Miracle-dz/UniFAR-FTUs huggingface dataset:
Goal-Oriented Learning In Experimental Software Engineering By Using Rough Sets

Rough set theory is a foundation for a series of analysis methods which has been successfully applied in many real-life problems of various areas. Main focus of the approach is to explain experimental data by establishing cause-eeect relationships. We have applied the approach in the context of experimental software engineering to support analysis and interpretation of measurement data within interactive feedback sessions. Measurement was performed following the Goal/Question/Metric-approach. As a case study, we report experiences from a project at Allianz Lebensversicherungs-AG devoted to better understanding of and subsequent improvement of exi-bility in software development. Experiences are reported from both application and methodological point of view. 1 MOTIVATION High quality of software products and processes becomes more and more important for every company. The Quality Improvement Paradigm (QIP) (Basili et al., 1994) ooers a general framework for the performance of improvement-oriented software development. In the context of the QIP, the Goal/Question/Metric (GQM) paradigm (Rom-bach, 1991) is a systematic approach for goal-oriented measurement. The GQM approach de-nes a goal which becomes reened into questions and further into metrics. According to the met-rics, measurement data is captured. The interpretation of the measurement data by the project team members takes place in well-structured, organized meetings, the so-called feedback sessions. The overall process to apply goal-oriented measurement within the QIP-based incremental learning cycle is called goal-oriented learning. This paper describes the application of goal-oriented learning at Allianz Lebensversicherungs-AG as part of the SoftQuali project (Kempter and Leippert, 1996). The main emphasis is on the use of the Rough Set analysis approach for preparing and performing feedback sessions. The QIP ooers a systematic strategy for learning and improvement of software processes and products within an organization. The performance of the QIP provides two feedback cycles: the project speciic feedback cycle and the organizational feedback cycle. The project cycle is meant to control project performance at project completion time, the organizational cycle is meant to accumulate reusable experiences that are applicable to other projects. The QIP is strongly related with the GQM paradigm. The GQM paradigm is a mechanism for deening and evaluating a set of operational goals using measurement. The idea of the GQM approach is to deene a goal which consists of the object, purpose, quality focus, the viewpoint and the environment where the measurement will be carried out. In the GQM plan the goal is reened into metrics via questions using a top-down approach. …
Evolution of Human-Competitive Agents in Modern Computer Games

Modern computer games have become far more sophisticated than their ancestors. In this process the requirements to the intelligence of artificial gaming characters have become more and more complex. This paper describes an approach to evolve human-competitive artificial players for modern computer games. The agents are evolved from scratch and successfully learn how to survive and defend themselves in the game. Agents trained with standard evolution against a training partner and agents trained by coevolution are presented. Both types of agents were able to defeat or even to dominate the original agents supplied by the game. Furthermore, we have made a detailed analysis of the obtained results to gain more insight into the resulting agents.
Analysis, Modeling, and Simulation of Hadoop YARN MapReduce

Despite much work in simulation of MapReduce applications and in optimizing scheduling to minimize workload execution time, we still need models and tools to understand the performance of the individual tasks of a MapReduce application. Armed with this knowledge, we can characterize applications and model task operation to determine their performance in a given cluster setting. We have developed and analyzed a model of MapReduce tasks that abstracts their operation at a balanced level, avoiding too much detail that slows down analysis and requires knowledge of the inner workings of the cluster system, but at sufficient detail to be able to generalize to multiple application classes and cluster systems. The model, and an accompanying simulator, are evaluated against execution of real MapReduce applications, and show very promising results.
Research on the Convergent Performance of the Auxiliary Problem Principle Based Distributed and Parallel Optimization Algorithm

The convergent performance of the auxiliary problem principle based distributed and parallel optimization algorithm (APPBDPOA) is dependent on its parameters and power network partition. Because of the complexity of the optimal power flow, it is difficult to obtain the sufficient and necessary conditions of convergence for APPBDPOA in theory. In this paper the convergent performance of APPBDPOA is studied through large numbers of tests. The relations between parameters and convergent performance and the influence of the power network partition on convergent speed are discussed. The conclusions can guide the parameter evaluation, which is significant for optimizing the power network partition and further speeding up APPBDPOA.
Cycle time of p-time event graphs

The dater equalities constitutes an appropriate tool which allows a linear description of Timed Event Graphs in the field of (max, +) algebra. This paper proposes an equivalent model in the usual algebra which can describe Timed and P-time Event Graphs. Considering 1-periodic behavior, the application of a variant of Farkas’ lemma allows the determination of upper and lower bounds of the production rate and necessary conditions of consistency.
Simulating Lévy Processes from Their Characteristic Functions and Financial Applications

