Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Model-Based Control for Neuromuscular Electrical Stimulation

Neuromuscular electrical stimulation (NMES) is a technology where skeletal muscles are externally stimulated by electrodes to help restore functionality to human limbs with motor neuron disorder. This dissertation is concerned with the model-based feedback control of the NMES quadriceps muscle group-knee joint dynamics. A class of nonlinear controllers is presented based on various levels of model structures and uncertainties. The two main control techniques used throughout this work are backstepping control and Lyapunov stability theory. In the first control strategy, we design a model-based nonlinear control law for the system with the exactly known passive mechanical that ensures asymptotical tracking. This first design is used as a stepping stone for the other control strategies in which we consider that uncertainties exist. In the next four control strategies, techniques for adaptive control of nonlinearly parameterized systems are applied to handle the unknown physical constant parameters that appear nonlinearly in the model. By exploiting the Lipschitzian nature or the concavity/convexity of the nonlinearly parameterized functions in the model, we design two adaptive controllers and two robust adaptive controllers that ensure practical tracking. The next set of controllers are based on a NMES model that includes the uncertain muscle contractile mechanics. In this case, neural network-based controllers are designed to deal with this uncertainty. We consider here voltage inputs without and with saturation. For the latter, the Nussbaum gain is applied to handle the input saturation. The last two control strategies are based on a more refined NMES model that accounts for the muscle activation dynamics. The main challenge here is that the activation state is unmeasurable. In the first design, we design a model-based observer that directly estimates the unmeasured state for a certain activation model. The second design introduces a nonlinear filter with an adaptive control law to handle parametric uncertainty in the activation dynamics. Both the observerand vii filter-based, partial-state feedback controllers ensure asymptotical tracking. Throughout this dissertation, the performance of the proposed control schemes are illustrated via computer simulations.
Learning Chinese through a twenty-first century writing workshop with the integration of mobile technology in a language immersion elementary school

ABSTRACT Digital mobile devices such as iPads have been around for many years and have been more and more popular in K-12 classrooms. Research has pointed out the advantages of using iPads in classrooms to enhance teaching, engage learning, and promote learning outcomes. iPads have been proven to be a useful and powerful digital mobile device in language learning including speaking, listening, reading, and writing. The purpose of the study was to investigate whether a technology integrated twenty-first century writing workshop had an influence on the ability and attitude towards writing in Chinese for second graders enrolled in the Mandarin Chinese program in a language immersion elementary school. Twenty-four students participated in the study. Recording technology (iPad recording app), an iPad camera, and the Book Creator App were integrated into the twenty-first century writing workshop. A pre-survey, post-survey, and observation were used to collect data. The results of the study confirmed that writing barriers decreased when the Chinese immersion program students utilized iPads in the writing workshop. Also, the writing ability and attitudes of the Chinese immersion program students improved after the completion of their twenty-first century writing workshop.
Word Embedding Attention Network: Generating Words by Querying Distributed Word Representations for Paraphrase Generation

Most recent approaches use the sequence-to-sequence model for paraphrase generation. The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words. Therefore, the generated sentences are often grammatically correct but semantically improper. In this work, we introduce a novel model based on the encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our proposed model generates the words by querying distributed word representations (i.e. neural word embeddings), hoping to capturing the meaning of the according words. Following previous work, we evaluate our model on two paraphrase-oriented tasks, namely text simplification and short text abstractive summarization. Experimental results show that our model outperforms the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a Chinese summarization dataset. Moreover, our model achieves state-of-the-art performances on these three benchmark datasets.
Spatiotemporal Manipulation of Hindbrain Development and Cancer Induction in Live Zebrafish via Optical Tools

