Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of various FFT methods for single tone detection and frequency estimation

The periodogram, implemented using the fast Fourier transform (FFT), is widely used for the detection and frequency measurement of single tones. This paper evaluates the detection and frequency estimation performance of the periodogram and its variants, such as the Welch and Bartlett methods and the polyphase-FFT. Performance results for the detection and frequency estimation performance of the periodogram and its variants are presented and compared. The standard periodogram generally gives the best detection performance and the minimum mean square frequency error for a fixed length of signal data. However, if the FFT length is fixed the Welch method gives the best performance.
Chemoselective ring construction from unsymmetrical 1,6-dienes via radical addition of sulfonyl halides

The sulfonyl radical-promoted cyclizations of 1,6-unsymmetrical dienes can be totally chemoselective. The addition of tosyl halides to various 1,6-dienes bearing both a nucleophilic and an electrophilic double bond clearly indicates that the attractive property is not related to the generally accepted electrophilic nature of tosyl radical. The chemoselectivity is likely to originate from the reversibility of the first step, i.e. tosyl radical addition to the double bond, which favors the formation of the adducts resulting from the one intermediate radical that cyclizes faster
Using Binary Code Instrumentation in Computer Security

The paper approaches the low-level details of the code generated by compilers whose format permits outside actions. Binary code modifications are manually done when the internal format is known and understood, or automatically by certain tools developed to process the binary code. The binary code instrumentation goals may be various from security increasing and bug fixing to development of malicious software. The paper highlights the binary code instrumentation techniques by code injection to increase the security and reliability of a software application. Also, the paper offers examples for binary code formats understanding and how the binary code injection may be applied.
Improving Statistical Parser by Recognition of Chinese Number and Quantifier Prefix in Machine Translation

By studying the Chinese number and quantifier prefix (CNQP) as a special language phenomenon in machine translation, this paper presents a CNQP recognition method, which is rule based and independent of word segmentation. The method expressed CNQPs compositions using Backus-Naur Form (BNF), and took the numeral as the active information and the quantifiers as the boundaries of the CNQPs. To avoid the word segmentation noise, a forward maximum matching method was used for obtaining the compositions of the CNQPs, which can be fed into the statistical parser for the analysis of the Chinese sentences. The experimental results indicate the proposed method as a pre-processing module can effectively improve the parsing results of the statistical parser without retraining on experimental data constructed manually, which can further enhance the translation qualities.
A microprocessor-based signature verification system

A microprocessor-based signature verification system is described. It is based on the fact that the pressure features of a signature, besides being invisible, are unique to the person signing it. The system extracts the various features from a signature pressure waveform and compares them with those previously stored in a database. Tolerances of the various features can easily be adjusted for different objectives to suit different environments. The test results for the verification of the system have been excellent. >
The Effect of Cognitive Workload, Job Enrichment Environment, and Spatial Ability on Goal Structures and Strategies

The cognitive strategies used in the performance of human-computer interaction tasks have often been described in terms of goal stack models which are equivalent to hierarchical structures of goals and sub-goals. Effects of cognitive workload, spatial ability and job enrichment (JE) environment were evaluated by using goal structures and the quality of output. Studies were performed on a memo text-editing task to evaluate methods to determine the cognitive goal structures by analyzing the keystrokes and the pauses between keystrokes. The keystrokes provide information about the nodes and goals in the structures while the pauses between keystrokes provide information about the hierarchical structure of the goals. The cognitive goal structures of individual subjects were analyzed quantitatively according to the efficiency of the strategy and the depth and breadth of the strategy. The measures of cognitive goal structures were affected by environmental manipulations of the task in an experimental study of subjects using a text-editor. In particular, in a high workload condition, performance depended upon the spatial ability of the subjects. The goal structures of high spatial subjects became shallower as workload increased and the goal structures of the low spatial subjects did not change appreciably. In the high JE condition, the high spatial subjects developed more efficient cognitive strategies while in the low JE condition these high spatial subjects were less efficient. For the low spatial subjects, job enrichment had no effect. Overall, high spatial subjects changed their cognitive goal structures and strategies dependent upon the environmental conditions whereas the low spatial subjects were invariant under different environmental conditions.
Finding nuggets in patent portfolios: core patent mining and its applications

