Miracle-dz/UniFAR-FTUs huggingface dataset:
Induction of the morphology of natural language : unsupervised morpheme segmentation with application to automatic speech recognition

In order to develop computer applications that successfully process natural language data (text and speech), one needs good models of the vocabulary and grammar of as many languages as possible. According to standard linguistic theory, words consist of morphemes, which are the smallest individually meaningful elements in a language. Since an immense number of word forms can be constructed by combining a limited set of morphemes, the capability of understanding and producing new word forms depends on knowing which morphemes are involved (e.g., “water, water+s, water+y, water+less, water+less+ness, sea+water”). Morpheme boundaries are not normally marked in text unless they coincide with word boundaries. The main objective of this thesis is to devise a method that discovers the likely locations of the morpheme boundaries in words of any language. The method proposed, called Morfessor, learns a simple model of concatenative morphology (word forming) in an unsupervised manner from plain text. Morfessor is formulated as a Bayesian, probabilistic model. That is, it does not rely on predefined grammatical rules of the language, but makes use of statistical properties of the input text. Morfessor situates itself between two types of existing unsupervised methods: morphology learning vs. word segmentation algorithms. In contrast to existing morphology learning algorithms, Morfessor can handle words consisting of a varying and possibly high number of morphemes. This is a requirement for coping with highly-inflecting and compounding languages, such as Finnish. In contrast to existing word segmentation methods, Morfessor learns a simple grammar that takes into account sequential dependencies, which improves the quality of the proposed segmentations. Morfessor is evaluated in two complementary ways in this work: directly by comparing to linguistic reference morpheme segmentations of Finnish and English words and indirectly as a component of a large (or virtually unlimited) vocabulary Finnish speech recognition system. In both cases, Morfessor is shown to outperform state-of-the-art solutions. The linguistic reference segmentations were produced as part of the current work, based on existing linguistic resources. This has resulted in a morphological gold standard, called Hutmegs, containing analyses of a large number of Finnish and English word forms.
Belief Exploration in a Multiple-Media Open Learner Model for Basic Harmony

This paper focuses on whether learners of basic music theory may find a multiple-media independent open learner model useful to explore their knowledge of harmony concepts. Learners were given the option to explore example beliefs held in their learner model as music notation, audio or text, and shown how their beliefs compared to those of an expert. Results suggest users are both willing and make use of the open learner model, and show individual preferences for media format in which to view their beliefs. Participants mostly explored incorrect knowledge even though more correct knowledge was present in the model, and made greater use of the views specific to the music domain (music notation, audio) when their model showed “incorrect knowledge”. Results indicate the potential to include multi-media information in open learner models in appropriate domains.
Study and Analysis of ECG Signal Using MATLAB & LABVIEW as Effective Tools

This paper deals with the study and analysis of ECG signal processing by means of MATLAB tool effectively. Study of ECG signal includes generation & simulation of ECG signal, acquisition of real time ECG data, ECG signal filtering & processing, feature extraction, comparison between different ECG signal analysis algorithms & techniques (i.e. Wavlet transform or so), detection of any abnormalities in ECG, calculating beat rate and so on using the most familiar and multipurpose MATLAB software along with LABVIEW. The proper utilization of MATLAB functions (both built-in and user defined), toolbox and Simulink can lead us to work with ECG signals for processing and analysis both in real time and by simulation with great accuracy and convenience.
What Makes a Good Footprint ?

2. Region approximation [1, 2]. Given a discrete set of points, and for each point whether it lies inside or outside some region of interest, we want to draw a polygon to represent that region. This problem can appear in two guises: (1) the region in question is well-defined, but we either do not know, or do not wish to spare the computational resources to represent, its exact boundary; (2) the region is ill-defined (‘vague’), and therefore we seek a ‘reasonable’ precisification of it:
Towards autonomic consolidation of heterogeneous workloads

Multicore architectures have become the standard both for general purpose PCs and for high-end servers. Such platforms provide an environment that invites consolidating several applications within the same processor to fully take advantage of the available parallelism. As multicore processors become the basic building blocks of data centers and cloud computing platforms, the problem of effective consolidation becomes very challenging as interference between collocated workloads may significantly affect their performance. Server virtualization has offered solutions to the problems of software heterogeneity and fault isolation but not to the problem of performance isolation. Consequently, there is a lot of research on resource partitioning that aims at performance isolation on multicores. Resource partitioning has been shown to be costly and may even result in inefficient resource usage, defeating the purpose of consolidation in data center and cloud computing platforms for reducing infrastructure and electricity costs. As such, consolidating applications is often conflicting with performance isolation, especially since performance degradation due to collocation may be difficult to characterize and foresee.
Design of an Ultra Low Power Clock Gating D Flip-Flop Using Quasi-Static Energy Recovery Logic

