Miracle-dz/UniFAR-FTUs huggingface dataset:
A Critical Review of the Unguided Loose Search (ULS) Process for Natural Language Based Extraction Technique on Relational Databases

Formulation of query statements by searchers for submission into relational databases and information retrieval systems have been a serious challenge that often lead to irrelevant search results. This is compounded by the level of uncertainty about the user’s information need and in some cases, unfamiliarity with retrieval system. Evidently, the World Wide Web presents a more established challenge in this area, considering the fact that searchers has little or no training on search techniques on the web. This paper recognizes fuzzy logic system and fuzziness as a tool required to close the gap between automated systems and human thinking. We realize this stiffness in query presentation as against the flexibility in human thinking and then consider the fuzzy concept as a tool that can be incorporated into a new system to overcome the syntactic problem presented in most relational operations. Thus, the paper proposes a novel approach of natural language query based problems. We propose the use of an Unguided Loose Search (ULS) which involves the use of local appropriator on a fuzzified Natural Language Interface. Our approach incorporates fuzziness in the interface, using the local appropriator, of the database systems rather than within the data itself. It allows freedom to users since they will not have to learn any specialized syntax such as that of SQL. The result shows that the new querying model called the EFUSQL model is applicable to real life users and can be incorporated into existing databases and query interfaces. The results show that naive users prefer the new system due to its flexibility and response time
Unsupervised classification of Antarctic satellite imagery using Kohonen's self-organising feature map

This paper describes an investigation into the use of Kohonen's self-organising feature map (SOM) for the classification of remotely sensed imagery of Antarctica. The SOM is an unsupervised neural network which is trained using unlabelled input data. The network consists of a grid of nodes and, after training, each node corresponds to a prototype vector in the input data space. In order to use the trained SOM as an image classifier it is necessary to calibrate the grid of prototype vectors whereby the prototype vectors are clustered and these clusters mapped to physical class labels. The K-means iterative clustering technique is demonstrated as a means of performing this clustering. However this method requires the user to specify the number of clusters to be formed. The U-matrix method is investigated as a way of identifying the number of clusters represented by the grid of prototype vectors.
Adaptive control of a robotic manipulator for soft catching of a falling object

This paper presents a controller design of a robotic manipulator for soft catching of a falling object. If a robotic system is able to catch a falling object softly, there will be many applications expected in human activities such as industry, welfare, nursing, housework and office work, because this ability allows a human operator or another robot system to move an object to the catching robot without any transportation systems such as an conveyor or a mobile structure. First, this paper considers a nonlinear decoupling control of a robotic manipulator. Next, a controller design is presented for catching a falling object with a small impact force. This controller consists of two parts: a position tracking controller that tracks a desired trajectory before contact between the object and the robot end-effector, and a force controller that is triggered after the contact. We employ a position-based impedance controller so that the entire control system can be constructed as a position-based controller. In order to achieve the soft catching, precise motion control is required to achieve the same velocity of the robot end-effector with a falling object when they are in contact. Hence, we employ an adaptive controller that consists of a feedback controller to compensate for disturbance such as friction and a feed forward controller to improve the tracking performance to the desired trajectory by adjusting controller parameters in real time. Experimental results with a falling raw egg demonstrate the effectiveness of the proposed approach.
Head and neck multimodality volumes visualization methods

This paper describes design strategies and algorithms which have been developed for visualization. registration, Volume of Interest (VoI) definition and measurements on multimodality single subject head and neck CT, MR, PET and surgical tumor specimen images. This work was part of a project that measured the usefulness of MR and PET images in three-dimensional radiotherapy treatment planning which was originally based only on CT images. The VoIs. which could be defined on any of the registered volume. were exported with the CT images to the treatment planning system, using DICOM. The major problem was the handling of large datasets with big differences in FOV and voxel sizes. We used object-oriented design and C++ programming language to develop object classes and methods for efficient data management. We used the eXtensible Markup Language (XMEL) standard to save the registration transformers and the VoIs. The registered volumes were resliced on the fly for visualization or VoI statistical information computing. Therefore, we avoided creation of large resliced dataset files. We used the new classes to extend two existing applications: interactive registration and RoI drawing. The paper describes: (i) the data handling problems. (ii) the developed objects relationship and methods and (iii) the two applications.
Using continuous limit techniques and stochastic computational modeling to predict the biological behavior of aggregating cells

