Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Information Access Approaches

Information retrieval, workflow, collaborative filtering, and the path model can be considered as members of the family of approaches to information access. Although details of nomenclature and technique may vary, each is meant to provide people with access to useful information. In this paper we take a broad view over information access, drawing from philosophy and semiology in constructing a framework for comparative discussion. We use this framework to examine the information representations that underlie these four approaches, looking at phenomena included and excluded, the sharing of information amongst the community of use, interaction in terms of models of user activity and presentation of results, adaptation of system behaviour, and the inter-relationships of the representation's components. With deeper understanding of relative strengths and weaknesses, and characteristic emphases and assumptions, we can improve our selection, combination, and development of information systems.
A case of pseudomyxoma peritonei in a 69 years old woman and review of literature

Pseudomyxoma peritonei (PMP) is a rare neoplasm characterized by the production of abundant mucin or gelatinous ascites with involvement of the peritoneal surface and omentum. Usually it is associated with benign or malignant mucinous tumor of the appendix or ovary but there are also cases described with other side as colon, rectum, stomach, gallbladder, biliary tract and so on The overall incidence is 1-2 per million per year, it is more common in women than in men (male: female ratio: 9:11). Signs and Symptoms of PMP are very a specific for this reason this disease is often discovered incidentally during other surgical procedure or diagnostic procedure of other pathology. Treatment ranges from debulking to hyperthermic intraperitoneal chemotherapy (HIPEC) with cytoreductive surgery (CRS). In this article we want to describe a case of PMP discovered during a diagnostic laparoscopy in a 69 years old woman with a specific symptoms and a diagnosis of peritoneal carcinomatosis at CT SCAN and to review the literature about this rare pathology.
Imaging spectrometry for submerged vegetation mapping in Lake Garda

Early results of an investigation carried out in Lake Garda, Italy for mapping submerged vegetation, by means of ground-based and aerial hyperspectral radiometry in the visible and near-infrared, are here presented. Even if the lake has been recently classified at the lowest limits of mesotrophy, the morphometric characteristics of its coastal zone in the southern portion and local climatic conditions allow the growth of lentic macrophytes communities, mainly Potamogeton natans, Vallinsneria spiralis and Lagarosiphon major. Then, the knowledge of the spatial distribution of macrophytes is of primary importance also because these plants, being primary producers, are indicators of the water quality status, and could also give information related to the climatic change (e.g. by the Lagarosiphon major, that in despite of its tropical origin, is very well adapted to the Lake Garda sub-alpine environment). Hyperspectral remote sensing, due to high spatial/spectral resolutions, may provide suitable tool to integrate observations collected from traditional in situ surveys. An experiment was performed in July 2000, within an EU-RTD Project. Two aerial surveyes almost simultaneously were performed at a relative altitude of 15,000 feets, having onboard different imaging spectrometers: Digital Airborne Imaging Spectrometer (DAIS) 7915, Reflective Optics System Imaging Spectrometer (ROSIS), and Multispectral Infrared and Visible Imaging Spectrometer (MIVIS).
Concurrent support of multiple page sizes on a skewed associative TLB

Some architecture definitions (e.g., Alpha) allow the use of multiple virtual page sizes even for a single process. Unfortunately, on current set-associative TLBs (translation lookaside buffers), pages with different sizes cannot coexist together. Thus, processors supporting multiple page sizes implement fully associative TLBs. In this research note, we show how the skewed-associative TLB can accommodate the concurrent use of multiple page sizes within a single process. This allows us to envision either medium size L1 TLBs or very large L2 TLBs supporting multiple page sizes.
A Descriptor Systems Toolbox for MATLAB

We describe a Descriptor Systems Toolbox implemented under MATLAB. This Toolbox relies on the object oriented approach for control systems analysis and design provided within the standard Control Toolbox of MATLAB. The basic approach to develop the Descriptor Systems Toolbox was to exploit the powerful matrix and system object manipulation features of MATLAB via flexible and functionally rich high level m-functions, while simultaneously enforcing highly efficient and numerically sound structure exploiting computations via the mex-function technology of MATLAB to solve critical numerical problems. The mex-functions are based on FORTRAN codes from LAPACK and SLICOT.
Empirical Comparative Study of Boosting and its Relatives

Transfer learning is an important research topic in machine learning and data mining that focuses on utilizing knowledge and skills learned in previous tasks to a novel but related task. This paper contributes to comparison between boosting for transfer learning and boosting. The results, in terms of the accuracy, weighted F-Measure, G-Mean, weighted GMPR, weighted precision and weighted AUC, are rigorously tested using the statistical framework proposed by Janez Demsar. Results show that the performance difference between TrAdaBoost and AdaBoost is less significant.
Data and metadata: development of a digital curriculum

