waashk/dblp huggingface dataset:
a new variable-length genome genetic algorithm for data clustering in semeiotics. this paper focuses on the introduction of a new evolutionary algorithm for data clustering, the self-sizing genome genetic algorithm. it is akin to a messy genetic algorithm and does not use a priori information about the number of clusters. a new recombination operator, gene-pooling, is introduced, while fitness is based on simultaneously maximizing intra-cluster homogeneity and inter-cluster separability. this algorithm is applied to clustering in dermatological semeiotics. moreover, a pathology addressing index is defined to quantify utility of found clusters in unambiguously addressing towards pathologies. comparison with other clustering tools is performed.
a novel grammar-based genetic programming approach to clustering. most of the classical methods for clustering analysis require the user setting of number of clusters. to surmount this problem, in this paper a grammar-based genetic programming approach to automatic data clustering is presented. an innovative clustering process is conceived strictly linked to a novel cluster representation which provides intelligible information on patterns. the efficacy of the implemented partitioning system is estimated on a medical domain by exploiting expressly defined evaluation indices. furthermore, a comparison with other clustering tools is performed.
inductive inference of chaotic series by genetic programming: a solomonoff-based approach. a genetic programming approach to inductive inference of chaotic series, with reference to solomonoff complexity, is presented. it consists in evolving a population of mathematical expressions looking for the 'optimal' one that generates a given chaotic data series. validation is performed on the logistic, the henon and the mackey-glass series. the method is shown effective in obtaining the analytical expression of the first two series, and in achieving very good results on the third one.
digital geometry image analysis for medical diagnosis. this paper describes a new medical image analysis technique for polygon mesh surfaces of human faces for a medical diagnosis application. the goal is to explore the natural patterns and 3d facial features to provide diagnostic information for fetal alcohol syndrome (fas). our approach is based on a digital geometry analysis framework that applies pattern recognition techniques to digital geometry (polygon mesh) data from 3d laser scanners and other sources. novel 3d geometric features are extracted and analyzed to determine the most discriminatory features that best represent fas characteristics. as part of the nih consortium for fasd, the techniques developed here are being applied and tested on real patient datasets collected by the nih consortium both within and outside the us.
boundary surface extraction and rendering for volume datasets. traditional iso-surface techniques focus on the extraction and rendering of contour surfaces. boundary surfaces, however, are often more interesting and useful to the applications as they are more natural representations of the objects embedded in the dataset. this paper describes an efficient boundary surface extraction and rendering approach for volume datasets. a volume dataset is first filtered using a laplacian of gaussian (log) filter to generate a zero-crossing field, from which boundary surface information is extracted. two types of surfaces can be generated: (1) zero-crossing surface; and (2) iso-surface boundaries. the zero-crossing surface can be extracted directly from the zero-crossing field as an iso-surface with a zero iso-value. original intensity values will then be attached to the vertices of the polygon mesh for flexible rendering. iso-surface boundaries are the iso-surfaces from the original volume that best approximate the zero-crossing boundaries. the iso-values of these iso-surface boundaries are obtained through a histogram analysis of the zero-crossing boundaries in a multi-scale space. the new approach provides a more efficient and accurate surface navigation technique for volume data exploration.
visualization of unstructured text sequences of nursing narratives. this paper presents a keyword-based information visualization technique for nursing record sequences. visualizing the trend information rooted in unstructured and fragmented abstract text data is a largely unaddressed problem. in our technique, multiple hierarchical keyword based visualizations are used to explore unstructured text data from nursing records. first, each text data set is broken up into a list of keywords to enable the visualization of keyword occurrences over time and the relative distribution of keywords. a graphical user interface is provided to enable selection and classification of keywords. users may select one or more data sets to compare, in addition to one or more groups of keywords to add to the visualization. colors are used to distinguish quickly and easily between groups of keywords present in the visualization. at the second level of hierarchy, keywords for visualization are discovered through a predetermined automatic detection and scoring based mechanism. the aggregate frequency trend of keywords from all data sets is also provided in both hierarchies as a way to visualize overall trends and analyze various events in time.
