Miracle-dz/UniFAR-FTUs huggingface dataset:
PATTERNS IN COLORED PETRI NETS

Colored Petri Nets (CPN) is a graphical language, which is extensively used for modeling and analysis of distributed systems with elements of concurrency. It has proven to be a good platform for modeling of process-aware information systems, workflow analysis, design of communication protocols, embedded systems, and distributed systems, etc. The challenge that we undertake in this report is to document a set of 34 empirically gathered patterns in Colored Petri Nets in the pattern format. The main goal of the CPN pattern catalog is to serve as a source of sound solutions, proven by experience, for problems appearing during modeling by means of CPN. Furthermore, the CPN patterns can be used as a domain language for communicating problems and solutions. In order to help developers in selecting a suitable pattern, we made classification of patterns and analyzed relationships between patterns for easy navigation through the CPN pattern language.
Programming Language Features for Web Application Development

Web programming remains difficult, even with cutting-edge libraries, because the execution model of the web environment is essentially different from the classic models. Unlike a batch program which sits between input and output streams, a web program sits between user activity in the browser and serverside resources such as a database. Furthermore the use of URLs as durable entry points to an application makes the environment fundamentally concurrent and re-entrant, a challenge and opportunity for supporting web programmers. This thesis makes four principal contributions to the technology for expressing web applications. First, it describes the features of Links, a new programming language with a unifiedmodel of the web environment, encompassing client and server. Among other things, the Links compiler can slice the program, generating JavaScript to run on the client and other code to run on the server, so that they interact transparently. To allow programmers to control the location of code, Links offers syntactic client and server annotations. The second contribution is a formal semantics of these client/server annotations, in the form of an “RPC calculus. ” Along with the calculus is provided a compilation technique that shows how these location annotations can be implemented in the web’s asymmetrical client/server setting, where the server acts only in response to the client’s requests. The third contribution is a description of a language feature, ‘formlets,’ which is an abstraction of HTML forms; as an abstraction, it allows reusing bundles of form elements, composing them hierarchically, and viewing their submitted data at an appropriate abstract type. And the fourth and final major contribution shows how to integrate relational database query expressions into a programming language while also extending those queries to allow nested data structures and functional abstraction.
Image denoising in computed tomography using learned discriminative dictionaries

Denoising of low-dose computed tomography (CT) images can substantially improve their diagnostic value. Learned overcomplete dictionaries have proved to be a powerful tool for this purpose. However, because the variability of the shape of features in a single CT image may be too high for a single dictionary to capture, previous studies have failed to exploit the true power of learned dictionaries for CT denoising. This study addresses this challenge by learning multiple dictionaries and using them to denoise CT images. Without loss of generality, we focus on the case where the CT image has two main parts with different structures. We jointly learn two dictionaries, one for each image part, such that each of the two dictionaries is very effective for representing patches from its corresponding image part but fails to effectively represent patches from the other part of the image. Dictionaries learned in this way will be able to effectively differentiate patches of the two image parts and denoise them. For denoising, we propose a soft segmentation strategy such that both dictionaries contribute to the denoised estimate of every patch in the image. We apply the proposed method on CT images with simulated Gaussian noise and also on low-dose CT images with realistic noise. Our results show that dictionaries learned with this approach are able to differentiate patches of structurally different parts of a CT image. In terms of denoising, this method achieves significantly better results than a method that learns two dictionaries independently and a method that learns only one dictionary. Our findings suggest that a single dictionary trained using the standard learning algorithms is unable to handle the large variety of structures that can be present in a CT image. Large improvements in denoising performance can be obtained if multiple dictionaries are learned for structurally different parts of the image. Further improvements can be achieved by training the dictionaries to differentiate patches that belong to the different image parts.
Enabling Clang to statically check MPI type safety

This paper introduces a static analysis method to check that the actual type of the data buffer and the type specified by MPI library constants match. The method is based on annotations in MPI library header files. Type checking is performed at compile time and has no run-time overhead. The paper also covers implementation of the method in Clang compiler and MPICH2 implementation of MPI.
Simulation on microgrid connected PV system under balance and unbalance fault

This paper presents an analysis in Matlab/Simulink of a three-phase photovoltaic system under balance and unbalance faults in Matlab/Simulink. The aim of this paper is to investigate the performance of the system under various types of fault. The simulation involved various types of faults occurring at different distances from the point of common coupling of the PV system. This paper also aimed to identify what type of fault that may severely damage the system. The simulation results presented in this paper show that the three-phase fault in the microgrid was severely affecting the system since it involved all the three phases of the system while the distance of the fault occurrence is less influenced in the system. The purpose of this research is to observe the effect on the system based on the types of faults occur and the distance faults occur.
A Sitting Posture Surveillance System Based on Kinect