The teaching potential of a problem-based learning (PBL-)oriented curriculum sparks a lot of enthusiasm, but the matrix-type organization of the curriculum elements calls for more powerful organizational tools than the block-oriented structure of previous curricula. Also, the multitude of digital elements available for teaching purposes present a fascinating development, but again the task of putting this offering into order is considerable. The aim of the present project was to set up a database-driven curriculum, organizing the administrative information in a way that allows the multiaxial access required by the new curriculum. Also, we wished to catalogue existing and upcoming digital teaching resources in a way that allowed a logical connection to the curricular activities. The database has a relational structure containing all the administrative information on lessons, as well as topic keywords and abstracts. An index table of the teaching resources forms a link to the curriculum database, and allows the resources to be searched based on curriculum information (topic, term, lecturer etc). Upcoming challenges include acquisition and quality assurance of teaching resources and teaching, as well as motivating the teachers and students. Automated data acquisition mechanisms and curricular activities available only online may be valuable elements in this ongoing process.
Parallel computation of the SAR distribution in a 3D human head model

This work presents a way of parallel computation of the Specific Absorption Rate distribution. The parallel program used in the computation was based on the FDTD (Finite-Difference Time-Domain) method [1,2,3]. In order to establish communication among the computational nodes, the MPI (Message Passing Interface) standard was used [4,5,6]. The presented example of a human head numerical model was built with the use of MRI (Magnetic Resonance Image) pictures.
Encoding techniques for complex information structures in connectionist systems

Two general information-encoding techniques called ‘relative-position encoding’ and ‘pattern-similarity association’ are presented. They are claimed to be a convenient basis for the connectionist implementation of complex, short-term information processing of the sort needed in commonsense reasoning, semantic/pragmatic interpretation of natural language utterances, and other types of high-level cognitive processing. The relationships of the techniques to other connectionist information-structuring methods, and also to methods used in computers, are discussed in detail. The rich interrelationships of these other connectionist and computer methods are also clarified. We detail the particular, simple forms that the relative-position encoding and pattern-similarity association techniques take in our own connectionist system, called Conposit, in order to clarify some issues and to provide evidence that the techniques are indeed useful in practice.
Transmission network architecture for metropolitan areas

A transmission network architecture for metropolitan areas with a hierarchical structure and distributed allocation of cross-connection functions is proposed. This scheme results in high full-rate transmission systems by efficiently using the cross-connect systems. Cross-connection functions are distributed on the main loop. It is found that the number of cross-connection nodes does not greatly influence total network cost. <<ETX>>
Automated detection of planes in 3-D point clouds using fast Hough transforms

Calibration of 3-D optical sensors often involves the use of calibration artifacts consisting of geometric features, such as 2 or more planes or spheres of known separation. In order to reduce data processing time and minimize user input during calibration, the respective features of the calibration artifact need to be automatically detected and labeled from the measured point clouds. The Hough transform (HT), which is a well-known method for line detection based on foot-of-normal parameterization, has been extended to plane detection in 3-D space. However, the typically sparse intermediate 3-D Hough accumulator space leads to excessive memory storage requirements. A 3-D HT method based on voting in an optimized sparse 3-D matrix model and efficient peak detection in Hough space is described. An alternative 1-D HT is also investigated for rapid detection of nominally parallel planes. Examples of the performance of these methods using simulated and experimental shape data are presented.
Real Time Facial Expression Recognition System with Applications to Facial Animation in MPEG-4

This paper discusses recognition up to intensities of mix of primary facial expressions in real time. The proposed recognition method is compatible with the MPEG-4 high level expression Facial Animation Parameter (FAP). In our method, the whole facial image is considered as a single pattern without any block segmentation. As model features, an expression vector, viz. low global frequency coefficient (DCT) changes relative to neutral facial image of a person is used. These features are robust and good enough to deal with real time processing. To construct a person specific model, apex images of primary facial expression categories are utilized as references. Personal facial expression space (PFES) is constructed by using multidimensional scaling. PFES with its generalization capability maps an unknown input image relative to known reference images. As PFES possesses linear mapping characteristics, MPEG-4 high level expression FAP can be easily calculated by the location of the input face on PFES. Also, temporal variations of facial expressions can be seen on PFES as trajectories. Experimental results are shown to demonstrate the effectiveness of the proposed method. key words: facial expression, PFES, MPEG-4 FAPs, frequency transforms, DCT
Proposed architecture of configurable, adaptable SoC