The simulation of a discrete sample path of a Lévy process reduces to simulating from the distribution of a Lévy increment. For a general Lévy process with exponential moments, the inverse transform method proposed in Glasserman and Liu [2010] is reliable and efficient. The values of the cumulative distribution function (cdf) are computed by inverting the characteristic function and tabulated on a uniform grid. The inverse of the cumulative distribution function is obtained by linear interpolation. In this article, we apply a Hilbert transform method for the characteristic function inversion. The Hilbert transform representation for the cdf can be discretized using a simple rule highly accurately. Most importantly, the error estimates admit explicit and computable expressions, which allow us to compute the cdf to any desired accuracy. We present an explicit bound for the estimation bias in terms of the range of the grid where probabilities are tabulated, the step size of the grid, and the approximation error for the probabilities. The bound can be computed from the characteristic function directly and allows one to determine the size and fineness of the grid and numerical parameters for evaluating the Hilbert transforms for any given bias tolerance level in one-dimensional problems. For multidimensional problems, we present a procedure for selecting the grid and the numerical parameters that is shown to converge theoretically and works well practically. The inverse transform method is attractive not only for Lévy processes that are otherwise not easy to simulate, but also for processes with special structures that could be simulated in different ways. The method is very fast and accurate when combined with quasi-Monte Carlo schemes and variance reduction techniques. The main results we derived are not limited to Lévy processes and can be applied to simulating from tabulated cumulative distribution functions in general and characteristic functions in our analytic class in particular.
Learning Efficient Disambiguation

This dissertation analyses the computational properties of current performance-models of natural language parsing, in particular Data Oriented Parsing (DOP), points out some of their major shortcomings and suggests suitable solutions. It provides proofs that various problems of probabilistic disambiguation are NP-Complete under instances of these performance-models, and it argues that none of these models accounts for attractive efficiency properties of human language processing in limited domains, e.g. that frequent inputs are usually processed faster than infrequent ones. The central hypothesis of this dissertation is that these shortcomings can be eliminated by specializing the performance-models to the limited domains. The dissertation addresses "grammar and model specialization" and presents a new framework, the Ambiguity-Reduction Specialization (ARS) framework, that formulates the necessary and sufficient conditions for successful specialization. The framework is instantiated into specialization algorithms and applied to specializing DOP. Novelties of these learning algorithms are 1) they limit the hypotheses-space to include only "safe" models, 2) are expressed as constrained optimization formulae that minimize the entropy of the training tree-bank given the specialized grammar, under the constraint that the size of the specialized model does not exceed a predefined maximum, and 3) they enable integrating the specialized model with the original one in a complementary manner. The dissertation provides experiments with initial implementations and compares the resulting Specialized DOP (SDOP) models to the original DOP models with encouraging results.
Mobility Characterization and Control of a Spherical Multi-Legged Robot

Mobility in rough terrain has long been a hindrance in ground-based robotic exploration. Historically, such tasks have been approached with wheeled platforms, which have difficulty surmounting obstacles of even moderate height, or with legged robots that can step over obstacles but require complex foot-placement planning and posture control. In this thesis, I present the latest Robotic All-Terrain Surveyor (RATS) prototype as a unique approach to locomotion in varied terrain through the ability to emulate multiple mobility modes using a single type of actuator. The novel robot has a spherical body roughly the size of a soccer ball with 12 legs symmetrically distributed around its surface. Each leg is a single-DOF pneumatic linear actuator, oriented normal to the spherical body. Thorough investigation of this prototype’s mobility and actuation behavior has demonstrated the feasibility of achieving tipping, hopping, and prolonged rolling behavior without the need for controlling body posture. Here I summarize the experimental results of this characterization and present an understanding of the system’s performance limitations in an effort to draw insight for controlling its movements. I also discuss the initial control strategies that have been developed to achieve multi-modal RATS locomotion, both in simulation and with the prototype. This paper concludes with a discussion of the numerous areas for future development to improve on the robot’s mobility performance and implement more sophisticated control techniques for locomotion.
Cortical Modulation of Spatial and Angular Tuning Maps in the Rat Thalamus

The massive feedback projections from cortex to the thalamus modulate sensory information transmission in many ways. We investigated the role of corticothalamic feedback projections on the directional selectivity (angular tuning) of neurons in the rat ventral posterior medial (VPM) nucleus to stimulation of their principal whisker. The angular tuning properties of single VPM neurons were compared before and after epochs of electrical stimulation of layer VI feedback neurons in the ipsilateral cortex under urethane anesthesia. Microstimulation of layer VI in “matched” (homologous) barrel columns sharpens the angular tuning curves of single VPM neurons that are tuned to the same direction as the stimulation site in the cortex. Further, microstimulation rotates the angular preference of VPM neurons initially tuned to a different direction toward the direction that cortical neurons prefer. Stimulation in “mismatched” (nonhomologous) barrel columns suppresses responses without consistent effects on angular tuning. We conclude that the primary sensory cortex exerts a significant influence on both spatial and angular tuning maps in the relay nuclei that project to it. The results suggest that the tuning properties of VPM cells in the behaving animal are continually modified to optimize perception of the most salient incoming messages.
An incremental semi rule-based learning model for cybersecurity in cyberinfrastructures

