Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust stabilization for uncertain time-delay systems containing saturating actuators

A class of uncertain time-delay systems containing saturating actuator is considered. Using a Razumikhin approach for the stability of functional differential equations, bounds on the time-delay are given such that the considered uncertain system is robustly stabilizable via constrained memoryless state feedback control laws. These bounds are given in terms of solutions of appropriate finite dimensional Riccati equations. <<ETX>>
12. Processing levels in foreign-language reading

Reading is a highly complex activity that can be described according to different interacting processing levels. These levels correspond to the use of specific kinds of knowledge. This paper presents a survey of reseach data on reading, with the focus on reading a foreign language, especially when related to previously acquired languages. Most of the data on the reading process come from cognitive psychology and deal with reading L1 English. Data and hypotheses on word recognition and sentence processing both in other languages and in foreign language reading are considered where possible.
An Agent Oriented Markov Model Framework for Service Composition

Service Composition Framework is becoming more and more important within the area of service management. Web service composition is to arrange multiple services into workflows supplying complex user needs. IT services and the related information from different sources are characterized as diverse, incomplete, heterogeneous, and geographically distributed. It is hard to consume these complicated services without knowledge assistant. Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest, used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. By using Bayesian network, the composition methods require more time to integrate the services in a complex service directory. To address this problem, a systematic way is proposed to handle the challenges of acquisition, structuring, and refinement of structured services, so an optimistic method like Markov Model is preferred along with an Agent to overcome this problem, and to ensure efficiency in service composition by providing accurate services in a less amount of time than the previous framework
Design of reconfigurable linear feedback shift register

Linear Feedback Shift Register(LFSR) is an important part of sequence cipher.This paper introduced a kind of reconfigurable LFSR design.As needed,it could be configured to be a random LFSR to run in GF(2),GF(28),GF(216) or GF(232) and in a certain length range.The prototype of the reconfigurable LFSR had been implemented in Altera's EP2S60F1020C5 FPGA and its highest run frequency was 100 MHz.It is proved through experiments that the reconfigurable LFSR design uses few hardware resources and its performance is stable.
High-Throughput GRID Computing for Life Sciences

The number of fully sequenced genomes increases daily, producing an exponential explosion of the sequence, annotation and metadata databases. Data analysis on a genome-wide level or investigation within a specific data repository has become a dataand calculation-intensive process occupying single computers and even larger computer clusters for month or even years. In most cases such applications can be subdivided into many independent smaller tasks. The smaller tasks are particularly suited to distribution over a computational GRID infrastructure, which drastically reduces the time to reach the final result. In our analysis of gene ontology data and their associations to gene products of any kind of organism in a search to find gene products with similar functionalities, we developed a system to divide the full search into a large number of jobs and to submit these jobs to the GRID infrastructure as long as all jobs are processed successfully, guaranteeing an analysis of the data without missing any information.
CITAVT-IV-A AUTONOMOUS LAND VEHICLE NAVIGATED BY MACHINE VISION

A new generation of autonomous land vehicle(ALV)-CITAVT IV developed by NUDT has reached a high speed of 75.6km/h in highway experiment in May 2000. CITAVT IV's system architecture, software design, functions and experiments are presented in this paper. In the research, the delay problem has been solved quite well.
97 Wavelet Transform Algorithm With Arithmetic Shift for Hardware

Based on the searching analysis of computer, the lifting coefficients of the 97 biorthogonal wavelet filter pair of CDF are expressed in arithmetic operation of tow shift registers. A arithmetic operation of shift registers are used to replace the multiplier with complicated architecture operating on floating-point. So it is more simpler and faster, and can be implemented by ASIC easily. In the end, the analysis of some simpler algorithms and the configuration of arithmetic shift are given.
Spam Filtering Based on Kernel Partial Least Squares Classification

The spam is one of the most serious problems to be resolved in the Internet.Recently,several spam filtering technologies have been proposed and applied to spam filtering,such as the Partial Least Squares(PLS) method.The PLS method can deal with the sparse data,the high dimensionalities and the multi-colinearity issues existing in the e-mail dataset.However,the latent content relationships among the e-mail data are,more often than not,nonlinear.This paper introduces the kernel function over PLS method to capture such non-linearity.Compared with PLSR method,the proposed KPLS model is proved with superior efficiency in the experiments on the Enron-Spam dataset.
The effects of rolloff-factor error on the BER performance of M-PSK signals

