Miracle-dz/UniFAR-FTUs huggingface dataset:
NOVEL MULTISTAGE PROBABILISTIC KERNEL MODELING IN HANDWRITING RECOGNITION

The design of handwriting recognition systems has been widely investigated in pattern recognition and machine learning literature. It was first attempted to enhance the system's performance by improving the recognition rate to reach $100\%$ which has not achieved yet. Despite the low misclassification error rate, there are still some misclassified test samples. This imposes a very high cost on the whole recognition system. The cost has to be reduced as much as possible which consequently leads to the consideration of reject option to prevent the recognition system from classifying test samples with high prediction uncertainty. The main contribution of this thesis is to propose a novel multistage recognition system that is capable of producing true prediction probability outputs and then reject test samples accordingly. An argument is supported that principally formulated probabilistic classifiers are the best reliable candidates to be utilized in the consideration of reject option. The implementation of reject option based on either non-probabilistic classifier's output score or conversion to probability measures is prone to mistake when compared to an accurate prediction probability output. The Convolutional Neural Network (CNN) is utilized as the automatic feature extractor that can properly harness the spatial correlation of the input raw handwritten images and extract a feature vector with strong discriminative properties. The SVM is used as a powerful classifier to accurately deal with the issue of big data sets. The authentic intuition of extracting the most informative training samples by using the distinguished support vector set from the SVM is also proposed. The Gaussian process classifier (GPC) in the Bayesian nonparametric modeling framework is introduced as the core element of the whole recognition system that can reliably provide an accurate estimate of the posterior probability of the class membership. Experiments under various inference methods, likelihood functions, covariance functions, and learning approaches are conducted in the hope of finding the best model configuration and parameterization. The models are evaluated on two popular handwritten numeral data sets known as MNIST and CENPARMI. The best GPC model in this multistage framework on MNIST can reach $100\%$ reliability rate with the lowest rejection rate of $1.48\%$, the best result achieved in the field. Another inherently probabilistic classifier, known as relevance vector machine (RVM), is also investigated. The RVM is formulated through the sparse Bayesian linear modeling to classification problems and it produces reliable prediction probability outputs. However, In comparison of the GPC with RVM, this argument is experimentally supported that the sparsity is not capable of improving the rejection performance on the data sets.
Prognostic significance of p16(INK4a) hypermethylation in non-small cell lung cancer is evident by quantitative DNA methylation analysis.

BACKGROUND p16(INK4a) is a tumor suppressor gene frequently inactivated by aberrant promoter hypermethylation. In the present study, p16(INK4a) methylation was evaluated in non-small cell lung cancer (NSCLC) using a quantitative assay and the clinical significance of the methylation was explored. MATERIALS AND METHODS A total of 244 tumor samples from formalin-fixed paraffin-embedded archives were examined in this study. p16(INK4a) methylation was analyzed by the fluorescence-based, real-time methylation-specific PCR assay, MethyLight. The quantitative methylation value was expressed as the percentage of methylated reference (PMR). RESULTS The median level of p16(INK4) methylation was 0.55 PMR (range 0.00-503.4). The p16(INK4) methylation value was significantly higher in males (p = 0.005) and in squamous cell carcinoma (p = 0.018). Prognostic analysis using the Cox proportional hazard model showed that the p16(INK4a) methylation value was a significant prognostic factor (odds ratio, 1.005; 95% CI, 1.003 to 1.008; p < 0.0001). The p16(INK4a) methylation value remained a significant prognostic factor (p = 0.0004) in multivariate analysis including age, gender, histological type and clinical stage. Specimens were then classified into hypermethylated or non-hypermethylated groups based on the p16(INK4a) methylation value using various cut-offs from 1 to 100 PMR. There was no significant difference in prognosis between the two groups using a cut-off value of 1 PMR. On the other hand, there was a significant difference using 6 PMR or more as the cut-off value (p < 0.01). CONCLUSION These results provide clear evidence for the prognostic significance of p16(INK4a) methylation in NSCLC using quantitative DNA methylation analysis. Careful assessment of DNA methylation is needed because qualitative methylation analysis may overestimate low levels of methylation, which have less clinical significance.
FACTORS INFLUENCING DEVELOPMENT LEVEL OF SETTLEMENTS IN SOUTHTRANSDANUBIA

