Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardinality and Density Measures and Their Influence to Multi-Label Learning Methods

Two main characteristics of multi-label dataset are cardinality and density, related to the number of labels of (each instance of) a multi-label dataset. The relation between these characteristics and multi-label learning performance has been observed with different datasets. However, the difference in domain dataset attributes also interfere on multi-label learning performance. In this work, we use a real dataset, named The Million Song Dataset, available in the internet, which presents the property of having too many labels associated to their instances (songs), as well as so many instances. In this work we present the processed datasets used to conduct our experiments, and we also describe our experiments results.
Design of Music Learning Assistant Based on Audio Music and Music Score Recognition

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.
Modelling supply networks and business cycles as unstable transport phenomena

Physical concepts developed to describe instabilities in traffic flows can be generalized in a way that allows one to understand the well-known instability of supply chains (the so-called ‘bull-whip effect’). That is, small variations in the consumption rate can cause large variations in the production rate of companies generating the requested product. Interestingly, the resulting oscillations have characteristic frequencies which are considerably lower than the variations in the consumption rate. This suggests that instabilities of supply chains may be the reason for the existence of business cycles. At the same time, we establish some links to queueing theory and between micro- and macroeconomics.
A fourth-order adaptive mesh refinement solver for Maxwell's Equations

We present a fourth-order accurate, multilevel Maxwell solver, discretized in space with a finite volume approach and advanced in time with the classical fourth-order Runge Kutta method (RK4). Electric fields are decomposed into divergence-free and curl-free parts; we solve for the divergence-free parts of Faraday's Law and the Amp\`ere-Maxwell Law while imposing Gauss' Laws as initial conditions. We employ a damping scheme inspired by the Advanced Weather Research and Forecasting Model to eliminate non-physical waves reflected off of coarse-fine grid boundaries, and Kreiss-Oliger artificial dissipation to remove standing wave instabilities. Surprisingly, artificial dissipation appears to damp the spuriously reflected waves at least as effectively as the atmospheric community's damping scheme.
Distance learning between image and class for object recognition

Object recognition is an active research topic in the computer vision community. Recently a novel Image-to-Class (I2C) distance has been proposed to handle this problem, which classifies images using a simple Naive-Bayes based nearest-neighbor (NBNN) classifier but provides surprisingly excellent performance. This new distance provides a novel direction that avoids feature quantization and shows better generalization capability than the traditional Image-to-Image (I2I) distance. However, the computation cost of calculating this distance is too expensive since its performance relies heavily on searching the nearest neighbor (NN) from a large number of training features, and the label information of the training data is not fully used, which limits its recognition performance. In this thesis, we aim to improve both the recognition performance and efficiency of this I2C distance as well as to extend its application field. First of all, we add a training phase to this distance for improving its recognition performance by learning a weighted I2C distance. A large margin optimization framework is proposed to learn the I2C distance function, which is modeled as a weighted combination of the distance from every local feature in an image to its NN in a candidate class. We learn these weights associated with local features in the training set by constraining the optimization such that the I2C distance from image to its belonging class should be less than that to any other class. To reduce the computation cost, we also propose two methods based on spatial division and hubness score to accelerate the NN search, which is able to largely reduce the online testing time while still preserving or even achieving a better classification accuracy. Secondly, we propose a distance metric learning method to further improve the performance of I2C distance by learning Per-Class Mahalanobis metrics. This Mahalanobis I2C distance is adaptive to different classes by combining with the learned metric for each class. These multiple Per-Class metrics are learned simultaneously by forming a convex
T1-T2 WEIGHTED MR IMAGE COMPOSITION AND CATALOGUING OF BRAIN TUMOR USING REGULARIZED LOGISTIC REGRESSION

In medical diagnosis, the functional and structural information of the brain as well as the impending abnormal tissues is very crucial and important with an MR image. A collective CAD system that detects and classifies the brain tumor by exploiting the structural information is presented. Magnetic Resonance Imaging (MRI) T1-weighted and T2-weighted images provides suitable variation of contrast between the different soft tissues of the brain which is suitable for detecting the brain tumor. Both the Magnetic Resonance (MR) image sequences are composited using the alpha blending technique. The tumor area in the MR images will be segmented using the Enhanced Watershed Segmentation (EWATS) algorithm. The feature extraction is a means of signifying the raw image data in its abridged form to ease the classification in a better way. An expert classification assistant is tried out to help the physicians to classify the detected MRI brain tumor in an efficient manner. The proposed method uses the Regularized Logistic Regression (RLR) for the efficient cataloguing of brain tumor in which it achieves an effective accuracy rate of 96%, specificity rate of 86% and sensitivity rate of 97%.
Active Learning for Chinese Dependency Parsing

