waashk/dblp huggingface dataset:
interchangeable consistency constraints for public health care systems. severe data quality problems exist in most public health care systems and inconsistent data sets often occur. consistency constraints can be used to define valid and invalid data. existing solutions of such constraints like rule systems are often difficult to maintain, not human-readable, and of a bad quality like containing contradictory rules. with in-daqu we present an approach that allows domain experts to easily create and maintain consistency constraints using an introduced domain-specific language. these constraints are being stored in an ontology, which allows for an automated inconsistency detection in the defined rules themselves. we identified several scenarios in which consistency constraints can be interchanged and exchanged between different participants. the approach has been successfully evaluated in the cancer registry of lower saxony.
constrained viterbi decoding for embedded user-customised password speaker recognition. embedded speaker recognition in mobile devices could involve several ergonomic constraints and a limited amount of computing resources. gmm/ubm systems have proved their efficiency in more classical contexts where good accuracy depends on a relatively large quantity of speech data. the proposed gmm/ubm extension addresses the situations with limited resources and takes advantage from the temporal structure of speech by using client-customised utterances harnessed by a markov model. new temporal information is then used to enhance discrimination with viterbi decoding increasing the gap between client and impostor scores. experiments on the myidea database are performed when impostors know the client-utterance and also when they do not, highlighting the potential of this new approach. a relative gain up to 64% in terms of eer is achieved when impostors do not know the client utterances and performance is equivalent to the gmm/ubm baseline system in other configurations.
an ontology-based software framework to provide educational data mining. this paper presents an ontology-based software framework for providing educational data mining applications, mainly offering flexibility of encapsulate mining techniques with semantic web services. it consists of an architecture based on three layers. a case study and illustrative scenario have been implemented to evaluate the framework feasibility. currently, the evaluation shows the pontential benefits for both the developer and the teacher.
client-side load balancer using cloud. web applications' traffic demand fluctuates widely and unpredictably. the common practice of provisioning a fixed capacity would either result in unsatisfied customers (underprovision) or waste valuable capital investment (overprovision). by leveraging an infrastructure cloud's on-demand, pay-per-use capabilities, we finally can match the capacity with the demand in real time. this paper investigates how we can build a large-scale web server farm in the cloud. our performance study shows that using existing cloud components and optimization techniques, we cannot achieve high scalability. instead, we propose a client-side load balancing architecture, which can scale and handle failure on a milli-second time scale. we experimentally show that our architecture achieves high throughput in a cloud environment while meeting qos requirements.
a standard-driven implementaion of ws-bpel 2.0. we present a systematic study of the ws-bpel 2.0 standard based on two complementary methods: the process of constructing a new high-level ws-bpel implementation driven by the structure of the standard, and an empirical evaluation of existing interpretations of the standard reflected in five widely available ws-bpel-implementations, both commercial and open source. in doing so we uncover a number of new ambiguities. most notably, ws-bpel's integration of xpath 1.0, the data access component of ws-bpel, turns out to be inconsistent with the xpath standard itself, which is evidenced by substantially differing results produced by existing implementations on test cases constructed to exercise their interpretation. the core concepts in ws-bpel have been formalized and analyzed successfully previously. our choice to study the standard by constructing a high-level, standard-driven implementation rather than an abstract, mathematical formalization has made it feasible to cover the complete standard, notably the integration with xpath. given ws-bpel's design goal of being platform-independent the inconsistencies are arguably a serious concern since they cannot be attributed to the quality of any particular implementation.
wms-extracting multiple sections data records from search engine results pages. in this paper, we develop an automatic wrapper for the extraction of multiple sections data records from search engine results pages. in the information extraction world, less attention has been focused on the development of wrappers for the extraction of multiple sections data records. this is evidenced by the fact that there is only one automatic wrapper, mse developed for this purpose. using the separation distance of data records and sections, mse is able to distinguish sections and data records and extract them from search engine results pages. in this study, our approach is the use of dom tree properties to develop an adaptive search method which is able to detect, differentiate, and partition sections and data records. the multiple sections data records labeled are used to pass through a few filtering stages, each filter is designed to filter out a particular group of irrelevant data until one data region containing the relevant records is found. our filtering rules are designed based on visual cue such as text and image size obtained from the browser rendering engine. experimental results show that our wrapper is able to obtain better results than the currently available mse wrapper.
