Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of LDPC codes in image transmission over Rayleigh fading channel

In this paper, we apply a new error-correcting code - low-density parity-check (LDPC) code - as the efficient channel coding technique in image communication on Rayleigh fading channel. The LDPC schemes with two different code rates are simulated. Results show that the LPDC codes have very good performance in image recovering over Rayleigh fading channel. By using a regular LPDC code with parameter R=1/2 and N=2048, the image can be recovered perfectly at about 5.5dB with rather low time delay and system complexity.
A practical method for software quality control via program mutation

A novel method of program mutation is presented that increases the feasibility, effectiveness, and efficiency of searching for those errors that have escaped the activities of Afterers and competent programmers. It is based on syntax direction and it is aided by the language semantics. This means that the scope of a program mutation (i.e. the mutation rules of the method) and its corresponding mutants are rigorously directed by a syntax and related semantics as defined by the tester. A paradigm for the mutation syntax and semantics when limited to Boolean expressions and the corresponding test coverage metrics are given. <<ETX>>
Population initialization in the context of a biased, problem-specific mutation

Several aspects of incorporating domain-specific knowledge into evolutionary computations are discussed. Some interesting properties are described which have been observed during deeper investigations of an algorithm for telecommunication network design. The algorithm was presented by J. Arabas (1995) and compared to other design techniques by S. Kozdrowski et al. (1997) showing comparable or better performance. In this paper, most attention is paid to the proper initialization of the population and to its interference with a knowledge-based mutation operator. Experimental results for several design problems are included to exemplify the observations.
IDEF6: A Design Rationale Capture Method Concept Paper

Abstract : When explicitly captured, design rationale typically exists in the form of unstructured textual comments. In addition to making it difficult, if not impossible to find relevant information on demand, lack of a structured method for organizing and providing completeness criteria for design rationale capture makes it unlikely that important information will be documented. Unlike design methods which serve to document WHAT a design is (Design Specification), the IDEF6 Design Rationale Capture Method is targeted at capturing WHY a design is the way it is -- or WHY it is not manifested in some other form -- together with HOW the final design configuration was reached. IDEF6 was intended to be a method with the representational capability to capture information system design rationale and associate that rationale with the design models and documentation for the end system. Thus, IDEF6 attempts to capture the logic underlying the decisions contributing to, or resulting in, the final design. The explicit capture of design rationale serves to help avoid repeating past mistakes, provides a direct means for determining the impact of proposed design changes, forces the explicit statement of goals and assumptions, and aids in the communication of final system specifications. IDEF, Knowledge acquisition, Design, Information engineering, Method, Design rational, Integration, Systems engineering.
Extending landmarks analysis to reason about resources and repetition

The identification of important planning subgoals, referred to as landmarks, has been shown to be useful in speeding up plan generation in a number of different planning architec- tures [8]. Here, we report on work in progress to extend some features of this domain analysis using landmarks. In particular we consider: the extraction of resource abstracted landmarks; and the identification of landmark repetition along with a count of the minimum number of times a landmark will need to be repeated. We illustrate our ideas with an example from a benchmark domain and consider ways in which this information could be exploited at plan time.
Ada COCOMO and the Ada Process Model

Abstract : Recently, three software development approaches have motivated the development of a revised version of COCOMO: the use of the Ada programming language, the use of incremental development, and the use of the Ada process model capitalizing on the strengths of Ada to improve the efficiency of software development. This paper presents the portions of the revised Ada COCOMO dealing with the effects of Ada and the Ada process model. The remainder of this section of the paper discusses the objectives of Ada COCOMO. Section 2 describes the Ada Process Model and its overall effects on software development effort and schedule. Section 3 presents the structure and features of Ada COCOMO, and discusses the rationale behind the changes made from the earlier version of COCOMO (called 'standard COCOMO' in the remainder of the paper). Section 4 summarizes the current status of Ada COCOMO, including its calibration to date and its currently available implementations; and Section 5 presents the resulting conclusions.
Some recent advances in learning and adaptation for uncertain feedback control systems

