Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Java Util Concurrent parking contention in the IBM J9 Virtual Machine

Java Util Concurrent (JUC) is a widely used library providing support for multithreaded applications. JUC provides a variety of tools such as explicit locks, thread pools, blocking queues etc. Many of these constructs use thread parking as a means of synchronizing threads. Measuring thread parking contention can potentially provide information which can be used to accelerate Java applications through the identification of bottlenecks. To the best of our knowledge, no such tool exists, possessing the ability to run as part of the JVM while not otherwise modifying the JUC base classes. IBM’s J9 Virtual Machine, which is an implementation of a Java Virtual Machine (JVM), has been modified to store parking contention data and output the results to the screen or to Java dumps. We also show that our tool has a negligible effect on the performance of the JVM. Furthermore, we have investigated coding patterns involving JUC locks and have used our tool to identify bottlenecks and consequently accelerate the Java code. We conclude that our instrumentation to the JUC thread parking implementation provides useful information to developers hoping to optimize applica-
A monitoring approach to facilitate run-time verification of software in deeply embedded systems

With embedded systems growing in hardware and software complexity, it is becoming increasingly difficult to avoid failures, despite costly design and testing efforts. Monitoring the behaviour of an embedded system's software is useful both during the product testing phase and in the field environment and can contribute to confidence in the overall system. Run-time verification combines monitoring with formal methods by checking specified rules against run-time observations. Embedded systems pose some problems for run-time verification, as their limited resources may preclude internal monitoring features and their self-contained design may inhibit sufficient observation of the software execution by an external monitor. The main aim of the research outlined in this thesis is to investigate the feasibility of using run-time verification as a monitoring approach for software executing in an embedded system. An experimental evaluation framework is described that supports the study of this approach. The embedded system comprises a Java Optimised Processor (JOP) soft processor, instantiated in the fabric of an FPGA (field programmable gate array). The experimental system employs the Java-MaC (Java Monitoring and Checking) run-time verification method, arranged to indirectly monitor the run-time behaviour of a software application executing on JOP. The experimental framework successfully allows run-time verification of a software application executing on the FPGA. A case study is presented that demonstrates that the monitoring approach is sufficient to observe a number of parameters used in an example ‘railroad crossing’ run-time verification scenario. A second case study aims to verify that a set of tasks scheduled under the rate-monotonic algorithm is scheduled at run time as predicted by static offline analysis. The results of the case studies provide detail on the potential and limitations of the monitoring arrangement. The gathered data is used to support recommendations for monitoring embedded systems, presented at the conclusion of this thesis.
Advances in factor analysis and structural equation models

The articles in this book develop the basic ideas of causal modeling and illustrate them for a wide range of applications in the social sciences. Causal modeling requires researchers to think through all the conceptual implications associated with a given specification. A causal model is said to be misspecified (and inadequate) if the assumptions are incorrect and do not allow the desired causal interpretation of the parameters. A common source of misspecification derives from the omission of a major causative factor that is correlated with at least 1 predictor variable included in the model. Causal modeling in the social sciences is especially challenging and complex because the causes of a given outcome are typically unknown or not susceptible to direct observation. Moreover the observed patterns among the variables may be consistent with a variety of causal explantations. Thus it is often desirable to formulate and test a variety of models. Some models may be ruled out on comon sense grounds. For example although educational aspirations may differ by race aspirations cannot cause race. Other models might be rejected only if they yield inferences inconsistent (within statistical limits) with the pattern of relationships actually observed. Causal modeling can be used to state theory more exactly to test theory more precisely and to yield a more thorough understanding of data. The methods in this book do not provide final answers to the question of model specification. What they offer the researcher is the flexibility to formulate and test a variety of causal models and to guide the analysis toward more adequate explanations of the relationships embedded in data.
Standardization in information systems

