Source Coding

We deal with zero-delay source coding of a vector-valued Gauss-Markov source subject to a mean-squared error (MSE) fidelity criterion characterized by the operational zero-delay vector-valued Gaussian rate distortion function (RDF). We address this problem by considering the nonanticipative RDF (NRDF) which is a lower bound to the causal optimal performance theoretically attainable (OPTA) function (or simply causal RDF) and operational zero-delay RDF. We recall the realization that corresponds to the optimal "test-channel" of the Gaussian NRDF, when considering a vector Gauss-Markov source subject to a MSE distortion in the finite time horizon. Then, we introduce sufficient conditions to show existence of solution for this problem in the infinite time horizon (or asymptotic regime). For the asymptotic regime, we use the asymptotic characterization of the Gaussian NRDF to provide a new equivalent realization scheme with feedback which is characterized by a resource allocation (reverse-waterfilling) problem across the dimension of the vector source. We leverage the new realization to derive a predictive coding scheme via lattice quantization with subtractive dither and joint memoryless entropy coding. This coding scheme offers an upper bound to the operational zero-delay vector-valued Gaussian RDF. When we use scalar quantization, then for r active dimensions of the vector Gauss-Markov source the gap between the obtained lower and theoretical upper bounds is less than or equal to 0.254r + 1 bits/vector. However, we further show that it is possible when we use vector quantization, and assume infinite dimensional Gauss-Markov sources to make the previous gap to be negligible, i.e., Gaussian NRDF approximates the operational zero-delay Gaussian RDF. We also extend our results to vector-valued Gaussian sources of any finite memory under mild conditions. Our theoretical framework is demonstrated with illustrative numerical experiments.

Code completion helps improve developers' programming productivity. However, the current support for code completion is limited to context-free code templates or a single method call of the variable on focus. Using software libraries for development, developers often repeat API usages for certain tasks. Thus, a code completion tool could make use of API usage patterns. In this paper, we introduce GraPacc, a graphbased, pattern-oriented, context-sensitive code completion approach that is based on a database of such patterns. GraPacc represents and manages the API usage patterns of multiple variables, methods, and control structures via graph-based models. It extracts the context-sensitive features from the code under editing, e.g. the API elements on focus and their relations to other code elements. Those features are used to search and rank the patterns that are most fitted with the current code. When a pattern is selected, the current code will be completed via a novel graph-based code completion algorithm. Empirical evaluation on several real-world systems shows that GraPacc has a high level of accuracy in code completion. D R A F T 1 Display display = new Display(); 2 Shell shell = new Shell(display); 3 ... 4 Button button = new Button(shell, SWT.PUSH); 5 button.setText( "OK"); 6 button.setSize(new Point(40,20)); 7 button.setLocation(new Point(200,20)); 8 ... 9 shell .pack(); 10 shell .open(); 11 while (! shell .isDisposed()) { 12 if (! display .readAndDispatch()) 13 display .sleep() ; 14 } 15 display .dispose(); D R A F T 1 Display display = new Display(); 2 Shell shell = new Shell(display); 3 ... 4 Button button = new Button(shell, SWT.PUSH); 5 FormData formData = new FormData(); 6 button.

The exact expression for the bit error rate (BER) of rectangular quadrature amplitude modulation (QAM) is given. The presented closed-form formula is independent of the bit mapping in use. It is thus particularly useful in the analysis of modulation schemes employing non-Gray mapping, e.g., those designed for iterative (turbo) demapping. Compared to the so-called expurgated bound and the union bound, our expression is shown to accurately predict the BER, which is particularly important in the low signal-to-noise ratio (SNR) range at which strong error-correcting codes ensure satisfactory performance.

When changing a source code entity (e.g., a function), developers must ensure that the change is propagated to related entities to avoid the introduction of bugs. Accurate change propagation is essential for the successful evolution of complex software systems. Techniques and tools are needed to support developers in propagating changes. Several heuristics have been proposed in the past for change propagation. Research shows that heuristics based on the change history of a project outperform heuristics based on the dependency graph. However, these heuristics being static are not the answer to the dynamic nature of software projects. These heuristics need to adapt to the dynamic nature of software projects and must adjust themselves for the peculiarities of each changed entity. In this paper we propose adaptive change propagation heuristics. These heuristics are metaheuristics that combine various previously researched heuristics to improve the overall performance (precision and recall) of change propagation heuristics. Through an empirical case study, using four large open source systems; GCC (a compiler), FreeBSD (an operating system), PostgreSQL (a database), and GCluster (a clustering framework), we demonstrate that our adaptive change propagation heuristics show a 57% statistically significant improvement over the top-performing static change propagation heuristics.