This paper presents low power clock gating adiabatic D flip-flop using single phase sinusoidal power clock scheme. We propose the clock gated single phase Quasi-Static Energy Recovery Logic (QSERL) D flip-flop at 90nm CMOS technology. In the previously proposed QSERL logic, two phase sinusoidal power clocks were used that increased the hardware complexity and clocking issues. In this paper, single phase QSERL is proposed to reduce the hardware complexity and clocking issues. The clock gating technique is applied to a QSERL D flip-flop during idle periods of clock. The proposed scheme works efficiently with reduced power loss. All the simulations have been performed using Cadence spectre simulator.
A Web Prediction Pattern Recommendation Algorithm

Recommender system can predict and recommend end users their probably most interesting commodities after abstracting their interest and preference through their browsing information. In this paper, we accordingly abstract user preference to predict the users’ coming access webpage to prefetch them. Through an optimized clustering and sequence mining algorithm, a recommender system is realized to predict user preference webpage. Experiments prove the precision and applicability of system
Data Hiding in Halftone Images Using Error Diffusion Halftoning Method with Adaptive Thresholding

DHSED is a watermarking algorithm to embed hidden binary visual patterns in two error diffused halftone images such that the hidden patterns can be visually inspected when the images are overlaid. A drawback of the DHSED method is that if the image has large areas of the same grey-level, hiding data causes some edge effects which may reveal the hidden data. In this paper we propose an improvement to the DHSED which fairly reduces the edge effects.
Fault insertion testing of a novel CPLD-based fail-safe system

According to the standard IEC 61508 fault insertion testing is required for the verification of fail-safe systems. Usually these systems are realized with microcontrollers. Fail-safe systems based on a novel CPLD-based architecture require a different method to perform fault insertion testing than microcontroller-based systems. This paper describes a method to accomplish fault insertion testing of a system based on the novel CPLD-based architecture using the original system hardware. The goal is to verify the realized safety integrity measures of the system by inserting faults and observing the behavior of the system. The described method exploits the fact, that the system contains two channels, where both channels contain a CPLD. During a test one CPLD is configured using a modified programming file. This file is available after the compilation of a VHDL-description, which was modified using saboteurs or mutants. This allows injecting a fault into this CPLD. The other CPLD is configured as fault-free device. The entire system has to detect the injected fault using its safety integrity measures. Consequently it has to enter and/or maintain a safe state.
Optimal Rate-Diverse Wireless Network Coding

This paper proposes an encoding/decoding framework for achieving the optimal channel capacities of the two-user broadcast channel where each user (receiver) has the message targeted for the other user (receiver) as side information. Since the link qualities of the channels from the base station to the two users are different, their respective single-user non-broadcast channel capacities are also different. A goal is to simultaneously achieve/approach the single-user non-broadcast channel capacities of the two users with a single broadcast transmission by applying network coding. This is referred to as the rate-diverse wireless network coding problem. For this problem, this paper presents a capacity-achieving framework based on linear-structured nested lattice codes. The significance of the proposed framework, besides its theoretical optimality, is that it suggests a general design principle for linear rate-diverse wireless network coding going beyond the use of lattice codes. We refer to this design principle as the principle of virtual single-user channels. Guided by this design principle, we propose two implementations of our encoding/decoding framework using practical linear codes amenable to decoding with affordable complexities: the first implementation is based on Low Density Lattice Codes (LDLC) and the second implementation is based on Bit-interleaved Coded Modulation (BICM). These two implementations demonstrate the validity and performance advantage of our framework.
Stochastic neural firing properties in neurons of a cerebellar control system

The cerebellar system for the voluntary control of arm-hand movements involves a large number of different neuron types. These neurons are located both inside the cerebellum but also in extracerebellar brain structures, which provide processed motor and sensory information to the cerebellum. The various different neuron types have different morphology, receive different types, number and patterns of synaptic inputs and have different firing rates and kinetics. However, a common trait for all neurons is that their firing properties have clear stochastic components, which is evident in recordings from the neurons recorded in brain preparations when all synaptic inputs is removed (in vitro). Here, we take advantage of a unique, comprehensive database of the various neuron types present within the cerebellar arm-hand control system, recorded from the brain in vivo, to provide a comparative description of their spike firing patterns. Although the inter-spike intervals for most cell types in this system can be described by a simple type of distribution characteristic for stochastic neurons, it is only for a few exceptional cases that the consecutive inter-spike intervals are independent of each other. We conclude that the spike patterns of these neurons may be the result of multi-factorial sources of variability that include the patterns in the various synaptic inputs that neurons receive in vivo and the inherent stochasticity of spike generation.
Plans as a Means for Guiding a Reinforcement Learner