Modern life has made more and more people become sedentary, and long periods of unhealthy posture can lead to myopia, cervical spine and lumbar disease. Therefore, this paper presents a sitting posture surveillance system based on Microsoft Kinect. By getting joints’ three-dimensional position information from Kinect’s real-time skeleton tracking technology, and input the data into computer. After processing the joints’ location information with posture recognition algorithm, computer recognizes the user’s sitting posture, and warns the incorrect postures.
Configurable sparse distributed memory hardware implementation

A configurable hardware implementation of Kanerva's Sparse Distributed Memory has been developed using advanced structures. The system consists of the host computer, address unit and memory unit. The address and memory units have been implemented with commercially available components to two functioning boards, and they perform the Hamming distance comparison and memory storage functions. In order to achieve effective hardware realization the units are designed for highly parallel processing. The host computer is used to edit, compile, and down-load the programs to be run in the units. The performance estimations are also presented. <<ETX>>
Prediction of transcription indexability

Prediction of transcription indexability This paper presents a semantic confidence measure that aims to predict the relevance of automatic transcripts for a task of Spoken Document Retrieval (SDR). The proposed predicting method relies on the combination of Automatic Speech Recognition confidence measure and a Semantic Compacity Index, that estimates the relevance of the words considering the semantic context in which they occurred. Experiments are conducted on the French Broadcast news corpus ESTER 2, by simulating a classical SDR usage scenario : users submit text-queries to a search engine that is expected to return the most relevant documents regarding the query. Results demonstrate the interest of using semantic level information to predict the transcription indexability.
Combining Evolutionary Algorithms and Language Games to Simulate Language Emergence

This thesis is focused on naming games and other language games and how they can be used to simulate the emergence of human languages. One specific naming game model proposed in the literature is reviewed and examined in detail. It is successfully reimplemented with the intention of verifying the results presented in the original article. The resulting implementation serves as a starting point for the development of new naming game model. The proposed new model incorporates elements known from evolutionary algorithms into the naming game framework. The model is implemented and used for simulations of language evolution in several scenarios. The results are discussed in relation to other naming game models and human language evolution. The thesis is concluded with a summary of what has been achieved and an outline of possible areas for future work in the field.
MECC scheduling algorithm in vehicular environment for uplink transmission in LTE networks

Single Carrier Frequency Division Multiple Access (SC-FDMA) is chosen because of the lower peak-to-average power ratio (PAPR) value in uplink transmission. However, the contiguity constraint is one of the major constraint presents in uplink packet scheduling, where all RBs allocated to a single UE must be contiguous in the frequency-domain within each time slot to maintain its single carrier. This paper proposed an uplink-scheduling algorithm namely the Maximum Expansion with Contiguity Constraints (MECC) algorithm, which supports both the RT and NRT services. The MECC algorithm is deployed in two stages. In the first stage, the RBs are allocated fairly among the UEs. The second stage allocates the RBs with the highest metric value and expands the allocation on both sides of the matrix, M with respect to the contiguity constraint. The performance of the MECC algorithm was observed in terms of throughput, fairness, delay, and Packet Loss Ratio (PLR) for VoIP, video and best effort flows. The MECC scheduling algorithm is compared to other algorithms namely the Round Robin (RR), Channel-Dependent First Maximum Expansion (CD-FME), and Proportional Fairness First Maximum Expansion (PF-FME). From here, it can be concluded that the MECC algorithm shows the best results among other algorithms by delivering the highest throughput which is up to 81.29% and 90.04% than CD-FME and RR scheduler for RT and NRT traffic respectively, having low PLR and delay which is up to 93.92% and 56.22% of improvement than CD-FME for the RT traffic flow. The MECC also has a satisfactory level of fairness for the cell-edge users in a vehicular environment of LTE network.
Dual‐loop SFC scheme for BTB‐VSC‐HVDC interconnecting asynchronous AC grids