Study delivers an overview on general approach of economic development being most commonly connected to GDP per capita values The given index, however – beside its advantages – shows up a number of disadvantages e.g. it can be calculated only on county- (NUTS III) level. A regional statistical classifi cation system has already been created in Hungary but on the compulsory NUTS II level (regions) the development levels of counties, micro-regions and settlements are remarkably different. Development level is measured on settlement level by the author on the basis of 17 socio-economic and infrastructural factors. Results point out that differences between the development level of settlements failed to decrease, in contrary the gaps slightly increased by the end of the examined period as it is shown by the variation co-effi cient of annually calculated Complex Development Index.
Carcass physical composition and meat quality of cull cows of different genetic groups feedlot finished and slaughtered with distinct weights

The objective of this trial was to evaluate the carcass physical composition and meat quality of crossbred cull cows, from the second (G2- 3/4Charolais (C) Nellore (N) and 3/4NC) and third (G3 - 5/8CN and 5/8NC) generations of Charolais - Nellore rotational crossbreeding, feedlot finished and slaughtered at 465, 507 and 566 kg. At the beginning of the experiment, the average age, weight and body condition were, respectively, 8.5 years, 388.6 kg and 2.35 points. The diet, with 48:52 roughage:concentrate ratio, contained 12.5% crude protein and 2.99 Mcal of digestible energy per kg of dry matter. Carcass bone (16.55, 15.84 and 12.72%) and muscle percentages (63.5, 60.05 and 60.69%) decreased, whereas fat percentage increased (19.85; 24.32; 28.08%, respectively) as slaughter weight increased. The weight gain of the carcass edible portion between the extreme slaughter weights was 77.36 kg, being 37.32 kg of muscle (48.24%) and 40.04 kg of fat (51.76%). Meat tenderness evaluated by shear force, was significantly improved in animals slaughter at higher weight (4.94, 4.89 and 3.89 kgf/cm3, respectively). Meat color of 5/8NC cows was darker (2.87 points) in relation to 5/8CN cows (4.0 points). Meat of G3 animals was tender than G2 (4.16 vs 4.99 kgf/cm³). Higher juiciness was observed for 5/8NC meat than for 5/8CN (6.49 vs 4.16 points), as result of lower cooking loss of 5/8NC meat (17.61% vs 23.33%). Increasing cull cows slaughter weight is a method to increase rapidly beef meat production, inclusively improving their quality.
The SMACH High-Level Executive

Personal robotics applications often require the integration of hundreds of components. In robot operating systems (ROSs), such subsystems and primitive capabilities are usually encapsulated in ROS nodes. Even with encapsulation and well-documented messaging interfaces, writing maintainable code to make a large set of ROS nodes to act together to solve a problem is difficult. Solution strategies range from writing code in big if/else cascades and nested switch statements to using more powerful inference and task-planning systems. In this column, we introduce an approach based on nested state machines that has proven very effective at building real-ROS applications. Complex Appications Over the past couple of years, we have been exploring the trade-offs between task scripting and task planning for highlevel control in robot applications written on top of ROS. Scripting approaches let the programmer not only say exactly what the robot should do, but also require the programmer to explicitly describe recovery logic for all failure modes. Although these methods can be rapid for developing small applications, they do not scale well. When failures arise, robots are not like pure software systems: they cannot just reset the state of the world and retry. As a result, autonomous robotics applications require a large amount of additional work to describe how to recover from these failures in addition to the application’s nominal execution. Furthermore, our experience has shown that maintaining, extending, and fixing such scripts over time makes it more and more challenging to analyze or model the application. On the other end of the spectrum, instead of explicitly describing which actions to execute in an imperative programming language, more autonomy can be given to the robot to plan and execute tasks. There exist model-based task planning and inference systems based on classic artificial intelligence (AI), constraint satisfaction, and model checking. The model, in this case, describes constraints and relations relevant to the set of actions at the robot’s disposal. These systems aim to shift the burden of solving the application-specific problems from the developer to the autonomous system.
Optimization and validation of the solid-liquid extraction technique for determination of picloram in soils by high performance liquid chromatography