The complexity of reinforcement learning problems grows ex ponentially with the size of the state space, which renders realistic cases unsol vable and underlines the need for guidance. This thesis studies a hybrid agent archit ecture, in which the toplevel module reuses temporal knowledge in the form of plans t hat i extracts from a concurrently executing low-level reinforcement learner. The first contribution of this work are significant improvements of the original model and i mplementation of the agent architecture, resulting in a more effective knowledg e extraction and reuse. The second contribution is an extensive exploration of the syne rgy ffects that take place between both layers of the architecture. It is shown that the combination of state abstraction and the reuse of plans as temporal abstraction c an lead to a significantly shorter learning time of a reinforcement learning agent. Li kewise, the number of decisions to be made by the agent is reduced because a plan is a definite commitment to a course of actions that does not require intermediary rea soning. In addition, we demonstrate that the architecture enables the integration of plans as prior knowledge through a clear and convenient interface. Thus, partial and approximate solutions to the problem can be easily specified to significantly decrease learning time even further. To my beloved wife Agnieszka Beata — for patience, support an d encouragement throughout the course of this thesis.
Pre-Service Teachers’ Causal Attributions about FASD and Their Teaching Self-Efficacy

Children with Fetal Alcohol Spectrum Disorders (FASD) often display complex problem behaviours in the classroom. The purpose of this study was to examine how pre-service teachers’ attributions about the problems associated with FASD relate to their self-efficacy for working with affected children. Data were collected in the fall of 2009 and 2011 from 157 pre-service teachers studying at a Western Canadian University. Data were analyzed using multiple regression analyses to determine the extent to which four predictor variables (locus of causality, stability, personal control, and external control) predicted self-efficacy. Results revealed that stability and external controllability were significant predictors of teachers’ self-efficacy in working with children with FASDs. The extent to which a teacher expects to implement interventions for these children in the classroom also predicted self-efficacy. Results are discussed in relation to the development of an attributional retraining program to correct misattributions about FASD and foster teacher self-efficacy.
Based Malware Protection Model for Android Application Chit

stealing private information, sending sms, reading contacts and can even harm by exploiting data. Malwares are spreading around the world and infecting not only for end users but also for large organizations and service providers. Users can download/upload the APK files from third party servers and can install into their mobile devices. Most of the applications from trusted sources are not malware, but the third party server providing malwares in modified APK. In this paper, we present the permission based malware protection model for android application then use the self-organizing feature map algorithm. This is express to make small subsequent adjustments of the protection level and to improve the accuracy of the android permissions. We illustrate the effectiveness of android application permissions through extensive data analysis.
Cultural Capital at Work: How Cognitive and Noncognitive Skills Are Taught, Trained, and Rewarded in a Chinese Technical College

Purpose: Community and technical college student employability is a pressing concern in the United States and China. Policy makers focus on developing students’ human capital in the form of credentials and cognitive skills. However, the focus on completion overlooks the role that noncognitive skills and contextual factors may play in student employability. Method: In this exploratory study, conducted among educators and employers in the manufacturing and biotechnology fields in a large eastern Chinese city, we use a cultural capital framework to address the following questions: (a) What forms of cultural capital are valued by educators and employers? (b) How are these forms of capital being cultivated in the classroom? (c) How do considerations of cultural capital influence hiring and workplace training? and (d) What contextual factors influence these phenomena? Inductive thematic analyses of interviews, classroom observation, and notes from factory tours with eight educators and two employers were completed. Results: This study revealed a shared view that both cognitive and noncognitive skills are essential. Results also reveal a cultural predisposition within Chinese classrooms to lecturing but also a growing use of active learning techniques, a commitment to general education, the importance of “cultural fit” during the hiring process, and job quality and the cultural devaluation of the skilled trades as salient contextual factors. Conclusion: Implications for research, policy, and practice are considered, particularly the need for all students to acquire both cognitive and noncognitive skills as well as a more nuanced and culturally informed perspective on student employability.
Robust Regression Modeling with Sparsity