Patents are critically important for a company to protect its core business concepts and proprietary technologies. Effective patent mining in massive patent databases not only provides business enterprises with valuable insights to develop strategies for research and development, intellectual property management, and product marketing, but also helps patent offices to improve efficiency and optimize their patent examination processes. This paper describes the patent mining problem of automatically discovering core patents (i.e., novel and influential patents in a domain). In addition, the value of core patent mining is illustrated by revealing the potential competitive relationships among companies in their core patents. The work addresses the unique patent vocabulary usage which is not considered in traditional word-based statistical methods with a topic-based temporal mining approach that quantifies a patent's novelty and influence through topic activeness variations. Tests of this method on real-world patent portfolios show the effectiveness of this approach over state-of-the-art methods.
An Analysis of Students' Perceptions of the Value and Efficacy of Instructors' Auditory and Text-Based Feedback Modalities across Multiple Conceptual Levels

This article used work from the writing assessment literature to develop a framework for assessing the impact and perceived value of written, audio, and combined written and audio feedback strategies across four global and 22 discrete dimensions of feedback. Using a quasi-experimental research design, students at three U.S. universities were provided with text-based, audio-based, and a combination of text- and audio-based feedback over the course of a semester. A survey asking students to indicate their feedback preferences was administered and analyzed using multiple statistical techniques. The findings indicated that students preferred a combination of feedback modalities; however, selective delivery strategies may be preferable at various levels.
The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics

From the Publisher: Visualization is a part of every day life. From weather map generation of financial modelling to MRI technology in medicine to 3D graphics used in movies like Jurassic Park, examples of visualization abound. The book/CD package offers readers the opportunity to practice visualization using a complete C++ programming environment developed by the authors.
Two-dimensional device simulation program: 2DP

Mathematical details of a two-dimensional semiconductor device simulation program are presented, Applicability of the carrier transport model to shallow junction bipolar transistors is discussed. Use of this program to optimize device structures in new bipolar technology is illustrated by presenting calculated device characteristics for variations in a few selected process conditions, Software links that automatically transfer data from a two-dimensional process simulation program and to a quasi-three-dimensional device equivalent circuit model generation program are also discussed.
Statistical design techniques for D/A converters

Techniques for the design of high-resolution digital-to-analog converters (DACs) that use precision-matched components are described. Statistical models, based on test chip data, that realistically describe both local mismatches and longer range spatial correlations are investigated. These models have been incorporated into a special-purpose DAC analysis/simulation tool (DACSIM) to provide a powerful Monte-Carlo yield estimation capability. DACSIM also performs accurate DC simulation of a large class of resistor-network DAC circuits taking into account many important parasitic effects, including superposition nonlinearities. An error-corrected DAC architecture that uses the concept of a generalised decoding strategy programmable at test has been validated in a CMOS 13 bit segmented current-switched DAC. The approach exhibits the potential for attaining very high accuracy without trimming. >
From CML to a Model-Based Concurrent Specification Language

In this paper, we address the problem of specification and design of concurrent systems More accurately, we present the definition of a new specification language that is formal, wide-spectrum, model-based, concurrent, polymorphic, and strongly implicitly typed The lan guage is built upon a concurrent, functional, and imperative programming language Concurrent ML Specification aspects are supported thanks to the addition of some specification constructs and also by allowing axioms to appear in ML structures and signatures The resulting specification language is thus highly expressive though it embodies a restricted number of concepts We present here the motivations underlying the definition of such a language as well as the design choices Furthermore, we introduce the specification and development methodology and illustrate it on various examples We will see that many specification styles are allowed algebraic, applicative, state-based, concurrent applicative, and con current imperative
Detection of Blood Vessels in Breast Using Matched Filters

Now-a-days a variety of edge detection algorithms exist in literature, but they do not always lead to acceptable results in extracting various features in an image. In this paper, we address the problem of detecting blood vessels in breast images. Blood vessels usually have poor local contrast and the application of existing edge detection algorithms yield results which are not satisfactory. We introduce an operator for feature extraction based on optical and spatial properties of objects to be recognized. The gray level profile of the cross section of a blood vessel is approximated by a Gaussian shaped curve. The concept of matched filter detection of signals is used to detect linear segments of blood vessels in these images. The automatic detection of blood vessels could help physicians in diagnosing diseases.
Sequence Validation Based Extraction of Named High Cardinality Entities