Author(s): Feng, Zhiping | Advisor(s): Weiss, Shimon | Abstract: Living organisms are made of cells that are capable of responding to external signals by modifying their internal state and subsequently their extracellular context. Understanding the spatio-temporal dynamics of these complex interaction networks in biological systems is the subject of a field known as systems biology. To investigate these interactions (a necessary step before understanding or modeling them), one needs to develop means to control or interfere spatially and temporally with these processes and to monitor their responses on a fast timescale and with single-cell resolution. Among all the great tools to precisely perturb the dynamics of biological networks, light activation has been witnessed as an extremely powerful, non-invasive methodology, providing an accurate control over biomolecules’ activity at an unprecedented resolution. This dissertation exemplifies the advantages of using optical tools to investigate complex bioprocesses with studies in two different fields which are developmental biology and cancer biology. In the first project, we show how photo-isomerization of the 13-cis retinoic acid to the trans-isomer (and vice versa) could be used in studying retinoic acid’s role in hindbrain development during zebrafish embryogenesis. The second project employs photon uncaging to induce oncogenic expression, and models tumor evolution from single cells in the zebrafish. Developing and validating these tools in these two projects greatly facilities our understanding of several biological networks. Photo-isomerization of retinoic acid between all-trans and cis form helps illuminate retinoic acid’s unique spatial distribution and function on hindbrain development in a developing zebrafish embryo. And this again clarifies the long-standing controversy of retinoic acid acting as a morphogen in patterning vertebrate embryo. Photo-uncaging of caged cyclofen proves to be a novel and powerful tool to non-invasively manipulate tumor-associated gene expression. The work shown here could shed new light on cancer initiation and growth, and provide new tools for target validation and testing of novel anti-cancer drugs.
Locality regularization graph embedding in face verification

Graph embedding techniques attempt to construct a high locality projection in such a way that projected same class samples should be close to each other. However, estimation of population data locality could be severely biased due to limited number of training samples. This biased estimation could trigger overfitting problem, leading to poor generalization. In this paper, we propose three new dimensionality reduction techniques. Projection features are regularized by utilizing a local Laplacian matrix to better approach true data locality for higher locality preserving. These techniques are developed based on the manipulation of locality regularization. The difference between them would be the adoption of different local Laplacian matrices. In view of this, a common name is given, which is Locality Regularization Graph Embedding (denoted as LRGE). The robustness of these techniques is tested on CMU PIE and FERET databases. Experimental results validate the effectiveness of these techniques in face verification
LIS CURRICULUM REVIEW USING FOCUS GROUP INTERVIEWS OF EMPLOYERS

Introduction. The paper reports two focus group interviews of employers of LIS graduates in Pakistan conducted to seek their feedback on MLIS program of the Department of Library and Information Science at the University of the Punjab, Lahore. Method. Usefulness of focus groups for such purposes is described. Procedure of focus group interviews conducted is given in detail. Results. The results are presented according to the following questions of discussion. What competencies are required of LIS graduates in the changing LIS market in Pakistan? What changes are required in the objectives of MLIS program? What should be the entry requirements for MLIS program? What should be the structure of the program and sequence of courses? What changes are required in the course contents? What should be the curriculum implementation strategies? What physical facilities are needed to implement the curriculum? What other measures do you suggest to improve the quality of education at DLIS? Conclusion. The focus group was found an effective method to seek employer’s opinion about LIS curriculum.
CLASSIFICATION OF LEAF DISEASES IN APPLE USING SUPPORT VECTOR MACHINE

: India is an agricultural country, and 70% of the population depends on agriculture, hence heavy crop losses due to plant diseases results in loss of several billion dollars annually. This paper mainly concentrates on detection of leaf diseases. In rural areas and in developed countries, the naked eye observation of agricultural experts to detect the plant diseases is cumbersome. It takes too much of cost and time. This paper aims to provide a fast and cheap solution through automatic detection of leaf diseases and thus to improve the yield of the crops by detecting the plant diseases at the earlier stage. Using image processing, we can able to identify the type of disease in plant leaf by analyzing the color and texture feature. In our system first, color transformation is done from RGB to HSV and followed by color feature extraction. In color feature analysis, the color distribution of pixels is represented by the mean and standard deviation of the image and the local color information is represented by a binary bitmap. For texture feature extraction, first green pixels are masked as they are healthy region. This is followed by segmentation using K means clustering and identification of significant segments. From the selected segments, the Gray level co-occurrence matrix (GLCM) is found. From GLCM, the texture features are extorted. Using SVM diseased plant is discriminated from the healthy plant and classified based on diseases. Classification accuracy is measured through the performance measures such as precision and recall.
Real-time Head Motion Tracking for Brain Positron Emission Tomography using Microsoft Kinect V2

