Miracle-dz/UniFAR-FTUs huggingface dataset:
Building an Automated Q-A System Using Online Forums as Knowledge Bases

Question-Answer systems traditionally use expensive and difficult to produce structured knowledge bases. Recent systems have used unstructured natural language sources as their datasets, but most of those sources have been overly broad or difficult to extend. Online forums are a largely untapped source of information that can provide both depth and breadth when limited to a specific domain, as well as being adaptive to the introduction of new information. In this paper, I conjecture that online forums can be similarly and effectively used as an unstructured knowledge base for Question-Answer systems. I use a relatively simple summarization-based approach to analyze the effectiveness of answering questions about history using well-known and popular online forums. The results of the experiment are moderately negative, though further research may need to be performed to determine whether this is a result of the model architecture, the datasets used, or the inappropriateness of forums as a source of Question-Answer knowledge.
Adaptive Code with Non-Uniform Modulation on OFDM Subcarriers Modeling for Underwater Acoustic Environment

In this paper, an Adaptive Code NonUniform Modulation with Orthogonal Frequency Division Multiplexing (OFDM) System was evaluated in the underwater acoustic communication channel. This system was adopted from the Subcarrier arrangement of the OFDM standard of IEEE 802.11a, and improved with some modifications. There are three schemes in the proposed adaptive system. The first is the Hamming code with Binary phase-shift keying (BPSK) modulated, which is mapped for 12-bit subcarrier data. The second is the Bose-Chaudhuri-Hocquenghem (BCH) code with Quadrature phase-shift keying (QPSK) modulation which is for the other 12-bit subcarrier data. And the third is the Reed Solomon code with 16-Quadrature Amplitude Modulation (16- QAM) modulation that is mapped for 24-bit subcarrier data. The system modeling was evaluated in the shallow water acoustic environments. Bit error rate (BER) and signal-to-noise ratio (SNR) were analyzed to evaluate the proposed system performance. The evaluation results showed that the proposed system was able to improve the performance better than the fixed modulation or un-coded Non-uniform modulation. At the same bit error limit which is 0.001, BPSK, QPSK, 16-QAM, uncoded Non-uniform modulation, and the proposed system have its own SNR, i.e.; 24.9 dB, 8.5 dB, 10.3 dB, 7 dB, and 2 dB, respectively. The proposed system requires lower power to achieve an error rate of 0.001. In addition, between the proposed system and un-coded Non-uniform modulation, it has a coding gain of 5dB.
Mobile Traffic Analysis: a Survey

This report surveys the literature on analyses of mobile traffic collected by operators within their network infrastructure. This is a recently emerged research field, and, apart a few outliers, relevant works cover the period from 2005 to date, with a sensible densification over the last three years. We provide a thorough review of the multidisciplinary activities that rely on mobile traffic datasets, identifying major categories and sub-categories in the literature, so as to outline a hierarchical classification of research lines. When detailing the works pertaining to each class, we balance a comprehensive view of state-of- the-art results with punctual focuses on the methodological aspects. Our approach provides a complete introductory guide to the research based on mobile traffic analysis. It allows summarizing the main findings of the current state-of-the-art, as well as pinpointing important open research directions.
Flywheel vs. Supercapacitor as Wayside Energy Storage for Electric Rail Transit Systems

Energy storage technologies are developing rapidly, and their application in different industrial sectors is increasing considerably. Electric rail transit systems use energy storage for different applications, including peak demand reduction, voltage regulation, and energy saving through recuperating regenerative braking energy. In this paper, a comprehensive review of supercapacitors and flywheels is presented. Both are compared based on their general characteristics and performances, with a focus on their roles in electric transit systems when used for energy saving, peak demand reduction, and voltage regulation. A cost analysis is also included to provide initial guidelines on the selection of the appropriate technology for a given transit system.
RuleTailor: Optimizing Flow Table Updates in OpenFlow Switches With Rule Transformations

Software-defined networking (SDN) provides flexible network control, which has enabled new, more complex network control mechanisms. These approaches impose high demands on flow updates in OpenFlow switches. Existing SDN controllers and firmware have been optimized for high-speed network updates, but specific switch implementations differ in their behavior. Previous optimization schemes for updates ignore the diversity of instruction types and switch behavior. In this paper, we present RuleTailor, an efficient, measurement-based optimization framework for SDN flow updates, to overcome these limitations. In contrast to other measurement-based optimization frameworks, RuleTailor uses new techniques, such as instruction type transformation, pseudo deletion, and match field distance, which can work in both the control plane and the data plane. Our evaluation shows that RuleTailor achieves a performance of fewer than 12 milliseconds per update in a content-addressable flow table with 1k entries, outperforming the state-of-the-art measurement-based framework, Tango, by a factor of 10.
Stop Motion Animation as a Learning Media to Write a Short Story

