Miracle-dz/UniFAR-FTUs huggingface dataset:
Torpedo: topic periodicity discovery from text data

Although history may not repeat itself, many human activities are inherently periodic, recurring daily, weekly, monthly, yearly or following some other periods. Such recurring activities may not repeat the same set of keywords, but they do share similar topics. Thus it is interesting to mine topic periodicity from text data instead of just looking at the temporal behavior of a single keyword/phrase. Some previous preliminary studies in this direction prespecify a periodic temporal template for each topic. In this paper, we remove this restriction and propose a simple yet effective framework Torpedo to mine periodic/recurrent patterns from text, such as news articles, search query logs, research papers, and web blogs. We first transform text data into topic-specific time series by a time dependent topic modeling module, where each of the time series characterizes the temporal behavior of a topic. Then we use time series techniques to detect periodicity. Hence we both obtain a clear view of how topics distribute over time and enable the automatic discovery of periods that are inherent in each topic. Theoretical and experimental analyses demonstrate the advantage of Torpedo over existing work.
Low Energy Implementation of Robust Digital Arithmetic in Sub/Near-Threshold Nanoscale CMOS: For Ultrasound Beamforming

This thesis will show combinatorial digital design using the 65nm transistor technology operating in near/sub-threshold region. Designing a 16By9Bit adder optimized with regard to power consumption with a speed requirement of 50MHz per operation for micro-beamforming. To optimize the addition of the 16, 9 bit numbers, studies of different building block are performed to find the best building blocks optimized for low power consumption, robustness and regular layout design without breaking the speed requirement. A new digital building block for standard digital building blocks optimized for subthreshold performance are proposed. In addition there will be shown a way to make regular layout designs. As a final result there will be shown a 16by9bit adder layout design with a delay equal to 17.7nS = 56.5MHz with a power consumption of 25uW at 20degrees and delay equal to 10nS = 100MHz with a power consumption of 36.2$uW at 80degrees. The design are build up from 6736 transistor and uses a area of 240um * 84um = 20.1mm^2.
Non-kinetic Tasks and Supporting Sociocultural Information

Abstract : In June 2011, the Engineer Research and Development Center started a research project aimed at understanding the connection between Civil Affairs (CA) tasks and the sociocultural information needed to support the conduct of those tasks. The project, called CALICO (Civil Affairs Language Informing Cultural Operations), used manual and automated content-analysis techniques to arrive at an understanding of what Army doctrine (as represented in CA training manuals) reveals about the connection between CA tasks and supporting sociocultural knowledge. One motivation for the work is the need to define CA tasks in such a way that they can be represented in a Battle Management Language (BML) that can support the production and interpretation of digital operations orders (DOOs). BMLs are at present able to support DOOs that include tasks in traditional, kinetic warfare (e.g., ATTACK, DEFEAT, TAKE, HOLD), but research is needed on the incorporation of non-kinetic tasks. The incorporation of non-kinetic tasks into a BML would enable DOOs to be constructed, issued, and interpreted for Phase 0 (Shaping) operations and Humanitarian Assistance and Disaster Relief (HA/DR) operations. Previous research demonstrated that non-kinetic tasks can be represented in ways consistent with BML architectures based on Lexical Functional Grammar, but research has not extended to the question of what sociocultural information is relevant to what task. CALICO analysis examines CA training texts tagged using a schema that emerged from the texts themselves rather than a taxonomy developed independently for another purpose (such as JC3IEDM). This paper describes the tagging effort as part of connecting specific non-kinetic tasks to the sociocultural knowledge that supports their execution.
Objetos 3D en pantallas táctiles interactuando de gestos a acciones

En los ultimos anos se han investigado metodos de interaccion para la manipulacion de objetos y la navegacion en escenas 3D en pantallas tactiles, pero dado el rapido crecimiento de la tecnologia, y la dificultad de manipular los 6 grados de libertad en las pantallas tactiles no se tiene un consenso del metodo de interaccion. En el trabajo se explora un enfoque que permite utilizar diversas tecnicas de interaccion en las pantallas multi-toque de forma sencilla, esto lo hace reconociendo la entrada en la pantalla como gestos y asignandolos a acciones en la escena, como caso de estudio se utiliza la libreria BIAS, verificando la flexibilidad y facilidad para utilizar diversas tecnicas de interaccion.
Effects of Problem-Based, Networked Hypermedia, and Cooperative Strategies on Visual Literacy Instruction.