One of the most useful Information Extraction (IE) solutions to Web information harnessing is Named Entity Recognition (NER). Hand-coded rule methods are still the best performers. These methods and statistical methods exploit Natural Language Processing (NLP) features and characteristics (e.g. Capitalization) to extract Named Entities (NE) like personal and company names. For entities with multiple sub-entities of higher cardinality (e.g. linux command, citation) and which are non-speech, these systems fail to deliver efficiently. Promising Machine Learning (ML) methods would require large amounts of training examples which are impossible to manually produce. We call these entities Named High Cardinality Entities (NHCEs). We propose a sequence validation based approach for the extraction and validation of NHCEs. In the approach, sub-entities of NHCE candidates are statistically and structurally characterized during top-down annotation process and guided to transformation into either value types (v-type) or user-defined types (u-type) using a ML model. Treated as sequences of sub-entities, NHCE candidates with transformed sub-entities are then validated (and subsequently labeled) using a series of validation operators. We present a case study to demonstrate the approach and show how it helps to bridge the gap between IE and Intelligent Systems (IS) through the use of transformed sub-entities in supervised learning.
A tool to increase information-processing capacity for consumer water meter data

Background: Water service providers invoice most South African urban consumers for the water they use every month. A secure treasury system generates water invoices at municipalities’ financial departments. Information about the water usage of customers initially comes from reading the water meters, usually located in gardens near the front boundaries of properties. Until as recently as 1990, the main purpose of the water meter readings was to generate invoices for water usage. There are various treasury systems for this purpose. Objective: The objective of this research article was to describe the development of Swift, a locally developed software tool for analysing water meter data from an information management perspective, which engineers in the water field generally use, and to assess critically the influence of Swift on published research and industry. This article focuses on water usage and the challenge of data interchange and extraction as issues that various industries face. Method: This article presents the first detailed report on Swift. It uses a detailed knowledge review and presents and summarises the findings chronologically. Results: The water meter data flow path used to be quite simple. The risk of breaches in confidentiality was limited. Technological advances over the years have led to additional knowledge coming from the same water meter readings with subsequent research outputs. However, there are also complicated data flow paths and increased risks. Users have used Swift to analyse more than two million consumers’ water meter readings to date. Studies have culminated in 10 peer-reviewed journal articles using the data. Seven of them were in the last five years. Conclusion: Swift-based data was the basis of various research studies in the past decade. Practical guidelines in the civil engineering fraternity for estimating water use in South Africa have incorporated knowledge from these studies. Developments after 1995 have increased the information processing capacity for water meter data.
The Ubiquitous User: A Reexamination of Carlson's Deserted Library

An examination of traditional output measures—circulation, reserve, in-house use, and reference transactions—from the Association of Research Libraries (ARL), California State University, State University of New York, University of Maryland, and other libraries reveals declines that may be deeper and more widespread than previously reported. This trend is evident against a background that includes (1) continuing increases in the cost of periodicals, (2) searching for a new paradigm, and (3) the emerging virtual library.
REAL-TIME AND EFFICIENT ALGORITHMS FOR FREQUENCY WARPING BASED ON LOCAL APPROXIMATIONS OF WARPING OPERATORS

Frequency warping is a modifier that acts on sound signals by remapping the frequency axis. Thus, the spectral content of the original sound is displaced to other frequencies. At the same time, the phase relationship among the signal components is altered, nonlinearly with respect to frequency. While this effect is interesting and has several applications, including in the synthesis by physical models, its use has been so far limited by the lack of an accurate and flexible real-time algorithm. In this paper we present methods for frequency warping that are based on local approximations of the warping operators and allow for real-time implementation. Filter bank structures are derived that allow for efficient realization of the approximate technique. An analysis of the error is also presented, which shows that both numerical and perceptual errors are within acceptable limits. Furthermore, the approximate implementation allows for a larger variety of warping maps than that achieved by the classical (non-causal) first-order allpass cascade implementation.
Context Inference Including Cause Reasoning and Prediction

