Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimal Checkpoint Interval- a Novel Checkpointing Approach for mobile Consumer Devices

-There are number of issues needs to handle in mobile computing systems like mobility, lack of stable storage on mobile nodes, disconnections, limited battery power and high failure rate of mobile nodes which causes loss of computation. Checkpointing is an attractive approach to introduce fault tolerance in mobile distributed systems transparently. A checkpoint is a local state of a process saved on the stable storage. However, a mobile consumer device is not considered to have sufficiently large and stable storage to store its checkpoint data. Therefore, a remote checkpoint technique is preferred in which the checkpoint data of a mobile device is kept in a remote checkpoint server instead of the mobile device. Since battery power is one of the most critical resources for mobile devices, it is important to identify optimal checkpoint intervals that minimize power consumption. The proposed method is used to recover the mobile devices from failures and minimizes power consumption.
Try a Little Bit of Teaching: A Dynamic Assessment of Word Decoding as a Kindergarten Predictor of Word Reading Difficulties at the End of Grade 1

ABSTRACT The present study examined the construct and predictive validity of a dynamic test of decoding. In theory, a dynamic test provides a direct measure of potential for learning. In this study, children were taught 3 novel letters and how to blend the sounds of those into new words, then they were tested on different words comprising the 3 letters. The study followed 171 children from kindergarten to the end of Grade 1. The dynamic test was found to add significantly to the longitudinal prediction of word reading difficulties at the end of Grade 1 even after controlling for a wide range of standard predictors. The dynamic test correlated strongly with concurrent measures of early reading, letter knowledge, and phoneme awareness but less strongly with vocabulary and nonverbal intelligence. It is suggested that the dynamic test taps learning of sublexical units and processing essential for initial reading development.
Inference of gene regulatory metwork (GRN) rrom gene expression data using k-means clustering and entropy based selection of interactions

Inferring regulatory network from gene expression data only is considered a challenging task in systems biology, and the introduction of various high-throughput DNA microarray technologies in the collection of expression data has significantly increased the amount of data to be analyzed by existing algorithms. All of these algorithms focus on different issues regarding the inference of gene regulatory network (GRN) and their methodologies work better only for certain types of datasets and/or regulatory networks. As a result, they have inherent limitations in dealing with different types of datasets. In this paper, we propose a novel method to infer gene regulatory network from expression data which utilizes Kmeans Clustering along with some properties of entropy from information theory. The proposed method has two main components, first grouping the genes of a dataset into given number of clusters and then finding statistically significant interactions among genes of each individual cluster and selected nearby clusters. To achieve this, an information theoretic approach based on Entropy Reduction is used to finally generate a regulatory interaction matrix consisting of all genes. The purpose of grouping genes in clusters based on the similarity of expression level is to minimize the search space of regulatory interactions among genes. The Entropy Reduction Technique (ERT) finds regulatory interactions with reduced number of genes. To assess the performance of our algorithm, we used datasets from DREAM5 – Network Inference challenge [6], DREAM4 – In Silico Network challenge [7] and one in silico dataset generated by GeneNetWeaver [8]. The performance of our algorithm was compared with the result of ARACNE, a popular information theoretic approach to reverse engineer gene regulatory network from expression dataset. We used precision and recall as performance measures. Our algorithm showed significant improvement in the precision and recall percentage over the network generated by ARACNE. We also compared our results among different threshold values and different numbers of clusters with three versions of our algorithm -No Clustering, Unmerged Clustering and Selected Merged Clustering.
WEKA: A Dynamic Software Suit for Machine Learning & Exploratory Data Analysis

This paper has been written on behalf of a popular Java-based software suit primarily developed with a vision to serve as a workbench for the implementation purpose of machine learning algorithms and an efficient tool for the data mining or data analysis tasks. The software got the name, WEKA, the first release was launched in 1993. Since then some rewrites and modifications were done. Through this paper, the review of this particular software has been carried out, including the history, overview of the workbench which consists of the GUI, the advantages and strengths of the application and advanced features.
VLSI programming

VLSI design is viewed as a programming activity. VLSI programs are described in CP-0 (Communicating Process-level 0), essentially a subset of CSP. By means of a few simple examples the authors introduce the CP-0 program notation and discuss its compatibility with VLSI. With larger examples (block sorters and FIR filters) they demonstrate how CP-0 programs can be derived from formal specifications and how to comply with performance-related requirements. <<ETX>>
Approximation and Refinement Techniques for Hard Model-checking Problems