It is necessary to have a large annotated Treebank to build a statistical dependency parser.Acquisition of such a Treebank is time consuming,tedious and expensive.This paper presents a method to reduce this demand via active learning,which selects the most uncertain samples for annotation instead of the whole training corpus.Experiments are carried out on the HIT-CIR-CDT,our results show that the parsing accuracy rises about 0.8 percent by active learning when using the same amount of training samples.In other words,for about the same parsing accuracy,we only need to annotate 70% of the samples as compared to the usual random selection method.
Adaptive Standby Mode Scheduling Method Based on Analysis of Activation Pattern for Improving User Experience of Low‐Power Set‐Top Boxes

The lowest power mode (passive‐standby mode) was proposed for reducing the power consumption of set‐top boxes in a standby state when not receiving content. However, low‐power set‐top boxes equipped with the lowest power mode have been rarely commercialized because of their low‐quality user experience. In the lowest power mode, they deactivates almost all of operational modules and processes, and thus require dozens of seconds for activation latency (that is, the latency for activating all modules of the set‐top boxes in a standby state). They are not even updated in a standby state because they deactivate their network interfaces in a standby state. This paper proposes an adaptive standby mode scheduling method for improving the user experience of such boxes. Set‐top boxes using the proposed method can analyze the activation pattern and find the frequently used time period (that is, when the set‐top boxes are frequently activated). They prepare for their activation during this frequently used time period, thereby reducing the activation latency and enabling their update in a standby state.
Candidate Path Selection Method for TCP Performance Improvement in Fixed Robust Routing

: Fixed robust routing is attracting attention as routing that achieves high robustness against changes in traffic patterns without conducting traffic measurement and performing dynamic route changes. Fixed robust routing minimizes the worst-case maximum link load by distributing traffic of every source-destination ( s-d ) router pair onto multiple candidate paths (multipath routing). Multipath routing, however, can result in performance degradation of Transmission Control Protocol (TCP) because of frequent out-of-order packet arrivals. In this paper, we first investigate the influence of multipath routing on TCP performance under fixed robust routing with a simulation using ns-2. The simulation results clarify that TCP throughput greatly degrades with multipath routing. We next propose a candidate path selection method to improve TCP throughput while suppressing the worst-case maximum link load to less than the allowed level under fixed robust routing. The method selects a single candidate path for each of a predetermined ratio of s-d router pairs in order to avoid TCP performance degradation, and it selects multiple candidate paths for each of the other router pairs in order to suppress the worst-case maximum link load. Numerical examples show that, provided the worst-case maximum link load is less than 1.0, our proposed method achieves about six times the TCP throughput as the original fixed robust routing
Variable Neighbourhood Search

The Variable Neighbourhood Search (VNS) is a recent metaheuristic for solving optimization problems whose basic idea is systematic change of neighborhood within a local search. In this paper we present basic rules of the VNS and its extensions. We briefly summarize the most relevant applications in Artificial Intelligence. These applications comprise classic tasks, like constraints satisfaction, planning or learning, in addition to computer-assisted discovery and heuristic solution of a variety of optimization problems. Finally we include some reflections on the essential aspects of the metaheuristics and the analysis of the heuristic solution procedures, and the contribution of the works with VNS for these questions.
Sequence-Function Protein Analysis Using Annotated Context Free Grammars ⁄