In this paper, in order to compress and enhance 2D images transmitted over wireless channels, a new scheme called Kalman-Turbo (KT) is introduced. In this scheme, the original image is partitioned into 2 N quantization levels and each of the N-bit planes is coded by Turbo encoder. After noise corruption of the channel, each of the noisy bit planes is processed iteratively by a joint equalization block, which is composed of low-complexity 2D binary Kalman filter and the Turbo decoder. In our Kalman filter, state equations are modified in order to take the advantage of two-dimensionality, and, previous soft decisions are taken into account to avoid divergence of Kalman filter. Simulation results show that the performance of the KT system is superior at low SNRs when compared with that of plain Turbo decoding algorithms. We can also achieve compression by any selection of N. Hence, we conclude that KT system will be a compromising approach in 2D image transmission.

We present UZE-CC, a universal fixed-to-variable length coding scheme achieving zero-error communication over any discrete memoryless channel with positive zero-error capacity, under minimal one-bit-per-round feedback. The scheme is sequence-individual — requiring no probabilistic assumptions on the channel or source — and offers both one-shot probabilistic guarantees and Las Vegas zero-error operation.
We establish tight performance bounds, including:

A constant-additive one-shot bound with optimal $\ln(1/\delta)$ scaling in the small-error regime,

A zero-error expected blocklength within a constant of the known-channel optimum,

Robustness to piecewise channel variation with $\tilde{O}(S\log(n/S))$ overhead,

Meta-family aggregation with minimal penalty.
A converse bound proves that no universal zero-error scheme can beat our guarantees by more than an additive constant, showing near-optimality.
Experiments on BSC, BEC, and Z-channels confirm overheads below 1 % in high-capacity regimes and under 3 % in the worst tested cases.

We present USW-X, a universal Slepian–Wolf coding framework that achieves sequence-individual performance guarantees without assuming any probabilistic source model. USW-X combines rateless 2-universal hashing with a Minimum Description Length (MDL) decoder and a tie-break mechanism to ensure zero-error decoding. Our main results include: (i) a one-shot bound guaranteeing effective rate within
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We present EUC-X, a universal, bit-agnostic lossless compressor that operates with a constant per-step shock bound independent of sequence length. The method aggregates Krichevsky-Trofimov (KT) predictors across contexts 0..K via multiplicative weights with fixed-share, which imposes a floor on expert weights and yields a constant bound on instantaneous regret against the best order. With a single constant threshold τ ⋆ , we obtain a universal shock lemma (USCL-const): |∆ t | ≤ 1 4 τ ⋆ for all t. We further incorporate a simple switching prior to achieve near-optimal redundancy Õ(S log(n/S)) for piecewisestationary sequences with S change points, and we lift the scheme to a meta-universal mixer over multiple families (CTW/PPM, LZ, BWT-based, RLE/periodic predictors), guaranteeing performance within a family-selection constant of the best constituent. Our analysis is sequence-individual (no distributional assumptions), and our implementation is quasi-linear with bounded arithmetic via stable quantization.

Bit error rate (BER) performance of convolutional coded quaternary DPSK (QDPSK) with Viterbi decoding is theoretically investigated in Rayleigh fading environments. The probability density functions of the path and branch metric values of Viterbi decoding are derived. The BER's after decoding due to additive white Gaussian noise (AWGN) and cochan-ne1 interference are theoretically analyzed. Rate 1/2 codes and their symbol punctured high-rate codes are considered, and the optimal symbol positions for deletion to minimize the BER after decoding are presented for the codes with constraint length K = 3-7. It is shown that Viterbi decoding considerably reduces the desired signal-to-interference power ratio (SIR), as well as the signal energy per information bit-to-noise power spectrum density ration ( Eb /No), necessary to achieve a certain BER. The spectrum efficiency of the cellular mobile radio system, achievable by the use of the symbol punctured codes, is also evaluated.

In the context of multimedia wireless transmission, a link adaptation strategy is proposed, assuming that the source decoder may accept some remaining errors at the output of the channel decoder. Based on a mean bit error rate for erroneous frames criterion, a target bit energy-to-equivalent noise ratio is chosen. Under this constraint, a maximization of the minimum user information rate is applied through dynamic spreading gain and power control, allowing to guarantee a transmission for each and every user.

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We investigate distributed source coding of two correlated sources X and Y where messages are passed to a decoder in a cascade fashion. The encoder of X sends a message at rate R1 to the encoder of Y . The encoder of Y then sends a message to the decoder at rate R2 based both on Y and on the message it received about X. The decoder's task is to estimate a function of X and Y . For example, we consider the minimum mean squared-error distortion when encoding the sum of jointly Gaussian random variables under these constraints. We also characterize the rates needed to reconstruct a function of X and Y losslessly. Our general contribution toward understanding the limits of the cascade multiterminal source coding network is in the form of inner and outer bounds on the achievable rate region for satisfying a distortion constraint for an arbitrary distortion function d(x, y, z). The inner bound makes use of a balance between two encoding tactics-relaying the information about X and recompressing the information about X jointly with Y . In the Gaussian case, a threshold is discovered for identifying which of the two extreme strategies optimizes the inner bound. Relaying outperforms recompressing the sum at the relay for some rate pairs if the variance of X is greater than the variance of Y .

