Miracle-dz/UniFAR-FTUs huggingface dataset:
Perspectives of Foreign Language Teachers on Influences, Challenges, and Practices Affecting Language Choice

Perspectives of Foreign Language Teachers on Influences, Challenges, and Practices Affecting Language Choice Brant M Lloyd Department of Spanish and Portuguese, BYU Master of Arts In light of the 2010 recommendation of the American Council on the Teaching of Foreign Languages that teachers use the target language (TL) “as exclusively as possible,” teachers face the necessity of promoting target language use and assessing how their language choices influence their students. The purpose of this study was to analyze teachers’ perspectives and practices of whether to use students’ first language or the target language. Data from over 100 foreign language teachers, predominantly those in the K-12 sector, were collected through a survey in order to measure how teachers manage the phenomenon of language choice. The findings were analyzed descriptively, categorically, and inferentially to find relationships among teachers in general, as well as within subgroups of participants. Findings indicate that teachers felt using the target language was more of a challenge for academic purposes because of their belief that they should be conducted in the TL. Academic tasks were also reported involving a greater quantity of speech functions, which made them more difficult to complete in the target language. Additional findings showed that teachers in general were most often influenced by student attitudes, motivation, and proficiency as well as their own attitudes, motivation, and proficiency. For certain subgroups, however, when teachers focused more on their own proficiency, motivation, training and experience they were more likely to reach 90% TL use or more. Furthermore, it was found that promoting the target language effectively equated with incorporating many strategies that supported TL goals. The most prevalent techniques found for promoting TL use were preparing for making input comprehensible, giving praise and recognition, and explaining its importance at the beginning of the year. Recommendations for increasing the ability of teachers to face the challenges of TL use are to simplify and increase the academic, social, and managerial purposes for which it can be used, prioritize and focus on the most influential factors of language choice (LC) within teachers’ control, and form and promote a plan for TL use built upon a foundation of most effective techniques and practices for optimizing language choices.
Review of Railroad Facility Maintenance Technology using UAV

This paper which is intended to review UAV-based railroad facility maintenance technologies includes the analysis of the tendency on how to utilize growing UAV technologies for railroad facility maintenance. In the United States and European countries, task forces dedicated to railroad facility maintenance using UAV have been established to conduct the facility diagnosis and safety & security process through competent operator, analyzer and developer and UAV has been used more commonly for catenary, supports, station structure and railroad bridge. The factor to be considered before applying UAV to the inspection of railroad facility is drafty conditions and particularly, UAV used in railroad sector is small in size which is vulnerable to environmental factors. To deal with such shortcomings, the study on disturbance resistance algorithm by modeling the attitude control and drafty environment and controller design to enhance hovering accuracy have been actively underway currently. However, in a bid to be able to apply UAV to railroad facility management, it is necessary to solve such problems as sensor error and abnormal GPS signal first.
Survey On Service Recommendations Techniques

Service Recommender systems are nothing but the guide for a user or tools that provide appropriate recommendations to users in a personalized way. Now-a-days the amount of customers, services and online information is increasing rapidly causing a big data analysis problem for service recommender system. Also , the traditional recommender system has arise inefficiency and scalability problems while processing such large amount of data. The another issue with this systems are they do not consider different users choices and provide same ratings and recommendations to different users. So to overcome the all issues a new approach is used that uses keyword query to denote user interest and gives recommendations to users according to their interest. It uses user based collaborative filtering algorithm for that purpose.
A packet scheduling scheme in wireless CDMA data network

In order to provide service for a large amount of non real-time data traffic, packet switching mode is to be used in the 3G system. A packet scheduling algorithm, PDSTTF (prediction-based delay-constrained shortest transmission time first), based on transmission time prediction, is first established. It is designed for a cellular CDMA system. Because of the constraints of a discrete rate set and a fixed scheduling interval in practical systems, a refined scheduling algorithm, M-PDSTTF (modified PDSTTF), which adopts a residual power allocation model, is then developed. Users' channel condition, packet transmission time and packet delay bound are all considered in M-PDSTTF. Simulation results show that M-PDSTTF can obtain better packet loss probability performance than EDF (see Andrews, M. et al., Bell Labs Technical Journal, Sept., 1999) and HOLPRO (see Varsou, A.C. and Poor, H.V., IEEE VTC'2000 Fall, 2000) algorithms.
Mechanical Engineering Series

