Miracle-dz/UniFAR-FTUs huggingface dataset:
Instruction cache tuning for RTOS-driven multitasking applications

With the advent of mobile and handheld devices, power consumption in embedded systems has become a key design issue. Recently, it has been shown that cache requirements of the applications vary widely and a significant amount of energy can be saved by tuning the cache parameters according to the needs of the application. To this end, techniques have been proposed to tune the cache for single-task based systems but no work has been done to extend these techniques to multitasking applications. In this research work, we present novel, lightweight and fast techniques for energy-sensitive tuning of the instruction cache hierarchy for multitasking applications. Cache tuning for RTOS-driven multitasking applications is achieved by intelligently separating the user tasks and RTOS components and profiling them in isolation to identify the nature of loops in them. We then apply the proposed techniques to tune a predictor based filter cache hierarchy for instructions for both single-task based applications and RTOS-driven multitasking applications. The proposed techniques are able to identify optimal or near-optimal filter and L1 cache sizes for all the applications tested and are up to an order of magnitude faster than exhaustive cache hierarchy simulation techniques. The proposed techniques are also highly scalable and can be relied upon to predict the instruction cache hit rate for any range of instruction cache sizes after a one-time simulation and profiling.
A Study on Out-of-vocabulary Word Modelling for a Segment-based Keyword Spotting System Chairman, Departmental Committee on Graduate Students a Study on Out-of-vocabulary Word Modeling for a Segment-based Keyword Spotting System Title: Senior Research Scientist

The purpose of a word spotting system is to detect a certain set of keywords in continuous speech. A number of applications for word spotting systems have emerged over the past few years, such as automated operator services, pre-recorded data indexing, and initiating human-machine interaction. Most word spotting systems proposed so far are HMM based. The most common approach consists of models of the keywords augmented with ller," or garbage" models, that are trained to account for non-keyword speech and background noise. Another approach is to use a large vocabulary continuous speech recognition system LVCSR to produce the most likely hypothesis string, and then search for the keywords in that string. The latter approach yields much higher performance, but is signiicantly more costly in computation and the amount of training data required. In this study, we develop a number of word spotting systems in an eeort to achieve performance comparable to the LVCSR, but with only a small fraction of the vocabulary. We investigate a number of methods to model the keywords and background, ranging from a few coarse general models for the background only, to reened phone representations, such as context-independent CI, and word-dependent WD, only for keywords models. The output hypothesis of the word spotter consists of a sequence of phones and keywords, and there is no constraint on the numberof keywords perutterance. The word spotters were developed using the segment-based SUMMIT speech recognition system. The task is to detect sixty-one keywords from continuous speech in the ATIS corpus. The training, development, and test sets are speciically designed to contain the keywords in appropriate proportions. The keyword set consists of thirty-nine cities, nine airlines, seven days of the week, and six other frequent w ords. We h a ve a c hieved performance of 89.8 Figure of Merit FOM for the LVCSR spot-ter, 81.8 using CI phone-words as ller models, and 79.2 using eighteen more general models. 3 Acknowledgments I would like t o deeply thank my thesis supervisor, Victor Zue, for his guidance, support , and valuable advise. I thank Jim Glass for his support and patience, especially during my rst days in the world of speech recognition. I would also like to thank: Mike McCandless for helping me understand the system and being available every time I needed help; Michelle Spina for her encouragement and for being an outstanding oocemate and friend; Stephanie Senee for her …
Video object tracking based on position prediction guide CAMSHIFT

In this article, a novel algorithm-position prediction guide continuously adaptive mean shift procedure (CAMSHIFT)-is proposed for tracking objects in video sequences. CAMSHIFT is incorporated with the position prediction algorithm makes the scale adaptation of CAMSHIFT is improved, tracking efficiency increased and the computation complexity is reduced. Preliminary experimental results show the algorithm performs well. Meanwhile, the algorithm success in indicating new objects appeared; objects disappeared; objects collisions and the consistency of the objects in consecutive sequences.
The effects of explicit instruction on L2 learners’ acquisition of verb–noun collocations