The paper considers the source description problem with average distortion and per-symbol reproduction cost constraints. The source description cost-distortion function is then defined as the minimum of a weighted sum of the rate and the expected per-symbol reproduction cost subject to average distortion constraint. This function is evaluated for (i) binary source, Hamming loss and reproduction cost of 0 for a 0 and 1 for 1, (ii) Gaussian source, squared error distortion, and average power constraint on reproduction, and (iii) Gaussian Wyner-Ziv source coding with side information setting and average power constraint on reproduction. The results are compared to the description cost in the classical case.

This paper deals with a universal coding problem for a certain kind of multiterminal source coding network called a generalized complementary delivery network. In this network, messages from multiple correlated sources are jointly encoded, and each decoder has access to some of the messages to enable it to reproduce the other messages. Both fixed-to-fixed length and fixed-to-variable length lossless coding schemes are considered. Explicit constructions of universal codes and the bounds of the error probabilities are clarified by using methods of types and graph-theoretical analysis.

Transmission of multimedia, especially transmission of image/video information, requires high reliability and high digital transmission speed. But, in case of mobile communication systems, since transmission reliability is degraded by multipath fading also the frequency bandwidth is severely limited, it is difficult to realize the multimedia transmission without applying techniques to overcome these limitations. For achieving a high quality and high speed in digital image transmission in a band-limited fading channel, Hierarchical modulation is a better method. This method is also constellation is used to give different degrees of error protection for the most important information of the image content. A higher order i.e. 256-HQAM where non-uniform signal constellation with hierarchical oriented OFDM modulator has been proposed. Proposed method is used to provide lesser BER and Bandwidth efficiency in the compressed image data at lower channel Signal to Noise Ratio (SNR). An information theoretical approach is applied to analyze the performance of proposed technique. Using the approach, it is quantitatively evaluated how much the proposed technique outperforms. BER and PSNR of reconstructed image of proposed methodology, which include uniform and non-uniform constellations, over AWGN environments, have been calculated to analyze the performance. The performance of proposed and existing technique has been evaluated by doing simulation using MATLAB 7.6 (R2008a).

In avionics and other critical systems domains, adequacy of test suites is currently measured using the MC/DC metric on source code (or on a model in model-based development). We believe that the rigor of the MC/DC metric is highly sensitive to the structure of the implementation and can therefore be misleading as a test adequacy criterion. We investigate this hypothesis by empirically studying the effect of program structure on MC/DC coverage. To perform this investigation, we use six realistic systems from the civil avionics domain and two toy examples. For each of these systems, we use two versions of their implementation-with and without expression folding (i.e., inlining). To assess the sensitivity of MC/DC to program structure, we first generate test suites that satisfy MC/DC over a non-inlined implementation. We then run the generated test suites over the inlined implementation and measure MC/DC achieved. For our realistic examples, the test suites yield an average reduction of 29.5% in MC/DC achieved over the inlined implementations at 5% statistical significance level.

The fixed slope lossy algorithm derived from the kthorder adaptive arithmetic codeword length function is extended to the case of finite-state decoders or trellis-structured decoders. It is shown that when this algorithm is used to encode a stationary, ergodic source with a continuous alphabet, the Lagrangian performance (i.e., the resulting compression rate plus times the resulting distortion) converges with probability one to a quantity computable as the infimum of an information-theoretic functional over a set of auxiliary random variables and reproduction levels, where > 0 and 0 is designated to be the slope of the ratedistortion function R(D) of the source at some D; the quantity is close to R(D) + D when the order k used in the arithmetic coding or the number of states in the decoders is large enough. An alternating minimization algorithm for computing the quantity is presented; this algorithm is based on a training sequence and in turn gives rise to a design algorithm for variable-rate trellis source codes. The resulting variable-rate trellis source codes are very efficient in low-rate regions (below 0:8 bits/sample). With k = 8, the mean-squared error encoding performance at the rate 1=2 bits/sample for memoryless Gaussian sources is comparable to that afforded by trellis-coded quantizers; with k = 8 and the number of states in the decoder = 32, the mean-squared error encoding performance at the rate 1=2 bits/sample for memoryless Laplacian sources is about 1 dB better than that afforded by the trellis-coded quantizers with 256 states. With k = 8 and the number of states in the decoder = 256, the meansquared error encoding performance at the rates of a fraction of 1 bit/sample for highly dependent Gauss-Markov sources with correlation coefficient 0:9 is within about 0.6 dB of the distortionrate function. Note that at such low rates, predictive coders usually perform poorly.