The Mechanical Engineering Series presents advanced level treatment of topics on the cutting edge of mechanical engineering. Designed for use by students, researchers and practicing engineers, the series presents modern developments in mechanical engineering and its innovative applications in applied mechanics, bioengineering, dynamic systems and control, energy, energy conversion and energy systems, fluid mechanics and fluid machinery, heat and mass transfer, manufacturing science and technology, mechanical design, mechanics of materials, microand nano-science technology, thermal physics, tribology, and vibration and acoustics. The series features graduate-level texts, professional books, and research monographs in key engineering science concentrations.
Learning and Exploiting Relational Structure for Efficient Inference

Learning and Exploiting Relational Structure for Efficient Inference Aniruddh Nath Chair of the Supervisory Committee: Professor Pedro Domingos Computer Science & Engineering One of the central challenges of statistical relational learning is the tradeoff between expressiveness and computational tractability. Representations such as Markov logic can capture rich joint probabilistic models over a set of related objects. However, inference in Markov logic and similar languages is #P-complete. Most existing tractable statistical relational representations are very limited in expressiveness. This dissertation explores two strategies for dealing with intractability while preserving expressiveness. The first strategy is to exploit the approximate symmetries frequently found in relational domains to perform approximate lifted inference. We provide error bounds for two approaches for approximate lifted belief propagation. We also describe propositional and lifted inference algorithms for repeated inference in statistical relational models. We describe a general approach for expected utility maximization in relational domains, making use of these algorithms. The second strategy we explore is learning rich relational representations directly from data. First, we propose a method for learning multiple hierarchical relational clusterings, unifying several previous approaches to relational clustering. Second, we describe a tractable high-treewidth statistical relational representation based on Sum-Product Networks, and propose a learning algorithm for this language. Finally, we apply state-of-the-art tractable learning methods to the problem of software fault localization.
Improving software fault injection

It has become well-established that software will never become bug-free, which has spurred research in mechanisms to contain faults and recover from them. Since such mechanisms deal with faults, fault injection is necessary to evaluate their effectiveness. However, little thought has been put into the question whether fault injection experiments faithfully represent the fault model designed by the user. Correspondence with the fault model is crucial to be able to draw strong and general conclusions from experimental results. The aim of this paper is twofold: to make a case for carefully evaluating whether activated faults match the fault model and to gain a better understanding of which parameters affect the deviation of the activated faults from the fault model. To achieve the latter, we instrumented a number of programs with our LLVM-based fault injection framework. We investigated the biases introduced by limited coverage, parts of the program executed more often than others and the nature of the workload. We evaluated the key factors that cause activated faults to deviate from the model and from these results provide recommendations on how to reduce such deviations.
Interpretation of User Comments for Detection of Malicious Websites

Automated understanding of natural language is a challenging problem, which has remained open for decades. We have investigated its special case, focused on identifying relevant concepts in natural-language text in the context of a specific given task. We have developed a set of general-purpose language interpretation techniques and applied them to the task of detecting malicious websites by analyzing comments of website visitors. In this context, concepts are related to behavior or contents of websites, such as presence of pop-ups and false testimonials. The developed algorithms are based on probabilistic topic models and other dimensionality reduction techniques applied to a special case of multi-label text classification, where concepts are output labels. We integrate information about the target task with other relevant information, including relations among concepts and external knowledge sources using a concept graph. The system iterates between training a topic model on the partially labeled data and optimizing the parameters and the label assignments. We analyze several alternative versions of this mechanism, such as one that measures the quality of separation among topics and eliminates words that are not discriminative. For the task of detecting malicious websites, we have developed an approach that applies machine-learning techniques to the automatically collected data about websites and achieves 98% precision and 95% recall. We present a crowdsourcing system for collecting multiple-choice and free-text comments from website visitors, which is especially useful when other sources of information are insufficient or unreliable. We improve detection performance by considering the text features in the comments about the website. This performance gain is greater when using unstructured free-text comments than using multiple-choice comments. Finally, we have evaluated the performance of our language interpretation framework, and shown that the performance gain from the extracted concepts is related to the popularity of the website and task-based concepts are complementary to text features for obscure websites. iii Acknowledgement
The impact of mathematics education research and brain-learning research on student performance in Algebra I

A large gap exists between mathematics education research and brain-learning research and classroom instructional practice. The researchers conducted an ex post facto study on the implementation of a research-based Algebra I curriculum built around high-cognitive demand tasks and student performance in Algebra I. Two groups of students were selected for the study. One group enrolled in Algebra I course that followed a typical textbook-driven curriculum and the second group enrolled in a course that followed a research-based curriculum with high-level tasks. The researcher used propensity score matching and multilevel modeling to compare the effects of the two curricula to determine whether student growth occurred. No significant difference existed in student performance between the traditional and research-based curricula. Additionally, African American and Hispanic student performance decreased and White student performance increased Grade 8 to Grade 9.
Improving syntactic tree alignment through rule-based error correction