The scope of the current research work was to evaluate the potential of the latest version of Microsoft Kinect sensor (Kinect v2) as an external tracking device for head motion during brain imaging with brain Positron Emission Tomography (PET). Head movements constitute a serious degradation factor in the acquired PET images. Although there are algorithms implementing motion correction using known motion data, the lack of effective and reliable motion tracking hardware has prevented their widespread adoption. Thus, the development of effective external tracking instrumentation is a necessity. Kinect was tested both for Siemens High-Resolution Research Tomograph (HRRT) and for Siemens ECAT HR PET system. The face Application Programming Interface (API) ’HD face’ released by Microsoft in June 2015 was modified and used in Matlab environment. Multiple experimental sessions took place examining the head tracking accuracy of kinect both in translational and rotational movements of the head. The results were analyzed statistically using one-sample Ttests with the significance level set to 5%. It was found that kinect v2 can track the head with a mean spatial accuracy of µ0 < 1 mm (SD = 0,8 mm) in the y-direction of the tomograph’s camera, µ0 < 3 mm (SD = 1,5 mm) in the z-direction of the tomograph’s camera and µ0 < 1 ◦ (SD < 1 ◦ ) for all the angles. However, further validation needs to take place. Modifications are needed in order for kinect to be used when acquiring PET data with the HRRT system. The small size of HRRT’s gantry (over 30 cm in diameter) makes kinect’s tracking unstable when the whole head is inside the gantry. On the other hand, Kinect could be used to track the motion of the head inside the gantry of the HR system.
Measuring the User Experience, Second Edition: Collecting, Analyzing, and Presenting Usability Metrics

Measuring the User Experience was the first book that focused on how to quantify the user experience. Now in the second edition, the authors include new material on how recent technologies have made it easier and more effective to collect a broader range of data about the user experience. As more UX and web professionals need to justify their design decisions with solid, reliable data, Measuring the User Experience provides the quantitative analysis training that these professionals need. Thesecond editionpresents new metrics such as emotional engagement, personas, keystroke analysis, and net promoter score. It also examines how new technologies coming from neuro-marketing and online market research can refine user experience measurement, helping usability and user experience practitioners make business cases to stakeholders. The book also contains new research and updated examples, including tips on writing online survey questions, six new case studies, and examples using the most recent version of Excel. Learn which metrics to select for every case, including behavioral, physiological, emotional, aesthetic, gestural, verbal, and physical, as well as more specialized metrics such as eye-tracking and clickstream data. Find a vendor-neutral examination of how to measure the user experience with web sites, digital products, and virtually any other type of product or system. Discover in-depth global case studies showing how organizations have successfully used metrics and the information they revealed. Companion site, www.measuringux. com, includes articles, tools, spreadsheets, presentations, and other resources to help you effectively measure the user experience
Web Science 2.0: Identifying Trends through Semantic Social Network Analysis

We introduce a novel set of social network analysis based algorithms for mining the Web, blogs, and online forums to identify trends and find the people launching these new trends. These algorithms have been implemented in Condor, a software system for predictive search and analysis of the Web and especially social networks. Algorithms include the temporal computation of network centrality measures, the visualization of social networks as Cybermaps, a semantic process of mining and analyzing large amounts of text based on social network analysis, and sentiment analysis and information filtering methods. The temporal calculation of betweenness of concepts permits to extract and predict long-term trends on the popularity of relevant concepts such as brands, movies, and politicians. We illustrate our approach by qualitatively comparing Web buzz and our Web betweenness for the 2008 US presidential elections, as well as correlating the Web buzz index with share prices.
MUSICAL NOISE REDUCTION BY PROCESSING SPECTROGRAM OF SPECTRAL SUBTRACTION OUTPUT

This paper addresses a novel method for musical noise reduction. Musical noise is undesired result of using spectral subtraction algorithm for de-noising speech signals. The existence of musical noise makes the speech signal unnatural and annoying for human hearing system. The proposed method applies image-processing techniques on the spectrogram of spectral subtraction output. To detect musical noise blocks, we present a method that is based on the symmetry in the adjacency of each musical noise block. To assess the performance, we have used two objective measures: Segmental SNR (SegSNR), and Log-Likelihood Ratio (LLR) distance. We have also examined the comparative results using informal subjective listening tests. It has been shown that the proposed method effectively suppresses musical noise without noticeable effect on speech intelligibility.
A Framework for the Risk Assessment and Management of Gene Drive Technology in Contained Use

