Miracle-dz/UniFAR-FTUs huggingface dataset:
Fundamental Algorithms

1. (a,b)-Trees As we saw in the previous section, the efficiency of standard operations on binary search trees depends on the maximum tree height. Using height balancing, we ensure that trees cannot degenerate linearly but instead have logarithmic height. Let us extend this approach to more general trees. Motivation: assume tree nodes are stored in secondary storage (hard disk). Comparisons of keys of binary trees would be too time expensive due to mechanical positioning of the read-write head of the hard drive. Reading blocks of data (sectors, pages, etc.) is relatively fast provided the read-write head is positioned. Idea: store blocks of data in nodes of trees ! Advantages: faster access to the data and decreasing height of trees ! (a,b)-Trees have been invented by Rudolf Bayer and Edward M. McCreight (1976). 1. (a,b)-Trees As we saw in the previous section, the efficiency of standard operations on binary search trees depends on the maximum tree height. Using height balancing, we ensure that trees cannot degenerate linearly but instead have logarithmic height. Let us extend this approach to more general trees. Motivation: assume tree nodes are stored in secondary storage (hard disk). Comparisons of keys of binary trees would be too time expensive due to mechanical positioning of the read-write head of the hard drive. Reading blocks of data (sectors, pages, etc.) is relatively fast provided the read-write head is positioned. Idea: store blocks of data in nodes of trees ! Advantages: faster access to the data and decreasing height of trees ! (a,b)-Trees have been invented by Rudolf Bayer and Edward M. McCreight (1976). 1. (a,b)-Trees As we saw in the previous section, the efficiency of standard operations on binary search trees depends on the maximum tree height. Using height balancing, we ensure that trees cannot degenerate linearly but instead have logarithmic height. Let us extend this approach to more general trees. Motivation: assume tree nodes are stored in secondary storage (hard disk). Comparisons of keys of binary trees would be too time expensive due to mechanical positioning of the read-write head of the hard drive. Reading blocks of data (sectors, pages, etc.) is relatively fast provided the read-write head is positioned. Idea: store blocks of data in nodes of trees ! Advantages: faster access to the data and decreasing height of trees ! (a,b)-Trees have been invented by Rudolf Bayer and Edward M. McCreight (1976). 1. (a,b)-Trees As we …
Parameter Tuning of a Hybrid Treecode-FMM on GPUs

Treecodes are O(N logN) hierarchical N -body algorithms, which have traditionally been used for applications in astrophysics, in a low-accuracy regime. Fast multipole methods (FMM) are O(N) hierarchical N -body algorithms that have been used in a variety of applications, often in the high-accuracy regime. Both algorithms are known to perform well on massively parallel heterogeneous systems, with the treecode showing stronger speed-ups due to higher computational intensity. We propose a hybridization of the two algorithms and aim to determine whether such approach can best either algorithm on its own. We study the behavior of the hybrid treecode-FMM for di↵erent accuracies, along with its performance
High-performance real-time motion control for precision systems

Digital motion controllers are used almost exclusively in automated motion control systems today. Their key performance parameters are controller execution speed, timing consistency, and data accuracy. Most commercially available controllers can achieve sampling rates up to 20kHz with several microseconds of timing variation between control cycles. A few state-of-the art control platforms can reach sampling rates of around 100kHz with several hundred nanoseconds of timing variation. There exist a growing number of emerging high-speed high-precision applications, such as diamond turning and scanning probe microscopy, that can benefit from digital controllers capable of faster sampling rates, more consistent timing, and higher data accuracy. This thesis presents two areas of research intended to increase the capabilities of digital motion controllers to meet the needs of these high-speed high-precision applications. First, it presents a new high-performance real-time multiprocessor control platform capable of 1MHz control sampling rates with less than 6ns RMS control cycle timing variation and 16-bit data acquisition accuracy. This platform also includes software libraries to integrate it with Simulink for rapid controller development and LabVIEW for easy graphical user interface development. This thesis covers the design of the control platform and experimentally demonstrates it as a motion controller for a fast-tool servo machine tool. Second, this thesis investigates the effect of control cycle timing variations (sampling jitter and control jitter) on control performance, with an emphasis on precision positioning degradation. A new approximate discrete model is developed to capture the effects of jitter, enabling an intuitive understanding of it’s effects on the control system. Based on this model, analyses are carried out to determine the relationship between jitter and positioning error for two scenarios: regulation error from jitter’s interaction with measurement noise; and tracking error from jitter’s interaction with a deterministic reference command. Further, several practical methods to mitigate the positioning degradation due to jitter are discussed, including a new jitter compensator that can be easily added to an existing controller. Through simulations and experiments performed on a fast-tool servo machine tool, the model and analyses are validated and the positioning degradation arising from jitter is clearly demonstrated.
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