Approximation and Refinement Techniques for Hard Model-Che cking Problems Mihaela Bobaru (née Gheorghiu) Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2009 Formal verification by model checking verifies whether a syst em satisfies some given correctness properties, and is intractable in general. We focus on s everal problems originating from the usage of model checking and from the inherent complexity of model checking itself. We propose approximation and iterative refinement techniques and demonstrate that they help in making these problems tractable on practical cases. Vacuit y detection is one of the problems, relating to the trivial satisfaction of properties. A simil ar problem is query solving, useful in model exploration, when properties of a system are not fully known and are to be discovered rather than checked. Both of these problems have solution sp ace structured as lattices and can be solved by model checking using those lattices. The lattic es, in the most general formulation of these problems, are too complex to be implemented effi ci ntly. We introduce a general approximation framework for model checking with lattices a nd instantiate this framework for the two problems, leading to algorithms and implementation s that can obtain efficiently partial answers to the problems. We also introduce refinement techni ques that consider incrementally larger lattices and compute even the partial answers g radually, to further abate the size explosion of the problems. Another problem we consider is th e s ate-space explosion of model checking. The size of system models is exponential in the num ber of state variables and that renders model checking intractable. We consider systems co posed of several components running concurrently. For such systems, compositional ver ification checks components individually to avoid composing an entire system. Model checkin g a individual component uses
Decision Tree and Decision Forest Algorithms: On Improving Accuracy, Efficiency and Knowledge Discovery

The “Digital Revolution” has blessed the human civilization with enormous amount of “DATA”. The challenges of automatically analyzing these data has augmented the need for developing sophisticated means for data mining. In the field of data mining, classification plays a very important role in both predicting the “class” of an unseen instance and discovering patterns in data. In today’s data-driven world, classification is being applied in our day-to-day activity. Thus, the importance of improving prediction accuracy and simplifying knowledge discovery from classifiers are paramount. Decision tree is one of the most popular classifiers that is capable of predicting unseen instances with high accuracy and generating human-interpretable knowledge. Besides, due to their sensitive nature decision trees are often used as base classifiers to form ensembles of decision trees. An ensemble of decision trees, popularly known as a decision forest is said to be more robust to noise(s) and more accurate than a single decision tree. Many decision tree and decision forest building algorithms have been proposed in literature. However, the existing algorithms have various limitations that give us room for further improvement. Furthermore, decision forests are more memory-intensive and less knowledge-extractable than a single decision tree. Hence, in this thesis we propose several novel algorithms for improving accuracy of decision trees and decision forests, then propose a technique to reduce the size of decision forests while retaining or increasing the ensemble accuracy, and finally propose a framework for effective knowledge discovery from decision forests. In order to validate the proposed algorithms/techniques, we carry out extensive experiments on several publicly available data sets. The experimental results indicate that the proposed algorithms/techniques clearly improve the current state-of-the-art in applicable areas.
SUPPORT VECTOR MACHINES (SVMS) BASED FRAMEWORK FOR CLASSIFICATION OF FALLERS AND NON-FALLERS

The elderly population is growing at a rapid pace, and falls are a significant problem facing adults aged 65 and older in terms of both human suffering and economic losses. Falls are the leading cause of mortality among older adults, and non-fatal falls result in reduced function and poor quality of life for older adults. Although much is known about the mechanisms and contributing risk factors relevant to falls, falls still remain a significant problem associated with this age group. Therefore, new strategies and knowledge need to be introduced to understand and prevent falls. Studies show that early detection of impaired mobility is critical to the prevention of falls. In this study, the relationship between gait and postural parameters and falls among elderly participants using wearable inertial sensors was investigated. As such, the aim of this study is to investigate the critical gait and postural parameters contributing to falls, then further to classify fallers and non-fallers by utilizing gait and postural parameters and machine learning techniques, e.g. support vector machines (SVMs). Additionally, as the assessment of fall risk is linked to noisy environment, it is important to understand the capability of the SVM classifier to effectively address noisy data. Therefore, the robustness of the SVM classifier was also investigated in this study. In summary, the presented work addresses several challenges through research on
Analysing developers' sentiments of code reviews an empirical study

