Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid multi-class text categorization based on SVM-DT

How to improve the text categorization efficiency as well as keeping high speed is a research problem. Several factors are effected the processing of the decision tree construction, such as, the degree, the balancing degree, the constructing way, the group number and the division degree between groups etc. Considered the various roles between the above factors, a comprehensive algorithm to construct the SVM-DT (Support Vector Machine - Decision Tree) is proposed. In this method, three conditions are considered respectively. The text categorization experiments on massive corpus demonstrate that the algorithm can improve the efficiency in some degree and decrease the training and testing time largely at the same time. The algorithm to construct the SVM-DT is feasible and adaptable.
FPGA hardware implementation of the LZMA compression algorithm

Software-based LZMA( Lempel Ziv Markov-chain algorithm) nondestructive compression algorithm is very slow and consumes too much CPU( central processing unit) resources during the data compression process,as a result it can not meet the requirements of real-time systems. On the basis of the improved algorithm,a hardware accelerator for LZMA was designed with FPGA( field programmable gate array) implementation. The hardware accelerator is composed of LZ77 compressor,range encoder and send out controller.Ping-pong operation,parallel matching method with high performance,pipeline processing structure and other acceleration techniques were used to speed up LZMA compression algorithm. While at the same time,data compressed by the circuit is still compatible with standard LZMA file format. The compression rate of the circuit is speeded up to 125 Mb / s,nearly 10 times faster than that of the software based LZMA. The processing relative data of each clock is speeded up nearly 200 times. Results from the experiments on ML605 basing on a Virtex-6 FPGA development kit,show the accelerator is correct and feasible.
Trajectory Planning for Automated Driving Based on Ordinal Optimization

This paper proposes an approach based on Ordinal Optimization (OO) to solve trajectory planning for automated driving. As most planning approaches based on candidate curves optimize the trajectory curve and the velocity profile separately, this paper formulates the problem as an unified Non-Linear Programming (NLP) model, optimizing the trajectory curve and the acceleration profile (acceleration is the derivative of velocity) simultaneously. Then a hybrid optimization algorithm named OODE, developed by combining the idea of OO and Differential Evolution (DE), is proposed to solve the NLP model. With the acceleration profile optimized “roughly”, OODE computes and compares “rough” (biased but computationally-easier) curve evaluations to select the best curve from candidates, so that a good enough curve can be obtained very efficiently. Then the acceleration profile is optimized again “accurately” with the selected curve. Simulation results show that good enough solutions are ensured with a high probability and our method is capable of working in real time.
Energy Conduction Model and Its Application in Medical Image Segmentation

This paper proposes an energy conduction model(ECM) based on the level set framework,which takes advantage of the heat conduction equation to construct the image energy.After comparing the image intensity distribution with the spatial distribution of the temperature field,an energy conduction function is defined,which well simulates the process of heat conducting.The advantage of the ECM is that it captures the global feature of an image and takes the local intensity information into account.Thus,ECM is able to accurately segment medical images with inhomogeneity and noise,as well as for the medical images with multi-targets.Synthetic and real medical images are tested with ECM,which shows its robustness and efficiency.
Implementing Motion Planning Software for Gretchen's Manipulator

Gretchen is a mobile Fetch robot with a seven degree of freedom manipulator. Her current software generally creates motion plans that add a great deal of unnecessary movement to the solution path. This is a significant problem due to the fact unnecessary manipulator movement causes an increase to the probability of collision. The goal of this project is to replace Gretchen's current motion planning software so that it produces nearly optimal paths. After applying a greedy smoothing heuristic to a Rapidly Exploring Random Tree algorithm, this project's software produces paths that are both direct and smooth in position, acceleration, and velocity. Furthermore, this software can plan to avoid simple obstacles that have already been placed into Gretchen's environment. In the future, this software could be improved by producing trajectories that speed up manipulator movement.
Studies on Microbial Electrochemical Cells Using Different Anode Respiring Bacteria

s/ Poster presentations 1. Qaiser F. Khan, Bradley G. Lusk, Prathap Parameswaran, Naeem Ali, Abdul Hameed, Bruce E. Rittmann, and César I. Torres. Characterization of the electrical current generation capabilities of the thermophile Thermoanaerobacter presudethanolicus, using a Microbial Electrochemical Cell. 114th General Meeting, American Society for Microbiology, May 17-2
on en tiempo real para mejorar la calidad de servicio del Middleware

