Interactive fisheye views use distortion to show both local detail and global context in the same display space. Although fisheyes allow the presentation and inspection of large data sets, the distortion effects can cause problems for users. One such problem is lack of memorability the ability to find and go back to objects and features in the data. This thesis examines the possibility of improving the memorability of fisheye views by adding historical information to the visualization. The historical information is added visually through visit wear, an extension of the concepts of edit wear and read wear. This will answer the question Where have I been? through visual instead of cognitive processing by overlaying new visual information on the data to indicate a users recent interaction history. This thesis describes general principles of visibility in a space that is distorted by a fisheye lens and defines some parameters of the design space of visit wear. Finally, a test system that applied the principles was evaluated, and showed that adding visit wear to a fisheye system improved the memorability of the information space.

Computing the similarity of two strings or sequences is one of the most important fundamental in computer field, and it has been widely studied for several decades. In the last decade, it gained the researchers' attentions again because of the improvements of the hardware computation ability and the presence of huge amount of data in biotechnology. In this dissertation, we pay attention to computing the edit distance between two sequences where the block-edit operations are involved in addition to the character-edit operations. Previous researches show that this problem is NP-hard if recursive block moves are allowed. Since we are interested in solving the editing problems by the polynomial-time optimization algorithms, we consider the simplified version of the edit distance problem. We first focus on the longest common subsequence (LCS) of run-length encoded (RLE) strings, where the runs can be seen as a class of simplified blocks. Then, we apply constraints to the problem, i.e. to find the constrained LCS (CLCS) of RLE strings. Besides, we show that the problems which involve block-edit operations can still be solved by the polynomial-time optimization algorithms if some restrictions are applied. Let X and Y be two sequences of lengths n and m, respectively. Also, let N and M, be the numbers of runs in the corresponding RLE forms of X and Y, respectively. In this dissertation, first, we propose a simple algorithm for computing the LCS of X and Y in O(NM + min{ p_1, p_2 }) time, where p_1 and p_2 denote the numbers of elements in the bottom and right boundaries of the matched blocks, respectively. This new algorithm improves the previously known time bound O(min{nM, Nm}) and outperforms the time bounds O(NM log NM) or O((N+M+q) log (N+M+q)) for some cases, where q denotes the number of matched blocks. Next, we give an efficient algorithm for solving the CLCS problem, which is to find a common subsequences Z of X and Y such that a given constrained sequence P is a subsequence of Z and the length of Z is maximized. Suppose X, Y and P are all in RLE format, and the lengths of X, Y and P are n, m and r, respectively. Let N, M and R be the numbers of runs in X, Y, and P, respectively. We show that by RLE, the CLCS problem can be solved in O(NMr + min{q_1 r + q_4, q_2 r + q_5 }) time, where q_1 and q_2 denote the numbers of elements in the south and east boundaries of the partially matched blocks on the first layer, respectively, and q_4 and q_5 denote the numbers of elements of the west and north pillars in the bottom boundaries of all fully matched cuboids in the DP lattice, respectively. When the input strings have good compression ratios, our work obviously outperforms the previously known DP algorithms and the Hunt-Szymanski-like algorithms. Finally, we consider variations of the block edit distance problem that involve character insertions, character deletions, block copies and block deletions, for two given sequences X and Y. In this dissertation, three variations are defined with different measuring functions, which are P(EIS, C), P(EI, L) and P(EI, N). Then we show that with some preprocessing, the minimum block edit distances of these three variations can be obtained by dynamic programming in O(nm), O(nm log m) and O(nm^2) time, respectively, where n and m are the lengths of X and Y.

In this study, the focus is on predicting the subcellular localization of a protein, since subcellular localization is helpful in understanding a protein&rsquo / s functions. Function of a protein may be estimated from its sequence. Motifs or conserved subsequences are strong indicators of function. In a given sample set of protein sequences known to perform the same function, a certain subsequence or group of subsequences should be common / that is, occurrence (frequency) of common subsequences should be high. Our idea is to find the common subsequences through clustering and use these common groups (implicit motifs) to classify proteins. To calculate the distance between two subsequences, traditional string edit distance is modified so that only replacement is allowed and the cost of replacement is related to an amino acid substitution matrix. Based on the modified string edit distance, spectral clustering embeds the subsequences into some transformed space for which the clustering problem is expected to become easier to solve. For a given protein sequence, distribution of its subsequences over the clusters is the feature vector which is subsequently fed to a classifier. The most important aspect if this approach is the use of spectral clustering based on modified string edit distance.

