accurate segmentation of optic disc from fundus images is an essential step to develop automatic screening device for eye pathologies. Automatic and correct segmentation of optic disc from non-uniformly illuminated or abnormal/affected fundus images is still a challenging issue. The proposed work presents a computer vision based method for optic disc segmentation from variety of normal, affected and blurred/non uniform illuminated fundus images. The methodology involves separation of super pixels from fundus images followed by removal of false positives like reflections, exudates, choroid vessels using analysis of geometrical features for correct optic disc segmentation. The proposed method was tested on both normal and abnormal/affected fundus images obtained from local eye hospital and achieved 90.78% overlapping ratio. The proposed OD segmentation method is robust and computationally cheap which makes it applicable for real time.
Reflecting on Reflecting: Fostering Student Capacity for Critical Reflection in an Action Research Project

Participatory forms of action research for community engagement require researchers to continually, critically reflect on the process and emerging findings. Yet, for many academic supervisors and students, this is a relatively new experience. This article is an account of the learning of one supervisor as she attempted to help doctoral students master this skill essential for the successful implementation of any action research project. A qualitative analysis of the data generated from students’ written and oral reflections reveals that the various interventions were helpful in fostering student capacity for critical reflection on several levels, but also highlights the challenges students experienced as they grappled to learn the skill. This account of learning may be beneficial as a guide to other supervisors and students who are struggling to master the elusive skill of critical reflection.
Image retrieval using topological structure of user sketch

Content-based image retrieval system retrieves an image from a database using visual information. Among approaches to expressing visual aspects in queries, "query by sketch" is most convenient and expressive. However, the query drawn by the user is typically quite different from the original image and makes it difficult to retrieve the intended image effectively. In this paper, a new method to retrieve the intended image from imperfect queries is presented. The user-drawn query image is converted to a prime edge graph that represents the topological structure of the objects in the image. The experimental results show that the system retrieves the intended image even from a partial or shifted query.
Crude Oil Spot Price Forecasting Based on Multiple Crude Oil Markets and Timeframes

This study proposes a multiple kernel learning (MKL)-based regression model for crude oil spot price forecasting and trading. We used a well-known trend-following technical analysis indicator, the moving average convergence and divergence (MACD) indicator, for extracting features from original spot prices. Additionally, we factored in the possibility that movements of target crude oil prices may be related to other important crude oil markets besides the target market for the prediction time horizon since traders may find price movement information within other relevant crude oil markets useful. We also considered multiple timeframes in this study since trends may differ across different timeframes and, in fact, traders may use their own timeframes. Therefore, for forecasting target crude oil prices, this study emphasizes on features pertaining to other important crude oil markets and different timeframes in addition to features of the target crude oil market and target timeframe. Moreover, the MKL framework has been used to fuse information extracted from different sources and timeframes of the same data source. Experimental results show that out-of-sample forecasting using the MKL method is superior to benchmark methods in terms of root mean square error (RMSE) and average percentage profit ( APP ). They also show that the information from multiple timeframes is useful for prediction, but that from another crude oil market is not.
Self-organising social processes of task allocation

In literature self-organising social behaviour is often described by means of a brain metaphor. These descriptions either discuss some general principles or propose a systemstheoretical analysis, but have not been related to existing psychological theory. In this paper we propose a way of describing self-organising social processes of task allocation by using principles derived from a neural-network model and from theories of cognitiveand social psychology.
SIMULATING OZONE EFFECTS ON FOREST PRODUCTIVITY: INTERACTIONS AMONG LEAF‐, CANOPY‐, AND STAND‐LEVEL PROCESSES

Ozone pollution in the lower atmosphere is known to have adverse effects on forest vegetation, but the degree to which mature forests are impacted has been very difficult to assess directly. In this study, we combined leaf-level ozone response data from independent ozone fumigation studies with a forest ecosystem model in order simulate the effects of ambient ozone on mature hardwood forests. Reductions in leaf carbon gain were determined as a linear function of ozone flux to the leaf interior, calculated as the product of ozone concentration and leaf stomatal conductance. This relationship was applied to individual canopy layers within the model in order to allow interaction with stand- and canopy-level factors such as light attenuation, leaf morphology, soil water limitations, and vertical ozone gradients. The resulting model was applied to 64 locations across the northeastern United States using ambient ozone data from 1987 to 1992. Predicted declines in annual net primary production ranged from 3 to 16% with greatest reductions in southern portions of the region where ozone levels were highest, and on soils with high water-holding capacity where drought stress was absent. Reductions in predicted wood growth were slightly greater (3- 22%) because wood is a lower carbon allocation priority in the model than leaf and root growth. Interannual variation in predicted ozone effects was small due to concurrent fluc- tuations in ozone and climate. Periods of high ozone often coincided with hot, dry weather conditions, causing reduced stomatal conductance and ozone uptake. Within-canopy ozone concentration gradients had little effect on predicted growth reductions because concen- trations remained high through upper canopy layers where net carbon assimilation and ozone uptake were greatest. Sensitivity analyses indicate a trade-off between model sensitivity to available soil water and foliar nitrogen and demonstrate uncertainties regarding several assumptions used in the model. Uncertainties surrounding ozone effects on stomatal function and plant water use efficiency were found to have important implications on current predictions. Field measurements of ozone effects on mature forests will be needed before the accuracy of model predictions can be fully assessed.
Patterns of Evolution in the Practice of Distributed Software Development: Quantitative Results from a Systematic Review