Classification algorithms are popular data mining methods that are widely used in intrusion detection systems. However, they tend to show good performance on off-line data and their accuracy drop when they are deployed to realtime intrusion detection systems that receive data streams constantly. In other words, the problem with a conventional classifier model, when applied to realtime detection systems, is that it is likely to perform poorly against new data that is different from the patterns against which the model was trained. This work presents an adaptive classification framework for a realtime intrusion detection system that changes its behavior according to the patterns occurred in the new received data. The performance of the system is constantly monitored and whenever it drops under a certain level of accuracy, the classifier model is retrained to learn new patterns and eliminate the obsolete ones from its knowledge. Further, the framework uses a semi rule-based approach to classification in order to make the model more understandable for human experts and facilitate the user interference in the learning process.
Parallel text searching in serial files using a processor farm

This paper discusses the implementation of a parallel text retrieval system using a microprocessor network. The system is designed to allow fast searching in document databases organised using the serial file structure, with a very rapid initial text signature search being followed by a more detailed, but more time-consuming, pattern matching search. The network is built from transputers, high performance microprocessors developed specifically for the construction of highly parallel computing systems, which are linked together in a processor farm. The paper discusses the design and implementation of processor farms, and then reports our initial studies of the efficiency of searching that can be achieved using this approach to text retrieval from serial files.
Motion Planning for Stepping On/Off Obstacles by Humanoid Robot

This paper presents a humanoid robot BHR-2 capable of performing stepping on and off a flat obstacle of known dimension. The trajectory comprises of two phases for the task completion. The first phase starts with the left foot raised from the ground and reaching the obstacle base tracking the trajectory points generated by spline interpolation. The end of the first phase is the state when both feet are on the obstacle. The second phase starts with the final point of the first phase. First, the left foot is raised slightly above the obstacle and then step forward to descend to the ground followed by right foot accordingly. The dynamic stability during the execution of stepping on/off is ensured by incorporating the ZMP criterion. The motion planning for stepping on/off is simulated accurately and is also verified through experimentation.
Relationship Between Cytogenetic Complexity and Peritumoral Edema in High-Grade Astrocytoma

Background The purpose of the study is to reveal the association of cytogenetic compltyexi and peritumoral edema volume (PTEV) and its prognostic significance in high-grade astrocytoma patients by culturing patient tumor cells. Methods Twenty-seven high-grade astrocytoma patients were divided into three groups according to karyotype complexity: normal, non-complex karyotype (NCK), and complex karyotype (CK). Endothelial growth factor receptor (EGFR) amplification was detected by FISH, and its association with chromosome 7 abnormalities was analyzed. Mean PTEV of each group was compared by ANOVA to evaluate the relationship between PTEV and cytogenetic complexity. Results The PTEV of patients in normal (n=6), NCK (n=8), and CK (n=13) groups were 24.52±17.73, 34.26±35.04, and 86.31±48.7 cm3, respectively (P=0.005). Ten out of 11 patients with EGFR amplification showed abnormalities in chromosome 7. The mean PTEV of EGFR-amplified and non-amplified groups were 80.4±53.7 and 41.3±37.9 cm3, respectively (P=0.035). The average survival of patients with PTEV less than 90 cm3 was 30.52±26.11 months, while in patients with PTEVs over or equal to 90 cm3, it was 10.83±5.53 months (P=0.007). Conclusions The results show an association of complex karyotype with the PTEV of high-grade astrocytoma. EGFR amplification plays a significant role in the formation of peritumoral edema, causing PTEV to increase, which is related with survival. This implies that cytogenetic karyotype can be applied as a prognostic factor.
A Novel Effect of MARCKS Phosphorylation by Activated PKC: The Dephosphorylation of Its Serine 25 in Chick Neuroblasts