Large amount of data is generated by applications used in basic-science research and development applications. The size of data introduces great challenges in storage, analysis and preserving privacy. This dissertation proposes novel techniques to efficiently analyze the data and reduce storage space requirements through a data compression technique while preserving privacy and providing data security. We present an efficient technique to compute an analytical query called spatial distance histogram (SDH) using spatiotemporal properties of the data. Special spatiotemporal properties present in the data are exploited to process SDH efficiently on the fly. General purpose graphics processing units (GPGPU or just GPU) are employed to further boost the performance of the algorithm. Size of the data generated in scientific applications poses problems of disk space requirements, input/output (I/O) delays and data transfer bandwidth requirements. These problems are addressed by applying proposed compression technique. We also address the issue of preserving privacy and security in scientific data by proposing a security model. The security model monitors user queries input to the database that stores and manages scientific data. Outputs of user queries are also inspected to detect privacy breach. Privacy policies are enforced by the monitor to allow only those queries and results that satisfy data owner specified policies.

A majority of the data transmitted and processed today has an inherent structured high-dimensional nature, either because of the process of encoding using high-dimensional codebooks for providing a systematic structure, or dependency of the data on a large number of agents or variables. As a result, many problem setups associated with transmission and processing of data have a structured high-dimensional aspect to them. This dissertation takes a look at two such problems, namely, communication over networks using network coding, and learning the structure of graphical representations like Markov networks using observed data, from an information-theoretic perspective. Such an approach yields intuition about good coding architectures as well as the limitations imposed by the high-dimensional framework. Th e dissertation studies the problem of network coding for networks having multiple transmission sessions, i.e., multiple users communicating with each other at the same time. The connection between such networks and the information-theoretic interference channel is examined, and the concept of interference alignment, derived from interference channel literature, is coupled with linear network coding to develop novel coding schemes off ering good guarantees on achievable throughput. In particular, two setups are analyzed – the first where each user requires data from only one user (multiple unicasts), and the second where each user requires data from potentially multiple users (multiple multicasts). It is demonstrated that one can achieve a rate equalling a signi ficant fraction of the maximal rate for each transmission session, provided certain constraints on the network topology are satisfi ed. Th e dissertation also analyzes the problem of learning the structure of Markov networks from observed samples – the learning problem is interpreted as a channel coding problem and its achievability and converse aspects are examined. A rate-distortion theoretic approach is taken for the converse aspect, and information-theoretic lower bounds on the number of samples, required for any algorithm to learn the Markov graph up to a pre-speci fied edit distance, are derived for ensembles of discrete and Gaussian Markov networks based on degree-bounded graphs. The problem of accurately learning the structure of discrete Markov networks, based on power-law graphs generated from the con figuration model, is also studied. The eff ect of power-law exponent value on the hardness of the learning problem is deduced from the converse aspect – it is shown that discrete Markov networks on power-law graphs with smaller exponent values require more number of samples to ensure accurate recovery of their underlying graphs for any learning algorithm. For the achievability aspect, an effi cient learning algorithm is designed for accurately reconstructing the structure of Ising model based on power-law graphs from the con figuration model; it is demonstrated that optimal number of samples su ffices for recovering the exact graph under certain constraints on the Ising model potential values. / text

The past decade has seen a tremendous growth in the amount of biomedical literature, specifically in the area of bioinformatics. As a result, biomedical text categorization has become a central task for providing researchers with literature appropriate for their specific information needs. Pan et al. have explored a method that automatically identifies information-bearing sentence fragments within scientific text. Their proposed method aims to automatically classify sentence fragments into certain sets of categories defined to satisfy specific types of information needs. The categories are grouped into five different dimensions known as Focus, Polarity, Certainty, Evidence, and Trend. The reason that fragments are used as the unit of classification is that the class value along each of these dimensions can change mid-sentence. In order to automatically annotate sentence fragments along the five dimensions, automatically breaking sentences into fragments is a necessary step. The performance of the classifier depends on the sentence fragments. In this study, we investigate the problem of automatic fragmentation of biomedical sentences, which is a fundamental layer in the multi-dimensional fragment classification. In addition, we believe that our proposed fragmentation algorithm can be used in other domains such as sentiment analysis. The goal of sentiment analysis is often to classify the polarity (positive or negative) of a given text. Sentiment classification can be conducted at different levels such as document, sentence, or phrase (fragment) level. Our proposed fragmentation algorithm can be used as a prerequisite for phrase-level sentiment categorization which aims to automatically capture multiple sentiments within a sentence. / Thesis (Master, Computing) -- Queen's University, 2009-09-25 10:08:04.429