Distributed Software Development is recent as a research area. With the evolution of its practice, more questions have emerged, and more research has been conducted. Consequently, this resulted in an increase in the existent literature. At the same time, the diversity of industry experience in the last ten years has been used to develop successful practices. We lack, however, knowledge of patterns of evolution in the practice of distributed software development that have been identified and proposed in the literature. In this paper, we present findings from the quantitative analysis of a systematic review of the literature of distributed software development. The goal of the review was to identify papers that either describe existing models referring to patterns of evolution in the practice of distributed software development, or discuss the need for such models.
Analysis & Generative Model for Trust Networks

Trust Networks are a specific kind of social network where edges in the network have positive and negative signs connoting friendship/trust and antagonism/distrust respectively. While the last few years have seen a rich body of work on generative models for social networks, there hasn’t been much work on understanding in what ways trust networks differ from social networks and to create generative models for them. In this work, we analyze the network structure and properties of two online trust networks – Epinions and Slashdot – using theories of structural balance and social capital from social psychology and propose a generative model for undirected graphs based on these theories that produces synthetic graphs which agree with observed data on key network properties. A generative model for directed graphs is part of ongoing work.
Term Distillation for Cross-DB Retrieval

In cross-DB retrieval, the domain of queries differs from the retrieval target in the distribution of that of term occurrences. This causes incorrect term weighting in the retrieval system which assigns to each term a retrieval weight based on the distribution of term occurrences. To resolve the problem, we propose \term distillation" which is a framework for query term selection in cross-DB retrieval. The experiments using the NTCIR patent retrieval test collection demonstrate that term distillation is e(cid:11)ective for cross-DB retrieval.
The effect of relationship quality on individual perceptions of social responsibility in the US

Social responsibility (SR) has been of continuing interest in the U.S. and around the world. Organizations make a wide variety of SR decisions that represent differing viewpoints. While a number of definitions of SR exist, many of these definitions indicate that SR decisions may be viewed as existing of various facets, such as legal/regulatory, financial/economic, ethical, environmental, and voluntary. While drivers of SR have been proposed, there has been limited research at a micro-level on how individuals perceive SR activities by the organizations where they work. Based on a prior qualitative study (Thornton and Byrd, 2013) that found SR decisions are related to several traits and influenced by relationships, a model was proposed and tested in this research. The traits found relevant in the qualitative research were conscientiousness, especially in the sense of being responsible, and self-efficacy. Relationship quality was assessed based on positive and negative emotional attractors as proposed in intentional change theory. Perceptions of individuals in management and non-management showed that relationship quality mediated the effect of conscientiousness and general self-efficacy on the SR. Because there are multiple facets, the author made use of Carroll’s (1991) pyramid of SR to identify activities that business owners and managers consider relevant. The findings indicate that conscientiousness is related to specific SR activities in the areas of legal/regulatory, ethical and discretionary dimensions while general self-efficacy is related to financial/economic and legal/regulatory dimensions. The presence of relationship quality enhanced the effects of both conscientiousness and general self-efficacy on the various SR dimensions. This suggests that individuals perceived SR activities along different traits and that enhancing these traits might improve perceptions of SR decisions.
Scalable Security Models for Assessing Effectiveness of Moving Target Defenses

Moving Target Defense (MTD) changes the attack surface of a system that confuses intruders to thwart attacks. Various MTD techniques are developed to enhance the security of a networked system, but the effectiveness of these techniques is not well assessed. Security models (e.g., Attack Graphs (AGs)) provide formal methods of assessing security, but modeling the MTD techniques in security models has not been studied. In this paper, we incorporate the MTD techniques in security modeling and analysis using a scalable security model, namely Hierarchical Attack Representation Models (HARMs), to assess the effectiveness of the MTD techniques. In addition, we use importance measures (IMs) for scalable security analysis and deploying the MTD techniques in an effective manner. The performance comparison between the HARM and the AG is given. Also, we compare the performance of using the IMs and the exhaustive search method in simulations.
A New Trajectory Similarity Measure for GPS Data