ABSTRACT - The objective of this study was to optimize and validate the solid-liquid extraction(ESL) technique for determination of picloram residues in soil samples. At the optimizationstage, the optimal conditions for extraction of soil samples were determined using univariateanalysis. Ratio soil/solution extraction, type and time of agitation, ionic strength and pH ofextraction solution were evaluated. Based on the optimized parameters, the following methodof extraction and analysis of picloram was developed: weigh 2.00 g of soil dried and sievedthrough a sieve mesh of 2.0 mm pore, add 20.0 mL of KCl concentration of 0.5 mol L -1 , shakethe bottle in the vortex for 10 seconds to form suspension and adjust to pH 7.00, withalkaline KOH 0.1 mol L -1 . Homogenate the system in a shaker system for 60 minutes andthen let it stand for 10 minutes. The bottles are centrifuged for 10 minutes at 3,500 rpm.After the settlement of the soil particles and cleaning of the supernatant extract, an aliquotis withdrawn and analyzed by high performance liquid chromatography. The optimized methodwas validated by determining the selectivity, linearity, detection and quantification limits,precision and accuracy. The ESL methodology was efficient for analysis of residues of thepesticides studied, with percentages of recovery above 90%. The limits of detection andquantification were 20.0 and 66.0 mg kg
Overlapping and Hierarchical Community Discovery Based on Spectral Method

With the development of community detection algorithm, traditional clustering methods are hardly capable enough to meet our needs. This paper proposes a new algorithm based on spectral method that can identify the overlapping and hierarchical communities in complex networks. Experiments on artificial and real datasets are conducted to verify the algorithm by comparing it with other algorithms. The experimental results show that our algorithm can efficiently identify overlapping communities in complex networks and find hierarchies of the communities.
Out With the Old and in With the New? An Empirical Comparison of Supervised Learning Algorithms to Predict Recidivism

Recent research has produced mixed results as to whether newer machine learning algorithms outperform older, more traditional methods such as logistic regression in predicting recidivism. In this study, we compared the performance of 12 supervised learning algorithms to predict recidivism among offenders released from Minnesota prisons. Using multiple predictive validity metrics, we assessed the performance of these algorithms across varying sample sizes, recidivism base rates, and number of predictors in the data set. The newer machine learning algorithms generally yielded better predictive validity results. LogitBoost had the best overall performance, followed by Random forests, MultiBoosting, bagged trees, and logistic model trees. Still, the gap between the best and worst algorithms was relatively modest, and none of the methods performed the best in each of the 10 scenarios we examined. The results suggest that multiple methods, including machine learning algorithms, should be considered in the development of recidivism risk assessment instruments.
Techniques for Automated Software Evolution