Identiflcation and understanding of protein function is a fundamental task in the analysis of complex biological systems. Protein structures and functions are encoded in the underlying amino acid sequence. The exact relationship between primary structure of the protein, its three-dimensional structure and its function is one of the fundamental unanswered questions in biology. The mapping of protein sequences with respect to their structure and function may beneflt from the analogy that we have in the structuring of languages, particularly in the assignment of meaning to words. Starting from Searls in 1993 [4] the analogy between biology and linguistic has been studied by a growing number of researchers. Thus, language analysis [1] has found applications to biological sequences, using various types of \vocabulary", for example the nucleotides in the case of DNA (e.g. [5]), and the standard 20 amino acids in the case of proteins (e.g. [6]). We made use of Context-Free Grammars (CFGs) and a protein classiflcation algorithm to develop Annotated Context Free Grammars (ACFGs). ACFGs are CFGs in which non terminal symbols are annotated using an n-gram Bayesian classifler. ACFGs are used to analyse the connection between protein chains and protein functions and can be applied to the interpretation and detection of amino acids involved in difierent functional regions. ACFGs are built using a bottom-up analysis, inspired by LZ77 compression algorithm [7], starting with the input of all protein sequences and attempting to rewrite them backward to the starting symbol. The analysis procedure locates the most basic motifs of a given length substituting them with a new non terminal symbol annotated with the most likely domain of the motifs. The most basic motifs are the most frequent motifs occurring in the protein family according to the theory of ⁄ This research is founded by the BioBITs Project (Converging Technologies 2007, area: Biotechnology-ICT)
Speech source separation using the IVA algorithm with multivariate mixed super Gaussian student's T source prior in real room environment

Independent vector analysis (IVA) can theoretically avoid the permutation problem in frequency domain blind source separation by using a multivariate source prior to retain the dependency between different frequency bins of each source. In this paper a mixture of multivariate super Gaussian distribution and multivariate Student's t distribution is adopted as a source prior for the IVA algorithm. The Student's t distribution due to its heavy tail nature can better model the high amplitude information in the frequency bins and at the same time a dependent super Gaussian distribution can be adopted to model other information in the frequency bins. The weight of both distributions is varied in the source prior mixture and their separation performance is tested over different scenarios by using the image and binaural room impulse responses (BRIRs). Detailed simulation results confirms that the IVA algorithm with the proposed mixed multivariate source prior can consistently achieve better separation performance.
WordNet in Malay Language

World Wide Web contains huge amount of data available in different languages across the world. Web browsers are tools used to display the data in graphical forms. With the evolution of Web 3.0, data has become an important part of human daily tasks, where it is used to process information, and formulate important decision rules for many organizations. Current tools used to conceptualize data are catered for some of the world well known languages such as English. However, these tools may not be able to support other languages as there are a wide range of languages with different syntax and representation. In this paper, we present a novel lexical semantic based database tool called MalayWordNet, specifically written for Malay language. Our tool is helpful for high end semantic based applications which use Malay language as part of their data presentation.
Nuclear And Radiochemistry Fundamentals And Applications

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Learning Analytics for 21st Century Competencies

Many educational institutions are shifting their teaching and learning towards equipping students with knowledge, skills and dispositions that prepare them for lifelong learning, in a complex and uncertain world. These have been termed “21 st century competencies”. Learning Analytics approaches in general offer different kinds of computational support for tracking learners’ behaviour, managing educational data, visualizing patterns and providing rapid feedback, both to educators and learners. This special issue brings together a diverse range of learning analytics tools and techniques can be deployed in the service of building 21 st century competencies. We introduce the research and development challenges, and introduce the research and practitioner papers accepted to this issue before concluding with some brief reflections on the collection and the relevance of a complex systems perspective for framing this topic.
DOM-based Information Extraction for the Web Sources

At present, the Web becomes a major channel for people to obtain information. However, there exist inherent drawbacks in the HTML language used to represent information of Web pages. The HTML tags only tell the browsers how to display the information on the screen, but no any semantics in them. So the HTML document is not suited to be a way of data exchange for computer to process. Based on DOM and inductive learning, the paper presents a novel approach to semi automatically generate Java classes which can be dominant part of a wrapper for Web sources. The paper is an important part of the research on integrated query processing over hetergeneous data sources.
Printed circuit board (PCB) loss characterization up-to 20 GHz and modeling, analysis and validation