This study examined the effects of varying instructional strategies on learning in emerging technology-based visual literacy instruction. The study used a completely crossed 2 (organizational strategy) x 2 (delivery strategy) x 2 (management strategy) factorial design, post-test only control group design. Organizational strategies employed included criterion-referenced, objectivist instruction and problem-based, constructivist instruction. Delivery strategies employed included local, computer-based instruction and networked hypermedia-based (World Wide Web) instruction. Management strategies implemented involved participation by individuals as well as cooperative dyads. Undergraduate education students (n=138) were randomly assigned to eight treatment groups and a control group and received self-paced instructional treatments. Learners were assessed on performance (achievement), instructional efficiency, and efficiency of learner strategies employed (process efficiency) . Achievement measures for all groups showed significantly higher levels of achievement when compared to the appended control group. Using multivariate and univariate analyses of variance, data were examined to determine what effect varying instructional strategies had on learning. Results indicated no statistically significant differences between any treatment groups. However, some strong data trends were observed. Recommendations for further research are included. (Contains 86 references.) (Author/DLS) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** Effects ofProblem-Based, Networked Hypermedia, and Cooperative Strategies on Visual Literacy Instruction U S DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) O This document has been reproduced as received from the person or organization originating it. O Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. Mary B. Shoffner Georgia State University David W. Dalton Kent State University "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY ,
Signal Processing Methods for Audio Classification and Music Content Analysis

Signal processing methods for audio classification and music content analysis are developed in this thesis. Audio classification is here understood as the process of assigning a discrete category label to an unknown recording. Two specific problems of audio classification are considered: musical instrument recognition and context recognition. In the former, the system classifies an audio recording according to the instrument, e.g. violin, flute, piano, that produced the sound. The latter task is about classifying an environment, such a car, restaurant, or library, based on its ambient audio background. In the field of music content analysis, methods are presented for music meter analysis and chorus detection. Meter analysis methods consider the estimation of the regular pattern of strong and weak beats in a piece of music. The goal of chorus detection is to locate the chorus segment in music which is often the catchiest and most memorable part of a song. These are among the most important and readily commercially applicable content attributes that can be automatically analyzed from music signals. For audio classification, several features and classification methods are proposed and evaluated. In musical instrument recognition, we consider methods to improve the performance of a baseline audio classification system that uses mel-frequency cepstral coefficients and their first derivatives as features, and continuous-density hidden Markov models (HMMs) for modeling the feature distributions. Two improvements are proposed to increase the performance of this baseline system. First, transforming the features to a base with maximal statistical independence using independent component analysis. Secondly, discriminative training is shown to further improve the recognition accuracy of the system. For musical meter analysis, three methods are proposed. The first performs meter analysis jointly at three different time scales: at the temporally atomic tatum pulse level, at the tactus pulse level, which corresponds to the tempo of a piece, and at the musical measure level. The features obtained from an accent feature analyzer and a bank of combfilter resonators are processed by a novel probabilistic model which rep-
Using EMBT to produce foreign language subtitles

Due to limited budgets and an ever-diminishing timeframe for the production of foreign language subtitles, pressure on subtitle companies is at an all-time high. Although translation technologies are ubiquitous in other areas of translation, especially localisation, and have been helping translators work more efficiently for a number of years now (Lagoudaki, 2006), it is strange to note that subtitle companies have been slower to jump on the bandwagon. Recent research from both academia and the industry (O'Hagan, 2003; Carroll, 2004; Gambier, 2005) suggests that the inroads made in natural language processing and machine translation could go a long way to alleviating some of this pressure. In this thesis, we set out to establish how example-based machine translation (EBMT) can be used to speed up the subtitling process, thus improving the throughput of the subtitler, and also as a means of automatically producing foreign language subtitles which subtitle companies may not normally provide, even though they would be extremely helpful for the viewing public. Through the development of the modular corpus-based MT engine, MaTrEx (Stroppa et al., 2006), and the collection of a large amount of subtitle data extracted from over 50 full-length features (Armstrong et al., 2006a), we were able to apply a number of EBMT techniques to produce subtitles for the language directions German-English and English-German. These machine-produced subtitles were evaluated using a range of both well-established automatic metrics common to machine translation as well as some novel manual evaluation strategies. Both automatic metrics and the human evaluation were very useful in the developmental process where we were able to isolate and fix errors made by our system. In addition, through obtaining a human's perspective on the subtitles produced by our system, we were able to gauge the acceptability of these subtitles for public viewing, and have provided a solid grounding for future research into the acceptability of (semi-) automatically generated subtitles.
Spectral Feature Selection for Data Mining

Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection. The book explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs. It presents the intrinsic ideas behind spectral feature selection, its theoretical foundations, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. The authors also cover feature selection and feature extraction, including basic concepts, popular existing algorithms, and applications. A timely introduction to spectral feature selection, this book illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. Readers learn how to use spectral feature selection to solve challenging problems in real-life applications and discover how general feature selection and extraction are connected to spectral feature selection.
Framework for Instructional Technology: Methods of Implementing Adaptive Training and Education

Abstract : In adaptive instructional environments, instructional interventions and/or content are adapted to an individual learner's competence level, goals, or other characteristics. The intention behind adaptation is to maintain the optimal level of challenge for each individual student, to provide support, and to correct misconceptions. This report provides a framework in which to consider various technology-based adaptive instructional techniques. The Framework for Instructional Technology (FIT) lays out four categories of adaptive techniques: Corrective Feedback, Support, Micro-sequencing, and Macro-sequencing. Under each category, FIT specifies five levels or approaches to adapting that correspond to degree of adaptive sophistication and complexity of implementation. With few exceptions, evidence supporting the use of higher levels of adaptation is lacking. This is because the systematic comparison of different implementation approaches has not been conducted. The report provides recommendations for combining different levels of FIT with different levels of interactive multimedia instruction. FIT can be used to clearly describe adaptive behaviors and to guide future research investigating the efficacy of different adaptive instructional interventions.
Community detection in disease-gene network based on principal component analysis

The identification of communities is imperative in the understanding of network structures and functions.Using community detection algorithms in biological networks, the community structure of biological networks can be determined, which is helpful in analyzing the topological structures and predicting the behaviors of biological networks. In this paper, we analyze the diseasome network using a new method called disease-gene network detecting algorithm based on principal component analysis, which can be used to investigate the connection between nodes within the same group. Experimental results on real-world networks have demonstrated that our algorithm is more efficient in detecting community structures when compared with other well-known results.
Focus on First Certificate

A complete integrated course for students preparing for the Cambridge First Certificate in English examination. It covers all the skills tested in the five papers, including the changed interview format of Paper 5. Exam practice sections provide additional material for homework and self-study. There is an accompanying teacher's book and two cassettes. Sue O'Connell is also the author of "Focus on Proficiency".
Classifying Everyday Activity Through Label Propagation With Sparse Training Data

We solve the problem of activity verification in the context of sustainability. Activity verification is the process of proving the user assertions pertaining to a certain activity performed by the user. Our motivation lies in incentivizing the user for engaging in sustainable activities like taking public transport or recycling. Such incentivization schemes require the system to verify the claim made by the user. The system verifies these claims by analyzing the supporting evidence captured by the user while performing the activity. The proliferation of portable smart-phones in the past few years has provided us with a ubiquitous and relatively cheap platform, having multiple sensors like accelerometer, gyroscope, microphone etc. to capture this evidence data in-situ. In this research, we investigate the supervised and semi-supervised learning techniques for activity verification. Both these techniques make use the data set constructed using the evidence submitted by the user. Supervised learning makes use of annotated evidence data to build a function to predict the class labels of the unlabeled data points. The evidence data captured can be either unimodal or multimodal in nature. We use the accelerometer data as evidence for transportation mode verification and image data as evidence for recycling verification. After training the system, we achieve maximum accuracy of 94% when classifying the transport mode and 81% when detecting recycle activity. In the case of recycle verification, we could improve the classification accuracy by asking the user for more evidence. We present some techniques to ask the user for the next best piece of evidence that maximizes the probability of classification. Using these techniques for detecting recycle activity, the accuracy increases to 93%. The major disadvantage of using supervised models is that it requires extensive annotated training data, which expensive to collect. Due to the limited training data, we look at the graph based inductive semi-supervised learning methods to propagate the labels among the unlabeled samples. In the semi-supervised approach, we represent each instance in the data set as a node in the graph. Since it is a complete graph, edges interconnect these nodes, with each edge having some weight representing the similarity between the points. We propagate the labels in this graph, based on the proximity of the data points to the labeled nodes. We estimate the performance of these algorithms by measuring how close the probability distribution of
Learning Enterprise Malware Triage from Automatic Dynamic Analysis