This paper addresses the design of joint source-channel variable-length codes with maximal free distance for given codeword lengths. While previous design methods are mainly based on bounds on the free distance of the code, the proposed algorithm exploits an exact characterization of the free distance. The code optimization is cast in the framework of mixed-integer linear programming and allows to tackle practical alphabet sizes in reasonable computing time.

Local File Inclusion (LFI) vulnerabilities continue to pose significant security risks to web applications despite being well-documented for over a decade. This paper presents a comprehensive analysis of LFI vulnerabilities, examining their evolution from basic path traversal attacks to sophisticated exploitation techniques that leverage filter bypasses and protocol wrappers. Through systematic investigation of 85 real-world LFI vulnerabilities discovered between 2020-2024, we identify emerging patterns in exploitation methodologies and propose a novel classification framework that categorizes LFI vulnerabilities based on both attack vectors and underlying file system access mechanisms. Our research introduces an enhanced detection methodology that demonstrated a 78% reduction in false positives compared to traditional approaches while maintaining a 96.3% detection rate during controlled testing. Additionally, we propose a defense-in-depth framework specifically tailored for LFI mitigation, which achieved a 99.1% protection rate across diverse web application environments. This paper contributes to the security literature by bridging the gap between theoretical understanding of LFI vulnerabilities and practical implementation of effective countermeasures in modern web application architectures, with particular attention to emerging risks in containerized and serverless deployment environments.

We introduce an open-ended test grounded in algorithmic probability that can avoid benchmark contamination in the quantitative evaluation of frontier models in the context of their Artificial General Intelligence (AGI) and Superintelligence (ASI) claims. Unlike other tests, this test does not rely on statistical compression methods (such as GZIP or LZW), which are more closely related to Shannon entropy than to Kolmogorov complexity and are not able to test beyond simple pattern matching. The test challenges aspects of AI, in particular LLMs, related to features of intelligence of fundamental nature such as synthesis and model creation in the context of inverse problems (generating new knowledge from observation). We argue that metrics based on model abstraction and abduction (optimal Bayesian `inference') for predictive `planning' can provide a robust framework for testing intelligence, including natural intelligence (human and animal), narrow AI, AGI, and ASI. We found that LLM model versions tend to be fragile and incremental as a result of memorisation only with progress likely driven by the size of training data. The results were compared with a hybrid neurosymbolic approach that theoretically guarantees universal intelligence based on the principles of algorithmic probability and Kolmogorov complexity. The method outperforms LLMs in a proof-of-concept on short binary sequences. We prove that compression is equivalent and directly proportional to a system's predictive power and vice versa. That is, if a system can better predict it can better compress, and if it can better compress, then it can better predict. Our findings strengthen the suspicion regarding the fundamental limitations of LLMs, exposing them as systems optimised for the perception of mastery over human language.

We present an agnostic signal reconstruction method for zero-knowledge one-way communication channels in which a receiver aims to interpret a message sent by an unknown source about which no prior knowledge is available and to which no return message can be sent. Our reconstruction method is agnostic vis-à-vis the arbitrarily chosen encoding-decoding scheme and other observer-dependent characteristics, such as the arbitrarily chosen computational model, probability distributions, or underlying mathematical theory. We investigate how non-random messages encode information about their intended physical properties, such as dimension and length scales of the space in which a signal or message may have been originally encoded, embedded, or generated. We focus on image data as a first illustration of the capabilities of the new method. We argue that our results have applications to life and technosignature detection, and to coding theory in general.

Conventional cryptography deals with the encryption and decryption of traditional textual data. The advent of networked multimedia systems will make continuous media streams, such as real time audio and video, increasingly pervasive in future computing and communications environments. It is thus important to secure networked continuous media from potential eavesdroppers. In this paper, we consider the process of real-time encryption and decryption for video streams. We implement a software-only security enhanced MPEG player. The security enhanced player implements a protection hierarchy by specializing the encryption scheme based on MPEG's coding sequences. Encryption may be performed on only I frames, I and P frames, on all I, P and B frames. Increased protection incurs more overhead as more encryption is done. Our security enhanced MPEG player incurs small average overheads in terms of achievable frame rate compared with the unmodified MPEG player, depending on the MPEG frame size, encoding format, and encryption method used, with speeds fast enough for most multimedia Internet applications. This is demonstrated by its integration with Vosaic, a real time multimedia World Wide Web browser. We also observe that increased compression actually results in less cryptographical overhead, due to the fact that more compression means less data, as well as longer dependencies between MPEG frames. Our work shows that video streams can also be encrypted and decrypted while satisfying the real time requirements in the present day Internet.

