Miracle-dz/UniFAR-FTUs huggingface dataset:
A Joint Admission Control and Bandwidth Allocation Scheme for Packet-Switched Wireless Networks

In this paper, we propose a joint admission control and bandwidth allocation scheme for packet-switched multiservices cellular networks. The main idea of the scheme is to adjust the acceptance ratios and the bandwidth allocated for different types of services in order to maintain maximum bandwidth utilization while guaranteeing quality of service (QoS) requirements for users. First, we define a total user utility function as the measure of how efficiently the bandwidth resource is used. Then, by using a Markovian queueing model, we obtain the optimal connection acceptance ratios and bandwidth allocation strategy that maximize total user utility. Simulation results demonstrate that using our scheme, bandwidth resource is most efficiently utilized and at the same time, the call-level and packet-level QoS requirements for both voice and data users are guaranteed.
Trends in reliability and test strategies

Software fails because it contains faults. If we could be certain that our attempts to fix a fault had been effective and that we had introduced no new faults, we would know that we had improved the software's reliability. Unfortunately, no single testing method can be trusted to give accurate results in every circumstance. There are at least two areas of uncertainty in testing. First, we cannot predict when a failure will occur as a result of choosing an input that cannot currently be processed correctly. Second, we do not know what effect fixing a fault will have on the software's reliability. Most of today's reliability models consider the software system to be a black box, using indirect measures of failure by making strong assumptions about the test cases. White-box testing measures, which are direct measures, are generally not used in software-reliability models. The assumption is that the more the program is covered, the more likely that the software is reliable. Hence we must develop new testing strategies. For example, we do not understand very well the notion of stress as it applies to software; what effect the dependence between versions of the same application has on reliability; the relationship between software reliability and white-box testing measures; and how to achieve the ultra-high reliability levels obtained through formal verification and fault-tolerance methods. These are just some of the areas for future research into improved testing strategies. >
Cell Cluster Image Segmentation on Form Analysis

In this paper, a segmentation algorithm based on form information is proposed for separation of touching and overlapping cells. The method integrating morphological smoothing with holes filling is employed in a pre-processing stage. Then the overlapping cell clusters are identified through a polygon approximation. Later the separation is implemented, which consists of the detecting concave points on the contours and determining the separation lines. The algorithm can split complicated large cell clusters. In addition, this algorithm can be adopted for other applications, where separation between touching and overlapping particles is needed.
NOVEL APPROACH FOR THERECOGNITION OF THE FACES OF EUNUCHS

Face can be considered as multidimensional visual stimuli. Eunuchs are the special kind of human being regarding the gender and their faces are also different in nature since these faces do not belong to either male or female gender. In this paper, a modified Local Binary Pattern (LBP) technique is used for extracting feature of eunuch faces and neural network based algorithm is used for face recognition. Recognition is done by Multilayer Feed Forward Neural Network with back proapgation learning rule. Here real life face images of the eunuchs are taken from North East India people and performance evaluation metrics like acceptance ratio and execution time are calculated.
A Flexible and Memory Efficient Data Structure for GPU-based Polyhedral Grid Raycasting

The foundation for unstructured grid volume visualization via raycasting is a compact and easily traversable representation of the grid’s topology. This data structure is used to query every cell intersecting a viewing ray in order to sample the data volume and accumulate corresponding color and opacity information. Current techniques mainly deal with tetrahedral volumes. However, with the ongoing evolution of simulation technology more complex grid structures containing not only tetrahedral but general convex polyhedral cells have been introduced. We propose a cell face-centered data structure capable of representing such polyhedral grids efficiently while still allowing for fast viewing ray propagation for GPU-based raycasting. By choosing to abandon an explicit cell representation we avoid storing redundant information and allow for a fast and straight-forward addressing scheme within the data structure. The benefits of our approach are demonstrated by comparing it to other state-of-the-art volume rendering methods.
The KIT-LIMSI Translation System for WMT 2014

This paper describes the joined submission of LIMSI and KIT to the Shared Translation Task for the German-toEnglish direction. The system consists of a phrase-based translation system using a pre-reordering approach. The baseline system already includes several models like conventional language models on different word factors and a discriminative word lexicon. This system is used to generate a k-best list. In a second step, the list is reranked using SOUL language and translation models (Le et al., 2011). Originally, SOUL translation models were applied to n-gram-based translation systems that use tuples as translation units instead of phrase pairs. In this article, we describe their integration into the KIT phrase-based system. Experimental results show that their use can yield significant improvements in terms of BLEU score.
Expert search for radio and television: a case study amongst Dutch broadcast professionals