La utilizacion de aplicaciones de diferente naturaleza dentro de un mismo entorno, entorno heterogeneo, se esta extendiendo gracias a la incorporacion de tecnicas de virtualizacion a los servidores. Compartir un servidor ofrece ventajas sobretodo en terminos de eficiencia de energia, utilizacion del espacio o mantenimiento. La virtualizacion anade ventajas en la separacion de las diferentes aplicaciones o entornos. Aun asi los gestores de recursos para entornos heterogeneos tienen como principal dificultad ofrecer calidad de servicio (QoS) a diferentes aplicaciones, entornos o cargas. Una aplicacion que realice streaming y otra que realice calculo intensivo, normalmente , no colisionaran ya que los recursos utilizados son diferentes. Por el otro lado, colisionaran dos aplicaciones que trabajen con la CPU. Nuestra propuesta ofrece la posibilidad de introducir dentro de estos gestores de recursos la capacidad de predecir este tipo de entornos, en concreto transaccionales y Grid, para aumentar la QoS y el rendimiento. Las predicciones han de utilizar tecnicas de simulacion ya que la mayoria de las veces el sistema no sera representable mediante tecnicas analiticas, por ser un sistema saturado o tener caracteristicas dificiles de representar. La simulacion es una tecnica utilizada para predecir el comportamiento de sistemas en multitud de areas. Las simulaciones de componentes hardware son muy comunes, dado el coste de construccion de los sistemas simulados (procesadores, memorias.). Sin embargo, el uso de la simulacion en entornos complejos, como es el middleware, y su aplicacion en gestores de recursos tiene un uso muy bajo. Nosotros proponemos simulaciones ligeras capaces de obtener resultados utilizables en estos entornos. Entre las aportaciones y contribuciones de la tesis tenemos: (i) utilizacion de metodos de simulacion para incrementar el rendimiento y la calidad de servicio de estos sistemas. (ii) ampliacion de un sistema de monitorizacion global para aplicaciones mixtas (JAVA y C) que nos ofrece la posibilidad de conseguir informacion de lo que ocurre en el middleware y de relacionarlo con el sistema. (iii) creacion de un gestor de recursos capaz de repartir los recursos en un entorno heterogeneo utilizando la prediccion para tener en cuenta diferentes parametros de calidad de servicio. En la tesis se muestran los mecanismos de creacion de los distintos simuladores, las herramientas de obtencion de datos y monitorizacion, asi como mecanismos autonomos que pueden alimentarse de la prediccion para producir mejores resultados. Los resultados obtenidos, con gran impacto en la QoS en el gestor creado para Globus, demuestran que los metodos aplicados en esta tesis pueden ser validos para crear gestores de recursos inteligentes, alimentados de las predicciones del sistema para tomar decisiones. Finalmente, utilizamos las simulaciones realizadas incorporandolas dentro de un prototipo de gestor de recursos heterogeneo capaz de repartir los recursos entre un entorno transaccional y un entorno Grid dentro del mismo servidor.
Object- and spatial-level quantitative analysis of multispectral histopathology images for detection and characterization of cancer

The main goal of this dissertation is the development and discussion of techniques for higher-level image analysis, i.e., object-level analysis, of breast cancer imagery. Established cytologic (cell) criteria can be contradictory, and even histologic (tissue) criteria (considered the gold standard for diagnosis) are subject to varied interpretation. There is thus a need to quantitatively define characteristics of breast cancer to bettor coordinate clinical care of women presenting breast masses. We propose here an approach for such quantitative analysis, Quantitative Object- and spatial Arrangement-Level Analysis (QOALA), using expert (pathologist) input to guide the classification process. The main contributions in this work are four-fold. First, quantitatively analyze the utility of multispectral imagery for classification and segmentation tasks in histopathology imagery. Second, we develop object-level segmentations for several histologic classes, as a quantitative object-level segmentation metric. Third, we extract a comprehensive set of both object- and spatial-level features which are used in a feature selection framework for classification of objects and imagery. Fourth, we extend the concepts of object-level features to higher-level image objects, analyze the utility of these high-level objects for image classification, and introduce the concept of a probabilistic graph-based model of imagery. Overall, QOALA yields very good object- and image-level classification performances. More specifically, the object-level features as implemented in QOALA are versatile and general enough to elicit important information from even imperfectly segmented objects. Additionally, the use of non-nuclear features, namely features of cytoplasm and stoma have good classification performance, often exceeding that of nuclei. Higher-level features display a potential to increase both object- and image-level classification performance.
FPGA Implementation by using XBEE Transceiver