We study the joint source channel coding problem of transmitting an analog source over a Gaussian channel in two cases -(i) the presence of interference known only to the transmitter and (ii) in the presence of side information known only to the receiver. We introduce hybrid digital analog forms of the Costa and Wyner-Ziv coding schemes. Our schemes are based on random coding arguments and are different from the nested lattice schemes by Kochman and Zamir that use dithered quantization. We also discuss superimposed digital and analog schemes for the above problems which show that there are infinitely many schemes for achieving the optimal distortion for these problems. This provides an extension of the schemes by Bross et al to the interference/side information case. We then discuss applications of the hybrid digital analog schemes for transmitting under a channel signal-to-noise ratio mismatch and for broadcasting a Gaussian source with bandwidth compression. v 1+ P σ 2 . This optimal performance can be achieved by two very simple schemes. The first one is separate source and channel coding, where the source is first quantized and the quantization index is transmitted using an optimal code for the AWGN channel. The second scheme is uncoded (analog) transmission with power scaling , where the source is not explicitly quantized. Recently, it was shown by Bross, Lapidoth and Tinguely [6] that there is a family of infinitely many schemes that are optimal, which contains the separation based scheme and uncoded (analog) transmission as special cases.

Generative Artificial Intelligence (Gen AI) has revolutionized education by enabling personalized learning in computer programming, improving engagement and outcomes. Despite its potential, challenges like accuracy, coherence, and relevance persist, necessitating targeted solutions to maximize its educational impact. A systematic literature review (SLR) was conducted following PRISMA guidelines, analyzing studies from 2019-2024 across databases like IEEE Xplore, ACM Digital Library, and Scopus. The multi-stage selection process identified 42 articles out of an initial 120, focusing on adaptability, relevance, coherence, and accuracy in AI-driven educational tools. Key factors enhancing Gen AI effectiveness were adaptability (33%), contextual relevance (24%), coherence (21%), and evaluation metrics (12%). Prompt engineering (10%) emerged as a critical strategy. Adaptive systems dynamically tailored content to learners, while relevance-enhancing tools aligned materials with educational goals. Evaluation metrics and coherence frameworks improved logical and functional accuracy. Findings highlight Gen AI's transformative potential in programming education, demonstrating improved engagement and alignment between theoretical and practical learning. However, challenges in coherence, accuracy, and ethical concerns like fairness and bias remain areas for further exploration. Generative AI offers scalable opportunities for personalized programming education. Addressing accuracy, coherence, and ethical challenges will enhance its integration into learning environments. Future research should focus on long-term evaluations, advanced evaluation frameworks, and ethical guidelines to ensure inclusive AI use.

An antidictionary code is a lossless compression algorithm using an antidictionary which is a set of minimal words that do not occur as substrings in an input string. The code was proposed by Crochemore et al. in 2000, and its asymptotic optimality has been proved with respect to only a specific information source, called balanced binary source that is a binary Markov source in which a state transition occurs with probability 1/2 or 1. In this paper, we prove the optimality of both static and dynamic antidictionary codes with respect to a stationary ergodic Markov source on finite alphabet such that a state transition occurs with probability p (0 < p ≤ 1).

In this paper, we analyze several adaptive and static data models of arithmetic compression. These models are represented by using Upgraded Petri net as our original class of the Petri nets. After the iterative processes of modeling, simulation and analysis, the models are transformed into an application. The models refer to one-pass and two-pass arithmetic coding where a set of symbols refers to bytes or nibbles. The frequency of a symbol is represented as an unsigned 32-bit integer. Original software for modeling and simulations of Upgraded Petri net, PeM (Petri Net Manager) is developed and used for all models described in this paper. All models are observed in the experiments by using a created application over a standard set of files. Experimental results are presented and compared.

In this paper, we analyze several adaptive and static data models of arithmetic compression. These models are represented by using Upgraded Petri net as our original class of the Petri nets. After the iterative processes of modeling, simulation and analysis, the models are transformed into an application. The models refer to one-pass and two-pass arithmetic coding where a set of symbols refers to bytes or nibbles. The frequency of a symbol is represented as an unsigned 32-bit integer. Original software for modeling and simulations of Upgraded Petri net, PeM (Petri Net Manager) is developed and used for all models described in this paper. All models are observed in the experiments by using a created application over a standard set of files. Experimental results are presented and compared.

Reliable image and video communications over noisy channels has been a great challenge especially for the transmission of large volume of data over unreliable and bandwidth limited channels. One technique to deal with this problem involves taking an existing source encoder and then protecting the bits using appropriate channel coding method. However, channel coding techniques add redundant bits resulting in an increase of the required bandwidth. Also, in the case of multimedia communications, the compressed source exhibits different level of importance among different portions of the encoded bit stream which calls for Unequal Error Protection (UEP). The unequal nature of bit importance that compressed source exhibits can be addressed by exploiting UEP within Hierarchical Quadrature Amplitude Modulation (HQAM) where different signalling points in a constellation are placed at different distances from the adjacent symbols according to their importance. This results in different bit er...