This study proposes a dual-loop supplementary frequency control (SFC) scheme for back-to-back voltage source converter high-voltage DC (BTB-VSC-HVDC) interconnecting asynchronous AC grids, to provide frequency support for each other after a large disturbance. The proposed dual-loop SFC consists of the frequency-active power (f–P) loop and the frequency-reactive power (f–Q) loop. The former deployed on the P-loop of VSC-HVDC can provide frequency support for the disturbed grid during the primary frequency regulation and improve the steady-state characteristics of the frequency response, while the latter attached to the Q-loop of VSC-HVDC supports virtual inertia to ameliorate the transient characteristics of the frequency response. Simulation studies are conducted based on the equivalently simplified model of Southwest Power Grid and Hubei Power Grid interconnected by Chongqing-Hubei BTB-VSC-HVDC. Simulation results show that the proposed SFC scheme can effectively improve the transient and steady-state characteristics of the system frequency response under a wide range of operating condition.
Context-Aided Tracking with Adaptive Hyperspectral Imagery

A methodology for the context-aided tracking of ground vehicles in remote airborne imagery is developed in which a background model is inferred from hyperspectral imagery. The materials comprising the background of a scene are remotely identified and lead to this model. Two model formation processes are developed: a manual method and a novel autonomous method that exploits an emerging adaptive, multipleobject-spectrometer instrument. A multiple-hypothesis-tracker is incorporated, which utilizes background statistics to form track costs and associated track maintenance thresholds. Traditionally, these statistics are uniform constants, but the advent of the background model allows for spatially-varying statistics. In an experiment designed to isolate the problem into a simple and parametric single-target situation, contextaided tracking is shown to improve aggregate tracking performance by 50% in certain operating conditions. A semi-automated background modeling approach is shown to qualitatively arrive at a reasonable background model with minimal operator intervention. A novel, adaptive, and autonomous approach is given which converges to a 66% correct background model in 1 18 th the time of the baseline – a 95% reduction in sensor acquisition time – then transitions to the 100% correct model in the steadystate. Finally, the context-aided system is demonstrated in a high-fidelity tracking testbed. The context-aided tracking improves certain aggregate tracking metrics by 4% relative to a system using uniform background statistics – an important finding nevertheless diluted by benign content in the scenario. Another metric is proposed as the most salient measure of performance gain in the context-aided system, showing a dramatic 30% reduction in error relative to the best-performing uniform background statistic system. The analysis demonstrates that context-aided tracking with adaptive hyperspectral data is a viable approach.
Short Text Segmentation for Improved Query Processing

: Understanding short texts is crucial to many applications, but challenges are many. Short texts do not always detect the syntax of a written language. As a result, traditional natural language processing tools, ranging from part-of-speech tagging to dependency parsing, cannot be easily applied. Semantic knowledge is required in order to better understand short texts. In this work, a model for short text understanding which exploits semantic knowledge is used. The results show that semantic knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are both effective and efficient in discovering semantics of short texts. A query data set containing short text is pre-processed by removing punctuation marks, numbers, stop words removal and stemming. Then POS tagging is performed and Co-Occurrence network is formed to obtain the semantic similarity. Term Similarities are calculated and finally clustering is done using K-Means, hierarchical, PAM, CLARA, DIANA and AGNES methods and the result reveals that hierarchical clustering gives optimal clusters.
Visual Human-Computer Interaction

Time-efficient acquisition of reflectance behavior together with surface geometry is a challenging problem. In this study, we investigate the impact of system parameter uncertainties when incorporating a data-driven BRDF reconstruction approach into the standard pipeline of a structured light scanning system. The parameters investigated include geometric detail of scanned objects; vertex positions and normals; and position and intensity of light sources. To have full control of uncertainties, experiments are carried out in a simulated environment, mimicking an actual structured light scanning setup. Results show that while uncertainties in vertex positions and normals have a high impact on the quality of reconstructed BRDFs, object geometry and light source properties have very little influence on the reconstructed BRDFs. With this analysis, practitioners now have insight in the tolerances required for accurate BRDF acquisition to work.
CMOS amplifiers incorporating a novel slew rate enhancement technique

A novel technique for enhancing the slew rate in CMOS amplifiers is described. The enhancement is provided by an auxiliary circuit which is automatically activated during transients. The main amplifier is not stressed with large currents, thus minimizing power dissipation and avoiding signal swing problems. This technique is quite generic, and can be applied to a variety of amplifier structures. An experimental operational transconductance amplifier incorporating this technique has been fabricated. A slew rate of 1 V/ mu s with a 10000 pF load capacitance has been achieved, while requiring a quiescent power dissipation of less than 1.5 mW. <<ETX>>
Analysing Big Data to Build Knowledge Based System for Early Detection of Ovarian Cancer