by Richard S. Gejji Mathematical modeling and computational simulations can be used to predict the average behavior of many cell-cell interactions. In this thesis, results on modeling the predatory bacteria myxococcus xanthus and the slug forming amoeba dictyostelium discoideum are presented as well as results on random placement of non-overlapping cells in an individual based model, and continuous limits of discrete stochastic systems. Periodic reversals in systems of self-propelled rod shaped bacteria can lead to ordering of cells, thus enabling them to effectively resolve traffic jams formed during swarming and maximize their swarming rate. A connection is described between a one dimensional cell-based stochastic model of reversing non-overlapping bacteria and a non-linear diffusion equation in [35] and this thesis. BoltzmannMatano analysis is used to determine the nonlinear diffusion equation corresponding to the specific reversal frequency. It is shown that cell populations with high reversal frequencies are able to spread out effectively at high densities. If the cells rarely reverse then they are able to spread out at lower densities but are not able to spread out at higher densities. Richard S. Gejji The amoeba Dictyostelium is able to sense the location of other members of its species and aggregate, using chemotaxis. During aggregation, the amoeba creates and follows the gradient of a diffusive chemical. Through local behavioral chemotactic rules, the cells are able to cluster together and create slugs. PDE equations describing this behavior have been derived in [78]. Existence, uniqueness and boundedness of solutions of this equation are proven in [5] and this thesis. The 1D steady state solutions and their stability are also analyzed. It is predicted that stable solutions correspond to biologically realizable aggregates, and conditions for determining whether or not certain amoeba aggregation patterns will occur in nature are presented. To my loving wife Sonje, whose encouragements have made this possible.
Online Graduate Instruction: What Faculty Consider Reasonable In Relation to What Students Expect

In this investigation, the author utilized a modified Delphi technique to validate whether graduate level online faculty and learners could achieve a consensus of opinion relative to a range of instructional practices commonly associated with online education. A list of administrative guidelines pertaining to online instruction collected from an assortment of post-secondary institutions in the United States was examined by participants to address whether a consensus could be established between what online faculty and online students perceived as important instructional practices in online delivery. The findings of this study offer insight into how administratively driven instructional practices in the areas of Communication, Presence & Engagement, and Timeliness/Responsiveness relate to the expectations of online learners, and the realisms of online faculty.
Robust control of a pH control plant

We are going to present how we designed robust controllers for a laboratory plant, which produces a dissolution of HCl of constant concentration despite variations in the plant parameters. To design the controllers by loopshaping we used the CC control package and the ACSL simulation software. The designed control system allowed us to operate in a wide range of pH values in spite of the variation of the plant parameters and disturbances. The performance at every working point is satisfactory, and the system is stable in the desired range of parameter variations.
Person Re-Identification via Locally Biased Metric Learning

Person re-identification, which means matching person across non-overlapping cameras in a surveillance camera network, has attracted more and more attention. A lot of metric learning based methods, which generally learn a new distance function under two pair-wise constrains, i.e. similar constrain and dissimilar constrain, were proposed to address the challenging problem due to significant appearance variances caused by pose changes, lighting variations and image resolution differences. However, these methods attempt to satisfy all similar constrains and dissimilar constrains, which may be conflict and cannot be simultaneously satisfied in the practical application. In this paper, we propose a new local metric learning method based KISS metric learning. Comparative experiments conducted on three public standard datasets have shown the promising prospect of the proposed method.
CLOUD SECURITY AND DIGITAL SIGNATUREA ROAD MAP

The use of cloud computing is rapidly increasing now a days. Cloud is an internet based service delivery model which provides internet based services, computing and storage for users in all market including financial, health care, government and so on. Cloud is a model where user is provided services by CSP (Cloud Service Provider). There are also disadvantages with cloud computing mainly with the security concern. Especially data security and privacy protection issues remain the primary problem in cloud computing services. It is required to protect the stored data and applications in clouds from hackers and intruders. Cryptography is an essential tool that helps to assure our data storage and accuracy in cloud environment. There are different types of cryptographic algorithms. This article briefly discusses on the basics of cloud, security issues associated with cloud and it briefly explains the existing cryptographic algorithms that can be used to improve the security in cloud environment. Finally, this paper describes about the basic mechanism of digital signature and encryption.
NoSQL in Higher Education. A Case Study