On a very abstract level, an information system consists of a set of system elements which communicate with each other. Communication is an unproductive operation, so the time needed to communicate data should be kept as short as possible and, to put it in monetary terms, the opportunity costs for communication should be kept small. Now, communicating data is more than just transmitting it, but it consists in large parts of converting data structures that are used by one system element into data structures that are used by another system element. Such conversion can be avoided, if the system elements use a common standard of data structures. Since establishing a standard at a system element incurs standardization costs, a decision maker has to check, if the cost savings gained by standardized communication outweigh the costs for installing the standard. In a recent paper, it is claimed that this so-called standardization problem is an NP-hard optimization problem. We will demonstrate that this is not true, but in fact the standardization problem can be solved in polynomial time by solving a minimum cut problem.
DATABASE THREAT ANALYSING TOOL (DTAT) FOR TOP-K OBJECTS QUERIES

Top-k pairs monitor the general functionality restricts the work space of sub domains processing the result which influence some query. There are many top- k queries which significantly query over k-closest pairs which answers wide variety of top-k pairs. When dataset defines every row which retrieves highest scoring query that has efficient evaluation will be active research topic with different instants. Efficient query for contents such as buffer will have clear idea for efficient maintenance of constant of top-k queries. For updating and querying the complexity of algorithms top-k query implements them for maintaining their query pairs. In our proposed model we can make top-k pairs querying process so speed and make their transaction faster with verification. It also helps prevention of leeching attack in database which can also leads to SQL injection. Here we have the technique which helps finding out the set of code which is defenseless and vulnerable to attacks. So here we develop tool termed Database Threat Analyzing Tool (DTAT) which helps make those places guarded and defensible.
Development and the Use of Chemistry Teaching Video Materials by Internet Browser

We have developed multimedia type chemistry CAL software by Netscape Navigator browser.We have converted CAL software, made by ExpandBook form, into HTML form, whose theme is for physical chemistry experiments. Multimedia type chemistry CAL material with video movie really helps students to visualize chemistry operations in practical experiments. Anyone can make a teaching document by HTML language easily if one memorizes a procedure. When we tried the intranet use of CAL software in a remote place (separate room) and video movies of CAL, it produced smooth movement and was useful for the learning student.
Comparison of different feature classifiers for brain computer interfaces

Changes in EEG power spectra related to the imagination of movements may be used to build up a direct communication channel between brain and computer (brain computer interface; BCI). However, for the practical implementation of a BCI device, the feature classifier plays a crucial role. We compared the performance of three different feature classifiers for the detection of the imagined movements in a group of 6 normal subjects by means the EEG. The feature classifiers compared were those based on the hidden Markov models (HMM), the artificial neural network (ANN) and on the Mahalanobis distance (MD). Results show a better performance of the MD and ANN classifiers with respect to the HMM classifier.
Funktionale Sicherheit nach ISO 26262 in der Konzeptphase der Entwicklung von Elektrik/Elektronik Architekturen von Fahrzeugen

Die Entwicklung von softwarebasierten Fahrzeugsystemen unter Befolgung des neuen Standards IO 26262 erfordert ein gemeinsames Verstandnis sowie die Verzahnung des Vorgehens in beiden Domanen. Ziel dieser Arbeit ist die Berucksichtigung von Anforderungen der funktionalen Sicherheit wahrend der Modellierung von Elektrik/Elektronik Architekturen, ihre formale Zuteilung zu Modellinhalten sowie die Unterstutzung nebenlaufiger und nachfolgender Aktivitaten der Fahrzeugentwicklung.
Optimization problems in telecommunications and the internet