MARCKS (Myristoylated Alanine-Rich C Kinase Substrate) is a peripheral membrane protein, especially abundant in the nervous system, and functionally related to actin organization and Ca-calmodulin regulation depending on its phosphorylation by PKC. However, MARCKS is susceptible to be phosphorylated by several different kinases and the possible interactions between these phosphorylations have not been fully studied in intact cells. In differentiating neuroblasts, as well as some neurons, there is at least one cell-type specific phosphorylation site: serine 25 (S25) in the chick. We demonstrate here that S25 is included in a highly conserved protein sequence which is a Cdk phosphorylatable region, located far away from the PKC phosphorylation domain. S25 phosphorylation was inhibited by olomoucine and roscovitine in neuroblasts undergoing various states of cell differentiation in vitro. These results, considered in the known context of Cdks activity in neuroblasts, suggest that Cdk5 is the enzyme responsible for this phosphorylation. We find that the phosphorylation by PKC at the effector domain does not occur in the same molecules that are phosphorylated at serine 25. The in situ analysis of the subcellular distribution of these two phosphorylated MARCKS variants revealed that they are also segregated in different protein clusters. In addition, we find that a sustained stimulation of PKC by phorbol-12-myristate-13-acetate (PMA) provokes the progressive disappearance of phosphorylation at serine 25. Cells treated with PMA, but in the presence of several Ser/Thr phosphatase (PP1, PP2A and PP2B) inhibitors indicated that this dephosphorylation is achieved via a phosphatase 2A (PP2A) form. These results provide new evidence regarding the existence of a novel consequence of PKC stimulation upon the phosphorylated state of MARCKS in neural cells, and propose a link between PKC and PP2A activity on MARCKS.
DESIGN TO DISCOVER : SECURITY ANALYTICS WITH 3 D VISUALIZATION ENGINE

When predicting and containing malware or botnet infections, we can use some ideas from studies of disease epidemics. The malware research community has long dedicated its expertise to dissecting malware binaries and examining footprints on victim systems – studying the disease and the patient in an isolated environment, if using the disease study as an analogy. Using the same analogy, our approach is different. Instead of studying the bad actors (diseases) themselves, we study a collective pool of victims (patients) and the behaviour of bad actors (diseases). We have built mathematical and statistical models for identifying the correlations of symptoms/IOCs (indicators of compromise), which help to further pinpoint unknown bad actors. Our data-driven analytical models help us effectively extend intelligence from an initial event onwards incrementally. At the same time, intelligence exploration can greatly be assisted by the use of a sophisticated visualization engine, as the simple truth is that a picture is worth a thousand words. We will introduce our in-house-developed OpenGL visualization engine that supports large-scale data 3D graphing and interactive intelligence searching. In our presentation, we will have a number of 3D demos to accompany our use cases and some very interesting ways of looking at data and intelligence. The design of the system facilitates an improved user experience for our day-to-day intelligence researchers.
An Architecture and Programming Framework for Dynamic Reconfigurable Computing Systems

Dynamic reconfigurable computing (DRC) system is becoming increasingly attractive with its potential to combine high performance and rich functionality. But problems exist in practical application of DRC, such as that designers need to know the architectural and physical details of reconfigurable device. To address this issue, a framework with hybrid architecture and transparent programming model has been proposed in this paper, which allows designers develop applications independently of the underlying physical devices. The hybrid architecture consists of microprocessors and reconfigurable hardware accelerators with corresponding control and management units. Hardware and software functions are described with function libraries that can be called in same manner by application designers. Compilation and synthesis processes are discussed to map the system description to the hybrid architecture. It is believed that this framework will be helpful to increase the development efficiency on reconfigurable computing platform.
Comparison between FEM and BEM for Real-time Simulation

AbstractComputer simulation of object deformation has wide applications in areas such as movies, computer graphics, computer games, etc. Numerous methods have been proposed to simulate deformable objects. A common practice of simulating deformable objects is to use physically-based approaches which include the mass-spring system and the finite element method (FEM). The mass-spring system only gives a coarse estimation of object deformation whereas the FEM requires generating solid volumetric elements which is a tedious and time consuming process. The use of boundary element method (BEM) allows objects to be deformed without generating solid volumetric elements.In order to achieve real-time deformation, all these methods require time-consuming pre-computation process. In this paper, a comparison is made between the FEM and the BEM techniques. We propose to adopt linear elements of boundary element technique for real-time applications. The method not only allows physically-based and real-time deformation, i.
Design and evaluation of an IDMA cooperative relay free-space optical system

In this paper, we illustrate our attempt in an effective method of free space optical (FSO) communications. Firstly, a pulse-position modulated (PPM) optical interleave-division multiple-access (OIDMA) system is designed. Secondly, to alleviate the degrading effects of atmospheric turbulence, cooperative relay technique is adopted by OIDMA for achieving spatial diversity and robust transmission performance. Thirdly, to study the convergence behavior of the proposed relay-assisted optical PPM-OIDMA system, extrinsic information transfer (EXIT) charts are analyzed. By deriving the trajectories of the exchanged extrinsic information from EXIT charts, the time of iterations required for convergence can be easily predicted. The effects of scintillation, thermal noise, background noise and multi-user interference have been taken into consideration.