This study investigates the relative effectiveness of two methods for teaching a set of English verb–noun collocations: form-focused instruction (FFI) and concept-based instruction (CBI). The sample comprised 73 Chinese-speaking learners of English taking intermediate English courses, who were randomly assigned to one of three groups: an FFI intervention group, a CBI intervention group, and a control group. In order to determine the effects of the two instructional methods, the participants’ collocational knowledge was measured in three ways: via form recall testing, form recognition testing, and meaning recall testing. Specifically, all of the participants took pretests, immediate posttests, and one-week delayed posttests. The results of a comparison of the learning gains between the three groups showed that the CBI and FFI approaches each had a positive effect on establishing initial form–meaning relationships for verb–noun collocations. The CBI group significantly outperformed the FFI group on both collocational acquisition and retention. This result suggests that CBI’s focus on learning verbal meanings through image schemas and conceptualization practice is associated with a deeper understanding of English verb–noun collocations. The study concludes with pedagogical implications for teaching second language (L2) collocations.
Biological implications of high‐energy cosmic ray induced muon flux in the extragalactic shock model

A ∼62 My periodicity in fossil biodiversity has been observed in independent studies of paleontology databases over ∼0.5Gy. The period and phase of this biodiversity cycle coincides with the oscillation of our solar system normal to the galactic disk with an amplitude ∼70 parsecs and a period ∼64 My. Our Galaxy is falling toward the Virgo cluster, forming a galactic shock at the north end of our galaxy due to this motion, capable of accelerating particles and exposing our galaxy's northern side to a higher flux of cosmic rays. These high‐energy particles strike the Earth's atmosphere initiating extensive air showers, ionizing the atmosphere by producing charged secondary particles. Secondary particles such as muons produced as a result of nuclear interactions are able to reach the ground and enhance the biological radiation dose. Using a Monte Carlo simulation package CORSIKA, we compute the biological dose resulting from enhanced muon exposure from cosmic rays and discuss their implications for terrestrial biodiversity variations.
Development of a model for sEMG based joint-torque estimation using Swarm techniques

Over the years, numerous researchers have explored the relationship between surface electromyography (sEMG) signal with joint torque that would be useful to develop a suitable controller for rehabilitation robot. This research focuses on the transformation of sEMG signal by adopting a mathematical model to find the estimated joint torque of knee extension. Swarm techniques such as Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) were adapted to optimize the mathematical model for estimated joint torque. The correlation between the estimated joint torque and actual joint torque were determined by Coefficient of Determination (R2) and fitness value of Sum Squared Error (SSE). The outcome of the research shows that both the PSO and IPSO have yielded promising results.
Energy Efficient Fault Tolerance for High Performance Computing (HPC) in the Cloud

With cloud computing, a large number of Virtual Machines (VMs) can be provisioned to form high performance computing (HPC) to run computation-intensive applications using the Hardware as a Service (HaaS) model. Fault Tolerance (FT) for HPC in the cloud is increasingly a challenging issue, because any fault during the execution would result in re-running the application, which will cost time, money and energy. There has been a significant increase in energy consumption of HPC systems in cloud as a result of rerunning application and fault tolerance (e.g., redundant computing). In this paper we present energy efficient fault tolerance for HPC in the cloud. We develop a generic FT algorithm for HPC systems in the cloud. Our algorithm uses proactive processlevel migration approach, however it does not rely on a spare node or redundant computing prior to prediction of a failure. Our experimental results obtained from a real cloud execution environment show that the energy utilization for HPC in the cloud while providing fault tolerance can be reduced by as much as 30%.
Improving throughput in multihop wireless networks

One of the main characteristics of wireless ad hoc networks is their node-centric broadcast nature of communication, leading to interferences and spatial contention between adjacent wireless links. Due to such interferences, pessimistic concerns have been recently raised with respect to the decreasing network capacity in wireless ad hoc networks when the number of nodes scales to several orders of magnitude higher. Such studies assume uniformly distributed nodes in the network and randomized traffic patterns. In this paper, we argue that in all cases of end-to-end data communications-including one-to-k unicast and multicast data dissemination as well as k-to-one data aggregation-the maximum achievable end-to-end data throughput (measured on the sources) heavily depends on the strategy of arranging the topology of transmission between sources and destinations, as well as possible per-node operations such as coding. An optimal strategy achieves better end-to-end throughput than an arbitrary one. We present theoretical studies and critical insights with respect to how these strategies may be designed so that end-to-end throughput may be increased. After all, under all circumstances-in either a lightly loaded or a congested network-increasing end-to-end throughput from its baseline is always beneficial to applications using ad hoc networks to communicate
A Steerable Dyadic Wavelet Transform and Interval Waveletsfor Enhancement of Digital