Rolloff-factor is a key parameter of M-PSK signals. It depicts the bandwidth and envelope fluctuation of the signals. This paper analyzed the effect of rolloff-factor error on the BER performance in the receiver with a theoretical method. The result shows the effect is very small. Computer simulations prove the correction of the derivation. It is useful for the receiver design in non-cooperative communication.
Researching of Chinese Text Clustering Based on Latent Semantic Index

This paper discusses the status quo and some problems of Chinese Text Clustering, introduces vector space model, expatiates the theory of Latent Semantic Index and the process of Chinese text clustering based on the model,finally compares the two models with an experiment,results of the experiment show that Latent Semantic Index can get better effect.
On performance of positioning with Wireless LAN in parking lot

In this paper, we evaluate the performance to the efficiency of positioning with Wireless LAN in the parking lot. We evaluate the performance of positioning with Scene Analysis by Wireless LAN in typical parking lot of outdoor. A variety of conditions to reveal the efficiency of positioning with Wireless LAN in parking lot. Arrangement of the wireless LAN access point, assumed users, positioning efficiency of the vehicle in static and dynamic, and direction of the vehicle are listed. We evaluate effects for positioning performance by the variety of conditions.
Sequential pattern mining with uncertain data

Ge, Jiaqi Ph.D., Purdue University, May 2016. Sequential Pattern Mining with Uncertain Data. Major Professor: Yuni Xia. In recent years, a number of emerging applications, such as sensor monitoring systems, RFID networks and location based services, have led to the proliferation of uncertain data. However, traditional data mining algorithms are usually inapplicable in uncertain data because of its probabilistic nature. Uncertainty has to be carefully handled; otherwise, it might significantly downgrade the quality of underlying data mining applications. Therefore, we extend traditional data mining algorithms into their uncertain versions so that they still can produce accurate results. In particular, we use a motivating example of sequential pattern mining to illustrate how to incorporate uncertain information in the process of data mining. We use possible world semantics to interpret two typical types of uncertainty: the tuple-level existential uncertainty and the attribute-level temporal uncertainty. In an uncertain database, it is probabilistic that a pattern is frequent or not; thus, we define the concept of probabilistic frequent sequential patterns. And various algorithms are designed to mine probabilistic frequent patterns efficiently in uncertain databases. We also implement our algorithms on distributed computing platforms, such as MapReduce and Spark, so that they can be applied in large scale databases. Our work also includes uncertainty computation in supervised machine learning algorithms. We develop an artificial neural network to classify numeric uncertain data; and a Näıve Bayesian classifier is designed for classifying categorical uncertain data streams. We also propose a discretization algorithm to pre-process numerical uncertain data, since many classifiers work with categoric data only. And experimen-
Chidamber and Kemerer Object-Oriented Measures: Analysis of their Design from the Metrology Perspective

During the last decade, software product measurement field has known many improvements and becomes an emerging field of software engineering. Based on the used programming approaches such as object-oriented, structured programming, etc., different kinds of measures are proposed in the literature. These sets of measures were defined many years ago to measure software artifacts, for example, source code and design, However, such measures need to be verified and validated based on the lessons learned from the measurement and metrology concepts. This paper focuses on software product measures, in particular, the object-oriented measures. Moreover, it aims at analyzing the design of a wellknown and one of the most used object-oriented measures, that is, the Chidamber and Kemerer measures suite. In addition, this paper provides an investigation of the extent to which this set of measures addresses the metrology concepts related to the software measurement design.
Efficient Community Detection in Heterogeneous Social Networks

Community detection is of great importance which enables us to understand the network structure and promotes many real-world applications such as recommendation systems. The heterogeneous social networks, which contain multiple social relations and various user generated content, make the community detection problem more complicated. Particularly, social relations and user generated content are regarded as link information and content information, respectively. Since the two types of information indicate a common community structure from different perspectives, it is better to mine them jointly to improve the detection accuracy. Some detection algorithms utilizing both link and content information have been developed. However, most works take the private community structure of a single data source as the common one, and some methods take extra time transforming the content data into link data compared with mining directly. In this paper, we propose a framework based on regularized joint nonnegative matrix factorization (RJNMF) to utilize link and content information jointly to enhance the community detection accuracy. In the framework, we develop joint NMF to analyze link and content information simultaneously and introduce regularization to obtain the common community structure directly. Experimental results on real-world datasets show the effectiveness of our method.
Theoretical study toward rationalizing inelastic background analysis of buried layers in XPS and HAXPES