Optimization problems occur in diverse areas of telecommunications. Some problems have become classical examples of application for techniques in operations research, such as the theory of network flows. Other opportunities for applications in telecommunications arise frequently, given the dynamic nature of the field. Every new technique presents different challenges that can be answered using appropriate optimization techniques. In this dissertation, problems occurring in telecommunications are discussed, with emphasis for applications in the Internet. First, a study of problems occurring in multicast routing is presented. Here, the objective is to allow the deployment of multicast services with minimum cost. A description of the problem is provided, and variations that occur frequently in some of these applications are discussed. Complexity results are presented for multicast problems, showing that it is NP-hard to approximate these problems effectively. Despite this, we also describe algorithms that give some guarantee of approximation. A second problem in multicast networks studied in this dissertation is the multicast routing problem. Its objective is to find a minimum cost route linking source to destinations, with additional quality of service constraints. A heuristic based on a Steiner tree algorithm is proposed, and used to construct solutions for the routing problem. This construction heuristic is also used as the basis to develop a restarting method, based on the greedy randomized adaptive search procedure (GRASP). The last part of the dissertation is concerned with problems in wireless networks. Such networks have numerous applications due to its highly dynamic nature. Algorithms to compute near optimal solutions for the minimum backbone problem are proposed, which perform in practice much better than other methods. A distributed version of the algorithm is also provided.
Detection Efficiency Mismatch and Finite-Key-Size Attacks on Practical Quantum Cryptography Systems

Security verification for the physical implementation of a cryptography system is an important step to ensure the security level promised by theory. As has been shown many times, any physical device has characteristics and behavior that deviate from theoretical expectations. Frequently, those lead to new security loopholes. This thesis presents three experimental studies of attacks on quantum key distribution (QKD) systems. The first is the detection efficiency mismatch on free-space systems, which takes advantage of alignment imperfections in Bob’s detector to control detection efficiencies. The experiment was done on a polarization-encoding free-space receiver to find the detection efficiencies of each detector for different spatial modes of an incoming photon. Those results were put into an optimization program, which modeled an intercept-andresend attack on a non-decoy Bennett-Brassard 1984 (BB84) protocol. The result shows that an adversary is able to gain information about the key without being detected by Alice and Bob. The second study is an experimental test of reliability of a spatial filter (a pinhole), which is proposed as a countermeasure for the previous attack. The result shows that, by sending a high-power laser beam focused on the pinhole, the pinhole can be widened without affecting other components in the receiver. Thus, the ability to perform a spatial mode detection efficiency-mismatch attack is recovered. The last experiment is a demonstration of Eve’s ability to force a commercial system to distill a key from a raw key of a short length, where the asymptotic assumption of security claimed by the manufacturer might not hold. It was shown that this could be done by inducing transmission loss in the channel at an appropriate time.
Finite Elements Contact Modelling of Planetary Gear Trains

The paper presents the application aspects of computer technologies concerning the process of creating theoretical contact models of planetary gear trains using Abaqus/Explicit. The necessary assumptions, constrains and specific features of these gear drives are discussed in details. The models created are appropriate and useful tools for computer simulation research of the dynamic behaviour of planetary gear trains.
Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM). This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.
Super Resolution of HARDI images Using Compressed Sensing Techniques

Effective techniques of inferring the condition of neural tracts in the brain is invaluable for clinicians and researchers towards investigation of neurological disorders in patients. It was not until the advent of diffusion Magnetic Resonance Imaging (dMRI), a noninvasive imaging method used to detect the diffusion of water molecules, that scientists have been able to assess the characteristics of cerebral diffusion in vivo. Among different dMRI methods, High Angular Resolution Diffusion Imaging (HARDI) is well known for striking a balance between ability to distinguish crossing neural fibre tracts while requiring a modest number of diffusion measurements (which is directly related to acquisition time). HARDI data provides insight into the directional properties of water diffusion in cerebral matter as a function of spatial coordinates. Ideally, one would be interested in having this information available at fine spatial resolution while minimizing the probing along different spatial orientations (so as to minimize the acquisition time). Unfortunately, availability of such datasets in reasonable acquisition times are hindered by limitations in current hardware and scanner protocols. On the other hand, post processing techniques prove promising in increasing the effective spatial resolution, allowing more detailed depictions of cerebral matter, while keeping the number of diffusion measurements within a feasible range. In light of the preceding developments, the main purpose of this research is to look into super resolution of HARDI data, using the modern theory of compressed sensing. The method proposed in this thesis allows an accurate approximation of HARDI signals at a higher spatial resolution compared to data obtained with a typical scanner. At the same time, ideas for reducing the number of diffusion measurements in the angular domain to improve the acquisition time are explored. Accordingly, the novel method of applying two distinct compressed sensing approaches in both spatial and angular domain, and combining them into a single framework for performing super resolution forms the main contribution provided by this thesis.
3DFaceNet: Real-time Dense Face Reconstruction via Synthesizing Photo-realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.
Sequential Pattern Mining Methods: A Snap Shot