Big Data and NoSQL technologies are simultaneously marketing hypes and tools that could significantly change the database and application development landscape. This paper argues that, despite the negation and revolt suggested by their name, NoSQL data stores are rather a complement of traditional transactional database systems. The paper is focused on the module implemented at the Faculty of Economics and Business Administration dedicated to web and application servers log analysis. Using MongoDB -- a popular NoSQL product, JavaScript, Pyhon environment and Tornado web server, the module extracts data from large log files and provides useful information about users who have not succeeded to connect to applications such as Blackboard system, faculty portal and web site, about the usage of workstations and labs and other information for which NoSQL technologies can complement relational databases. For a better understanding, beside the module architecture and the solution itself, the authors also try to draw the big picture for the NoSQL technologies and the current context in the data storage environment.
Performance optimization of CSMA/CA based ad hoc networks

Ad hoc wireless networks introduce a number of challenges for protocol designers. Stations using such networks are constrained by factors such as limited bandwidth, link errors, and collisions. Changes are needed at various levels of the protocol stack, but most importantly at the Medium Access Control (MAC) layer. The medium access mechanism in multi-hop wireless networks should minimize collisions, and take care of the hidden and exposed node problems. It should also be flexible enough to allow all nodes to maximize the utilization of available bandwidth. Due to the broadcast nature of wireless transmission, when two hosts are communicating, all other hosts within the range of the two hosts must defer their communication in order to avoid collision. This results in significant throughput degradation as the number of active hosts increases. Studies have shown that wireless networks provide dismal capacity in the presence of many contending nodes in a given area. In the recent past, research has been carried out to improve the throughput of wireless networks and various approaches have been proposed. Some of the areas of enhancements include power control, antenna directivity, multi-channel capability, concurrent transmission, multi-rate, etc. In this thesis, the focus of my work is on the design of new and innovative methods to enhance the throughput and delay performance of ad hoc networks using CSMA/CA-based MAC protocols. In the later chapters, we highlight three new MAC layer schemes and a link layer hop-by-hop flow control scheme. The first MAC scheme is a multi-channel MAC protocol that is capable of enhancing the throughput of CSMA/CA protocols in a multi-channel 1 ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library
Development and validation of an automated essay scoring engine to assess students’ development across program levels

As English as a second language (ESL) populations in English-speaking countries continue to grow steadily, the need for methods of accounting for students’ academic success in college has become increasingly self-evident. Holistic assessment practices often lead to subjective and vague descriptions of learner language level, such as beginner, intermediate, advanced (Ellis & Larsen-Freeman, 2006). Objective measurements (e.g., the number of error-free T-units) used in second language production and proficiency research provide precise specifications of students’ development (Housen, Kuiken, & Vedder, 2012; Norris & Ortega, 2009; Wolfe-Quintero, Inagaki, & Kim, 1998); however, the process of obtaining a profile of a student’s development by using these objective measures requires many resources, especially time. In the ESL writing curriculum, class sizes are frequently expanding and instructors’ workloads are often high (Kellogg, Whiteford, & Quinlan, 2010); thus, time is at its limits, making the accountability for students’ development difficult to manage. The purpose of this research is to develop and validate an automated essay scoring (AES) engine to address the need for resources that provide precise descriptions of students’ writing development. Development of the engine utilizes measures of complexity, accuracy, fluency, and functionality (CAFF), which are guided by Complexity Theory and Systemic Functional Linguistics. These measures were built into computer algorithms by using a hybrid approach to natural language processing (NLP), which includes the statistical parsing of student texts and rule-based feature detection. Validation follows an interpretive argument-based approach to demonstrate the adequacy and appropriateness of AES scores. Results provide a mixed set of validity evidence both
Influence Maximization in Continuous Time Diffusion Networks

The problem of finding the optimal set of source nodes in a diffusion network that maximizes the spread of information, influence, and diseases in a limited amount of time depends dramatically on the underlying temporal dynamics of the network. However, this still remains largely unexplored to date. To this end, given a network and its temporal dynamics, we first describe how continuous time Markov chains allow us to analytically compute the average total number of nodes reached by a diffusion process starting in a set of source nodes. We then show that selecting the set of most influential source nodes in the continuous time influence maximization problem is NP-hard and develop an efficient approximation algorithm with provable near-optimal performance. Experiments on synthetic and real diffusion networks show that our algorithm outperforms other state of the art algorithms by at least ∼20% and is robust across different network topologies.
An efficient watershed transform computation method