Automatic alignment of parallel treebanks often display regular errors that can be corrected by improving the alignment model. However, if the aligner is statistical, often much more training data is needed to properly address these errors. In some cases, a rule-based approach to error correction may provide a quick and convenient solution. We present an approach that highlights problematic phenomena which enables us to pinpoint systematic error patterns for which we can devise rules for correction. Finally, we investigate the application of two manually constructed rules on a large parallel treebank.
Compound Control of Electromagnetic Linear Actuator Based on Fuzzy Switching

Due to the motion control system of electromagnetic linear actuator (EMLA) is a nonlinear system with poor controllability, single control strategy has been difficult to meet the requirements of its control. A compound control strategy based on inverse system control (ISC) and proportional-integral (PI) is designed in this paper. Switching between two algorithms, which is based on the fuzzy rules, prevents the control algorithm to jitter and jump. System model is build under Matlab/Simulink to do simulation analysis. The designed controller is integrated into the system simulation model and the system software of digital signal processor (DSP) controller. Simulation and test results show that the compound control strategy using fuzzy switching rules achieves the smooth transition of two control algorithms, and the goal of any position location, and continuous adjustment in 0~4mm lift. Positioning accuracy is up to ± 0.02mm, while the response time is less than 10ms.
Design of the RISC-V Instruction Set Architecture

Author(s): Waterman, Andrew Shell | Advisor(s): Patterson, David | Abstract: The hardware-software interface, embodied in the instruction set architecture (ISA), is arguably the most important interface in a computer system. Yet, in contrast to nearly all other interfaces in a modern computer system, all commercially popular ISAs are proprietary. A free and open ISA standard has the potential to increase innovation in microprocessor design, reduce computer system cost, and, as Moore’s law wanes, ease the transition to more specialized computational devices.In this dissertation, I present the RISC-V instruction set architecture. RISC-V is a free and open ISA that, with three decades of hindsight, builds and improves upon the original Reduced Instruction Set Computer (RISC) architectures. It is structured as a small base ISA with a variety of optional extensions. The base ISA is very simple, making RISC-V suitable for research and education, but complete enough to be a suitable ISA for inexpensive, low-power embedded devices. The optional extensions form a more powerful ISA for general-purpose and high-performance computing. I also present and evaluate a new RISC-V ISA extension for reduced code size, which makes RISC-V more compact than all popular 64-bit ISAs.
An Efficient Approach for Enhancing the Security of Amazigh Text using Binary Tree

Now a day’s Cryptography is one of the broad areas for researchers. Due to its importance, several cryptography techniques are adopted by many authors to secure the data, but still there is a scope to improve the previous approaches. The main of our research is to develop a novel Approach for enhancing the security of Amazigh Text using binary tree. The plaintext considered is the combination of Unicode characters. This paper contributes in the area of elliptic curve cryptography by encrypting data using matrix approach and using the concept of tree traversal method for enhancing the security of the encrypted points. The security goals were enhanced by making it difficult for attacker to predicate a pattern as well as speed of the encryption/decryption scheme. The results show strength of the algorithm.
A Deep Learning Approach to the Classification of 3D Models under BIM Environment

In recent years, the extensive use of BIM has brought a revolutionary change for the construction industry. As an important technology to support BIM, three-dimensional digital technology has become a hot academic research. The 3D geometric model is the main data expression of modes in BIM environment. But because of the complexity of 3D modeling, the maintenance of the 3D model library in BIM environment will spend a lot of time and cost. The traditional CAD 3D modeling has accumulated a large number of 3D models for the BIM project to reuse. Using the 3D model classification technology can quickly classify the existing 3D model, and save a lot of cost. Deep learning is in recent years a new method of machine learning. In this paper, we use Stacked Auto-Encoders (SAE) to classify 3D models under the environment of BIM. Experiments show that, the method proposed in the paper has achieved good results in the 3D model classification, which provides a new idea for the development of BIM.
Sharing Expertise: The Next Step for Knowledge Management

There are numerous ways to handle knowledge within organizations. Indeed, knowledge management has been a flourishing commercial area for almost ten years, and one can point to many precursors within organizations as well. Knowledge management – regardless of its title or position in history – has always been an important, though not necessarily frequent, aspect of organizational life. It would be difficult to imagine a modern corporation that did not occasionally reflect and improve its methods of handling communications, data, and information – or try to learn from its experience. In this chapter, we move from the metaphor of knowledge management to a new metaphor, expertise sharing, which promotes focusing on the inherently collaborative and social nature of the problem.
Streaming of Markov Sources over Burst Erasure Channels