towards the introduction of qos information in a component model. assuring quality of service (qos) properties is critical in the development of component-based distributed systems. this paper presents an approach to introduce qos constraints into a coalgebraic model of software components. such constraints are formally captured through the concept of a q-algebra which, in its turn, can be smoothly integrated in the definition of component combinators.
wikipedia driven autonomous label assignment in wrapper induced tables with missing column names. as the volume of information available on the internet is growing exponentially, it is clear that most of this information will have to be processed and digested by computers to produce useful information for human consumption. unfortunately, most web contents are currently designed for direct human consumption in which it is assumed that a human will decipher the information presented to him in some context and will be able to connect the missing dots, if any. in particular, information presented in some tabular form often does not accompany descriptive titles or column names similar to attribute names in tables. while such omissions are not really an issue for humans, it is truly hard to extract information in autonomous systems in which a machine is expected to understand the meaning of the table presented and extract the right information in the context of the query. it is even more difficult when the information needed is distributed across the globe and involve semantic heterogeneity. in this paper, our goal is to address the issue of how to interpret tables with missing column names by developing a method for the assignment of attributes names in an arbitrary table extracted from the web in a fully autonomous manner. we propose a novel approach by leveraging wikipedia for the first time for column name discovery for the purpose of table annotation. we show that this leads to an improved likelihood of capturing the context and interpretation of the table accurately and producing a semantically meaningful query response.
expert finding in question-answering websites: a novel hybrid approach. question answering websites are becoming an ever more popular knowledge sharing platform. on such websites, people may ask any type of question and then wait for someone else to answer the question. however, in this manner, askers may not obtain correct answers from appropriate experts, and knowledge sharing through question answering websites is interfered. recently, various approaches have been proposed to automatically find experts in question answering websites. in this paper, we propose a novel hybrid approach to effectively find experts for the category of the target question in question answering websites. our approach considers user subject relevance, user reputation and authority of a category in finding experts. the experiment results show that our proposed methods outperform other conventional methods.
similar triangles and orientation in plane elementary geometry for coq-based proofs. in plane elementary geometry, the concept of similar triangles not only forms an important foundation for trigonometry, but it also can be used to solve many geometric problems. the notion of orientation allows us to remove the usual ambiguities in presentation of object. in this paper, we present the formalization of these notions in coq. we also introduce their properties and how they are applied to the proof of two theorems: the ptolemy's theorem and the intersecting chords theorem.
improving complex distributed software system availability through information hiding. for distributed software systems, ensuring their availability under intentional attacks is critical. traffic analysis, conducted by the attacker, could reveal the protocol being carried out by the components. furthermore, having inferred the protocol, the attacker can use the pattern of the messages as a guide to the most critical components. we thwart these directed attacks by using message forwarding to reduce traffic differences, thus diverge attackers from targeted attack to random attack, which probabilistically prolongs the availability of important components in the system. the simulation results also show that message forwarding effectively balance the traffic flow and hence indicate the validity of our approach.
probabilistic anti-spam filtering with dimensionality reduction. one of the biggest problems of e-mail communication is the massive spam message delivery. everyday, billion of unwanted messages are sent by spammers and this number does not stop growing. helpfully, there are different approaches able to automatically detect and remove most of these messages, and a well-known ones are based on bayesian decision theory. however, many machine learning techniques applied to text categorization have the same difficulty: the high dimensionality of the feature space. many term selection methods have been proposed in the literature. nevertheless, it is still unclear how the performance of naive bayes anti-spam filters depends on the methods applied for reducing the dimensionality of the feature space. in this paper, we compare the performance of most popular methods used as term selection techniques with some variations of the original naive bayes anti-spam filter.
art: adaptive, reliable, and fault-tolerant task management for computational grids. the main goal of art is reducing the number of replications by using checkpointing and rollback scheme for each replication. in art, the minimum number of replications is adaptively selected based on analysis of probability of successful execution within the given deadline and reliability requirement of each task. simulation results show that art outperforms existing mechanisms.
metaself: an architecture and a development method for dependable self-* systems. this paper proposes a software architecture and a development process for engineering dependable and controllable self-organising (so) systems. our approach addresses dependability by exploiting metadata to support decision making and adaptation based on the dynamic enforcement of explicitly defined policies. control is obtained by actively modifying metadata, policies or components. we show how this applies to two different systems: (1) a dynamically resilient web service system; and (2) an industrial assembly system with self-adaptive and so capabilities.