The sentiment (i.e., general positive or negative attitude) towards another person, entity or event significantly influences a person’s decision-making process. It is one of the most important factors that influences interactions among the stakeholders for different application areas in many domains. Hence, various types of approaches have been proposed in order to detect sentiment accurately. However, it is not formally analyzed whether sentiments can impact the outcomes of that activity, expressed during software development activities, like peer code review. The objective of this study is to identify the factors influencing review comments and the impact of sentiments on the outcomes of associated review requests. On this goal, we manually rated 1000 review comments to build a training dataset and used that dataset to evaluate eight sentiment analysis techniques. We found a model based on Gradient Tree Boosting (GTB), a supervised learning algorithm, providing the best accuracy to distinguish among positive, negative, and neutral review comments. To the best of our knowledge, this is the first approach that implemented supervised learning methods in the context of code review. We achieved as high as 74% accuracy in sentiment detection which is significantly higher than existing lexicon based analyzers (50% accuracy). We have also validated it with human raters. Using our GTB based model, we classified 10.7 million review comments from 10 popular open source projects. The results suggest that larger code reviews (e.g., measured in terms of number of files or code churn) are more likely to receive negative review comments and those negative review comments not only may increase review interval (i.e., time to complete a code review) but also may decrease code acceptance rate. Based on these findings, we recommend developers to avoid submitting large code review requests and to avoid authoring negative review comments. The results also suggest that the reviewers authoring higher number of negative
Classification of walking pattern using acceleration waveform in elderly people

We attempted to classify walking on level ground from walking on a stairway using a waist acceleration signal. A tri-axial accelerometer was fixed to the subject's waist and the three acceleration signals were recorded by a portable data logger at a sampling rate of 256 Hz. Eleven healthy, elderly subjects were asked to walk through a corridor and up and down a stairway as a single sequence, without any instruction. The data were analyzed using a discrete wavelet transform. Walking patterns were classified using two parameters; one was the ratio between the power of wavelet coefficients which were corresponded to locomotion and total power in the anteroposterior direction (RPA). The other was the ratio between root mean square of wavelet coefficients at the anteroposterior direction and that at the vertical direction (RAV). Walking up stairs could be distinguished by the smallest value in RPA from other walking patterns. Walking down stairs could be discriminated from level walking using RAV. It was possible to classify the walking pattern using acceleration signals in elderly people.
Supporting Scalable Data Analytics on Large Linked Data

Linked data are the de-facto standard in publishing and sharing data on the web. To date, we have been inundated with large amounts of ever-increasing linked data in constantly evolving structures. The proliferation of the data and the need to access and harvest knowledge from distributed data sources motivate us to revisit several classic problems in query processing and query optimization. The problem of answering queries over views is commonly encountered in a number of settings, including while enforcing security policies to access linked data, or when integrating data from disparate sources. We approach this problem by efficiently rewriting queries over the views to equivalent queries over the underlying linked data, thus avoiding the costs entailed by view materialization and maintenance. An outstanding problem of query rewriting is the number of rewritten queries is exponential to the size of the query and the views, which motivates us to study problem of multiquery optimization in the context of linked data. Our solutions are declarative and make no assumption for the underlying storage, i.e., being store-independent. Unlike relational and XML data, linked data are schemaless. While tracking the evolution of schema for linked data is hard, keyword search is an ideal tool to perform data integration. Existing works make crippling assumptions for the data and hence fall short in handling massive linked data with tens to hundreds of millions of facts. Our study for keyword search on linked data brought together the classical techniques in the literature and our novel ideas, which leads to much better query efficiency and quality of the results. Linked data also contain rich temporal semantics. To cope with the ever-increasing data, we have investigated how to partition and store large temporal or multiversion linked data for distributed and parallel computation, in an effort to achieve load-balancing to support scalable data analytics for massive linked data.
Visual hull construction, alignment and refinement for human kinematic modeling, motion tracking and rendering

The abilities to build precise human kinematic models and to perform accurate human motion tracking are essential in a wide variety of applications. Due to the complexity of the human bodies and the problem of self-occlusion, modeling and tracking humans using cameras are challenging tasks. In this thesis, we develop algorithms to perform these two tasks based on the shape estimation method Shape-From-Silhouette (SFS) which constructs a shape estimate (known as Visual Hull) of an object using its silhouettes images. In the first half of this thesis we extend the traditional SFS algorithm so that it can be used effectively for the human related applications. To perform SFS in real-time, we propose a fast testing/projection algorithm for voxel-based SFS algorithms. Moreover, we combine silhouette information over time to effectively increase the number of cameras (and hence reconstruction details) for SFS without physically adding new cameras. We first propose a new Visual Hull representation called Bounding Edges. We then analyze the ambiguity problem of aligning two Visual Hulls. Based on the analysis, we develop an algorithm to align Visual Hulls over time using stereo and an important property of the Shape-From-Silhouette principle. This temporal SFS algorithm combines both geometric constraints and photometric consistency to align Colored Surface Points of the object extracted from the silhouette and color images. Once the Visual Hulls are aligned, they are refined by compensating for the motion of the object. The algorithm is developed for both rigid and articulated objects. In the second half of this thesis we show how the improved SFS algorithms are used to perform the tasks of human modeling and motion tracking. First we build a system to acquire human kinematic models consisting of precise shape and joint locations. Once the kinematic models are built, they are used to track the motion of the person in new video sequences. The tracking algorithm is based on the Visual Hull alignment idea used in the temporal SFS algorithms. Finally we demonstrate how the kinematic model and the tracked motion data can be used for image-based rendering and motion transfer between two people.
Implementation of MIMO-OFDM System using ML decoder