The approach of inelastic background analysis was previously demonstrated to be a useful tool for retrieving the depth distribution of buried layers with an accuracy, which is better than 5% even for some complex samples. This paper presents a study that attempt at rationalizing the approach by exploring how to make the best choice of the inelastic mean free path and the inelastic scattering cross section, which are the two main input parameters needed in the analysis. To this end, spectra from buried layers were created with Quases‐Generate software. The layers consisted of Si 1s recorded at 6099 eV and Au 4d recorded at 1150 eV kinetic energy buried under overlayers of Si, Au, Al, polymer, or Ta. Spectra from samples with a wide range of buried layer thickness and overlayer thickness were created. Subsequently, these spectra were analyzed with Quases‐Analyze software and for each case the analysis was done with different combinations of the input parameters. Among these, the best choice for all cases was to use an effective IMFP and effective inelastic scattering cross section with relative weights being half the thickness of the buried layer and the full thickness of the overlayer. This general formula together with a new version of the software makes the inelastic background analysis of buried layers faster and easier to apply even for nonspecialists.
Automated measurement of spontaneous surprise

Humans exhibit a rich variety of facial expressions reflecting their emotional state. As pointed out by [6], automatically measuring facial expressions to infer emotional states is a challenging task, amongst others because there are no uniquely defined representational units of facial expressions for the basic emotions. In natural circumstances, spontaneous facial expressions tend to be subtle and highly context-dependent, which makes their formal representation very difficult. We have adopted an approach that performs computational analyses on video data acquired from carefully designed experiments for the measurement of facial expressions (see, e.g. [4]). This paper reports on our approach to measure spontaneous surprise. Two main obstacles in automatically measuring spontaneous surprise are of a behavioral and a technical nature. The behavioral obstacle is to elicit and identify spontaneous surprise. The technical obstacle is to develop a computational method to measure the facial expressions associated with spontaneous surprise. In order to deal with the first obstacle two of the authors (MS and JK) developed a novel experimental paradigm to elicit spontaneous surprise in humans. The paradigm is described in Section 2. The second obstacle is addressed by the Spontaneous Surprise Measurement method presented in Section 3. The setup for the experimental evaluation of the method is described in Section 4 and the results are reported in Section 5. Finally, Section 6 is a concluding discussion.
Digital video inpainting detection using correlation of hessian matrix

The use of digital video during forensic investigation helps in providing evidence related to crime scene. However, due to freely available user friendly video editing tools, the forgery of acquired digital videos that are used as evidence in a law suit is now simpler and faster. As a result, it has become easier for manipulators to alter the contents of digital evidence. For instance, inpainting technique is used to remove an object from a video without leaving any artefact of illegal tampering. Therefore, this paper presents a technique for detecting and locating inpainting forgery in a video sequence with static camera motion. Our technique exploits statistical correlation of Hessian matrix (SCHM) to detect and locate tampered regions within a video sequence. The results of our experiments prove that the technique effectively detect and locate areas which are tampered using both texture and structure based inpainting with an average precision rate of 99.79% and an average false positive rate of 0.29%.
Spectral Techniques for Heterogeneous Social Networks

Social networks represent a set of participants and the pairwise relationships between them. There are several different types of networks, such as directed networks, networks with typed edges, dynamic networks and signed networks, as well as composition of different types of networks. Each individual behaves in certain ways in particular situations. Each social situation could represent a status over a time interval for a dynamic network or a specific relationship or role, such as a working relationship or friendship in a network with typed edges, or an incoming role or outgoing role in a directed network. In much social network analysis, edges are only positively weighted, and also of a single type. Ignoring the qualitative differences of relationships rules out several interesting kinds of analysis. I develop a novel way to analyze such networks by considering the qualitatively different social roles that each individual can play in a network. Each individual is represented by copies corresponding to the roles. Each role or status and the corresponding connections define a subgraph. I model the subgraph as a layer, and show how to weight the edges connecting the layers to produce a consistent spectral embedding. This embedding can be used to compute social network properties of graphs of different types, to predict edges, edge types, i and edge direction, as well as to track the change of role over time. I illustrate the approaches using synthetic and real-world datasets. Furthermore, conventional Laplacian approaches are designed for graphs with positively weighted edges and do not deal with signed graphs, which have positively and negatively weighted edges. I derive spectral analysis methods for signed graphs and extend the methods for graph based semi-supervised learning. Using real-world data, I show that they produce robust results.
TEXT-INDEPENDENT SPEAKER VERIFICATION: A COMPARATIVE ANALYSIS STUDY