Abstract : Adversaries employ malware against victims of cyber espionage with the intent of gaining unauthorized access to information. To that end, malware authors intentionally attempt to evade defensive countermeasures based on static methods. This thesis analyzes a dynamic analysis methodology for malware triage that applies at the enterprise scale. This study captures behavior reports from 64,987 samples of malware randomly selected from a large collection and 25,591 clean executable files from operating system install media. Function call information in sequences of behavior generate feature vectors from behavior reports from the les. The results of 64 experiment combinations indicate that using more informed behavior features yields better performing models with this data set. The decision tree classifier attained a max performance of 0.999 area under the ROC curve and 99.4% accuracy using argument information with function sequence lengths from 11-14. This methodology contributes to strategic cyber situation awareness by fusion with fast malware detection methods, such as static analysis, to change the game of malware triage in favor of cyber defense. This method of triage reduces the number of false alarms from automatic analysis that allows a 97% workload reduction over using a static method alone.
Les choix techniques sont des choix pédagogiques : les dimensions multiples d’une expérience de formation à distance

Les choix techniques necessaires a la conception d’un enseignement a distance ne peuvent passer avant ou apres les choix pedagogiques, car ils sont la materialite meme de ces choix pedagogiques, tout comme l’usage est au cœur de la conception technique de tout produit. Un cadre theorique est propose pour comprendre la diversite des points de vue a prendre en compte dans cette demarche qui debouche sur une conception distribuee de l’enseignement. Le compte rendu de l’activite de developpement d’un DESS sous Learning Space au sein de l’UTC permet d’illustrer ce cadre d’analyse et surtout de montrer le travail de discussion constante entre les points de vue au sein d’un meme projet.
Automatic Language Discrimination.

Abstract : A language discrimination algorithm was developed that utilizes single steady-state phoneme-like reference sounds. The algorithm makes use of the relative frequency of occurrence of these reference sounds and nearest-mean decision strategy. The reference sounds were determined interactively and have been shown to have good language specificity. The number of reference sounds required is small enough to allow real-time operation. Standardization of each speaker's long-term average spectrum removed much of the data variation that was due to variations in data recording conditions. This standardization provided a 41 percent decrease in the number of test speaker misclassifications. In a performance test involving 50 test speakers of five languages, 80 percent correct classification was achieved. Excellent discrimination among L1, L3, and L5 was attained, while additional references with more language specificity are needed for discriminating between L2 and L4. (Author)
UCR Time Series Data Mining Archive

A silicon carbide body with a density of at least 85% is machined to required specification and then fired at a temperature ranging from 1400 DEG C to 2100 DEG C in a firing atmosphere ranging in pressure from 10 3 torr to 25 torr to increase its fracture strength by at least 10%.
Signal processing in digital communications

Preface. Introduction. Deterministic Signal Processing. Probability, Random, and Stochastic Signal Processing. Channel Characterization and Distortion. Channel Estimation and Blind Identification. Adaptive Equalization. Multichannel Modulation, DMT and OFDM. Discrete-Time Synchronization. Appendixes. About the Author. Index.
Implementation and Verification of a 7-Stage Pipeline Processor

This report details the implementation of a 7-stage processor pipeline using VHDL. The report excludes discussion on instruction and data caches. The pipeline stages are balanced with respect to timing. The pipeline design is verified using embedded microprocessor benchmarks. The synthesized pipeline design is evaluated in terms of timing, area, and power. Implementation and evaluation of the branch predictors are emphasized, as they are vital part of a processor pipeline. Prior to branch predictor implementation, several branch predictor configurations have been evaluated, for their performance, through simulations using SimpleScalar tool. Based on the simulation results, few best performing predictors have been implemented and verified by integrating them into a processor pipeline. The pipeline design is synthesized using Cadence Encounter RTL compiler in order to extract area and power estimates. Based on the synthesis results, evaluation of the pipeline and its function units has been carried out. The multiplier and the branch predictor unit are identified as the most critical with respect timing. Solutions have been suggested to improve the timing balance between the pipeline stages. It has also been evaluated that even though the branch predictors contribute for a significant improvement in the performance of a pipeline, they also account for 45% of total area and 65% of total power of the pipeline. The effect of caches on the pipeline timing, area, and power is not considered for evaluations.