In this paper, we propose a watershed computation method that is faster than and is devoid of certain drawbacks experienced by a traditional immersion-based watershed algorithm. Flooding of catchment basins in our algorithm always starts from precomputed regional minima and conditional neighborhood comparisons while processing the eight neighboring pixels of a labeled center pixel. This invariably results in thin continuous watershed lines. Better time complexity of the proposed algorithm relative to the traditional algorithm stems from the fact that the labels of various neighboring pixels are determined at the same time. The results of running both the proposed and the traditional algorithm on different images clearly establish the superiority of the proposed algorithm.
Improving AS relationship inference using PoPs

The Internet is a complex network, comprised of thousands of interconnected Autonomous Systems. Considerable research is done in order to infer the undisclosed commercial relationships between ASes. These relationships, which have been commonly classified to four distinct Type of Relationships (ToRs), dictate the routing policies between ASes. These policies are a crucial part in understanding the Internet's traffic and behavior patterns. This work leverages Internet Point of Presence (PoP) level maps to improve AS ToR inference. We propose a method which uses PoP level maps to find complex AS relationships and detect anomalies on the AS relationship level. We present experimental results of using the method on ToR reported by CAIDA and report several types of anomalies and errors. The results demonstrate the benefits of using PoP level maps for ToR inference, requiring considerable less resources than other methods theoretically capable of detecting similar phenomena.
Analysis and reduction of sense-amplifier offset

The offset of sense amplifiers used for dynamic RAMs (DRAMs) at the megabit level is investigated. An analytic expression which permits an easy calculation of the different offset contributions is derived. The influence of the transistor size and the effect of decoupling devices between bit lines and amplifier are discussed. The sensitivity can be improved if a preamplifier is used. Here, a circuit that doubles the sense signals by switching two capacitors between bit line and reference line is presented. The performance of the preamplifier has been verified through its realization on a test chip. >
Software for prediction and estimation with applications to high-dimensional genomic and epidemiologic data

Three add-on packages for the R statistical programming environment (R Core Team, 2013) are described, with simulations demonstrating performance gains and applications to real data. Chapter 1 describes the relaxnet package, which extends the glmnet package with relaxation (as in the relaxed lasso of Meinshausen, 2007). Chapter 2 describes the widenet package, which extends relaxnet with polynomial basis expansions. Chapter 3 describes the multiPIM package, which takes a causal inference approach to variable importance analysis. Section 3.7 describes an analysis of data from the PRospective Observational Multicenter Major Trauma Transfusion (PROMMTT) study (Rahbar et al., 2012; Hubbard et al., 2013), for which the multiPIM package is used in conjunction with the relaxnet and widenet packages to estimate variable importances.
Evaluation of Prerequisite Skills Testing for Solid Mechanics

The use of a prerequisite skills exam for a Solid Mechanics course in the Aerospace Engineering degree curriculum is evaluated. The prerequisite skills exam was implemented as a type of mastery exam on prerequisite skills needed for successful comprehension of Solid Mechanics, allowing students multiple attempts to pass the exam for credit towards their grade in Solid Mechanics. This began several semesters ago on a trial basis, and has now spread to a number of other courses in Aerospace Engineering. Both qualitative and quantitative evaluations of the value of the prerequisite skills exam are performed, both as justification for continuation of the exam and towards the creation of self-assessment tools in anticipation of the next ABET visit. Qualitative evaluations include short questionnaires given to students just after the entire class completes the prerequisite skills testing, and again during the week prior to final exams. Quantitative evaluation consists of isolating specific portions of the Solid Mechanics final exams that require the use of the skills tested during the prerequisite skills exam, and comparing them to student performance during semesters prior to the implementation of this testing. Details of the exam, results from the evaluations, and additional observations by the instructor are provided.
Parallel, distributed-memory computation of the translation operator in three dimensions

A new algorithm for the parallel, distributedmemory computation of the translation operator in the threedimensional (3D) Multilevel Fast Multipole Algorithm (MLFMA) is presented. In the MLFMA, translation operators with L multipoles need to be evaluated in O(L) directions. The key property of the proposed algorithm is that such translation operator is evaluated in only O(logL) time, using P = O(L) parallel processes. This relationship between P and L occurs naturally in parallel MLFMA implementations that rely on a hierarchical distribution of radiation pattern sampling points and their associated radiation patterns. Numerical results show that the proposed algorithm outperforms a baseline parallel algorithm by a factor of ten.