To study the concept of Self Adaptive Networked computing Elements (SANE) we developed a configurable platform based on the Xilinx EDK and Xilinx System Generator tools. The platform is built around a MicroBlaze CPU with a set of standard peripherals such as DDR RAM controller and RS232 interface - denoted as "Master", extended with a set of several "reprogrammable Accelerators" connected to the MicroBlaze "Master" via fast simplex links (FSL).
1/f dynamics adaptable attractor selection and synchronizability in noise-driven multistable neuronal networks

We study minimal multistable systems of coupled model neurons with combined excitatory and inhibitory connections. With slow potassium currents, multistability of several firing regimes with distinctively different firing rates is observed. In the presence of noise, there is noise-driven switching between these states of which transient dynamics have 1/f-type power spectra. The selection between higher- and lower-frequency oscillations depends on external inputs, which results in coherence between the periodic input and the system's firing rate. Without slow potassium currents, there are multistable solutions in which two inhibitory neurons fire synchronously or anti-synchronously. Addition of a small amount of noise results in increased synchronizability of the two neurons depending on the level of external inputs. These results suggest adaptable dynamics of multistable neural attractors to external inputs enhanced by additional noise.
Multiresolutional quality assessment of SAR azimuth sequence using a spatiotemporal analysis technique

Speckle noise and phase errors are two major a sources of quality degradation for synthetic aperture radar imageries. In this work, we address this problem with the proposal of a spatio-temporal metric to benchmark these degradations by analyzing an azimuthal image sequence. Preliminary results of the metrics with and without multiresolutional formulation are reported.
Wicked problems in designing open online learning

This paper will report on the “wicked” problems encountered when designing an online course with bounded content in an unbounded learning environment. It will describe the dilemmas faced and decisions made by academics in an Australian university challenged by an institutional initiative to design radical, disruptive learning experiences making use of readily available online media. This bounded/unbounded environment demands new roles for instructors in adopting innovative pedagogies and teaching and learning strategies. It also creates changing and challenging roles for course designers as they deal with ill-defined parameters and unknown audiences. In this paper, we propose a novel methodology for making curricular decisions in ill-defined spaces.
A Neural Network Model for Pitch Perception

In this paper we will examine the human ear-and cochlear mechanics in particular-in order to better understand the signals sent to the brain. We will also discuss some theories of pitch perception. We propose a neural network model to examine the sensitivity discrepancy in general and pitch perception in particular, with emphasis on the neural representation of pitch perception. The resulting model concentrates on the preprocessing of the auditory stimulus, reducing it to a simple pattern classification problem. Western music defines an octave as an interval that represents a frequency ratio of 2: 1. The most commonly used scale is the dodecaphonic (twelvetone), well-tempered scale. It divides the octave into 12 evenly spaced halftones, 6 percent (12VN2) apart in frequency. Frequency detection is performed by an array of sensory hairs in the cochlea in the ear. The characteristic frequency of these hairs depends on their location along the cochlea and increases monotonically along the array. Each hair behaves as a bandpass filter with a bandwidth of approximately 10 percent of the characteristic frequency. The human auditory system is sensitive to as little as . 3 percent to 1.5 percent changes at certain frequencies (Evans 1982). This sensitivity discrepancy between the human auditory system and its components may be explained by neural processing performed by the brain on the output from the cochlea. The Human Ear
The Compilation of a Maritime English Corpus for ESP Learners

One of the contributions of corpus linguistics is to provide real features of language use. In particular, specialized corpora in English can contribute to presenting a language used in specific areas which is very different from General English. For this reason, it is necessary to compile a corpus of Maritime English (ME) which can offer authentic ME language usage for English for Specific Purposes (ESP) learners and seafarers. Unfortunately, current ME methodologies are not likely to be effective and efficient in that they are restricted to words and phrases recommended by the International Maritime Organization (IMO) or other official maritime organizations. In fact, ESP learners and seafarers have to learn the more realistic language used in the maritime community, reducing the gap between ME and General English. In other words, building on their previous General English knowledge, they must acquire a language which they can use every day in their jobs in the community. Our study describes the compilation of a one million word corpus of ME and its descriptive features and discusses related issues.
Evaluation of the assessment component of a system for shaping vocal behavior in severely speech-disabled individuals

A study was conducted of the assessment component of a computer-based system for performing vocal shaping in severely speech-deficient individuals. The resulting correlations between experienced human speech evaluators and a computer indicate that computer-aided vocal shaping of severely speech-disabled individuals is a realistic possibility. Such quantitative results have never been obtained before with the developmentally handicapped. <<ETX>>
Category Level Object Discovery Using Dynamic Topic Model