Objective: Generally there are numerous approaches to give security information that is being imparted. However, what if the security is assured irrespective of the hackers are from the noise. The Information Security is essential concern towards each communication system. Methods/Analysis: XBee is a PAN technology based on the IEEE 802.15. 4 standard. Unlike Bluetooth or wireless USB devices, XBee devices have the ability to form a mesh network between nodes. Meshing is a type of daisy chaining from one device to another. Finding: This technique allows the short range of an individual node to be expanded and multiplied, covering a much larger area. This paper describes a design of desired security for data communication by designing standard algorithm for encryption and decryption. The process occur in this module was based on encryption and decryption. The source information is generated by a bit file and this will be encrypted and is sent to destination through XBee modules. The receiving system will check the data and decrypt according to a specific algorithm and displays on the LED. Applications/Improvement: The present algorithm is implemented in Verilog HD Land simulated using Xilinx ISE simulator tool. The design is implemented on Xilinx Spartan-3 EFPGA development board. This project has applications in encoding and decoding operations for security purposes.
Research on the Extraction Method of Explicit Attribute-View Pairs in Product Review

The fine-grained affective analysis of product reviews is a fine-grained mining of the content of reviews, which has important research significance. The explicit attribute-viewpoint extraction is one of its key research issues. Due to the complex structure and colloquial features of online product reviews, traditional models are not ideal for the extraction of explicit attribute-views pairs. To solve this problem, this paper proposes an extraction method based on deep learning and integrating positional relationship information. By using the Bi-directional Long Short-Term Memory Network to overcome the problem of long distance dependence, the author makes full use of the context, combines the attention mechanism to reduce the noise weight of sentence-level informal text, and integrates the feature of position relation to enrich the feature information. The experimental results show that compared with other models, the network model proposed in this paper has improved the call rate and F1 value.
Introducing Open Government Data

ABSTRACT This article provides an overview of open government data. It outlines what it is, provides examples, and summarizes library engagement with these data. The professional and academic literature and other web sources were examined along with government policies and portals. Open government data is a fairly recent and evolving phenomenon that promotes government transparency and invites citizen participation and innovative reuses of public data. Libraries have been responding to this release of data in a number of ways including offering data literacy instruction and special services and programs.
Forward vehicle detection based on shadow features and adaboost

A single camera based on-road forward vehicle detection system was developed to detect vehicles in front of the driver in a driver assistance system to improve vehicle safety.The shadows underneath vehicles are detected to generate vehicle hypotheses.An adaptive road threshold method and a dark areas merge approach are used to resolve the grayscale variation on the road and the distortion of the shadow border.A gradient-based adaboost method is used to verify the hypotheses.Kalman filters are then used to track objects to improve system performance.The system was tested with series of images taken on roads.The results show that the system can effectively detect vehicles in front of the driver on real time.
Size and compositional dependent effects of marine aerosol on cloud condensation nuclei

v Acknowledgements . vi 1. Motivation and study objectives . 1 2. Review of the current state of knowledge . 3 2.1 Aerosol definitions, sources, and composition. 3 2.2 Aerosol cloud formation and interactions . 6 2.3 Marine aerosol and climate . 13 2.3.1 Marine aerosol composition . 15 2.3.2 Pollution in the marine environment . 22 2.3.3 Marine cloud condensation nuclei . 24 2.4 Ice nucleating particles . 26 3. Methods and measurements . 29 3.1 Sampling locations . 29 3.1. 1 PEGASO Cruise, Southern Ocean (Jan 2nd Feb 12th, 2015) . 29 3.1. 2 Mace Head Atmospheric Research Station, North East Atlantic . 31 3.2 Supporting instrumentation . 32 3.2.1 Condensation particle counter . 32 3.2.2 Particle sizing by electrical mobility diameter . 34 3.2.3 Aerosol Mass Spectrometer . 36 3.3 Cloud condensation nuclei counter . 37 3.3.1 Calibration . 39 3.3.2 Polydispersed set-up. 43 3.3.3 Monodispersed set-up . 44 3.4 Black carbon instrumentation . 44 3.5 Interpreting data . 45 3.5. 1 Air Mass Analysis . 46 3.5. 2 Köhler theory . 47 3.5. 3 ĸ-Köhler theory and aerosol hygroscopicity . 49 3.5. 4 Degree of neutralisation . 50
From scarcity to bounty: how Galateas can turn your scarce short queries into gold

With the growth of digital libraries and the digital library federation in addition to partially unstructured collections of documents such as web sites, a large set of vendors are offering engines for retrieving content and metadata via search requests by the end user (queries). In most cases these queries are short unstructured fragments of text in different languages that are difficult to make sense of because of the lack of context. When attempting to perform automatic translation of these queries, using machine learning approaches, the problem becomes worse as aligned corpora are almost inexistent for such types of linguistic data. The GALATEAS European project concentrates on analyzing language-based information from transaction logs and facilitates the development of improved navigation and search technologies for multilingual content access, in order to offer digital content providers an innovative approach to understanding users' behaviour.
Open Domain Factoid Question Answering System