a meta model for representing arbitrary meta model hierarchies. "the only constant is change" is an often contemplated sentence in literature which refers to the re-occurring need for adaption of existing information systems -- especially of that category which handles models. here, applying a change often means to re-generate and deploy a complete chain of tools ranging from repositories over modeling environments to frameworks that use models to produce some output (e.g. code generators). using an orthogonal classification as atkinson and k&uuml;hne [3] suggested could resolve this issue. therefore, we developed a meta model that can be used for representing arbitrary meta model hierarchies and can thus be used as a linguistic meta model within the orthogonal classification. one of its features is its support for advanced meta modeling paradigms such as (extended) powertypes, clabjects and deep instantiation.
a novel music recommender by discovering preferable perceptual-patterns from music pieces. nowadays, advanced information and communication technologies ease the access of music pieces. however, it is still hard for the users to find what she/he prefers from a huge amount of music works. to solve this problem, most music recommenders based on collaborative filtering (called cf) utilize the rating logs to predict the user's preference. unfortunately, cf-like recommenders cannot capture the user's preference effectively due to the gap between the complicated musical contents and diverse user preferences. to reduce the gap, in this paper, we propose a novel recommender that integrates musical contents mining and collaborative filtering to achieve high-quality music recommendation. for musical contents mining, the proposed perceptual patterns derived by two-stage clustering are adopted as a kind of musical genes to support music recommendation. for collaborative filtering, pattern-based preference prediction can imply the user's preferred music effectively. the experimental results reveal that our proposed recommender well outperforms the existing recommenders in terms of recommendation quality.
fangs: high speed sequence mapping for next generation sequencers. next generation sequencing machines are generating millions of short dna sequences (reads) everyday. there is a need for efficient algorithms to map these sequences to the reference genome to identify snps or rare transcripts and to fulfill the dream of personalized medicine. we present a fast algorithm for next generation sequencers (fangs), which dynamically reduces the search space by using q-gram filtering and pigeon hole principle to rapidly map 454-roche reads onto a reference genome. fangs is a sequential algorithm designed to find all the matches of a query sequence in the reference genome tolerating a large number of mismatches or insertions/deletions. using fangs, we mapped 50000 reads with a total of 25 million nucleotides to the human genome in as little as 23.3 minutes on a typical desktop computer. through our experiments, we found that fangs is upto an order of magnitude faster than the state-of-the-art techniques for queries of length 500 allowing 5 mismatches or insertion/deletions.
automatically detecting and classifying noises in document images. image filtering to remove noise in document images follows two different approaches. the first one uses human classification of the noise present in an image for identifying a noise filter to use. the second approach is to blindly apply a batch of filters to an image. the former approach, although widely used, may insert noise in the filtering process due to the incorrect classification of the noise or even unsuitable filtering parameters. this paper presents a new paradigm for document image filtering. it aims at doing a more accurate and computationally efficient document cleanup by pre-characterizing the noise that is present in the document based on a set of human labeled training samples. the current focus of the project is on pre-characterization of the following types of noise: back-to-front interference or bleed through, skew and orientation, blur and framing.
evolutionary model tree induction. model trees are a particular case of decision trees employed to solve regression problems. they have the advantage of presenting an interpretable output with an acceptable level of predictive performance. since generating optimal model trees is a np-complete problem, the traditional model tree induction algorithms make use of a greedy heuristic, which may not converge to the global optimal solution. we propose the use of the evolutionary algorithms paradigm (ea) as an alternate heuristic to generate model trees in order to improve the convergence to global optimal solutions. we test the predictive performance of this new approach using public uci datasets, and compare the results with traditional greedy regression/model trees induction algorithms.
prom-oogle: data mining and integration of on-line databases to discover gene promoters. the vast number of on-line biological and medical databases available can be a great resource for biomedical researchers. however, the different types of data and interfaces available can be overwhelming for many biomedical researchers to learn and make effective use of. moreover, the available resources lack needed integration. here we focus on an important task in medical research: to provide researchers with promoter analysis for a given gene. prom-oogle is a web based data mining tool that provides a means for researchers to take a gene name of interest and obtain its promoter sequence in return after automatic integration of text databases. additionally, the program is capable of returning multiple promoters from different genes allowing researchers to study how promoters regulate genes. this tool facilitates the process of acquiring information on a promoter and may lead to interesting discoveries.