Gaussian mixture models (GMMs) remain the state of the art technique for modeling spectral envelope features for speech recognition systems. This paper presents a comparative analysis of the performance of three estimation algorithms Expectation Maximization (EM), Greedy EM Algorithm (GEM) and Figueiredo-Jain Algorithm (FJ) based Gaussian mixture models (GMMs) for text-independent speech biometrics verification. The simulation results are showed significant performance achievements. The test performance of, EER=0.26 % for "EM", EER=0.21 % for "GEM" and EER=0.16 % for "FJ", show that the behavioral information scheme of speech biometrics is more robust and have a discriminating power, which can be explored for identity authentication.
MULTEXT-East "1984" annotated corpus 4.0

The novel "1984" by George Orwell is the central component of the MULTEXT-East corpus. This parallel and sentence aligned corpus contains the novel in the English original (about 100,000 words in length), and its translations into a number of languages. This version of the corpus contains the linguistically annotated texts, with each word tagged by its lemma and its MULTEXT(-East) morphosyntactic description (MSD, i.e., a fine-grained feature-structure based PoS tag). The structurally annotated texts are a separate submission (http://hdl.handle.net/11356/1044), also with somewhat different languages.
Survey of the state of the art in human language technology

Spoken language interfaces to computers is a topic that has lured and fascinated engineers and speech scientists alike for over ve decades. For many, the ability to converse freely with a machine represents the ultimate challenge to our understanding of the production and perception processes involved in human speech communication. In addition to being a provocative topic, spoken language interfaces are fast becoming a necessity. In the near future, interactive networks will provide easy access to a wealth of information and services that will fundamentally aaect how people work, play and conduct their daily aaairs. Today, such networks are limited to people who can read and have access to computers|a relatively small part of the population even in the most developed countries. Advances in human language technology are needed for the average citizen to communicate with networks using natural communication skills using everyday devices, such as telephones and televisions. Without fundamental advances in user-centered interfaces, a large portion of society will be prevented from participating in the age of information, resulting in further stratiication of society and tragic loss in human potential. The rst chapter in this survey deals with spoken language input technologies. A speech interface, in a user's own language, is ideal because it is the most natural, exible, eecient, and economical form of human communication. The following sections summarize spoken input technologies that will facilitate such an interface. 1 2 Chapter 1: Spoken Language Input Spoken input to computers embodies many diierent technologies and applications, as shown in Figure 1.1. In some cases, as shown at the bottom of the gure, one is interested not in the underlying linguistic content, but the identity of the speaker, or the language being spoken. Speaker recognition can involve identifying a speciic speaker out of a known population, which has forensic implications, or verifying the claimed identity of a user, thus enabling controlled access to locales (e.g., a computer room) and services (e.g., voice banking). Speaker recognition technologies are addressed in section 1.7. Language identiication also has important applications, and techniques applied to this area are summarized in section 8.7. When one thinks about speaking to computers, the rst image is usually speech recognition, the conversion of an acoustic signal to a stream of words. After many years of research, speech recognition technology is beginning to pass the threshold of practicality. The last decade has witnessed dramatic improvement in speech recognition …
A fast solution technique for large unit commitment problem using heuristic algorithm

This paper, based on the characteristics of Unit commitment (UC) problem, establishes a set of heuristic expert rules to determine the priority list, the serial number of each selected based unit,the total number of generation unit, and to regulate the generation plan for meeting the minimum up/down time limits of units. By applying these heuristic rules to solve large scale UC problem, the scale of the UC problem and computational time are reduced. The simulation results prove the correctness and validity of the proposed heuristic rules.
Cognitive Context Detection for Adaptive Automation

An important research challenge in Human Machine Systems (HMS) is to create machines that are able to better understand human behavior so that the overall efficiency of the HMS can be enhanced through increased productivity and reduced safety risk. The research question posed in this paper is the following: Can an understanding of physiological behavior of humans be combined with statistical machine learning theory to develop predictive models that are able to accurately predict the cognitive difficulty experienced by humans? In this paper, we answer this question in the affirmative by demonstrating the use of two physiological measurements, pupil dilation and eye-gaze patterns, as indices of cognitive workload. Specifically, we demonstrate the possibility of cognitive context detection through machine learning and classification using eye-tracking data from NRL’s Supervisory Control Operations User Testbed (SCOUTTM), a flexible simulation environment that represents the tasks that a future UAS operator would engage in, while controlling multiple UAS.