Quantification of neuronal correlations in neuron population helps us to understand neural coding rules. Such quantification could also reveal how neurons encode information in normal and disease conditions like Alzheimer's and Parkinson's. While neurons communicate with each other by transmitting spikes, there would be change in calcium concentration within the neurons inherently. Accordingly, there would be correlations in calcium spike trains and they could have heterogeneous memory structures. In this context, estimation of mutual information rate in calcium spike trains assumes primary significance. However, such estimation is difficult with available methods which would consider longer blocks for convergence without noticing that neuronal information changes in short time windows. Against this backdrop, we propose a faster method which exploits the memory structures in pair of calcium spike trains to quantify mutual information shared between them. Our method has shown superior performance with example Markov processes as well as experimental spike trains. Such mutual information rate analysis could be used to identify signatures of neuronal behaviour in large populations in normal and abnormal conditions.

This work provides an algebraic framework for source coding with decoder side information and its dual problem, channel coding with encoder side information, showing that nested concatenated codes can achieve the corresponding rate-distortion and capacity-noise bounds. We show that code concatenation preserves the nested properties of codes and that only one of the concatenated codes needs to be nested, which opens up a wide range of possible new code combinations for these side information based problems. In particular, the practically important binary version of these problems can be addressed by concatenating binary inner and non-binary outer linear codes. By observing that list decoding with folded Reed-Solomon codes is asymptotically optimal for encoding IID q-ary sources and that in concatenation with inner binary codes it can asymptotically achieve the rate-distortion bound for a Bernoulli symmetric source, we illustrate our findings with a new algebraic construction which comprises concatenated nested cyclic codes and binary linear block codes.

In many scenarios, side information naturally exists in point-to-point communications. Although side information can be present in the encoder and/or decoder and thus yield several cases, the most important case that worths particular attention is source coding with side information at the decoder (Wyner-Ziv coding) which requires different design strategies compared to the the conventional source coding problem. Due to the difficulty caused by the joint design of random variable and reconstruction function, a common approach to this lossy source coding problem is to apply conventional vector quantization followed by Slepian-Wolf coding. In this thesis, we investigate the best rate-distortion performance achievable asymptotically by practical Wyner-Ziv coding schemes of the above approach from an information theoretic viewpoint and a numerical computation viewpoint respectively. From the information theoretic viewpoint, we establish the corresponding rate-distortion function RWZ (D) for any memoryless pair (X, Y ) and any distortion measure. Given an arbitrary single letter distortion measure d, it is shown that the best rate achievable asymptotically under the constraint that X is recovered with distortion level no greater than D ≥ 0 is RWZ (D) = min X [I(X; X) -I(Y ; X)], where the minimum is taken over all auxiliary random variables X such that Ed(X, X) ≤ D and X → X → Y is a Markov chain. Further, we are interested in designing practical Wyner-Ziv coding. With the characterization at RWZ (D), this reduces to investigating X. Then from the viewpoint of numerical computation, the extended Blahut-Arimoto algorithm is proposed to study the rate-distortion performance, as well as determine the random variable X that achieves RWZ (D) which provids guidelines for designing practical Wyner-Ziv coding. In most cases, the random variable X that achieves RWZ (D) is different from the random variable X that achieves the classical rate-distortion R(D) without side information at the decoder. Interestingly, the extended Blahut-Arimoto algorithm allows us to observe an interesting phenomenon, that is, there are indeed cases where X = X . To gain deep The author wishes to express her sincere gratitude and appreciation to all of them who have made this thesis successful and meaningful. The author is grateful to her supervisor, Dr. En-hui Yang, for his invaluable and constant guidance through out her master study period. He is more a mentor than a mere advisor to her. He set a high standard for her that really helps her grow.

Recent advances in multimedia technology opened the path for individual manipulation of the different audio objects within a multichannel mix, for both sampling and karaoke applications. This requires the transmission of these objects as an additional information. Informed Source Separation (ISS) is an adequate framework for this problem. Its main idea is not to transmit the objects themselves, but rather the parameters required to recover them using the mixtures and separation algorithms. In recent studies, the connection was made between ISS and source coding and the concept of coding-based ISS (CISS) was introduced. CISS differs from classical source coding in its use of the mixtures, which permits to reduce the bitrates required to convey audio objects compared to source coding alone with the same model. In this study, we extend existing work on CISS to the case of multichannel mixtures and demonstrate a considerable increase of performance over classical ISS.