This paper reports the results of a qualitative case study amongst eight Dutch broadcast professionals who frequently use the online search system of the The Netherlands Institute for Sound and Vision. The study is based on the assumption that the task affects the search strategy. Test subjects are observed and interviewed while following a scenario with tasks designed around three factors: nature of the content (audio or video), length and time restraints. Results show that the task does not affect the search strategies of the test persons and that the search strategies differs between test persons, not between tasks.
Patterns in Reactive Programming: from Transformative to Reactive

Functional Reactive Programming (FRP) was introduced in 1997, and it proposed the abstraction of streams of events and behaviors that nowadays are a staple in reactive programming solutions. Due to recent technological advancements, such as the Reactive Extensions library and the Elm language, and the Reactive Manifesto that was published in 2013, more and more developers are showing interest in the abstractions that reactive programming provides. When programs are divided into reactive and transformative programs, it is seen that most programs are reactive programs that depend upon external events and respond to them. Reactive programming is a paradigm that is meant to help building these kinds of programs by providing abstractions for expressing these reactions and automatically managing the flow of time and computation dependencies. There is quite a lot of research about fixing the underlying flaws of FRP, but little research has been conducted where the usage of patterns in reactive programming has been studied. In this thesis we study how some chosen patterns are changed when they are first implemented using an imperative style, and then re-implemented using a reactive style. We find that while most of our analyzed patterns work surprisingly well when using streams, there are some scenarios where the introduction of streams permeate a clear change in the pattern. During the analysis of the Model-View-Controller model, we see that streams seem to eliminate the need for ways of explicitly requesting the most up-todate data, at least during initialization – at least during the initialization phase. Analyzing the Chain of Responsibility and the Mediator patterns revealed that using streams somewhat diminishes the value of classes that act as “mediators”. In the Mediator pattern we saw that the Mediator class was important during the initialization phase in connecting all the modules, but once they were connected the Mediator did not necessarily do anything. The Chain of Responsibility would have undergone the most drastic transformation of all the patterns. If the pattern had been implemented by using streams in a sane way, the chain aspect of the pattern would have been lost completely. Forcibly retaining the chain would have imposed a needless limitation, and would have prevented the realization of the advantages that streams may provide.
A Combination Method of Artificial Potential Field and Improved Conjugate Gradient for Trajectory Planning for Needle Insertion into Soft Tissue

To reduce postoperative complications and unwanted side-effects, injury to vital tissues and/or nerves should be avoided in minimally invasive surgery. In this paper, a combined trajectory planning algorithm that uses an artificial potential field (APF) method in conjunction with an improved conjugate gradient algorithm (ICGA) is proposed. Fascial tissues create local minima (LM) when the APF method is applied, which may lead to needle oscillation. The proposed method finds sub-goal points using ICGA to escape from LM. Thus, a complete smooth trajectory passing through anatomical obstacles is obtained. Needle guidance and steering can be performed through a unicycle model. The optimal APF function is obtained by adjusting parameter n. When n=6, the distance between LM and the target reached its minimum (1.0198), which shows that Point 6 is closest to the target and n=6 performs higher search efficiency. The experimental simulation shows combination method has higher efficiency (13.45% higher) compared to APF method, which requires just a few tenths of a second on a standard PC.
Bringing the Cloud Underground: Lessons for Bringing the Next IT Revolution to Geoscience

Summary This article describes the emerging technology of cloud computing and the opportunity for cloud computing to advance the practice of geoscience. We discuss the underlying technologies and industry trends and ability of cloud computing to provide average practitioners with the computational resources to solve a new scale of problems. We discuss our experience with deployment of a cloud-based solution for large-scale geostatistical problem solving and provide guidance to others looking at cloud computing for geoscience applications.
Corrosion Prediction In Aging AircraftMaterials