For a variety of reasons, modern, non-trivial software systems must evolve to cope with change, including alterations in stakeholder requirements, environments in which the software is deployed, and dependent technologies, e.g., frameworks. Unfortunately, evolution and maintenance is an expensive, time-consuming, and errorprone task, especially when the system in question is large and complex. Typically, a change to a single program element requires changes to related, and often seemingly unrelated, elements scattered throughout the source code. To address this problem, approaches have emerged to mechanically assist developers with a wide range of software evolution and maintenance tasks, including migrating code to a new framework version, translating existing code to a new platform, and restructuring code to mirror an improved design. This assistance is typically provided in the form of extensions (plug-ins) to integrated development environments (IDEs) that afford (semi-) automated aid in carrying out these tasks, thus easing the burden associated with evolution and maintenance. In some approaches, the corresponding plug-in keeps track of the elements relevant to the change being implemented, with the IDE displaying only those elements. Other approaches attempt to automatically restructure code to improve such features as type safety while preserving semantics. Although existing approaches are useful in alleviating some of the burden associated with software evolution and maintenance, there are a number of situations where
Text Analysis Integration into a Medical Information Retrieval System: Challenges Related to Word Sense Disambiguation

We describe a high throughput, real-time modularized text analysis system integrated into a production medical information retrieval system at the Mayo Clinic. The system identifies clinically relevant entities and concepts from unstructured medical reports, e.g. drugs, diagnosis and concepts, as defined in RxNorm, SNOMED CT and UMLS. To achieve the desired performance, the text analysis components were either developed for or adapted to the clinical domain. In this paper, we discuss one of several required innovations, disambiguation of mapped concepts with respect to an ontology. The methodology for the identification of a set of problem ambiguities and the creation of a manually sense-tagged data set for each of them is presented. Based on this data set, methods for word sense disambiguation for clinical notes were developed. In this paper, we report one set of F-scores (range is 0.47-0.98) which are substantially above the majority sense baseline.
Exploring Linguistic Constraints in Nlp Applications

The key argument of this dissertation is that the success of an Natural Language Processing (NLP) application depends on a proper representation of the corresponding linguistic problem. This theme is raised in the context that the recent progress made in our field is widely credited to the effective use of strong engineering techniques. However, the intriguing power of highly lexicalized models shown in many NLP applications is not only an achievement by the development in machine learning, but also impossible without the extensive hand-annotated data resources made available, which are originally built with very deep linguistic considerations. More specifically, we explore three linguistic aspects in this dissertation: the distinction between closed-class vs. open-class words, long-tail distributions in vocabulary study and determinism in language models. The first two aspects are studied in unsupervised tasks, unsupervised part-of-speech (POS) tagging and morphology learning, and the last one is studied in supervised tasks, English POS tagging and Chinese word segmentation. Each linguistic aspect under study manifests itself in a (different) way to help improve performance or efficiency in some NLP application. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Mitchell P. Marcus
A Stereoscopic Approach for the Association of People Tracks in Video Surveillance Systems

analyses (COLLINS et al. 2001). The more accurate the object coordinates of people in the scene are known, the more detailed analyses can be conducted with respect to motion patterns or interactions between tracked people. In this paper we present an approach that generates consistent global tracks of people in non-crowded scenarios. The trajectories are calculated in a common reference frame from observations of multiple surveillance cameras. An important step in our work is the estimation of body height as well as a reliable association of tracks across partly overlapping views. For that purpose, stereoscopic analysis is applied to overlapping parts of images which are either generated randomly while scanning wide areas with several PTZ cam
Performance Projections of HPC Applications on Chip Multiprocessor (CMP) Based Systems

Performance Projections of HPC Applications on Chip Multiprocessor (CMP) Based Systems. (May 2011) Sameh Sh Shawky Sharkawi, B.S., The American University in Cairo; M.S., Texas A&M University Chair of Advisory Committee: Dr. Valerie Elaine Taylor Performance projections of High Performance Computing (HPC) applications onto various hardware platforms are important for hardware vendors and HPC users. The projections aid hardware vendors in the design of future systems and help HPC users with system procurement and application refinements. In this dissertation, we present an efficient method to project the performance of HPC applications onto Chip Multiprocessor (CMP) based systems using widely available standard benchmark data. The main advantage of this method is the use of published data about the target machine; the target machine need not be available. With the current trend in HPC platforms shifting towards cluster systems with chip multiprocessors (CMPs), efficient and accurate performance projection becomes a challenging task. Typically, CMP-based systems are configured hierarchically, which significantly impacts the performance of HPC applications. The goal of this research is to develop an efficient method to project the performance of HPC applications onto systems that utilize CMPs. To provide for efficiency, our projection methodology is automated (projections are done using a tool) and fast (with small overhead).
Mixture of discriminative learning experts of constant sensitivity for automated cytology screening