In the last decade or so, the high performance community is observing a paradigm shift with interconnection methodology for processing elements. Combining commercial off-the-shelf components to build supercomputers has provided users with an excellent price-to-performance ratio. At the same time, scientific applications ranging from molecular dynamics to ocean modeling are being designed with Message Passing Interface (MPI) being the de facto programming model. The insatiable computational requirements of the scientific applications has been continuously pushing the scale of these clusters. Increasing scale of these clusters has aggravated the occurrence of hot-spots in the network and reduced the mean time between failures of difference network components. In order to provide the best performance to the scientific applications, it is imperative that the MPI libraries are capable of avoiding network hot-spots and resilience to faults in the network. At the same time, InfiniBand has emerged as a popular interconnect, providing a plethora of modern features with open standard and high performance. In this dissertation, we focus on designing a communications and network fault tolerance layer with InfiniBand, which leverages the presence of multiple paths in the network for avoidance of hot-spots in the network and network fault tolerance. Much of the dissertation has been integrated with an open source effort, MVAPICH, which is
Electromyography and inertial sensor-based gesture detection and control

With the advent of new technology the need of interacting with machines has increased. In order to improve this interaction it is necessary to create simple and intuitive interfaces which are accessible to everyone. Gestures are naturally used in the real world to interact with objects or transmit information. The ability to capture, recognize and interpret gestures may thus enable humans to communicate with machines and provide more natural ways of human-computer interaction (HCI). This dissertation had as main objective the development of an algorithm that is able to perform gesture pattern recognition, using information from electromyography (EMG) and inertial measurement unit (IMU) sensors. The EMG provides information on muscular activity and the IMU allows the measurement of movement, for example, the velocity and orientation of body segments. Using the Myo gesture control armband, a device recently introduced in the market that incorporates seven EMG channels and one IMU, a comparison between the built system and a system in the market was possible. The built system used, to acquire data, four EMG channels from BITalino device placed in the forearm, in combination with a smartwatch to acquire data from the IMU. Twelve gestures were correctly identified, including hand contraction and extension, wrist extension and flexion, snap the fingers, among others. For pattern recognition, using both systems, the twelve gestures were recorded, and data was pre-processed to enhance muscle activation and signal segmentation. Then, the best set of features, from both sensors, was chosen and finally, different classification techniques were applied. In the end, all gestures were successfully recognized with no difference between the systems. The use of information from the two sensors proved to be essential. The ability to recognize gestures enables the creation of new interaction techniques.
Academic Self-Efficacy, Information Literacy, and Undergraduate Course-Related Research: Expanding Gross's Imposed Query Model

ABSTRACT The purpose of this article is to demonstrate the potential relevance of academic self-beliefs to Gross's imposed query model as they apply to undergraduate students, particularly at the IQ2 (transferred) and IQ3 (interpreted) stages. The author argues that, at these two stages, students will decide if they have the skills required to be successful, how much effort they will put into the research process, and if they will seek help from an expert information intermediary. Furthermore, the author proposes an expanded model of the IQ2 (transferred) and IQ3 (interpreted) stages to be tested by future research seeking to understand how undergraduate students navigate the course-related research process.
JAnalyzer, A Visual Static Analyzer for Java