An artificial neural network is developed to predict the corrosion behavior of different series of aluminum alloys when exposed to a variety of corrosive substances, short term and long term aircraft carrier exposures. Given the corrosion environment and time of exposure the neural network predicts the ASTM G34 corrosion rating and the resulting material loss. The trained and limited test results predicted from the neural network are in good comparison to the experimental data. The effects of corrosion environment and material type from neural network simulation are presented to illustrate the trends. Based on the preliminary results, the neural network approach to corrosion predictions is encouraging and can be used for a variety of materials and environments if more data is available. It is possible to use another neural network to predict the required exposure time to produce a particular corrosion classification in an environment. It is intended that the approach developed here will assist in the structural integrity evaluation of aging aircraft.
Image Retrieval Based on Color-Spatial Feature

Nowdays,content-based image retrieval are coming into research and color feature is mostly used.This paper presents a method of using the information of color,grey and space as image feature.This method is effective to image's rotation and scale,even if the slight change of illumination.The experiment results indicate that this method improves the retrieval effect.
CCD transfer method: standard for absolute performance of CCDs and digital CCD camera systems

This paper presents the CCD transfer concept, a characterization technique that has been successfully employed by the scientific CCD community for over two decades. The method has been shown to be an indispensable tool in characterizing, optimizing and calibrating CCDs and high performance digital camera systems in absolute terms. We introduce the CCD transfer technique, in its entirety, by presenting a complete set of transfer curves and support equations. Commercial digital camera groups, currently changing from relative to absolute performance standards, will find the CCD transfer approach beneficial.
UML Model Refactoring and Proof of Behavior Preservation Using Formalization

Program refactoring is a set of transformations that when applied to the program, change its structure while preserving its behavior. Model refactoring is the equivalent of program refactoring at the model level, so that models are refactored instead of programs. In this paper, a novel method is introduced for proof of behavior preservation using formal descriptions which is based on the utilization of a formal language for accurate and unambiguous description of the model and its behavior. In this approach, using a formalization language with operational meaning, it is proved that a refactoring preserves the behavioral characteristics of the model. This proof is based on the preconditions, postconditions, and invariants of the model. We use the formalization language ROOL (Refinement Object Oriented Language) for refactoring formalization and also present an approach for UML graph refactoring using control flow graphs that are based on graph transformation rules. In this method, the UML metamodel is transformed into the corresponding graph, and the refactoring is displayed as a control flow graph, and then all necessary preconditions are studied as graph transformation rules. Then if the necessary preconditions hold, the refactoring is applied and a new graph is produced. The AGG graph transformation tool was used for implementation of refactoring steps.
Vision-aided Cooperative Navigation for UAV Swarms

This paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a GPS receiver, and a vision system, to improve its navigation performance (in real time or in post processing phase) exploiting formation flying deputies equipped with GPS receivers. The key concept is to integrate differential GPS and visual tracking information within a sensor fusion algorithm based on the Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on the filtering algorithm. Then, flight testing strategy and experimental results are presented. In particular, cooperative navigation output is compared with the estimates provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magneticand inertial-independent accurate information.
OSA-MAC: A MAC Protocol for Opportunistic Spectrum Access in Cognitive Radio Networks

We present a MAC protocol for opportunistic spec- trum access (OSA-MAC) in cognitive wireless networks. The proposed MAC protocol works in a multi-channel environment which is capable of performing channel sensing to discover spectrum opportunities. For this MAC protocol, two channel selection methods are considered which trade the implementation complexity with throughput improvement. We then analyze the saturation throughput performance of the proposed MAC protocol under scenarios where the probabilities for each channel to be available to different secondary flows are the same or different. We then derive analytically the probability of col- lision of secondary users with primary users due to sensing errors. This analysis can be used, for example, to determine the requirement of sensing accuracy for secondary users, and to design an admission control method. We present numerical results to demonstrate the throughput performance of the OSA- MAC protocol and applications of the proposed analytical model.
A Computational Approach for Multi-Level Biological Complex Systems Analysis