listening the photos. in the music field, an open issue is represented by the creation of innovative tools for acquisition, preservation and sharing of information. the strong difficulties in preserving the original carriers, together dedicated equipments able to read any (often obsolete) format, encouraged the analog/digital (a/d) transfer of audio contents in order to make them available in digital libraries. unfortunately, the a/d transfer is often an invasive process. this work proposes an innovative and not-invasive approach to audio extraction from complex source material, such as shellac phonographic discs: pog (photos of ghosts) is the new system, able to reconstruct the audio signal from a still image of a disc surface. it is automatic, needs of low-cost hardware, recognizes different rpm and performs an automatic separation of the tracks; also it is robust with respect to dust and scratches.
a group-based security policy for wireless sensor networks. the protection of wireless sensor networks is an important task, especially for critical situations such as intrusion detection, tamper monitoring, or military applications. to date, much of the emphasis on protecting sensor networks has focused on key management and secure routing. however, as sensors become more capable and ubiquitous, the need for more fine-grained control over their resources grows. in this paper, we present a security policy for wireless sensor networks which provide designers and administrators the ability to fine-tune access to sensor resources. we build on the notion of group-based key establishment to show how group membership can be utilized in deploying a dynamic, robust, and flexible security policy for wireless sensor networks.
estimating node similarity from co-citation in a spatial graph model. co-citation (number of nodes linking to both of a given pair of nodes) is often used heuristically to judge similarity between nodes in a complex network. we investigate the relation between node similarity and co-citation in the context of the spatial preferred attachment (spa) model. the spa model is a spatial model, where nodes live in a metric space, and nodes that are close together in space are considered similar, and are more likely to link to one another. theoretical analysis of the spa model leads to a measure to estimate spatial distance from the link information, based on co-citation as well as the degrees of both nodes. simulation results show this measure to be highly accurate in predicting the actual spatial distance.
an overview and an empirical evaluation of uml-aof: an uml profile for aspect-oriented frameworks. in this paper we present uml-aof and an empirical evaluation about it. uml-aof is a profile that aims to make the design characteristics of aspect-oriented frameworks (aofs) more evident in models than standard profiles for aspect-oriented programming (aop). uml-aof gathers several stereotypes and tagged values which represent design and architectural details commonly found in aofs, such as some idioms, patterns and extension mechanisms. in order to evaluate the applicability of this profile, an empirical study was conducted to analyze the number of errors and the time spent by software engineers using both uml-aof and a conventional profile for aop. based on the collected data, we have observed that the number of errors as well the time spent analyzing the models were significantly better than using the conventional profile.
dynamic contract layers. design by contract (dbc) is a programming technique to separate contract enforcement from application code. dbc provides information about the applicability of methods and helps to narrow down the search space in case of a software failure. however, most dbc implementations suffer from inflexibility: contract enforcement can only be activated or deactivated at compile-time or start-up, contracts are checked globally and cannot be restricted in their scope such as to the current thread of execution, and contracts cannot be grouped according to the concerns they relate to. in this paper, we present dynamic contract layers (dcl) for fine-grained and flexible contract management. based on ideas from context-oriented programming, we extend dbc by a grouping mechanism for contracts, thread-local activation and deactivation of such groups, and selective contract enforcement at run-time. pydcl, our proof-of-concept implementation of dcl, is built onto contextpy, our cop extension for the python programming language. we evaluate our approach by applying pydcl contracts to the moin-moin wiki framework.
graph-based verification of static program constraints. software artifacts usually have static program constraints and these constraints should be satisfied in each reuse. in addition to this, the developers are also required to satisfy the coding conventions used by their organization. because in a complex software system there are too many coding conventions and program constraints to be satisfied, it becomes a cumbersome task to check them all manually. this paper presents a process and tools that allow computer-aided program constraint checking that work on the source code. we developed a modeling language called source code modeling language (scml) in which program elements from the source code can be represented. in the process, the source code is converted into scml models. the constraint detection is realized by graph transformation rules which are also modeled in scml; the rules detect the violation and extract information from the scml model of the source code to provide feedback on the location of the problem. the constraint violations can be queried from a querying mechanism that automatically searches the graph for the extracted information. the process has been applied to an industrial software system.