Streaming of Markov Sources over Burst Erasure Channels Farrokh Etezadi Doctor of Philosophy Graduate Department of Electrical and Computer Engineering (ECE) University of Toronto 2015 Real-time streaming communication systems require both the sequential encoding of information sources and playback under strict latency constraints. The central focus of this thesis is on the fundamental limits of such communication systems in the presence of packet losses. In practice packet losses are unavoidable due to fading in wireless channels or congestion in wired networks. While several ad hoc approaches are used to deal with packet losses in streaming systems, in this thesis we examine these approaches using an information theoretic framework. In our setup, the source process is a sequence of vectors sampled from a spatially i.i. d. and temporally a first-order stationary Markov distribution. The encoder sequentially compresses these source vectors into channel packets. The channel may introduce a burst erasure of length up to B in an unknown location during the transmission period, and perfectly reveals the rest of the packets to the destination. The decoder is interested in reconstructing the source vectors with zero delay, except those at the time of erasure and a window of length W following it. The minimum attainable compression rate for this setup R(B,W ), termed the rate-recovery function, is investigated for discrete source with lossless recovery, and Gauss-Markov sources with a quadratic distortion measure. The above setup introduces a new problem in network information theory. Our key contributions include: (1) Upper and lower bounds on the rate-recovery function for discrete memoryless sources and lossless recovery, which coincide in some special cases. (2) A new coding scheme for the Gauss-Markov sources and a quadratic distortion measure. This scheme can be interpreted as a hybrid between predictive coding and memoryless quantization-and-binning. (3) Extensions of our zero-delay setup to incorporate non-zero decoding delays. We further show that our proposed hybrid coding scheme yields significant performance gains over baseline schemes such as predictive coding, memoryless quantizationand-binning and interleaving, over statistical channels such as the i.i. d. erasure channel and the Gilbert Elliott channel, and performs close to optimally, over a wide range of channel parameters. While our information theoretic framework involves coding theorems for burst-erasure channels our resulting schemes are applicable for much broader class of erasure channels and can yield significant performance gains in practice.
Describing And Recognizing 3 D Objects Using Surface Properties

Thank you for downloading describing and recognizing 3 d objects using surface properties. Maybe you have knowledge that, people have look numerous times for their favorite readings like this describing and recognizing 3 d objects using surface properties, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some harmful bugs inside their computer.
Power-jitter trade-off analysis in digital-to-time converters

Digital-to-time converters are one of the main building blocks in time-domain signal processing. The jitter-power product is analysed and shown to scale up linearly as the full-scale delay range in current-mode logic implementations, and quadratically in CMOS logic. It is also shown that CMOS converters outperforms current-mode ones only when their output range is lower than about 1.4 times the clock period.
USABILITY TESTING AND THE RELATIONSHIP BETWEEN COMPUTER LITERACY AND EFFECTIVE USE OF A CHEMICAL KNOWLEDGE BASE BY FIRST-SEMESTER ORGANIC CHEMISTRY STUDENTS

Usability testing is a technique that allows for the examination of a specific user’s effectiveness, efficiency and satisfaction in achieving goals (Law, Hvannberg, 2002). This user-focused design process has been found to be particularly important in early site development. In this study, multiple interfaces of the knowledge base will be examined comparatively, changing only the aesthetics. Using a think-aloud process, users will be walked through seven scenarios in the IUPUI Chemistry Knowledge Base, and asked to vocalize their thoughts as they attempt each situation. Completion of user questionnaires and a post-test System Usability Scale (SUS) will provide recommendations from which improvements may be made to the design, layout and management of the Knowledge Base (Brooke, 1996).
PAPER Special Section on Advanced Processors Based on Novel Concepts in Computation Evaluation of a Field-Programmable VLSI Based on an Asynchronous Bit-Serial Architecture

SUMMARY This paper presents a novel asynchronous architecture of Field-programmable gate arrays (FPGAs) to reduce the power consumption. In the dynamic power consumption of the conventional FPGAs, the power consumed by the switch blocks and clock distribution is dominant since FPGAs have complex switch blocks and the large number of registers for high programmability. To reduce the power consumption of switch blocks and clock distribution, asynchronous bit-serial architecture is proposed. To ensure the correct operation independent of data-path lengths, we use the level-encoded dual-rail encoding and propose its area-efficient implementation. The proposed field-programmable VLSI is implemented in a 90 nm CMOS technology. The delay and the power consumption of the proposed FPVLSI are respectively 61% and 58% of those of 4-phase dual-rail encoding which is the most common encoding in delay insensitive encoding.