the recursive transpose-connected cycles (rtcc) interconnection network for multiprocessors. in this paper, we propose a new modular topology for interconnection networks, the recursive transpose-connected cycles (rtcc). the rtcc has a recursive definition quite similar to that of fractal graphs having interesting topological characteristics, making it suitable for utilization as the base topology of large-scale multicomputer interconnection networks. we study important properties of this topology such as diameter, bisection width and issues related to implementation, such as routing algorithms and the average message latency under vlsi layout constraints. in addition, we prove that the rtcc is a hamiltonian graph. we conclude that, insight of most of the above-mentioned properties, the rtcc is superior to conventional topologies such as the mesh and k-ary n-cube.
on the relationship between roles and power: preliminary report. this paper discusses a formal analysis of multi-agent systems based on organizational and normative concepts. social agents (individuals and groups) and multi-agent systems have an underlying structure which is underpinned by roles. power is legitimized within a social context and it stems from a role. in other words, agents draw their power from the authority positions that they hold in relation to other agents. how roles are intertwined with commitments, obligations, power, rights and authority relations is discussed.
efficient discovery of loosely structured motifs in biological data. in the last few years, the completion of the human genome sequencing showed up a wide range of new challenging issues involving raw data analysis. in particular, the discovery of information implicitly encoded in biological sequences is assuming a prominent role in identifying genetic diseases and in deciphering biological mechanisms. this information is usually represented by patterns frequently occurring in the sequences, also called motifs. because of biological observations, the class of structured motifs have received much attention. this paper gives a contribution in this setting by providing an efficient algorithm for the identification of novel classes of structured motifs, where several kinds of "exceptions" (whose biological relevance recently emerged in the literature) may be tolerated in pattern repetitions.
msa: a memory-aware utility accrual scheduling algorithm. whereas fairness can be the basis for general-purpose operating system scheduling policies, timeliness is the primary concern for real-time systems. as such, real-time schedulers permit uninterrupted, exclusive access to the cpu by a specific task to ensure its timely completion of execution. only a subset of tasks, however, can satisfy their timing constraints during processor overload conditions in a real-time system. utility accrual (ua) scheduling disciplines assure scalability and graceful performance degradation by identifying the subset of tasks to be granted the heavily contended system resources.furthermore, whereas general-purpose operating systems treat memory monolithically and indiscriminately service dynamic allocation requests, ua-schedulers can benefit from special memory management considerations during memory overload conditions. msa, the scheduling algorithm here presented is the first of its kind to treat memory as a ua-scheduler-managed resource. the scheduler is made aware of memory allocation requirements of each task throughout runtime and accordingly makes appropriate cpu and resource scheduling decisions. the algorithm is well-suited for use in resource-constrained embedded systems in a soft real-time environment.we have implemented msa in a posix real-time operating environment and measured its performance under various load conditions. our experimental results show overall performance gains over other memory-unaware ua scheduling algorithms during memory overload.
transformation of b specifications into uml class diagrams and state machines. we propose a rule-based approach for transforming b abstract machines into uml diagrams. we believe that important insight into the structure underlying a b model can be gained by representing it in uml, for example in order to explain the model to stakeholders that are not experts in the b formalism. we focus on the generation of class diagram and state machines. our approach does not prescribe a mechanic algorithm for translation, giving the modeler choices to adapt the resulting uml models as appropriate.
limits in modelling evolving computer-based systems. this paper explores the limitations of one technique for modelling computer-based systems with evolving requirements. a case study is introduced which highlights the importance of taking a multi-perspective on dependable computer-based systems. this should be reflected in the modelling technique. such considerations motivate our ongoing research agenda.
comparing images with distance functions based on attribute interaction. this paper presents two new families of distance functions for image comparison through their feature vectors. these families concern with the effects of the interaction of attributes when two images are compared. experiments were executed in order to corroborate the effectiveness of the new functions, leading to very promising results.