A question answering (QA) system provides direct answers to user questions by consulting its knowledge base. Since the early days of artificial intelligence in the 60’s, researchers have been fascinated with answering natural language questions. However, the difficulty of natural language processing (NLP) has limited the scope of QA to domain-specific expert systems. In recent years, the combination of web growth, improvements in information technology, and the explosive demand for better information access has reignited the interest in QA systems. The wealth of information on the web makes it an attractive resource for seeking quick answers to simple, factual questions such as “who was the first American in space? ” or “what is the second tallest mountain in the world? ” Yet today’s most advanced web search services (e.g., Google, Yahoo, MSN live search and AskJeeves) make it surprisingly tedious to locate answers to such questions. Question answering aims to develop techniques that can go beyond the retrieval of relevant Documents in order to return exact answers to natural language factoid questions, such as “Who is the first woman to be in space? ”, “Which is the largest city in India? ”, and “When was first world war fought? ”. Answering natural language questions requires more complex processing of text than employed by current information retrieval systems. This thesis investigates a number of techniques for performing open-domain factoid question answering. We have developed an architecture that augments existing search engines so that they support natural language question answering and is also capable of supporting local corpus as a knowledge base. Our system currently supports document retrieval from Google and Yahoo via their public search engine application programming interfaces (APIs). We assumed that all the information required to produce an answer exists in a single sentence and followed a pipelined approach towards the problem. Various stages in the pipeline include: automatically constructed question type analysers based on various classifier models, document retrieval, passage extraction, phrase extraction, sentence and answer ranking. We developed and analyzed different sentence and answer ranking algorithms, starting with simple ones that employ surface matching text patterns to more complicated ones using root words, part of speech (POS) tags and sense similarity metrics. The thesis also presents a feasibility analysis of our system to be used in real time QA applications.
Multi-scale Images Edge Detection Model Based on Grayscale Gap

Several new conceptions including function feature,total Gap and full Gap are proposed,and the Gap statis- tical model for image segmentation is established and the detail algorithm is developed.Also,we take an analysis of regular part and singular part in the Gap statistic model for image segmentation.And the relationship between parame- ters in the model and the self-similar function of area feature is derived.Compared with the famous model of Mumford- Shah functional,our model has lower complexity.
Self Prediction Of Performance Metrics For Dbms Workload

Database Management System (DBMS) workload involves homogenous as well as heterogeneous data and concurrent users. Humans are incapable to manage the versatile data and dynamic behavior of DBMS workload. There is a need of fast computations of current serverâ€™s loads and requirements, AI algorithms and machine learning techniques. Autonomic computing technology using types of workload Decision Support System (DSS) or Online Transaction Processing (OLTP) and its performance requirement can help servers, adaptation of the workloads. If we know the type of workload, we can design such systems that predict the identified workload performance and adapt the changes in the behavior of the workload. For managing the workload, we have to face number of problems for the DBMS to better perform. Before executing, we can predict and control the workload to tune the DBMS. Predicting performance of the workload is important for tuning a DBMS and makes the DBMS aware of itself making it autonomic. The optimizer and DBMS can tune itself accordingly. Evolving behavior of workload can be handled by making the system adaptive.We have developed a framework called Autonomic Workload Performance Predictor (AWPP) for predicting the performance of the workload making it adaptive to the changing behavior of the workload. Case-based reasoning approach is applied and results are compared with other well-known machine learning techniques to observe the accuracy and effectiveness and significance of AWPP framework. MySQL database management system is being used to execute different benchmark workload to validate the proposed workload performance prediction framework. For training and testing TPCH and TPC-C like queries are used as our representative workload. We have taken the various benchmark workloads of DSS and OLTP for experimentation. CBR approach produced effective, accurate and significant results while predicting the performance of workload using the information available before executing a workload and adapting the workload on evolution.These predictions will be helpful for optimizer and DBMSs algorithms as well as for workload management, capacity planning, system sizing.
Compressive Sensing in Wireless Sensor Networks – a Survey

Abstract When Donoho and Candes, Rosenberg and Tao separately introduced the concept of compressive sampling (CS) to the scientific community, the entire basis of traditional sampling was at jeopardy. Of course, what Nyquist had said still holds and is unquestionable, but to achieve communication standards at par with the traditional ones but with far fewer samples, is something that drove hundreds to work in this particular area of research. Since that time till now, it has been explored in various areas of science and engineering like astrophysics, optics, etc. and image processing, array processing and radar, distributed processing, networking, respectively. Among these, the application of CS in wireless sensor networks is our topic of elucidation here. Our paper aims to review most of the work that has been done so far in the above-mentioned area, so that the scientific community has a clear understanding of the immense potential CS holds for this field of study.