The utilisation of the CRISPR/Cas9 technology has sparked a renewed interest in gene drive mechanisms. These mechanisms of biased inheritance may yield promising applications in the fields of vector control and nature conservation. However, the same properties that will enable these applications may also pose a risk if organisms that are equipped with gene drive cassettes are unintentionally released into the environment. Although several groups of scientists and regulators have started to address these safety concerns, there are currently no dedicated guidelines published on the required risk assessment and minimal control measures applicable to gene drive organisms in contained use. To fill this gap, this paper describes a fundamental approach to assessing the risks of these organisms while handled in a contained laboratory environment. Based on the likelihood that an adverse effect will arise from the handling of a gene drive organism and the severity of this effect, three risk classes for contained use activities are presented. Finally, specific minimum requirements regarding physical measures and working practices are proposed according to the presented risk classes and tailored to activities with rodents, insects, and fungi, which are most likely to be used for gene drive applications in the near future.
Fine-Grained Emotion Elements Extraction and Tendency Judgment Based on Mixed Model

Nowadays, with the development of internet technology and electronic commerce, the Web storages huge number of product reviews comment by customers. Product reviews tend to be more objective in reflecting the real situation of the product, more and more customers post product reviews at merchant websites in order to make an informed choice. However, a large number of reviews made it difficult to track the comments and suggestions that customers made. In this paper, a fine-grained emotional element detection and emotional tendency judgment method based on conditional random fields (CRFs) and support vector machine (SVM) was proposed. This model introduces semantics and word meaning in CRF model to improve the robustness. In SVM model, deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure.
Using the structural location of terms to improve the results of text retrieval based approaches to feature location

Software maintenance and evolution make up a considerable portion of the time and effort spent during the life cycle of a software system. During the maintenance and evolution phase, the majority of a developer’s time is spent on program comprehension tasks. Feature location (i.e., identifying a starting point for a change), impact analysis (i.e., identifying all source elements involved in a change), and software summarization (i.e., automatically summarizing the responsibilities of a source element) are examples of such tasks. Recent research in these areas has focused on improving each process to ease the burden on developers and decrease the time spent in each task through the use of textual information, dependency graphs, and execution traces. Furthermore, the success of text retrieval in other areas (e.g., traceability) has initiated new studies in automating feature location by the use of text retrieval techniques, such as the vector space model (VSM), latent semantic indexing (LSI), and latent Dirichlet allocation (LDA). Some research has been done to improve LSI and VSM models by combining structural information (i.e., information regarding the creation and use of objects and methods within the code) with the corpus obtained from extracting text from source code. However, little research has focused on improving LDA and more sophisticated topic models (i.e., a statistical model of the abstract topics that occur in a corpus) with structural information. Furthermore, no study has looked at how a developer’s knowledge of a software system’s structure may be incorporated into text retrieval based feature location for software maintenance tasks.
The model manager: a multidimensional conceptual modeling tool in the CIM framework

Organizations of all sizes collect a vast quantity of data every year. A data warehouse facilitates strategic multidimensional analysis on these data, by providing a single integrated queryable interface for multiple heterogeneous data sources. However, this interface is at the logical level schema, which fails to take advantage of the fundamental concepts of multidimensional data analysis like facts, dimensions, hierarchies, etc. In this thesis, we discuss a conceptual modeling language from the Conceptual Integration Modeling framework that serves multidimensional data to the users at a much higher level of abstraction. We not only provide the formal semantics for the language, but we also supply conceptual models depicting real world data analysis scenarios. These models are backed up with rigorous mathematical definitions to dispel any ambiguity in their interpretation. We developed a fully functional graphical editor, called the Model manager, to enable users to draw conceptual models visually.
Comparison study of metaheuristics

Many existing studies have used hypothetical data to evaluate the performance of various metaheuristics in solving delivery route optimization. As empirical data impose characteristics of a particular problem, it is necessary to evaluate whether the problem characteristics may influence to the performance of metaheuristics. This article therefore attempts to compare the performance of metaheuristics, that is, genetic algorithm, ant colony optimization (ACO), particle swarm optimization, and simulated annealing (SA), to solve an empirical delivery problem in Yogyakarta, Indonesia. Two cases are developed to capture different characteristics of empirical data. The first case introduces delivery problem of one logistics operator and 58 retailers; the second case presents collaborative strategy in delivery problem, involving two logistics operators and 142 retailers. Results indicate that ACO and SA perform better with respect to less distance traveled for both cases and higher truck utility and lower number of routes for the second case.