Big data analysis plays a crucial role in the health care for early diagnosis of fatal disease. The data mining techniques are widely used for data analysis problem to discover valuable knowledge from a large amount of data. This paper uses the data mining methods such as feature selection and classification to provide a predictive model for ovarian cancer detection. A huge amount of dataset is gathered to build knowledge based system. Rough set theory is utilized to find the data reliance and reduce the feature set contained in the data set. The Hybrid Particle Genetic Swarm Optimization (PGSO) is used to optimize the selected features to efficiently classify the ovarian cancer, either normal or early or different stages of ovarian cancer. Multi class SVM is adopted as the classifier to classify normal or different stages of ovarian cancer using the optimized feature set. The experiment is done on different ovarian cancer dataset and the proposed system has obtained better results for all datasets.
A Minimalist Approach to Collocation Acquisition: Integrating Syntax and Semantics

How language, be it first (L1) or second (L2), is acquired has been the concern of much research and investigation, and perhaps no other phenomenon has witnessed such interest (Shormani, 2014b). One such area within language acquisition is collocability and how it presents a difficult aspect to L2 acquirers. This is so due to the fact that collocability involves formulaic language the mastering of which has been considered specific to native speakers of the language being learned. Thus, in this article, I propose a minimalist approach based on integrating syntax and semantics. The former concerns combining (collocating) two lexical items by means of Select and Merge operations, and the latter concerns what goes with what in a collocation based on feature specifications encoded on each lexis (i.e. word). My proposal is based on substantial evidence proving the availability of syntax and semantics in collocability, hence, abstracting from usage-based approaches. Thus, each collocation produced by Select and Merge (syntax) has to "pass" the semantic constraints manifested in the Collocating Feature Specification Rule (=CFSR). However, if this produced collocation fails to "pass" CFSR, it has to undergo acquisition once more in which parameters are reset and retri ggered through acquisition reorientation. The proposal places much emphasis on mental properties of Universal Grammar (UG), and the same is true concerning L2 acquisition settings in relation to providing L2 acquirers with "equal" linguistic input native s peakers have. The minimalist approach developed in this article stems from the fact that minimalism is seen as a theory, primarily concerned with language acquisition in its two spheres (i.e. L1 and L2). As far as L1 acquisition is concerned, minimalism sees it as acquiring only feature specifications manifested in parameters. L2 acquisition, however, is seen by minimalism, as merely acquiring features, peculiar to L2 being learned, which are different from and/or similar to those of the acquirer's L1, wher e the learner's only task is to reset and retrigger the UG parameters specific to L2, simply because UG principles are universal. Hence, our major task as linguists, language educators and teachers alike is how to make use of and benefit from the minimalist assumptions and hypotheses concerned with language acquisition in general, and those concerned with collocability in particular.
Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment

In order to meet Service Level Agreement (SLA) requirements, efficient scaling of Virtual Machine (VM) resources in cloud computing needs to be provisioned ahead due to the instantiation time required by the VM. One way to do this is by predicting future resource demands. The existing research on VM resource provisioning are either reactive in their approach or use only non-business level metrics. In this research, a Cloud client prediction model for TPC-W benchmark web application is developed and evaluated using three machine learning techniques: Support Vector Regression (SVR), Neural Networks (NN) and Linear Regression (LR). Business level metrics for Response Time and Throughput are included in the prediction model with the aim of providing cloud clients with a more robust scaling decision choice. Results and analysis from the experiments carried out on Amazon Elastic Compute Cloud (EC2) show that Support Vector Regression provides the best prediction model for random-like workload traffic pattern.
3D Point Cloud Reconstruction Based on the Finger Vascular Pattern

In the last few years the principle of having finger vein recognition for biometric identification has gained in popularity. Unfortunately, this procedure is very sensitive to the orientation of the finger. At the Biometric Pattern Recognition chair at the University of Twente a device has been designed to be able to capture three images of the finger veins. This paper is the first attempt to try to solve this orientation problem by creating a three-dimensional model of the finger veins using the device. A 3D point cloud of the finger veins of an index finger has been created using stereo reconstruction. Utilizing the camera parameters obtained by camera calibration, the images of two different angles can be processed. The 3D model could be reconstructed by creating a disparity map using the knowledge obtained by an algorithm that can isolate the finger. Two known test models that simulated the finger vein structure were created to estimate the accuracy of the 3D model. By reconstructing the models, it is shown that there is still improvement possible for future research. The method shown in this paper shows that it is in fact possible to get 3D models of the vascular system of the finger. The method still needs improvements in terms of speed and accuracy. This paper also provides several recommendations concerning the 3D model acquisition and improvements of the finger vein scanner used in this work.