Due to their intrinsic nature, biological entities are universally considered as complex systems. Over years, many different computational methods pertaining to the Systems Biology field, have been devised to unravel this complexity. However, when taken alone, most of times these methods are not able to provide a deep comprehension of structural, spatial and dynamical aspects of the systems under evaluation. For this reason, approaches exploiting different levels of analysis are today a hot research topic in different areas, such as the theoretical formalization of the method, and the development of computational tools for the integration of different modeling perspectives. In the present dissertation I developed a computational pipeline able to perform analyses exploiting, one after the other, the three main modeling frameworks for biological systems, gaining, from every level, a different type of information: i.e. identification of flux distributions and metabolic sub-phenotypes from the ensemble evolutionary FBA (a novel method inspired by Flux Balance Analysis); information on network structural properties and topological metrics from graph theory approaches; estimation of kinetic constants for mechanism-based modeling through the definition of an efficient version of the Particle Swarm Optimizer based on Fuzzy Logic. Moreover, I also redefined a network visualization strategy able to overlay flux values and topological metrics to network structure. In order to validate the proposed pipeline I also developed a “core model” of yeast metabolism from which I identified two ensembles of flux distributions (possible solutions) in agreement with the “Crabtree-positive” and “Crabtree-negative” metabolic phenotypes. Moreover, by means of a cluster analysis, devised methods were able to define groups inside each ensemble that I identified as putative “sub-phenotypes”. Lastly, I contributed to reconstruct four reduced metabolic “core models”, deriving from the Human Metabolic Atlas, and describing three tissue-specific cancer conditions and a reference state. From these models a relevant heterogeneity emerged between reference and cancer conditions in terms of metabolic flux values.
Human activity recognition in video: extending statistical features across time, space and semantic context

This thesis explores the problem of recognizing complex human activities involving the manipulation of objects in high resolution video. Inspired by human psychophysical performance, I develop and evaluate an activity recognition feature derived from the velocity histories of tracked keypoints. These features have a much greater spatial and temporal range than existing video features. I show that a generative mixture model using these features performs comparably to local spatio-temporal features on the KTH activity recognition dataset. I additionally introduce and explore a new activity recognition dataset of activities of daily living (URADL), containing high resolution video sequences of complex activities. I demonstrate the superior performance of my velocity history feature on this dataset, and explore ways in which it can be extended. I investigate the value of a more sophisticated latent velocity model for velocity histories. I explore the addition of contextual semantic information to the model, whether fully automatic or derived from supervision, and provide a sketch for the inclusion of this information in any feature-based generative model for activity recognition or time series data. This approach performs comparably to established methods on the KTH dataset, and significantly outperforms local spatio-temporal features on the challenging new URADL dataset. I further develop another new dataset, URADL2, and explore transferring knowledge between related video activity recognition domains. Using a straightforward feature-expansion transfer learning technique, I show improved performance on one dataset using activity models transferred from the other dataset.
An Investigation of the Information Practices of Education Doctoral Students

Academic libraries are increasingly re-envisioning their services to provide expanded outreach and segmented programming for specific user groups. Many academic libraries offer segmented services and programming for undergraduate groups such as first-year experience programs and general education programs. Currently, academic libraries are also identifying and expanding their services and programming to meet the unique needs of graduate groups. In conjunction with this focus, the roles of academic librarians are also expanding in the area of outreach. In essence, academic librarians are becoming more directly involved in aligning library services and programming with academic programs and promoting change within their institutions. Faced with the challenges of outreach and promoting change it is essential that librarians gain deeper insights about the perspectives and needs of graduate programs and graduate groups to effectively plan and align library services. The purpose of this design research study was to explore the organizational factors that influence how library services and library instruction are utilized in two doctoral programs in education at the University of Central Florida (UCF). Using a sequential mixed methods approach, quantitative data was collected in an online survey and qualitative data was collected in audio recorded interviews conducted with students enrolled in two doctoral programs in education, as well as program faculty, and academic librarians. Findings from this study were then used to describe a conjecture for an asynchronous online learning resource that applies elements outlined in Sandoval’s (2014) conjecture map model. Findings were also used to make recommendations about future planning for library outreach and the utilization of library services in the doctoral programs.
Classification of mammographic tissue using shape and texture features

The authors have performed a pilot study on the classification of regions of interest (ROI) containing normal tissue, biopsy-proven malignant masses, and biopsy-proven microcalcification (MCC) clusters using a mix of shape and texture features. Shape features included size, translation, and rotation invariant moments. Texture features included fractal-based features and spatial gray level dependence (SGLD) matrix features. The entropy was also computed for each ROI. The type of classifier used was a neural network based on the ALOPEX training algorithm. Best results using a database of 40 normal, 32 mass, and 20 MCC ROIs for training and 5 normal, 5 mass, and 4 MCC ROIs for testing were obtained using texture features and a binary tree neural network which splits the classification of the three types of tissue into two steps. Classification between normal ROIs and abnormal (mass+MCC) ROIs reached 95% training and 100% testing and classification of mass and MCC ROIs reached 98% training and 100% testing.