This paper describes two approaches for accomplishing interactive feature analysis by overcomplete mul-tiresolution representations. We show quantitatively that transform coeecients, modiied by an adaptive non-linear operator, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. Our results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. We design a lter bank representing a steerable dyadic wavelet transform that can be used for mul-tiresolution analysis along arbitrary orientations. Digital mammograms are enhanced by orientation analysis performed by a steerable dyadic wavelet transform. Arbitrary regions of interest (ROI) are enhanced by Deslauriers-Dubuc interpolation representations on an interval. We demonstrate that our methods can provide radiologists with an interactive capability to support localized processing of selected (suspicious) areas (lesions). Features extracted from multiscale representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology we can improve chances of early detection while requiring less time to evaluate mammograms for most patients. 1. INTRODUCTION Many cancers escape detection due to the density of surrounding breast tissue. For example, diierences in attenuation of the various soft tissue structures in the female breast are small, and it is necessary to use low levels of X-ray energy to obtain high contrast in mammographic lm. Since contrast between the soft tissues of the breast is inherently low and because relatively minor changes in mammary structure can signify the presence of a malignant breast tumor, the detection is more diicult in mammography than in most other forms of radiography. The radiologist must search for malignancy in mammographic features such as microcalciications, dominate and stellate masses, as well as textures of brous tissues ((broglandular patterns). Analyzing images along distinct orientations and resolutions is advantageous in many computational vision and image processing tasks. Filtering in a continuum of orientations 1] or other deformations 2, 3] can be performed by combining outputs of a nite set of lters. Such a scheme can be employed for de-composition/reconstruction as well: a pyramid implemented as a near-perfect reconstruction lter bank was previously designed in 4]. We present a lter bank implementation of a discrete dyadic wavelet transform that is constructed to make possible analysis along arbitrary orientations. We demonstrate the eeectiveness of our scheme for the enhancement of digital mammography. We shall present a novel method for accomplishing an interactive paradigm for adaptive contrast enhancement …
Website Search Engine Optimization: Geographical and Cultural Point of View

In this paper, we analyzed the influence of geographical area (Jordan) and a local culture on website search engine ranking, and identify the effect and the relationship of the local society keywords in increasing website ranking. Our analysis provides a foundation for understanding the search engine optimization in the local geographical area. In particular, it suggests that we could use social culture keywords to increase the website ranking in search engines that localize the search area such as Google.jo which localize the search for Jordan. The first part of the study started with the identification of keywords that were previously used by Jordanian users to search information related to Jordan prior to their interests. The frequently occurring keywords were then used to conduct the search engine ranking analysis in Jordan.
Distributed Rateless Coding Method for Wireless Sensor Networks Based on Data Oriented Exchanging Strategy ⋆

Performance of data recovery is important for network coding in wireless sensor networks. The newly proposed ‘packet-centric’ distributed rateless coding strategies present fairly good performance close to that of the optimized centralized rateless codes. Nevertheless, such strategies may waste a lot of computation and energy resources, because many coded packets may visit the sensor nodes repeatedly by random walk. In this paper, we propose a new strategy named Data Oriented Distributed Rateless Coding (DODRC). With DODRC, certain packets are kept unchanged and fewer packets shall walk across the network. Further more, DODRC can significantly decrease the probability to visits nodes which have already been visited previously, by making the coded packets move with larger probability toward the areas where more sensor nodes are deployed. The experimental result shows that, with DODRC, the performance of data recovery is improved by 25%, and the communication and computation cost is decreased.
How can i help you': comparing engagement classification strategies for a robot bartender

A robot agent existing in the physical world must be able to understand the social states of the human users it interacts with in order to respond appropriately. We compared two implemented methods for estimating the engagement state of customers for a robot bartender based on low-level sensor data: a rule-based version derived from the analysis of human behaviour in real bars, and a trained version using supervised learning on a labelled multimodal corpus. We first compared the two implementations using cross-validation on real sensor data and found that nearly all classifier types significantly outperformed the rule-based classifier. We also carried out feature selection to see which sensor features were the most informative for the classification task, and found that the position of the head and hands were relevant, but that the torso orientation was not. Finally, we performed a user study comparing the ability of the two classifiers to detect the intended user engagement of actual customers of the robot bartender; this study found that the trained classifier was faster at detecting initial intended user engagement, but that the rule-based classifier was more stable.
WHO guidance grounded in a comprehensive approach to sexual and reproductive health and human rights: topical pre-exposure prophylaxis

Two new microbicide products based on topical (vaginal) application of antiretroviral drugs – 1% tenofovir gel and the dapivirine ring – are currently in late‐stage clinical testing, and results on their safety and effectiveness are expected to become available in early 2015. WHO guidelines on the use of topical pre‐exposure prophylaxis (topical PrEP) are important in order to ensure that these new prevention products are optimally used.
Combination of optimization and robustness evaluation from practical point of view