Dielectric properties and losses are increasingly more important in signal link path characterization, as data rates increases. High-frequency effects such as dielectric losses, dispersion and skin-effect losses need to be considered. For ensuring signal integrity, it is important to characterize losses of printed circuit board (PCB) dielectrics. To extract dielectric properties of a stripline transmission medium, an analytical technique was used as described in [1]. Two test materials were studied, and the extraction procedure was refined. This technique was applied to microstrip transmission lines. Two analytical solutions were derived for extracting practical material parameters. A microstrip test vehicle was designed and the two extraction methods were applied to a set of test boards. A standard measurement protocol was developed. Results were analyzed and documented. An approach to characterize losses in printed circuit board (PCB) materials as a single value was studied. It was analytically proven that the theory was meaningful. Time-domain and frequency-domain techniques were developed to characterize the single value loss parameter. Measurements were taken with stripline test vehicles. Results validated the method to be a potential standard for characterizing losses. Several tools are available in the market for design and discovery related to signal integrity issues. Some of these tools were considered to model two simple validation problems. Propagation of a plane wave through a dielectric slab was analyzed with two different tools and results were also validated using calculations. A simple PCB was modeled with decoupling capacitors using several tools to see the effects of decoupling on impedance.
Modeling analysis and optimization for European network data merging

In this paper, the problem of building a consistent European network state based on the data provided by different Transmission System Operators (TSOs) is addressed. A hierarchical merging procedure is introduced and consists in the resolution of several Optimal Power Flow problems (OPFs). Results on the European network demonstrate the interest of this procedure on real-life cases and highlight the benefits of using a hierarchical multi-objective approach.
Archival and Retrieval of Lost Objects using Multi-feature Image Matching in Mobile Applications

The rapid development of mobile computing technologies, as well as enhanced wireless communications, has paved the way for the development of the e-Government and m-Government systems. With the aim of increasing the accessibility of government services, such systems are still proven to be under-utilized, especially in the area of retrieving lost personal objects. In this paper, we propose a multiplatform mobile application for reporting and retrieval of lost objects in an efficient manner, rather than going through a manual procedure of filling up forms. The backend sever runs an object retrieval algorithm on the reported lost and found objects in the database, which is comprised of a textual search, a geographic information system filter, an image matching algorithm using Speeded Up Robust Features, followed by color validation using L*a*b* color space. Experiments have been performed on 40 lost objects against the database of 50 found objects with the accuracy of 95%, while providing a precision value of 100% and a 90% of recall. The efficiency and overall good performance of the proposed system can help in reducing the manual labor, as well as in providing fast feedback to the users.
HRM in Small and Medium Sized Enterprises (SMEs)

Small and Medium-sized Enterprises (SMEs) are often deemed critical to the economy of many countries. The reason for this is twofold: first, there are so many smaller firms that they make up a large proportion of employment; secondly, because the way people are managed is regarded as central to the competitive standing of firms and industries (Boxall and Purcell, 2003). Such a recognition has provoked debate about the role of HRM as a means to enhance organisational effectiveness (Den Hartog and Verburg, 2004). What is problematic, however, is that much of the literature that espouses the virtues of HRM is almost exclusively derived from larger firms (Dundon and Wilkinson, 2004). The purpose of this chapter is to assess the nature and extent of HRM among SMEs. We adopt a generalist approach to HRM, which includes employment relations broadly defined . In this way the chapter can report on various practices while providing an analysis about the meanings and interpretations of management action in the context of a smaller rather than larger firm, mostly from a UK perspective. The chapter starts by asking what an SME is, commenting on the importance of SMEs for the economy as a whole and for human resource management specifically. Traditional approaches to HRM are then considered, assessing how they have been applied to smaller businesses. From this it is suggested that much theorising about HRM in SMEs is limited in that analysis often gravitates around one of two polarised perspectives: the ‘small is beautiful’ versus ‘bleak house’ scenario. (Wilkinson 1999). The remainder of the chapter then reviews current research evidence concerning the core dimensions of HRM among SMEs (e.g. informality, recruitment, training, employee involvement /voice and related management practices).
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Graphing Calculators in Teaching Statistical p-Values to Elementary Statistics Students.

The statistical output of interest to most elementary statistics students is the p-value, outputted in computer programs like SPSS, Minitab and SAS. Statistical decisions are sometimes made using these values without understanding the meaning or how these values are calculated. Most elementary statistics textbooks calculates p-values for z-tests while omitting t-tests, c tests, and F-tests because of the complexity of these distributions. All elementary statistics texts surveyed for this paper use Excel and Minitab output to report p-values for t-tests, c -tests, and F-tests. This paper shows an alternative for calculating p-values using the embedded statistical distributions in the graphing calculator regardless of the complexity of the statistical distributions.