Object-oriented (OO) programming has well-known benefits, producing reusable, modular, well-structured code. Nevertheless, program development is still hard, especially for large programs that consist of thousands of interacting objects. Faults in one method can propagate to others defined in different classes or even different packages. The programmer would benefit from a high-level, intuitive, graphical view of these method dependencies. Such a view would aid refactoring by revealing the degree of coupling between different parts of the program as well as save debugging time by allowing design faults to be visualized at implementation time in a straightforward way. Our research concentrated on Java due to its increasing popularity and platform independence. In contrast to classic imperative programming paradigms, the development of such a view is a non-trivial task for OO languages, because methods are typically invoked through dynamic dispatch . the type of the object on which the method will actually be invoked is not known at compile time. Such polymorphism has the benefit to the programmer of reusable code, but means that the relationship between caller and callee is 1:many rather than 1:1. A type analysis for Java is therefore required to synthesise a set of possible types for each object identifier in the program. Inferring these sets is a complex task, which we explain further below. Our research concentrated on Java due to its platform independence and increasing popularity. Our standalone tool, JAnalyzer, aids program development by: construction of call-graphs by state of the art analyses visual representation of inter-dependencies between methods, thus aiding refactoring comprehensible and responsive views of even very large call-graphs
Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.
Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security

It is our great pleasure to welcome you to the 8th ACM Symposium on Information, Computer and Communications Security - ASIACCS 2013, which will be held in Hangzhou, China, May 7-10, 2013. The inaugural ASIACCS was held in Taipei (2006). Since then ASIACCS have been held in Singapore (2007), Tokyo (2008), Sydney (2009), Beijing (2010), Hong Kong (2011), and Seoul (2012). ASIACCS is a major international forum for information security researchers, practitioners, developers, and users to explore and exchange the newest cyber security ideas, breakthroughs, findings, techniques, tools, and experiences. We welcome subm issions from academia, government, and industry presenting novel research on all theoretical and practical aspects of computer and network security. The conference received 216 paper submissions, and accepted 35 regular papers and 27 short papers for presentation at the conference. The final versions of the accepted papers, which the authors finalized on the basis of comments from the reviewers, were included in the proceedings. Each accepted paper was carefully reviewed by at least three program committee members on the basis of its significance, novelty, and technical quality. The reviewing process was double-blind. It was followed by a Web-based discussion. Based on the comments and scores given by reviewers, the final decisions on acceptance were made.
AN ADVANCED APPROACH FOR RULE BASED ENGLISH TO BENGALI MACHINE TRANSLATION

This paper introduces an advanced, efficient approach for rule based English to Bengali (E2B) machine translation (MT), where Penn-Treebank parts of speech (PoS) tags, HMM (Hidden Markov Model) Tagger is used. Fuzzy-If-Then-Rule approach is used to select the lemma from rule-based-knowledge. The proposed E2B-MT has been tested through F-Score measurement, and the accuracy is more than eighty percent.
Synthesizing communication-efficient distributed-memory parallel programs for block recursive algorithms

Distributed-memory multiprocessors have shown great promise as cost-effective machines for scalable high-performance supercomputing. However, developing efficient parallel programs using message-passing remains a difficult and error-prone task. In this thesis, we present a framework for synthesizing communication-efficient distributed-memory parallel programs for block recursive algorithms, such as the fast Fourier transform (FFT) and Strassen's matrix multiplication. This framework is based on an explicitly-parallel algebraic representation of the algorithms, which involves the tensor (Kronecker) product and other matrix operations. This representation is useful in analyzing the communication implications of computation partitioning and data distribution. In this framework, programs are synthesized under two different target program models. These two models are based on different ways of managing the distribution of data for optimizing communication. In the first model, distributions of arrays are kept static and so communication is needed whenever a processor requires non-local data elements. In the second model, distributions of the data arrays are dynamically changed to ensure that computation is localized in every computation step. This results in programs with different communication overhead characteristics due to use of different communication primitives for performing data movement. The first model uses point-to-point interprocessor communication primitives whereas the second model uses data redistribution primitives involving collective all-to-many communication. These two program models are shown to be suitable for different ranges of the problem size. The programs with redistributions have lower communication overhead than programs using a static distribution when the problem size is large. In order to achieve high-performance on distributed-memory machines, programs should be tailored to machine's architecture, such as the network topology. In the programs generated under the redistribution model, communication is optimized by using collective communication primitives tuned to the target machine's topology. The impact of communication overhead is further reduced by overlapping computation with communication. We present an overlapping technique which is suitable for distributed-memory machines with a wormhole or circuit-switched routed mesh or hypercube interconnection network. The methodology is illustrated by synthesizing communication-efficient programs for the FFT algorithms. We have incorporated this framework in the EXTENT portable parallel programming environment.
Integrated Scheduling for a Reliable Dual-OS Monitor