Optimization as well as robustness evaluation are key technologies of virtual product development. The optimization, i.e. improvement of product characteristics, has been an integral part of this development for several years now. On the other hand, the robustness of a construction, i.e. the reliable function within admissible boundaries, is becoming more and more focused on, recently. In fact, robustness is an additional demand on " optimized " designs. The optimization and robustness evaluation are either performed consecutively or simultaneously, and numerous methods are available for this. In the following, existing methods shall shortly be introduced and discussed from a practical point of view regarding their appliance in virtual prototyping processes. Three optimization method classes are discussed. These are: mathematical optimization methods using gradients, response surface methods, and stochastic search algorithms. Pareto optimization will be shortly mentioned. Within the robustness analysis, the sensitivity of the unavoidable scatter of environmental conditions and their impact on the most important structural responses is evaluated. Especially for nonlinear structural behavior it is mandatory to analyze the robustness with respect to the most important random variations of the design parameters. Robustness evaluation is restricted to relatively frequently occurring events. To cover rare events, methods of reliability analysis have to be employed. Additionally, methods for a simultaneous performance of the optimization and the robustness evaluation task are introduced. Two practical applications serve to point out potential application areas of optimization and robustness evaluation.
Aggregation of Service Level Agreements in the Context of Business Processes

One can observe that more and more companies are focusing on their core compenetency and are outsourcing parts or even complete business processes to service providers. Service Level Agreements (SLAs) are used as a contractual basis to define certain non-functional properties (e.g. response time) a service has to provide. To determine the SLA of a business process a priori, the SLAs of the invoked services need to be aggregated into a single SLA for the business process. This paper defines a method how a service provider can aggregate the SLAs of the individual services within a business process into a single SLA. This provides a service provider with the capability to annotate the service that the business process implements with an appropriate SLA. We propose a framework that helps in carrying out the SLA aggregation within business processes. The framework consists of two parts: (i) a formal model for SLAs and (ii) a concept to aggregate SLAs, where algorithms can be plugged in.
Toward Collaborative , Web-Based , 3 D Environments for the Investigation , Analysis , Annotation and Display of Virtual Crime Scenes

Modern day crime scene investigation methods are continually being enhanced by the application of new technologies used to better analyze crime scene data and improve the presentation of such data in the courtroom, helping to solve and prosecute crimes. This paper describes a new system called IC-CRIME that integrates several new technologies to meet both of these needs. IC-CRIME employs laser scanning to produce realistic 3D models of a crime scene which it then incorporates into a 3D game environment. IC-CRIME creates an integrated platform that encourages investigators and forensic experts to view, explore, and annotate these models at any time from any web browser. A key goal of the system is to expand opportunities for collaboration between professionals whose geographic locations and time commitments would otherwise limit their ability to contribute to the analysis of the actual crime scene.
Converting an HPSG-based Treebank into its Parallel Dependency-based Treebank

A treebank is an important language resource for supervised statistical parsers. The parser induces the grammatical properties of a language from this language resource and uses the model to parse unseen data automatically. Since developing such a resource is very time-consuming and tedious, one can take advantage of already extant resources by adapting them to a particular application. This reduces the amount of human effort required to develop a new language resource. In this paper, we introduce an algorithm to convert an HPSG-based treebank into its parallel dependency-based treebank. With this converter, we can automatically create a new language resource from an existing treebank developed based on a grammar formalism. Our proposed algorithm is able to create both projective and non-projective dependency trees.
Cognitive Reflection and the Diligent Worker: An Experimental Study of Millennials

Recent studies have shown that despite crucially needing the creative talent of millennials (people born after 1980) organizations have been reluctant to hire young workers because of their supposed lack of diligence. We propose to help resolve this dilemma by studying the determinants of task performance and shirking behaviors of millennials in a laboratory work environment. We find that cognitive ability is a good predictor of task performance in line with previous literature. In contrast with previous research, personality traits do not consistently predict either task performance or shirking behaviors. Shirking behaviors, as measured by the time participants spent browsing the internet for non-work purposes (Cyberloafing), were only explained by the performance on the Cognitive Reflection Test (CRT). This finding echoes recent research in cognitive psychology according to which conventional measures of cognitive ability only assess a narrow concept of rational thinking (the algorithmic mind) that fails to capture individuals’ capacity to reflect and control their impulses. Our findings suggest that hiring diligent millennials relies on the use of novel cognitive measures such as CRT in lieu of standard personality and intelligence tests.