In this paper we derive limit theorems for the conditional distribution ofX1givenSn=snasn→ ∞, where theXiare independent and identically distributed (i.i.d.) random variables,Sn=X1+··· +Xn, andsn/nconverges orsn≡sis constant. We obtain convergence in total variation of PX1∣Sn/n=sto a distribution associated to that ofX1and of PnX1∣Sn=sto a gamma distribution. The case of stable distributions (to which the method of associated distributions cannot be applied) is studied in detail.

Multiterminal source coding refers to separate encoding and joint decoding of multiple correlated sources. Joint decoding requires all the messages to be decoded simultaneously which is exponentially more complex than a sequence of single-message decodings. Inspired by previous work on successive coding, we apply the successive Wyner-Ziv coding, which is inherently a low complexity approach of obtaining a prescribed distortion, to the two-terminal source coding scheme. First, we consider 1-helper problem where one source provides partial side information to the decoder to help the reconstruction of the main source. Our results show that the successive coding strategy is an optimal strategy in the sense of achieving the rate-distortion function. By developing connections between source encoding and data fusion steps, it is shown that the whole rate-distortion region for the 2-terminal source coding problem is achievable using the successive coding strategy. Comparing the performance of the sequential coding with the performance of the successive coding, we show that there is no sum-rate loss when the side information is not available at the encoder. This result is of special interest in some applications such as video coding where there are processing and storage constraints at the encoder. Finally, we provide an achievable rate-distortion region for the m-terminal source coding.

Polar codes, introduced recently by Arıkan, are the first family of codes known to achieve capacity of symmetric channels using a low complexity successive cancellation decoder. Although these codes, combined with successive cancellation, are optimal in this respect, their finite-length performance is not record breaking. We discuss several techniques through which their finite-length performance can be improved. We also study the performance of these codes in the context of source coding, both lossless and lossy, in the single-user context as well as for distributed applications.

One of the most dangerous cybersecurity threats is control hijacking attacks, which hijack the control of a victim application, and execute arbitrary system calls assuming the identity of the victim program's effective user. System call monitoring has been touted as an effective defense against control hijacking attacks because it could prevent remote attackers from inflicting damage upon a victim system even if they can successfully compromise certain applications running on the system. However, the Achilles' heel of the system call monitoring approach is the construction of accurate system call behavior model that minimizes false positives and negatives. This paper describes the design, implementation, and evaluation of a Program semantics-Aware Intrusion Detection system called Paid, which automatically derives an applicationspecific system call behavior model from the application's source code, and checks the application's run-time system call pattern against this model to thwart any control hijacking attacks. The per-application behavior model is in the form of the sites and ordering of system calls made in the application, as well as its partial control flow. Experiments on a fully working Paid prototype show that Paid can indeed stop attacks that exploit nonstandard security holes, such as format string attacks that modify function pointers, and that the run-time latency and throughput penalty of Paid are under 11.66% and 10.44%, respectively, for a set of productionmode network server applications including Apache, Sendmail, Ftp daemon, etc.

A web application is a program that is executed on the web server should be written in security awareness to prevent malicious data reaches sensitive sinks that cause different types of vulnerabilities and affect in the behaviour of web application. In this paper an automated static source code analyser technique is proposed based on manual testing of a number of source code web application to build secure control flow path for mitigating SQL

Web applications play a very important role in many fields and become an integral part of the daily lives of millions of users to offer business and convenience services ,Most of the web applications increase their adoption of database systems as a back end to store critical information for the daily billions of transactions are done online .The SQL injection attacks (SQLIAs) one amongst the most dangerous vulnerabilities for Web applications, and represent a trick to SQL query as a serious threat to database server by input possibly such as form fields, The consequences of this type of attacks are poses serious security threats to web applications they allow attackers intelligently obtain unrestricted access to bypass the authentication databases systems or stolen sensitive information these databases contain might. In this paper we present a detailed survey on various kinds of SQLIAs types and also compared existing detection and prevention techniques against these attacks with proposed system in order to find out analytically to what extent they can be claimed effective. Results of an empirical study showed the proposed technique is give promising result in detect more SQLIAs types with respect to the existing tools.

The multiterminal source coding problem has gained prominence recently due to its relevance to sensor networks. In general, rate-distortion in multiterminal source coding is an open problem. However, variations of multiterminal source coding, such as the chief executive officer problem, are addressed in the literature. In addition, full and partial cooperation among the sensors, and reliability and robustness against sensor failures are also being investigated in the literature. In this thesis, multiterminal Gaussian source coding was investigated in the context of sensor networks. The rate-distortion region was parameterized for a pair of correlated Gaussian sources using an implicit transform parameter. The approach made use of established results in multiple descriptor coding using pairwise orthogonal transforms. vii