It is not enough to recognize the situations which currently occur simply. The current situations have the causes that they get to occur. The causes can just be generated and they can make the situations like the current states while the ones which occurred in the past have continued. Furthermore, if the causes which made the current situations don't disappear, they can continue to stay the same, get worse, or be changed to another situation. Therefore, limiting the range of context awareness to the situations which currently occur can be insufficient as the system which recognizes situations of the everyday world. Therefore, this study aims at problem-solving of two things. First, it recognizes situations without advance information. Second, it infers causes of situations and predicts how the situations will turn out in the future. To solve these problems, this study uses multiple sensor data fusion together using Dempster-Shafer Evidence Theory (DST) and Kalman Filter (KF). It recognizes situations under the conditions without any advance information through DST, infers causes of the current situations, and predicts how the current situations will turn out in the future. At this moment, BPA is important to recognize situations through DST and infer causes and state transition equation plays an important role in predicting arrangement through KF. The study carries out context inference and cause inference using DST. It describes the plan which infers causes of situations without advance information. It calculates required state transition equation to predict the progress of the research and infers how the causes revealed through DST by using it will arrange the current situations in the future by using KF.
A humming retrieval system based on music fingerprint

In this paper, we proposed an improved music information retrieval method utilizing the music fingerprint. The goal of this method is to represent the music with compressed musical information. Based on the selected MIDI files, which are generated automatically as our music target database, we evaluate the accuracy, effectiveness, and efficiency of this method. In this research we not only extract the feature sequence, which can represent the file effectively, from the query and melody database, but also make it possible for retrieving the results in an innovative way. We investigate on the influence of noise to the performance of our system. As experimental result shows, the retrieval accuracy arriving at up to91% without noise is pretty well
DATA VISUALIZATION AND CLUSTERING: AN APPLICATION TO GENE EXPRESSION DATA

In this work we present a multi-step process that, starting from raw datasets, brings them through preprocessing, preclustering and agglomerative clustering stages, exploiting a visual and interactive environment for data analysis and exploration. At the core of the process lies the idea of subdividing the process of data clusterization into two main steps: the first one aimed to reduce the size of data and the second one to present the reduced and human-understandable dataset to the user. This last step allows him to participate in the process of clustering and helps him figuring out the data underlying structures. The approach is actually implemented in a group of user-friendly tools under the MATLAB environment, featuring a number of classical and novel data processing, visualization, assessment and interaction methods.
Jess, the Java expert system shell

This report describes Jess, a clone of the popular CLIPS expert system shell written entirely in Java. Jess supports the development of rule-based expert systems which can be tightly coupled to code written in the powerful, portable Java language. The syntax of the Jess language is discussed, and a comprehensive list of supported functions is presented. A guide to extending Jess by writing Java code is also included.
The Scholarship of Teaching and Learning

This book is a guide to the scholarship of teaching and learning (SoTL) for scientists, engineers, and mathematicians teaching at the collegiate level in countries around the world. It shows instructors how to draw on their disciplinary knowledge and teaching experience to investigate questions about student learning. It takes them all the way through the inquiry process beginning with framing a research question and selecting a research design, moving on to gathering and analyzing evidence, and finally to making the results public. Numerous examples are provided at each stage, many from published studies of teaching and learning in science, engineering, or mathematics. At strategic points, short sets of questions prompt readers to pause and reflect, plan, or act. These questions are derived from the authors’ experience leading many SoTL workshops in the United States and Canada. The taxonomy of SoTL questions—What works? What is? What could be? —that emerged from the SoTL studies undertaken by the Carnegie scholars provides a useful framework at many stages of the inquiry process. The book addresses the issue of evaluating and valuing this work, including implications for junior faculty who wish to engage in SoTL. The authors explain why SoTL should be of interest to science, technology, engineering, and mathematics (STEM) faculty at all types of institutions, including faculty members active in traditional STEM research. They also give their perspective on the benefits of SoTL to faculty, to their institutions, to the academy, and to students.
3D CAD Model Retrieval Based on Feature Fusion

Existing 3D model retrieval techniques focus on the global feature more than local detailed feature are not applicable to 3D CAD models. This paper combines the local feature and global feature to satisfy the needs of mechanical design. We first analyze the limitations of Shape Description (SD) algorithm. Then propose an automatic feature extraction technology based on the local characteristics of the curvature distribution. Next we fuse the extracted feature using a novel method. Lastly, we design an improved K-nearest neighbor algorithm to retrieve models. Experimental results indicate the efficiency and feasibility of the proposed method.