Learning and adaptation are essential abilities for feedback control systems to improve performance under uncertainties and external disturbances. In the past decades, there are more and more research interests in developing feedback controllers with learning abilities to ensure stability or optimality of closed‒loop systems. In this guest editorial for the special issue, some recent advances in this area are introduced from three perspectives. The first one is about new developments in adaptive dynamic programming and reinforcement learning methods, which use function approximators such as neural networks to approximately solve the adaptive optimal control problem of uncertain nonlinear systems. The second perspective is related to the learning issues in adaptive control systems based on neural networks. The third perspective includes some new results to deal with uncertainties in feedback control systems based on traditional nonlinear control approaches such as multi‒step nonlinear model predictive control and nonlinear H‒∞ control.
Combined speech and audio coding by discrimination

We propose in this paper a general solution for combined speech and audio coding. Particularly, we describe a speech/music discrimination procedure for multi-mode wideband coding. The speech/music decision is updated only when a low-energy frame is detected, and kept unchanged otherwise. The signal is classified using second-order statistics of discriminant parameters. An experimental CELP/transform coder operating at 16 kbit/s is demonstrated. Results show improved performance when compared to single-mode encoding.
Bayesian Model Averaging: A Tutorial

Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident in- ferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for ac- counting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples. In these examples, BMA provides improved out-of- sample predictive performance. We also provide a catalogue of currently available BMA software.
Study of Fault Detection Based on Benzene Ring in Wireless Sensor Networks

Large scale and limited resources of wireless sensor networks(WSNs) increase the need to cope with node failures. This paper proposes a novel mechanism for the detection of node failures in WSNs. We designed a structure of benzene ring with symmetry. A benzene ring consists of a center node and several child nodes. Reliable communication links among these nodes. Each node maintains a list of its neighbors. The center node delegates detection service and make decisions about faulty nodes based on its child nodes’ available resources. Compared with other solutions, our mechanism can reduce energy consumption, decrease the number of neighborhoods and extend network lifetime. Simulation experiments show the performance and viability of the mechanism.
Model-Based Decomposition Using Non-Binary Dependency Analysis and Heuristic Partitioning Analysis

The two-phase method for model-based decomposition (Chen et al. 2005a) has two major functional components: dependency analysis and partitioning analysis. The functions of these two components are enhanced and generalized in this paper in order to improve the method’s capability. On the one hand, the non-binary dependency analysis is developed such that the two-phase method can handle both binary and non-binary dependency information of the model. The essence of this development is to properly select a resemblance coefficient for the quantification of couplings among the model’s elements. On the other hand, as the past version of partitioning analysis takes the enumerative approach to search decomposition solutions, the heuristic partitioning analysis is developed as an alterative to search a reasonably good solution in a shorter time. The working principle of the heuristic approach is to analyze the coupling structure of the model such that the weak coupling links among the model’s elements can be identified for model partitioning. At the end, a relief valve system is applied to illustrate and justify the newly developed method components.Copyright © 2006 by ASME
Summarizing Review of "Unequal" Reviewers

A frequently encountered problem in decision making is the following review problem: review a large number of objects and select a small number of the best ones. An example is selecting conference papers from a large number of submissions. This problem involves two sub-problems: assigning reviewers to each object, and summarizing reviewers’ scores into an overall score that supposedly reflects the quality of an object. In this paper, we address the score summarization sub-problem for the scenario where a small number of reviewers evaluate each object. Simply averaging the scores may not work as even a single reviewer could influence the average significantly. We recognize that reviewers are not necessarily on an equal ground and propose the notion of “leniency” to model this difference of reviewers. Two insights underpin our approach: (1) the “leniency” of a reviewer depends on how s/he evaluates objects as well as on how other reviewers evaluate the same set of objects, (2) the “leniency” of a reviewer and the “quality” of objects evaluated exhibit a mutual dependency relationship. These insights motivate us to develop a model that solves both “leniency” and “quality” simultaneously. We study the effectiveness of this model on a real-life dataset.
Speech recognition using dynamic neural networks

The authors present an algorithm for isolated-word recognition that takes into consideration the duration variability of the different utterances of the same word. The algorithm is based on extracting acoustical features from the speech signal and using them as the input to a sequence of multilayer perceptron neural networks. The networks were implemented as predictors for the speech samples for a certain duration of time. The networks were trained by a combination of the back-propagation and the dynamic time warping (DTW) techniques. The DTW technique was implemented to normalize the duration variability. The networks were trained to recognize the correct words and to reject the wrong words. The training set consisted of ten words, each uttered seven times by three different speakers. The test set consisted of three utterances of each of the ten words. The results show that all these words could be recognized. <<ETX>>
Latent Fingerprint Enhancement in Preprocessing Stage