One practical objective in an automated cytology screening task is to obtain as high as possible specificity (the percentage of normal slides being classified as normal) while attaining acceptable (predefined) constant sensitivity. In this paper, we propose a new learning algorithm which continuously improves the specificity while maintaining constant sensitivity for pattern classification problems. We further propose to integrate the pre-trained networks with constant sensitivities into the mixture of experts (MOE) network configuration. This enables each trained expert to be responsive to specific subregions of the input spaces with minimum ambiguity and thus produces better performance.
Clustering in Wireless Sensor Network using K-MEANS and MAP REDUCE Algorithm

A wireless sensor network (WSN) consists of a large number of small sensors with limited energy. Prolonged network lifetime, scalability, node mobility and load balancing are important requirements for many WSN applications. Clustering the sensor nodes is an effective technique to achieve these goals. The different clustering algorithms also differ in their objectives. We have proposed a new method to achieve these goals and the proposed method depends on MAP-REDUCE programming model and K-MEANS clustering algorithm. So, new clustering algorithm has been proposed to cluster the sensor nodes of a network. It uses MAP REDUCE and K MEANS algorithm for clustering. Network is divided into number of clusters, which we have taken as 5% of the total number of nodes of a network. Nodes are assigned to the cluster having minimum distance to the cluster head having maximum energy. The distance is calculated using Euclidean Distance Formula. We have also calculated the intra cluster and inter cluster distance for the cluster. We also found the end to end delay of packet transmission,energy consumption for the transmission. Initial simulations are performed to check how much we can lower the energy consumption by placing the cluster heads over the grid. We have considered two ways with which cluster heads can be placed over the grid, either place them randomly or keep some distance among them. For this results are found and checked. These results show that placing the cluster heads using some minimal distance performs well than placing them randomly.
Multimedia Conferencing Management Using Web Services Orchestration over Public Networks

The multimedia conference control and management interfaces are still in an early stage of development, and need to be extended in order to provide a high flexibility, scalability, and process management level that allows the deployment of multimedia conferencing solutions adapted to the user requirements. In this paper, we introduce the multimedia conferencing management using Web service and orchestration approach over public networks. For multimedia conferencing is a converged voice and data applications, which management involves both real-time communications and non-real-time information processing tasks, and mainly focus on the design and implementation for communication Web service and extensible BPEL (Business Process Execution language) based multimedia conferencing communication services process logics and also are encapsulated in a coarse grained Web service for the purposes of flexible multimedia conferencing process management. And also, we give the multimedia conferencing process management system and performance measurement and analysis. Finally, we give the conclusion and future work.
Ku Antigen Interaction with Apurinic/Apyrimidinic Sites: Nonhomologous End Joining Vs Base Excision Repair

The apurinic/apyrimidinic (AP) site is considered to be a common lesion in genomic DNA. It is widely accepted that, irrespective of their origin, AP sites are further processed by the base excision repair (BER) machinery, being the central intermediate of this process. Under special conditions, proteins, which recognize AP sites, are able to form covalent adducts with DNA. By combination of the cross-linking technique with mass-spectrometry analysis, Ku antigen (Ku) - the central player in nonhomologous end joining (NHEJ), the pathway of double-strand break (DSB) repair - was identified as a protein reactive to AP sites. Moreover, Ku was shown to be a 5'-dRP/AP lyase that acts near DSBs in NHEJ. The recent studies have demonstrated involvement of Ku in the different stages of BER. Here we make an overview of Ku roles in the NHEJ and BER pathways of DNA repair.