Sequential pattern mining (SPM) is an important data mining task of discovering time-related behaviours in sequence databases. Sequential pattern mining technology has been applied in many domains, like web-log analysis, the analyses of customer purchase behaviour, process analysis of scientific experiments, medical record analysis, etc. Increased application of sequential pattern mining requires a perfect understanding of the problem and a clear identification of the advantages and disadvantages of existing algorithms. SPM algorithms are broadly categorized into two basic approaches: Apriori based and Pattern growth. Most of the sequential pattern mining methods follow the Apriori based methods, which leads to too many scanning of database and very large amount of candidate sequences generation and testing, which decrease the performance of the algorithms. Pattern growth based methods solve all above problems and in addition, it works on projected database which minimize the search space. Paper reviews the existing SPM techniques, compares various SPM techniques theoretically and practically. It highlights performance evaluation of each of the techniques. Paper also highlights limitation of conventional objective measures and focused on interestingness measures. Finally, a discussion of the current research challenges and pointed out future research direction in the field of SPM.
Systems and methods for improving the ranking of news articles

A article in based on the quality of the associated source of information to improve the ranking of news articles. In The present invention receives the list of links (505), for each link, identifies the information source to which the link is associated (515), based in part on quality of at least the specified information source Te, ranking method results, characterized by ranking the list of links (520) are provided. FIELD 5
The Process of Process Modeling

Business process models have gained significant importance due to their critical role for managing business processes. In particular, process models support the common understanding of a company’s business processes, enable the discovery of improvement opportunities, and serve as drivers for the implementation of business processes. Still, a wide range of quality problems have been observed. For example, literature reports on error rates between 10% and 20% in industrial process model collections. Most research in the context of quality issues of process models puts a strong emphasis on the outcome of the process modeling act by analyzing the resulting model. However, it is rarely considered that process model quality is presumably dependent on the process followed to create the process model. This thesis strives for addressing this gap by specifically investigating the process of creating process models. In this context, different actions on several levels of abstraction might be considered, including elicitation and formalization of process models. During elicitation information is gathered, which is used in formalization phases for actually creating the formal process model. This thesis focuses on the formalization of process models, which can be considered a process by itself—the Process of Process Modeling (PPM). Due to the lack of an established theory, we follow a mixed method approach to exploratively investigate the PPM. This way, different perspectives are combined to develop a comprehensive understanding. In this context, we attempt to address the following research objectives. First, means for recording and performing a detailed analysis of the PPM are required. For this, a specialized modeling environment— Cheetah Experimental Platform (CEP)—is developed, allowing a systematic investigation of the PPM. Further, a visualization for the PPM, i.e., Modeling Phase Diagrams (MPDs), is presented to support data exploration and hypotheses generation. Second, we attempt to observe and categorize reoccurring behavior of modelers to develop an understanding on how process models are created. Finally, we investigate factors that influence the PPM to understand why certain behavior can be observed. The findings are condensed to form a model on the factors that influence the PPM. Summarized, this thesis proposes means for analyzing the PPM and presents initial findings to form an understanding on how the formalization of process models is conducted and why certain behavior can be observed. While the results cannot be considered an established theory, this work constitutes a first building block toward a comprehensive understanding of the PPM. This will ultimately improve process model quality by facilitating the development of specialized modeling environments, which address potential pitfalls during the creation of process models.