— The traffic in wireless networks has been showing an exponential growth over the last decade. In order to meet the demand, and support a continuation of this growth, bandwidth efficient, high data rate and multipath fading free communication system is required. Orthogonal frequency division multiplexing (OFDM) system with multiple input multiple output (MIMO) configuration fulfils the above demands. In the process of generation of MIMO-OFDM signal, binary data is passed through a turbo encoder and the output of the turbo encoder is modulated using Quadrature Amplitude Modulation (QAM). The QAM output is passed through Inverse Fast Fourier Transform (IFFT) to produce OFDM signal and this signal is transmitted using Space Time Block Codes (STBC) for MIMO implementation. In this project, the MIMO-OFDM signal is detected by using maximum likelihood (ML) method in which Viterbi Algorithm is used. This project presents how the OFDM signal is generated at the transmitter and detected at the receiver using ML method. Results are simulated using MATLAB software.
The American Association of Law Libraries

THIS YEAR,THE AMERICAN ASSOCIATION of Law Libraries is 57 years old. In light of this fact, if age alone could be used as a criterion for durability and accomplishment, then the AALL holds a seniority among the major professional library associations in this country, second only to that of the American Library Association from which it originally emerged. That the AALL has been durable is the object of this writing, With its accomplishments to date for both law librarianship and lawyers, there is no sign that it is growing old. Sometime prior to the 1906 annual conference of the American Library Association, a small group of men trained chiefly as lawyers and managing the libraries of a variety of political units decided among themselves that their work in a highly specialized research discipline called for the formation of an association which could more effectively represent their problems and the solutions to them than could a general library organization concerned primarily with public library administration. More practically speaking, there was also the matter of a needed compilation similar to the Jones Index to Legal Periodical Literature, which had last been supplemented in 1899. In addition, although there had been law libraries since the time that a collection of books could call itself that, there was nothing in print concerning their organization or management, nor was there any means of regularly exchanging the lessons of personal experience or training. In each instance, the AALL was to stimulate a solution to these problems, first with the start of publication in 1908 of the Index to Legal Periodicals and the Law Library Journal. Within the next few years, a noteworthy amount of writing by various members of the Association was to be done in an attempt to come to terms with the problems of organization and management, Thus, the American Association of Law Libraries was given life in
A sharp interface isogeometric strategy for moving boundary problems

Song, Tao PhD, Purdue University, December 2016. A Sharp Interface Isogeometric Strategy for Moving Boundary Problems. Major Professor: Ganesh Subbarayan, School of Mechanical Engineering. Moving boundary problems that include crack propagation, solidification and shape optimization occur in a variety of natural and engineered systems. A significant difficulty in solving the moving boundary problems, in addition to computationally modeling the moving interface, is to capture the interfacial behavior. Commonly, moving boundary problems are solved by diffuse interface techniques such as the phase field method that is challenged by high computational cost or by the level set method in which considerable care needs to be exercised to ensure stable evolution of the interface. The challenges with phase field method include the need to develop alternative (diffuse) mathematical forms of the governing equations that must be proven to converge to the sharp interface form. The phase-field equations are also usually non-linear and non-convex, and the diffuse transition region has to be very thin to converge to the physical solution. This last fact in turn requires the mesh to span several orders of magnitude in length scale and to be very refined near the interfacial region. In the case of level set method, the solution to the Hamilton-Jacobi equation needs stabilization to minimize the oscillation and an auxiliary velocity field equation needs to be posed and solved to extend the velocity from the interface into the domain. Finally, but most importantly, due to the geometrically implicit nature of these methods (the boundary is not explicitly represented, but inferred from the value of the phase field or level set parameter), the necessary geometric quantities such as normals and curvatures are difficult to compute with C 0 continuous finite elements and can be accurately computed only in the limit of mesh refinement.