Numerous researches related to stop motion animation as a learning media to compose a short story needs to be developed because writing skill cannot be separated from everyday life, especially language learning in school. This study aims to investigate the factors that influence the students’ writing ability in composing a short story, the perceptions about the use of stop motion animation, and the methods that were appropriate to the use of stop-motion animation as a learning media. This research was a qualitative study with a case study approach. The data collection technique used was in-depth interviews with several students and teachers as well as classroom observations. The results revealed that there were some issues encountered by the students in writing a short story, namely the students met some difficulties in expressing their thoughts and developing them into a short story and the students’ limitation of vocabularies. Teaching-learning process using the Contextual Teaching and Learning (CTL) method accompanied by stop motion animation as a media gained the students’ optimal learning to write a short story. Stop motion animation made the students joined the class enthusiastically.
Lepton: An automaton for Literate Executable Papers

‘’Lepton” is a tool for writing reproducible research papers and similar documents by extending literate programming to data analysis reports. ‘’Lepton” makes it easy to include scripts or complete programs, compilation and execution instructions, as well as execution results in the same file. Offloading execution to ‘’Lepton” makes the analysis operator-independent and easy to reproduce. In the spirit of literate programming, the plain text file format used by ‘’Lepton” is intended to be human-understandable as opposed to machine-readable, and simple enough to be usable without the software.
A Distributed Service Framework for the Internet of Things

This article introduces distributed service framework (DSF), a distributed service framework supporting development, and deployment of trustworthy and privacy-preserving distributed Internet of Things (IoT) and Industrial IoT (IIoT) applications. DSF provides a common protocol and infrastructure for secure service specification, registration, discovery, publishing, and subscription, over insecure public networks, and introduces mechanisms for service replication, supporting dynamic scalability of services, and delegation, allowing use on embedded and resource-constrained devices. DSF is an open protocol, with an open-source implementation including supporting library for application development, a command-line client for user interaction, and a daemon that manages services and data. DSF is qualitatively evaluated against existing approaches to developing distributed applications and common technologies used in the IIoT context and demonstrated through the development of an IIoT scenario.
Performance Analysis of Buck Power Converter Using Fuzzy Logic Controllers

In this paper the performance and design comparison of conventional fuzzy logic controller (CFLC) and the single input fuzzy logic controller (SIFLC) to regulate the output voltage of the buck power converter has been proposed. The CFLC is the two term controller it has two inputs error and the change of error it uses the 2-dimensional rule table, by using the idea of the signed distance method in the designing of the SIFLC, the 2-dimensional rule table for the CFLC can be converted into 1-dimensional rule table or linear control surface. The important feature is by means of signed distance method the two inputs CFLC can be converted into SIFLC with no significant change in the performance also as the rule table is reduced so SIFLC takes less time to execute its algorithm. To demonstrate that the performance of both controllers CFLC and SIFLC are identical a MATLAB simulation is carried out.
Layered information overlay on telexistence using augmented reality techniques

Telexistence is fundamentally a concept named for the general technology that enables a human being to have a real-time sensation of being at a place other than where he actually exists, and to interact with the remote environment [1] . We have achieved human-like neck movements to visually interact with a remote object in 3 dimensional spaces through previous versions of TELESAR We introduce TELESAR V which maps a user spinal1, neck, head and arm movements into a dexterous slave robot and allowing the operator to feel the robot body as his own body through visual, auditory, kinesthetic and fingertip tactile sensation. With TELESAR V, operator can perform tele-operations confidently with no prior practice. However, in Telexistence theory, there is an opportunity to provide information by using augmented reality to increase the robot capability and user’s satisfaction. The paper propose layered Information overlay system that can provide information according to distance of robot and object. The information such as object properties, personal information and visual remote controller are necessary for user because user can go anywhere user want and when user goes to some place that user is not familiar with, user need information to help him/her to achieve the goal. However, without layered based information system, user will get too much of detail and information that may disturb his/her vision that can make a trouble in the end. With this system, user can feel comfortable to look at the object he/she is
The Recruitment of African American Graduate Students at the University of Arkansas, 2000-2010