This thesis considers robust regression modeling with sparsity. In this study, we specifically focus on robust regression modeling based on γ-divergence with sparse regularization. The γ-divergence has been investigated for the i.i. d. problem and is renowned for exhibiting strong robustness. This implies that the latent bias can be sufficiently small even under heavy contamination. In this thesis, the γ-divergence is extended to the regression problem. The parameters in regression models are estimated by minimizing the objective function which is the empirical estimation of the γ-divergence with sparse regularization. We propose an efficient parameter estimation algorithm which has a monotone decreasing property for the objective function. In particular, we discuss a linear regression with the L1 regularization in detail. Further, we consider generalized linear models, which are natural extensions of linear regression. However, the parameter estimation algorithm obtained here is not always applicable to generalized linear models. Some models require a higher computational cost as the sample size becomes larger. To reduce this computational cost, we adopt a stochastic optimization approach which can largely reduce the computational cost per iteration. Further, two types of γ-divergence are compared under homogeneous and heterogeneous contaminations. We reveal the distinct difference between two types of γ-divergence in terms of robustness. One γ-divergence can exhibit the strong robustness for any parametric model under heterogeneous contamination. The other cannot in general except under homogeneous contamination or when the parametric model of the response variable belongs to a locationscale family in which the scale does not depend on the explanatory variables. Numerical experiments and real data analyses are performed for illustrating the effectiveness of the proposed methods and for supporting the theoretical properties which we proved.
Mechanics of Tissue Cutting During Needle Insertion in Biological Tissue

In percutaneous needle insertions, cutting forces at the needle tip deflect the needle and increases targeting error. Thus, modeling needle-tissue interaction in biological tissue is essential for accurate robotics-assisted needle steering. In this letter, dynamics of needle tip interaction with inhomogeneous biological tissue is described and the effects of insertion velocity, tissue mechanical characteristics, and needle geometry on tissue cutting force are studied. Needle interaction with biological tissue is divided into three distinct events and modeled. 1) Initial tissue puncturing, which starts by soft tissue deformation and continues until a crack is formed in the tissue. Employing a viscoelastic model of fracture initiation we have predicted the maximum puncturing force and force-displacement response of a needle in contact with a tissue. 2) Tissue cutting, which follows the crack propagation in tissue and is predicted using a novel energy-based fracture model. The model takes account of the needle tip geometry and the tissue mechanical characteristics. 3) Friction between tissue and needle shaft is estimated during needle insertion and retraction using a needle-tissue friction model. Using a needle driving robot ex vivo experiments are performed on a porcine tissue sample to identify the model parameters and validate the analytical predictions offered by the models.
Enhanced NSGA-II Algorithm for Solving Multi-objective Optimization Problems Using PBX Crossover and POM Mutation.

— Non-Dominated Sorting Genetic Algorithm (NSGA-II) is an algorithm given to solve the Multi-Objective Optimization (MOO) problems. NSGA-II is one of the most widely used algorithms for solving MOO problems. The present work proposed as advancement to the existing NSGA-II. It this method, combination of crossover and mutation operators is used other than that in the original NSGA-II, and the results are compared to see if which works better.
‘Expanding your mind’: the process of constructing gender-equitable masculinities in young Nicaraguan men participating in reproductive health or gender training programs

Background: Traditional forms of masculinity strongly influence men's and women's wellbeing. Objective: This study has two aims: (i) to explore notions of various forms of masculinities in young Nicaraguan men participating in programs addressing sexual health, reproductive health, and/or gender equality and (ii) to find out how these young men perceive their involvement in actions aimed at reducing violence against women (VAW). Design: A qualitative grounded theory study. Data were collected through six focus groups and two in-depth interviews with altogether 62 young men. Results: Our analysis showed that the informants experienced a process of change, labeled ‘Expanding your mind’, in which we identified four interrelated subcategories: The apprentice, The responsible/respectful man, The proactive peer educator, and ‘The feminist man’. The process showed how an increased awareness of gender inequities facilitated the emergence of values (respect and responsibility) and behavior (thoughtful action) that contributed to increase the informant's critical thinking and agency at individual, social, and political levels. The process was influenced by individual and external factors. Conclusions: Multiple progressive masculinities can emerge from programs challenging patriarchy in this Latin American setting. The masculinities identified in this study show a range of attitudes and behaviors; however, all lean toward more equitable gender relations. The results suggest that learning about sexual and reproductive health does not directly imply developing more gender-equitable attitudes and behaviors or a greater willingness to prevent VAW. It is paramount that interventions to challenge machismo in this setting continue and are expanded to reach more young men.