effective shape-based retrieval and classification of mammograms. this paper presents a new approach to support computer-aided diagnosis (cad) aiming at assisting the task of classification and similarity retrieval of mammographic mass lesions, based on shape content. we have tested classical algorithms for automatic segmentation of this kind of image, but usually they are not precise enough to generate accurate contours to allow lesion classification based on shape analyses. thus, in this work, we have used zernike moments for invariant pattern recognition within regions of interest (rois), without previous segmentation of images. a new data mining algorithm that generates statistical-based association rules is used to identify representative features that discriminate the disease classes of images. in order to minimize the computational effort, an algorithm based on fractal theory is applied to reduce the dimension of feature vectors. k-nearest neighbor retrieval was applied to a database containing images excerpted from previously classified digitalized mammograms presenting breast lesions. the results reveal that our approach allows fast and effective feature extraction and is robust and suitable for analyzing this kind of image.
a new similarity measure for histograms applied to content-based retrieval of medical images. this paper presents a new similarity measure to compare gray-level histograms, aiming at reducing both false positive and false negative results, in the context of medical images.
an improved hybrid genetic algorithm for the generalized assignment problem. we consider the generalized assignment problem in which the objective is to find a minimum cost assignment of a set of jobs to a set of agents subject to resource constraints. the presented new approach is based on a previously published, successful hybrid genetic algorithm and includes as new features two alternative initialization heuristics, a modified selection and replacement scheme for handling infeasible solutions more appropriately, and a heuristic mutation operator. tests are performed on standard test instances from the literature and on newly created, larger and more difficult instances. the presented genetic algorithm with its two initialization variants is compared to the previous genetic algorithm and to the commercial general purpose branch-and-cut system cplex. results indicate that cplex is able to solve relatively large instances of the general assignment problem to provable optimality. for the largest and most difficult instances, however, the proposed genetic algorithm yields on average the best results in shortest time.
time-frequency feature detection for time-course microarray data. gene clustering based on microarray data provides useful functional information to the working biologists. many current gene-clustering algorithms rely on euclidean-based distance metrics and fail to capture the time-dependent features of the data, usually corrupted by high levels of experimental noise. here we propose an algorithm capable of dealing with the noise through a time-frequency approach and related measure of correlation between time-course expressions of different genes (trajectories). the approach makes use of fast multi-resolution feature classification algorithms and allows for the desired functional characteristics (such as phase delay, activation/repression etc.) to be enhanced and detected.we have applied our algorithm to time-course microarray data of drosophila melanogaster (arbeitman et al., science, sep 27, 2002, page 2270--2275). we examined various relations among homeodomain genes (referred to as group h) and regulators of homeodomain genes (group rh) as follows: after normalization, the trajectories were projected on to cosbell wavelet basis. the four genes in group rh form two clusters: three of them stayed close to each other, and the last one, cg8651 (trithorax), was singled out. the group h genes, forming four clusters, showed functional features that are more similar to trithorax than the other three. we further analyzed ten homeodomain genes that have good correlations with trithorax in the wavelet basis. literature search showed that there are five genes thought to be in the downstream pathway of trithorax. although only two of these five genes were in the dataset available to the algorithm, it was able to identify both of these. our study suggests that timefrequency analysis provides a powerful tool for discovering the underlying regulatory networks when applied to time-course microarray data.
exploit sequencing to accelerate hot xml query pattern mining. speeding up query evaluation in large xml repositories becomes a challenging and all-important problem with vast xml-related applications arising. upon discovery of hot xml query patterns, indexing and caching can be effectively adopted for query performance enhancement. previous algorithms for finding hot query patterns basically introduced a straightforward generate-and-test strategy. in this paper, we present, solaria, an efficient algorithm for mining hot xml query patterns without candidate maintenance and costly tree-containment checking. efficient algorithm of sequence mining is involved in discovering frequent tree-structured patterns, which aims at replacing expensive containment testing with cheap parent-child checking in sequences. solaria deeply prunes unrelated search space for frequent pattern enumeration by parent-child relationship constraint. with the motivation of indexing and caching in xml query optimization, we also propose the derived algorithm solaria for mining hot "closed" xml query patterns which provide compact and complete structure information. by a thorough experimental study on various real-life data, we demonstrate the efficiency and scalability of solaria over the previous known alternative. solaria is also linearly scalable in terms of xml queries' size.