The purpose for conducting this qualitative study was to identify how the U of A approached the recruiting of African American graduate students over a ten year period of time. Based upon best practices for recruiting African American graduate students which was identified in current and scholarly literature in the field, the study examined the recruiting practices at U of A from 2000-2010. The personal interview was the instrument of choice, and within this time period, 40 interviews were conducted. The participants included the chancellor, college deans, faculty, graduate coordinators and recruiters from the various departments and colleges which offered graduate degree programs, personnel from the Office of Graduate Recruitment and Support Services, personnel from the Graduate School Admissions Office, and other administrators who were identified as being pertinent to this study. The findings presented models for best practices in the recruitment of minority students, especially African American graduate students. Relevant topics were discussed such as perceived barriers to the recruiting process, funding issues for graduate assistantships, and the perceptions of prospective minority students, campus faculty, and the general public regarding the campus climate. Conclusions and recommendations were also presented. This dissertation is approved for recommendation to the graduate council Dissertation Director: Dr. Michael T. Miller Dissertation Committee:
Reflective practice in nursing

List of Contributors vi Preface ix 1 An introduction to reflection 1 Chris Bulman 2 Developing skills for reflective practice 23 Sue Atkins and Sue Schutz 3 Writing to learn: writing reflectively 53 Sylvina Tate 4 Reflecting in groups 93 Bernadette Carter 5 An exploration of the student and mentor journey into reflective practice 121 Charlotte Maddison and Pam Sharp 6 Supervision for supervisors: icing on the cake or a basic ingredient for the development of clinical supervision in nursing? 150 John Driscoll and Paul Cassedy 7 A personal exploration of reflective and clinical expertise 177 Sue Duke 8 Assessing and evaluating reflection 201 Sue Schutz 9 Getting started on a journey with reflection 225 Chris Bulman Index 254
The Effect of Benazepril on Cats with Chronic Renal Failure

The study was carried out on 12 outpatient cats, age between 4 and 16 years, 7 males and 5 females, that were diagnosed with chronic renal insufficiency. In the first week, benazepril was administered orally, in dose of 0,25 mg/kg/day, after that the dose was increased to 0.5 mg/kg/day. Before and after 1, 4 and 12 weeks of benazepril administration, the blood pressure was measured by oscillometric method. For each cat, the systemic blood pressure value was calculated as the mean of five consecutive measurements. Blood and urine sample were collected before and after four and 12 weeks of the initiation of benazepril administration. After four weeks of benazepril administration, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and proteinuria, assessed by the urine protein-to-creatinine ratio, were all significantly lower (p<0,05) then the values recorded before the administration of benazepril. Benazepril had no significant influences concerning the blood biochemical parameters of renal profile.
Seamless Hourly Rainfall Ensemble Forecasts

Typically, Numerical Weather Prediction (NWP) Direct Model Output (DMO) provide deterministic precipitation forecasts. However, these contain no information regarding forecast uncertainty. Additionally, different NWP models have varying spatial and temporal resolutions and forecast period lengths. Many users require a seamless forecast system with a uniform resolution over the entire forecast period. NWP ensemble systems typically produce a limited number of ensemble members, due to computational resources and time constraints, by perturbing initial conditions and/or model physics, providing an insight into the forecast uncertainty. Global NWP ensembles typically contain no more than 50 ensemble members at coarse temporal and spatial resolutions, in regional NWP ensembles that have higher spatial and temporal resolutions at the expense of the number of members, typically less than 25, and maximum lead time. Limited area ensembles have even higher spatial and temporal resolutions, at the further expense of members and lead time. Some applications, for example hydrological, require high resolution, both spatial and temporal, ensembles with 50-100 members. This is computationally prohibitive for dynamical NWP ensembles. A large ensemble of rainfall forecasts has been generated by blending two deterministic NWP models with a stochastic model of forecast error, which has the space-time properties of observed rainfall. NWP uncertainties depend on the scale and forecast lead time, especially at long forecast lead times. By blending the models scale by scale, it is possible to recognise the increased skill of the models at short lead times and larger spatial scales. The stochastic model of forecast error is applied at each scale, adding increasingly more variability at small spatial scales and longer forecast lead times, while preserving the space-time structure of rainfall. The Bureau of Meteorology’s Australian Parallel Suite (APS, current version is APS1) Australian Community Climate and Earth-System Simulator (ACCESS) Global model (ACCESS G) (∼40 km × ∼40 km, 3 hourly out to 10 days) and ACCESS-R (Regional) (∼12 km ×∼12 km, hourly out to 3 days) models are blended over 1000 km × 1000 km domains. An ensemble of hourly, downscaled precipitation forecasts out to 10 days is produced with a resolution of 2 km × 2 km. A year of these ensemble forecasts (accumulated to daily totals) has been verified against the daily Australian Water Availability Project (AWAP) analyses (0.05◦ × 0.05◦ resolution). These results show the system is able to produce reliable forecasts out to five days over these domains. More recently, hourly (out to 10 days) ACCESS G forecasts (APS1 ACCESS G model outputting hourly fields, running in research mode) and ACCESS R have been used to create hourly forecast ensembles which are being compared to radar observations of rainfall.
A novel proximity graph: Circular neighborhood cell graph for histopathological tissue image analyzing