Most of the latent fingerprint images are incomplete with poor quality. Therefore to develop an automatic feature extraction of a latent fingerprint is a very challenging problem. A combination of different preprocessing stages will produce a better enhanced Latent fingerprint image. In the present study, an algorithm with a combination of FFT and Gaussian filter has been proposed and implement for image enhancement. The experimental results show that the enhanced images are acceptable for post-processing analysis.
An autotuned deconvolution algorithm with applications to aircraft landing tests matching and autopilot retuning

Many problems of practical interest may be set as inverse problems. We consider two such problems arising in aeronautical applications: the matching of in-flight recorded data with the outputs of a simulation tool, and the retuning (or improvement) of an existing controller. We show how to solve them by the means of an autotuned deconvolution tool.
Proceedings of the 2007 ACM workshop on Recurring malcode

On behalf of the program committee, it is my great pleasure to present the proceedings of the 5th ACM Workshop on Recurring Malcode (WORM). Internet-wide infectious epidemics have emerged as one of the leading threats to information security and service availability. Self-propagating threats, often termed worms, exploit software weaknesses, hardware limitations, Internet topology, and the open Internet communication model to compromise large numbers of networked systems. Malware is increasingly used as a beachhead to launch further malicious activities, such as installing spyware, deploying phishing servers and spam relays, or performing information espionage. Unfortunately, current operational practices still face significant challenges in containing these threats as evidenced by the rise in automated botnet networks and the continued presence of worms released years ago This year's workshop continues the efforts of the previous years by providing a forum for exchanging ideas, increasing understanding, and relating experiences on malicious code. To this end, we invited participation from a wide range of communities, including researchers and practitioners from academia, industry, and the government. The WORM program committee received 30 paper submissions from all over the world. All submissions were carefully reviewed by at least three members of the program committee and judged on the basis of scientific novelty, importance to the field, and technical quality. The final selection took place at the program committee meeting held in Boston, USA, on August 7th 2007. The program committee selected nine papers based on quality, focus, and the likelihood of stimulating productive discussion at the workshop. Moreover, the program committee also solicited invited talks from leading practitioners to share their perspectives
Algorithmic Program Debugging

The thesis lays a theoretical framework for program debugging, with the goal of partly mechanizing this activity. In particular, we formalize and develop algorithmic solutions to the following two questions: (1) How do we identify a bug in a program that behaves incorrectly? (2) How do we fix a bug, once one is identified? We develop interactive diagnosis algorithms that identify a bug in a program that behaves incorrectly, and implement them in Prolog for the diagnosis of Prolog programs. Their performance suggests that they can be the backbone of debugging aids that go far beyond what is offered by current programming environments. We develop an inductive inference algorithm that synthesizes logic programs from examples of their behavior. The algorithm incorporates the diagnosis algorithms as a component. It is incremental, and progresses by debugging a program with respect to the examples. The Model Inference System is a Prolog implementation of the algorithm. Its range of applications and efficiency is comparable to existing systems for program synthesis from examples and grammatical inference. We develop an algorithm that can fix a bug that has been identified, and integrate it with the diagnosis algorithms to form an interactive debugging system. By restricting the class of bugs we attempt to correct, the system can debug programs that are too complex for the Model Inference System to synthesize.
Modeling and classification of biological signals using least squares Prony-SVD AR modeling [underwater acoustics]

Examines various marine biological signals and estimates models of these signals using a least-squares Prony/SVD algorithm. Two classification procedures are presented; the first technique uses a back-propagation neural network, and the second method is based on various spectral distances. Performances of the methods are compared and the characteristics of each technique are discussed. <<ETX>>
Statistical learning for alloy design from electronic structure calculations