a methodology to provide and use interchangeable services. computing today requires the use of many software packages, but only a few packages are used on a daily basis. this infrequent usage pattern often does not justify purchasing full licenses and therefore motivates a need for a more flexible way to use and pay for the usage of software. this paper describes a design philosophy in which similar services provide the same interface to clients. services based on this design are interchangeable, allow payment per use, handle payment conveniently, are platform independent, and frequently do not require local installation. clients can therefore easily utilize resources based on application needs and services available at the time that the application is executing. an example implementation using this methodology is also discussed.
a new cache management algorithm for multimedia storage systems. every storage platform intended to fit the requirements of the multimedia systems must incorporate a disk scheduling mechanism and a cache architecture that can handle their special needs. on the other hand, the general trend is to make integrated storage platforms that meet the requirements of deterministic applications, multimedia systems, and traditional best-effort applications altogether. in this paper we propose a new multimedia cache algorithm that imposes several optimizations to the state of the art. this algorithm has been designed to become part of an integrated storage system, including a proposal for an admission control algorithm that ensures the response time of the system for the accepted requests. this paper also presents an evaluation of the algorithm and compares it with other algorithms described in the bibliography.
an analysis of modeling flaws in hl7 and jahis. health level 7 (hl7), one of the ansi standards organizations has a mission to provide standards for the execution, management, and integration of data to support clinical patient care. hl7 will play an important role in the implementation of the hipaa regulations. hl7 has developed a reference information model (rim), an object-oriented model of clinical data. jahis is a japanese organization that has developed extensions to this rim. instead of using the unified modeling language (uml), the standard notation for object-oriented software development, these two organizations have developed specialized object-oriented models. this has resulted in languages which are incompatible with the current use of uml. the consequences of this choice are the loss of the possible use of a large variety of existing models and patterns. what is worse, it will be difficult to add security specifications in their models, a critical aspect in the electronic interchange of medical records. we discuss here the shortcomings of hl7 and jahis as modeling languages and as languages in which to add security specifications. we also propose some solutions to this situation.
a debugging calculus for mobile ambients. advancements in network-aware computing has prompted the study of novel programming languages with advanced programming abstractions to support various forms of mobility and to coordinate and monitor the use of resources. this work addresses the issue of designing debuggers for network-aware programming languages. in our approach a debugger is viewed as being an extension of the underlying programming language with suitable debugging abstractions. we apply this idea to cardelli and gordon's ambient calculus [3]. the resulting debugger is designed to monitor and trace executions of mobile ambients by keeping track of causal informations about events of computations.
blinded-key signatures: securing private keys embedded in mobile agents. we present a new cryptographic primitive, the blinded-key signature, which allows the inclusion of private keys in autonomous mobile agents. this novel approach can be applied to many well-known digital signature schemes, such as rsa and elgammal.
prototype-based mining of numeric data streams. great organizations collect open-ended and time-changing data received at a high speed. the possibility of extracting useful knowledge from these potentially infinite databases is a new challenge in data mining. in this paper we propose an anytime incremental learning algorithm for mining numeric data streams. within supervised learning, our approach is based on prototypes and hypercubic decision rules, concerning with the simplicity of the model provided and the time complexity as primary goals. experimental results with synthetic databases of 100 gigabytes show a good performance from streams of data in continuous transformation.
discovering decision rules from numerical data streams. this paper presents a scalable learning algorithm to classify numerical, low dimensionality, high-cardinality, time-changing data streams. our approach, named scallop, provides a set of decision rules on demand which improves its simplicity and helpfulness for the user. scallop updates the knowledge model every time a new example is read, adding interesting rules and removing out-of-date rules. as the model is dynamic, it maintains the tendency of data. experimental results with synthetic data streams show a good performance with respect to running time, accuracy and simplicity of the model.
incremental rule learning based on example nearness from numerical data streams. mining data streams is a challenging task that requires online systems based on incremental learning approaches. this paper describes a classification system based on decision rules that may store up-to-date border examples to avoid unnecessary revisions when virtual drifts are present in data. consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbor algorithm. in addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another.
data streams classification by incremental rule learning with parameterized generalization. mining data streams is a challenging task that requires online systems based on incremental learning approaches. this paper describes a classification system based on decision rules that may store up--to--date border examples to avoid unnecessary revisions when virtual drifts are present in data. consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbor algorithm. in addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another.