We present a new algorithm for measuring the similarity between trajectories, and in particular between GPS traces. We call this new similarity measure the Merge Distance (MD). Our approach is robust against subsampling and supersampling. We perform experiments to compare this new similarity measure with the two main approaches that have been used so far: Dynamic Time Warping (DTW) and the Euclidean distance.
Towards pro-active adaptation with confidence: augmenting service monitoring with online testing

Service-based applications need to operate in a highly dynamic and distributed world. As those applications are composed of individual services, they have to react to failures of those services to ensure that the applications maintain their expected functionality and quality. Self-adaptation is one solution to this problem, as it allows applications to autonomously react to failures. Currently, monitoring is typically used to identify failures, thus triggering adaptation. However, monitoring only observes failures after they have occurred, which means that adaptation based on monitoring is reactive. This can lead to shortcomings like user dissatisfaction, increased execution times, and late response to critical events. Pro-active adaptation addresses those shortcomings, because in such a setting, the application detects the need for adaptation and thus can adapt before a failure will occur. However, it is important to avoid unnecessary pro-active adaptations, as they can lead to severe shortcomings, such as increased costs or follow-up failures. This means that when taking pro-active adaptation decisions it is key that there is confidence in the predicted future failures, i.e., pro-active adaptation should only be performed if there is certainty that the failure could in fact occur. To avoid unnecessary adaptations, we introduce an approach based on augmenting service monitoring with online testing to produce failure predictions with confidence. We demonstrate the applicability of our approach using a scenario from the eGovernment domain.
From 2 D Photos of Yourself to Virtual TryOn Dress on the Web

This paper describes a complete methodology for cloning and dressing people by using a website. The input is simple photographs or body measurements that anybody can produce in any environment. Then the web-based virtual-try-on allows users to see them dressed. The basic methodology uses a pre-calculated generic database to produce personally sized bodies and animate garments on a web application.
An analog neural computer with modular architecture for real-time dynamic computations

A multichip analog parallel neural network whose architecture, neuron characteristics, synaptic connections, and time constants are modifiable is described. The system has several important features, such as time constants for time-domain computations, interchangeable chips allowing a modifiable gross architecture, and expandability to any arbitrary size. Such an approach allows the exploration of different network architectures for a wide range of applications, in particular dynamic real-world computations. Four different modules (neuron, synapse, time constant, and switch units) have been designed and fabricated in a 2- mu m CMOS technology. About 100 of these modules have been assembled in a fully functional prototype neural computer. An integrated software package for setting the network configuration and characteristics, and monitoring the neuron outputs has been developed as well. The performance of the individual modules as well as the overall system response for several applications was tested successfully. Results of a network for real-time decomposition of acoustical patterns are discussed. >
Identification Of Variation Points Using Dynamic Analysis

In this position paper we investigate the use of dynamic analysis to determine commonalities and variation points as a first step to the migration of similar but separate versions of a software system into an integrated product line. The approach detects forks and merges in different execution traces as an indication of variation points. It is illustrated by a simple implementation, which is applied to an academic example. Finally we formulate a number of research issues that need to be investigated further.
A ug 2 00 5 Diffusion and networks : A powerful combination !

Over the last decade, an enormous interest and activity in complex networks have been witnessed within the physics community. On the other hand, diffusion and its theory, have equipped the toolbox of the physicist for decades. In this paper, we will demonstrate how to combine these two seemingly different topics in a fruitful manner. In particular, we will review and develop further, an auxiliary diffusive process on weighted networks that represents a powerful concept and tool for studying network (community) structures. The working principle of the method is the observation that the relaxation of the diffusive process towards the stationary state is non-local and fastest in the highly connected regions of the network. This can be used to acquire non-trivial information about the structure of clustered and non-clustered networks.
On problems of China commercial banks' compliance risk management

A long time, China's commercial banks have not been to compliance as an important source of risk to manage, but also did not put on risk management compliance should be an important position, and the Basle Committee on Banking Supervision on the banks of a series of compliance requirements there is a big gap compared. Therefore, improving banking compliance risk management has become an important task of commercial banks in China that brooks no delay. This Article has explored herein realization on compliance risk, analyzed major issues that exist in compliance risk management of commercial banks of this country, and conducted preliminary thinking and exploration on how to improve compliance system and management procedures of commercial banks and construct effective compliance risk management mechanism.