The objective of this thesis is to explore how statistical lea rning methods can contribute to the interpretation and efficacy of electronic structur e calculations. This study develops new applications of statistical learning and data mining methods to both semiempirical and density functional theory (DFT) calculations. Each of these classes of electronic structure calculations serves as templates for di ferent data driven discovery strategies for materials science applications. In our study of semi-empirical methods, we take advantage of the ability of data mining methods to quantitatively a ssess high dimensional parameterization schemes. The impact of this work includes the deve lopment of accelerated computational schemes for developing reduced order models. Another applic ation s the use of these informatics based techniques to serve as a means for est imating parameters when data for such calculations are not available. Using density of states (DOS) spectra derived from DFT ca lculations we have demonstrated the classification power of singular value decomposition m ethods to accurately develop structural and stoichiometric classifications of compounds. Buildi ng on this work we have extended this analytical strategy to apply the predictive c apacity of informatics methods to develop a new and far more robust modeling approach for DOS spectra, a ddressing an issue that has gone relatively unchallenged over two decades. By exploring a diverse array of materials systems (metals, ceramics, different crysta l structures) this work has laid the foundations for expanding the linkages between statistical learning a d statistical thermodynamics. The results of this work provide exciting new opportunit ies in computational based design of materials that have not been explored before.
Program changes and the cost of selective recompilation

When module boundaries are dissolved, a program can be seen simply as a collection of named objects (procedures, types, variables, etc.) that reference one another. Program designers impose order on such collections by grouping somehow-related names into modules. Informed by over twenty years of software engineering wisdom, we trust that modularization makes programs easier to control, maintain, and understand. However, not all of the effects of modularity are beneficial. This research suggests that most of the recompilations performed after a change to an interface are redundant and that this redundancy is a direct consequence of how we modularize software systems. This conclusion is based on the careful analysis of a small number of C and Ada programs. This analysis in turn is based on a model of software manufacture that specifically addresses the question of how much work has to be done to incorporate a given set of changes consistently into a given software product. In each program analyzed, the average name defined in an interface is used in 2 or 3 compilation units; the average interface is used in 7 to 25 units. Thus if a programmer were to change a single name chosen at random from an arbitrary interface and then, like the UNIX tool make, compile every compilation unit using that interface, we would expect between 6 and 9 out of every 10 compilations to be unnecessary. This phenomenon extends to the purposeful changes made during program development. For historic changes made to one program, fewer than half the compilations performed after an interface change were actually necessary, even though the data was treated conservatively both by grouping changes and by eliminating spurious interconnections. This work corroborates practical experience as well as observations made by other researchers, solidly tying together a collection of disparate evidence into a coherent picture of software manufacture. It validates the approach taken by some programming environments to use an underlying flat (i.e. non-modular) representation of program objects and, to the extent that recompilation costs reflect general program complexity, leads us to question some basic assumptions about modularization. ftn*This research was sponsored in part by a Xerox Special Opportunity Fellowship and in part by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976, Amendment 20, under contract number F33615-87-C-1499, monitored by the Avionics Laboratory, Air Force Wright Aeronautical Laboratories, Aeronautical Systems Division (AFSC), United States Air Force, Wright-Patterson AFB, Ohio 45433-6543. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the Xerox Corporation, the Defense Advanced Research Projects Agency or the US Government.
An Algorithm for Rate Allocation in a Packet-Switching Network With Feedback

As the speed and complexity of computer networks evolve, sharing network resources becomes increasingly important. thus, the issue of how to allocate the available bandwidth among the multitude of users needs to be addressed. Such allocation needs to be in some sense efficient and fair to different users. In this work the so-called maxmin fairness is chosen as the optimality criterion. The new distributed and asynchronous algorithm is suggested. The algorithm is shown to converge to the optimal rate allocation in a network with general topology under dynamic changes in the set of network users, individual user load and occasional route changes. An upper bound on convergence time is given. The algorithm is shown to be well-behaved in transience. Unlike previous work, the algorithm takes bandwidth consumed by feedback traffic into account. Further, an extension of the algorithm is suggested to address the problem of policing misbehaved users.
Hop-by-hop flow control as a method to improve QoS in 802.3 LANs

This paper describes a novel approach to providing high levels of quality of service (QoS) for real-time UDP-based flows transmitted within switched IEEE 802.3 LANs. There are some obvious similarities between controlling congestion and providing high levels of QoS. Hence, some methods invented in order to control congestion may also be applied to support QoS in QoS-aware LANs. Since real-time and elastic traffic have different expectations in relation to the level of service received, the presented scheme grants them different priority levels and also different treatment on a hop-by-hop basis. The modified 802.3x hop-by-hop flow control is advocated as a better congestion control solution than packet dropping. Due to lack of data packet losses, TCP traffic sources need to be provided with the necessary feedback. In the presented scheme this is done by means of an early congestion notification (ECN) mechanism. Simulation results are also presented.