Category level object discovery is important for a number of applications such as remote sensing image classification, and data mining in images and video sequences. This paper presents a novel unsupervised learning algorithm for discovering object category and their locations in video sequences. Both appearance consistency and motion consistency of local patches across frames are exploited. Video patches are first extracted and represented by spatial-temporal context words. A dynamic topic model is then introduced to learn object categories in video sequences. The proposed dynamic model can categorize and localize multiple objects in a single video. Experimental results on the CamVid dataset and the VISATTM dataset demonstrate the effectiveness of our method.
On-line map-matching framework for floating car data with low sampling rate in urban road networks

The performance of map matching has a significant effect on obtaining real-time traffic information. The floating car data (FCD) is of low-sampling rate, and urban road networks such as multi-layer roads can be particularly complex. Most of the current low-sampling-rate map-matching approaches use a fixed time interval, which can result in a lack of efficiency and accuracy if the initial point is not correctly matched. Moreover, the issue of handling data relating to multi-layer road networks remains open. To address these issues, a new on-line map-matching framework is proposed, comprising the confidence point and the maximum delay constraint dynamic time window. The framework performs map matching by self-adaptively choosing the appropriate timing and matching method according to the complexity of the local network to which the positioning point belongs. To distinguish elevated roads from normal roads, vehicle behaviour patterns on elevated roads are taken into account. Comparisons of the proposed algorithm, hidden Markov model algorithm, incremental algorithm and point-to-curve algorithm are conducted on two datasets. The empirical results show that the proposed algorithm outperforms the other algorithms. When the behaviour pattern on elevated roads is considered, the accuracy of these algorithms is also improved.
Challenges in Scaling Software-Based Self-Testing to Multithreaded Chip Multiprocessors

Functional software-based self-testing (SBST) has been recently studied by leading academic research groups and applied by major microprocessor manufacturers as a complement to other classic structural testing techniques for microprocessors and processor-based SoCs. Is the SBST paradigm scalable to testing multithreaded chip multiprocessors (CMPs) and effectively detect faults not only in the functional components but also in the thread-specific and core interoperability logic? We study the challenges in scaling existing software-based self-test capital (uniprocessor self-test programs and self-test generation techniques) to real, multithreaded CMPs, like Sun's OpenSPARC T1 and T2. Since this type of CMPs is built around well studied microprocessor cores of mature architecture (like SPARC v9 in the OpenSPARC case), tailoring, enhancing and scheduling of existing uniprocessor self-test programs can be an effective methodology for software-based self-test of CMPs.
E‐coaching systems: Convenient, anytime, anywhere, and nonhuman

Technologies continue to evolve to provide more compelling and interactive learning opportunities. Coaching has traditionally been face-to-face or by email. By combining the new technologies with coaching, learning developers now have the opportunity to develop an asynchronous, online, nonhuman coaching system, or e-coaching system. An e-coaching system can alleviate much of the coaching labor while making it more conducive to the coachee.
Linear Mix-net and a Scheme for Collecting Data from Anonymous Data Holders

To make e-society, e-governance and cloud computing systems be utilized more widely, this paper proposes a scheme to collect attribute values belong to same data holders and calculate functions of them without knowing correspondences between the attribute values and their holders or links among attribute values of same holders. Different from most of other schemes the proposed scheme is based on linear Mix-net that exploits secret key encryption functions such as linear equation based (LE-based) and multidimensional array based (MA-based) ones, therefore it can handle real numbers that appear in many important business and engineering applications efficiently in the same way as integers. In addition, anonymous tag based credentials used in the scheme ensure the correctness of calculation results. Although the scheme can calculate only linear combinations of attribute values when LE-based encryption functions are used, if they are replaced with MA-based ones, it can calculate also general polynomial functions of attribute values.
Role of fiber and healthy dietary patterns in body weight regulation and weight loss

The worldwide overweight and obesity pandemic is among the greatest public health challenges of our time [1,2]. From 1980 through 2013, the global prevalence of overweight and obesity increased by 28% for adults and 47% for children, resulting in an increase from about 850 million overweight or obese people in 1980 to over 2 billion in 2013 [2]. Elevated body mass index (BMI) or excessive adiposity in adulthood and increasingly in childhood is a growing risk factor for major chronic diseases such as diabetes, cardiovascular disease, non-alcoholic fatty liver disease, chronic kidney disease, and a number of obesity related cancers [3-12]. It is estimated that about 300,000 premature adult deaths each year are directly attributable to overweight or obesity in the US [13].