Abstract In this work the LCVD of copper and nickel from the precursor gases Cu(hfac)tmvs and Ni(CO)4 has been investigated. The in-house constructed LCVD system and processes and the practical utilisation of these in prototype integrated circuit edit work are described. The investigated process parameters include laser power, laser scan speed, precursor partial pressure and the effect of H2 and He carrier gases. The deposited metal conductor lines have been examined by LIMA, AFM, FIB secondary electron/ion micrography, and by electrical measurements. Furthermore, the study of experimental FIB circuit edit processes is carried out and discussed with particular emphasis on ion beam induced ESD damages. It is shown how the LCVD and FIB methods can be combined to create a novel method to carry out successfully circuit edit cases where both methods alone will fail. The combined FIB/LCVD- method is shown to be highly complementary and effective in practical circuit edit work in terms of reduced process time and improved yield. Circuit edit cases where both technologies are successfully used in a complementary way are presented. Selected examples of some special circuit edit cases include RF- circuit editing, a high resolution method for FIB-deposited tungsten conductor line resistance reduction and large area EMI shielding of IC surfaces. Based on the research it was possible for a formal workflow for the combined process to be developed and this approach was applied to 132 circuit edit cases with 85% yield. The combined method was applied to 30% of the total number of edit cases. Finally, the developed process and constructed system was commercialized.

L’empoisonnement est une infraction qui est apparue tardivement, bien que le poison soit depuis longtemps utilisé comme une arme criminelle redoutable. En 1682, il fait l’objet d’une réglementation spécifique qui le distingue du simple homicide et qui encadre de manière rigoureuse le commerce des substances vénéneuses. Depuis cette date, l’empoisonnement a toujours été incriminé de façon autonome dans le droit français. Aussi, cette nouveauté juridique soulève de nombreuses questions d’une part sur la constitution de l’infraction et, de l’autre, sur sa répression par les tribunaux. L’empoisonnement étant un crime difficilement démontrable, la question de la preuve se pose à chaque instant. Par conséquent, il faut interroger la justice criminelle pour comprendre l’apport de cette nouvelle législation et les spécificités qui sont propres au crime d’empoisonnement, tant dans le déroulement de la procédure criminelle que dans la sanction appliquée aux empoisonneurs. / Poisonning is a violation which appeared lately, though poison has been used as a powerful criminal weapon for a long time. In 1682, a specific regulation distinguished it from a manslaughter and supervised the trade of poisonous substances in a rigorous way. Since then, the poisoning has always been incriminated independantly in the French law. So a lot of question was raised by this new law ; in one hand on the constitution of the breach and on the other hand on its repression by the courts. As poisonning is a crime which is hard to proove, evidence have to be found all the time. Therefore, we have to ask the Criminal Justice to understand this new legislation and these specificities which are particular to poisonning, both in the progress of the criminal procedure and in the penalty applied to the poisoners.

This thesis describes the steps leading to the promotion Web site Fotovýběr and building the company's name. The aim is through marketing tools to increase awareness about the services offered and to propose such a marketing strategy for the future will ensure an adequate supply of site visitors, ie potential customers. At the beginning of the analysis is devoted to the company and its current activities, then I suggest specific marketing practices and finally evaluate the results and prospects for the future. The thesis should help the company Fotovýběr optimize their marketing activities.

The goal of this diploma thesis is to create system for network data diagnostics based on detecting and correcting spelling errors. The system is intended to be used by network administrators as next diagnostics tool. As opposed to the primary use of detection and correction spelling error in common text, these methods are applied to network data, which are given by the user. Created system works with NetFlow data, pcap files or log files. Context is modeled with different created data categories. Dictionaries are used to verify the correctness of words, where each category uses its own. Finding a correction only according to the edit distance leads to many results and therefore a heuristic for evaluating candidates was proposed for selecting the right candidate. The created system was tested in terms of functionality and performance.