Virtualization solutions aimed at the consolidation of a real-time operating system (RTOS) and a general-purpose operating system (GPOS) onto the same platform are gaining momentum as high-end embedded systems increase their computation power. Among them, the most extended approach for scheduling both operating systems consists of executing the GPOS only when the RTOS becomes idle. Although this approach can guarantee the real-time performance of the RTOS tasks and interrupt handlers, the responsiveness of GPOS time-sensitive activities is negatively affected when the RTOS contains compute-bound activities executing with low priority. In this paper, we modify a reliable hardware-assisted dual-OS virtualization technique to implement an integrated scheduling architecture where the execution priority level of the GPOS and RTOS activities can be mixed with high granularity. The evaluation results show that the proposed approach is suitable for enhancing the responsiveness of the GPOS time-sensitive activities without compromising the reliability and real-time performance of the RTOS.
New beginnings: Facilitating effective learning through the use of Web 2.0 tools

Web 2.0 is driving change both in the mainstream society and education. Web 2.0 enhances the experience by allowing users not to only download pre-packaged content but by empowering them to become active contributors and publishers. Web 2.0 affordances such as the ability to network, communicate, collaborate, co-create and aggregate knowledge offer considerable opportunities for learning and teaching. The growth of Web 2.0 technology and rising easiness to collaborate, communicate and cocreate provides an opportunity to move away from a transmission method of teaching to one that empowers learners to learn collaboratively through interaction with peers.
Collaborative filtering: A survey

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Automatic Test Case Generation for Modern Web Applications Using Population-Based Automatic Fuzzy Neural Network

Automatic test case generation is an approach to decrease cost and time in software testing. Although there have been lots of proposed methods for automatic test case generation of web applications, there still exists some challenges which needs more researches. The most important pr oblem in this area is the lack of a complete descriptive model which indicates the whole behaviors of web application as guidance for the generation of test cases with high software coverage. In this paper, test cases are generated automatically to test web applications using a machine learning method. The proposed method called RTCGW (R ule-based Test Case Generator for Web Applications) generates test cases based on a set of fuzzy rules that try to indicate the whole software behaviors to reach to a high level of software coverage. For this purpose a novel machine learning approach based on fuzzy neural networks is proposed to extract fuzzy rules from a set of data and then used to generate a set of fuzzy rules representing software behaviors. The fuzzy rule set is then used to generate software test cases and the generated test cases are optimized using an optimization algorithm based on combination of genetic and simulated annealing algorithms. Two benchmark problems are tested using the optimized test cases. The results show a high level of coverage and performance for the proposed method in comparison with other methods.
VERTa: a Linguistically-motivated Metric at the WMT15 Metrics Task

This paper describes VERTa’s submission to the 2015 EMNLP Workshop on Statistical Machine Translation. VERTa is a linguistically-motivated metric that combines linguistic features at different levels. In this paper, VERTa is described briefly, as well as the three versions submitted to the workshop: VERTa70Adeq30Flu, VERTa-EQ and VERTaW. Finally, the experiments conducted with the WMT14 data are reported and some conclusions are drawn.
An Update on Last Planner

The Last Planner system of production control has now been in use for a number of years. Its inventors provide an update consisting of a description of innovations and changes, thoughts on theoretical foundations, proposals regarding work structuring, phase scheduling and reliable promising, and recommendations for further development. Special emphasis is placed on the relationship between scheduling and production control, and also on the technique of phase scheduling to specify the handoffs that are the control foci for Last Planner.