The cell is the smallest unit of living beings, which has structural and functional properties. Almost all cell behaviors are regulated by various intracellular reactions initiated by the signaling. The signaling and the distance between cells influence each other. Thus, cell‐location‐based modeling and analyzing of histopathological tissues provide important information to the expert. In literature, methods such as distance‐based threshold, K‐Nearest Neighbor, Voronoi graphs, Delaunay triangulation, and colored graph have been used. However, circular neighborhood relationships of cells have not been considered by any CAD system so far despite of its crucial impact. Thus, we developed the circular neighborhood cell‐graph. Histopathological images of liver were classified by using features extracted from T‐Distance, K‐Nearest Neighbor, Voronoi, Delaunay, and the proposed cell‐graph. Then, the classification performances of the methods were compared. Experimental results show that liver tissue images can be classified with accuracy of 95.7% by using the features provided by the proposed cell‐graph model.
Massively Parallel Seesaw Search for MAX-SAT

The MAX-SAT problem is a generalization of the Boolean Satisfiability (SAT) problem. Given a Conjunctive Normal Form (CNF) of boolean variables, the aim is to find a boolean assignment to the variables in such a way, that maximum number of clauses are satisfied. There exists no polynomial time algorithm for MAX-SAT. Some existing algorithms use brute force technique, but they have exponential worst case time complexity. Randomization algorithms exist, but many suffer from local minima. This paper discusses the Seesaw Search technique for solving MAXSAT, which is designed to work on multiple cores of a CPU, utilizing their full potential, to improve the efficiency of the search.
Collaborative system and multi robots based on pneumatic muscle actuator

Designing a multi-robot system provides numerous advantages for many applications, such as low cost, multi-tasking and more efficient group work. While the rigidity of the robots used in industrial and medical application increase the probability of risk of injury. Therefore, many researches are done to increase the safety factor for robot-human interaction, as a result, either the separated between the human and robot is suggested or the force shutdown to robot system is applied. These solutions might be useful for industrial applications, nonetheless it is not for medical and the application require the direct interaction between the human and machine. To overcome the rigidity problem, a soft pneumatic muscle actuator PMA is used in this thesis to design a fully soft robot arm. The performances and the behaviours of these actuators are tested to enhance the force formula for the contraction and the extension PMAs. General length formulas are proposed in terms of the initial length in addition to the structure-based formulas for the tensile force and length. Three different novel actuators are proposed together with their kinematics. These actuators include: the self-bending contraction actuator SBCA, the double-bend pneumatic muscle actuator DB-PMA and the circular pneumatic muscle actuator CPMA. The presented actuators are used with the simple contraction and extension actuators to design different novel structures of continuum arms and end effectors. Then an efficient control system is proposed by using a parallel structure of the neural network NN and proportional P controller (PNNP controller). The presented continuum arms formed a multiple robot system to perform several tasks under the PNNP controller.
The Resolution of Gender Anaphora Reference with the Help of Kernel Trick Mechanisms

An article represents the results of the experiments constructing of conventional anaphora resolution system for gender-balanced corpora GAP. The main idea behind the conducted set of experiments is taking advantage over the kernel trick mechanism of deep word vectorization for various semantic and syntax features. We will show that it has a positive impact on system performance versus the traditional approach.
Learning (to learn) from few examples

The availability of large labelled datasets has played a crucial role in the recent success of deep neural networks. However, there are many situations in which training data is scarce. For instance, in the case of object tracking, such a limitation arises directly from the definition of the problem, which requires an estimate of the position of an object of interest in every frame of a video with the sole supervision of a bounding box in the first frame. Another scenario which can be doomed by data scarcity is classification. For example, one might want to identify the species of a rare insect with a single reference image, or train a tumour classification system from a few labelled MRI scans. For both tracking and classification, we propose techniques that sidestep per-task data scarcity by leveraging a large number of small episodes, each characterised by a limited training set. This strategy is particularly novel for tracking, for which for many years the standard approach has been to train a discriminative model exclusively online, while the video is streaming. Moreover, we examine how this general approach can be framed as "learning to learn", in the sense that the knowledge distilled within a training task is accrued and used across tasks. We show how such a framework allows one to devise systems that can be trained with a small amount of per-task data while also being dynamically tailored to the problem at hand. From a practical point of view, the proposed methods have a common focus on simplicity, efficiency and speed, achieved by exploiting the shape or redundancies of the data. All the code produced in this thesis is open source and available at http://www.robots. ox.ac. uk/~luca