The multiterminal source coding problem has gained prominence recently due to its relevance to sensor networks. In general, rate-distortion in multiterminal source coding is an open problem. However, variations of multiterminal source coding, such as the chief executive officer problem, are addressed in the literature. In addition, full and partial cooperation among the sensors, and reliability and robustness against sensor failures are also being investigated in the literature. In this thesis, multiterminal Gaussian source coding was investigated in the context of sensor networks. The rate-distortion region was parameterized for a pair of correlated Gaussian sources using an implicit transform parameter. The approach made use of established results in multiple descriptor coding using pairwise orthogonal transforms. vii

This paper considers the optimization of a class of joint source-channel codes described by finite-state encoders (FSEs) generating variable-length codes. It focuses on FSEs associated to joint source-channel integer arithmetic codes, which are uniquely decodable codes by design. An efficient method for computing the free distance of such codes using Dijkstra's algorithm is proposed. To facilitate the search for codes with good distance properties, FSEs are organized within a tree structure, which allows the use of efficient branch-and-prune techniques avoiding a search of the whole tree.

Notations absences et qui ont toujours trouver un mot d'encouragement. S'il est une chose que je peux affirmer, c'est d'avoir noué de belles amitiés partout où je suis allée, j'ai beaucoup de chance. Ed infine, ma non per importanza, ai miei amici, i quali sono sempre stati comprensivi quando non ho potuto essere presente e mi hanno sempre incoraggiata nei momenti piú difficili. Se posso vantarmi di qualcosa, è di aver stretto grandi amicizie durante questo percorso di studio e di questo mi ritengo molto fortunata. Tot slot, maar zeker niet onbelangrijk, voor mijn vrienden, die in staat zijn geweest mij te vergeven voor mijn afwezigheid en altijd een bemoedigend woord hadden. Ik prijs mezelf gelukkig dat ik goede vrienden heb gemaakt tijdens al mijn verblijven. Und nicht zuletzt danke ich meinen Freunden, die mir mein Fehlen immer verziehen und mich stets ermutigt haben. Wenn ich mit Recht behaupten kann, dass ich während aller meiner Aufenthalte wunderbare Freunde gefunden habe, so kann ich mich sehr glücklich schätzen.

In this paper we propose a Multiple Description Image Coding(MDIC) scheme to generate two compressed and balanced rates descriptions in the wavelet domain (Daubechies biorthogonal (9, 7) wavelet) using pairwise correlating transform optimal and application method for Generalized Multiple Description Coding (GMDC) to image coding in the wavelet domain. The GMDC produces statistically correlated streams such that lost streams can be estimated from the received data. Our performance test shown that the proposed method gives more improvement and good quality of the reconstructed image when the wavelet coefficients are normalized by Gaussian Scale Mixture (GSM) model then the Gaussian one ,.

During software development, design rules and contracts in the source code are often encoded through regularities, such as API usage protocols, coding idioms and naming conventions. The structural regularities that govern a program can aid in comprehension and maintenance of the application, but are often implicit or undocumented. Tool support for extracting these regularities from the source code can provide developers useful insights. But building such tool support is not trivial, in particular, because the informal nature of regularities results in frequent deviations and exceptions to these regularities. We propose an automated approach, based on association rule mining, to discover the structural regularities that govern the source code of a software system. We chose this technique because of its resilience to exceptions. In general, tool support for mining regularities tends to discover a huge amount of rules, making interpretation of the results hard and time-consuming. To ease the interpretation, we reduce the results to a minimal canonical form, and group them to obtain a more rational description of the discovered regularities. As an initial feasibility study of our approach, we applied it on two open-source systems, namely IntensiVE (Smalltalk) and FreeCol (Java).

In this paper the relation between nonanticipative rate distortion function (RDF) and Bayesian filtering theory is further investigated on general Polish spaces. The relation is established via an optimization on the space of conditional distributions of the so-called directed information subject to fidelity constraints. Existence of the optimal reproduction distribution of the nonanticipative RDF is shown using the topology of weak convergence of probability measures. Subsequently, we use the solution of the nonanticipative RDF to present the realization of a multidimensional partially observable source over a scalar Gaussian channel. We show that linear encoders are optimal, establishing joint source-channel coding in real-time.

In this paper we analyze the probabilistic matching of sources with memory to channels with memory so that symbol-by-symbol code with memory without anticipation are optimal, with respect to an average distortion and excess distortion probability. We show achievability of such a symbolby- symbol code with memory without anticipation, and we show matching for the Binary Symmetric Markov source (BSMS(p)) over a first-order symmetric channel with a cost constraint.

Eight new properties of absolute moment block truncation coding (AMBTC) are presented with proof. The main purposes of this paper are twofold: (1) provide fundamental insights into the AMBTC algorithm and (2) show that AMBTC is the optimum choice among the I-bit moment preserving quantizers for achieving the minimum mean square error and the minimum computations. Coding performance simulated in the full-band and subband environments further demonstrates its consistency with the theoretical performance bounds.

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