waashk/dblp huggingface dataset:
efficient inline caching without dynamic translation. inline caching is a very important optimization technique for interpreters, effectively eliminating the overhead in dynamic typing. unfortunately, inline caches are mostly used together with dynamic translation, which is expensive in terms of implementation costs. we present efficient inlinecaching techniques that do not require dynamic translation.
evolutionary testing of object-oriented software. it is estimated that 80% of software development cost is spent on detecting and fixing defects. to tackle this issue, a number of tools and testing techniques have been developed to improve the existing testing framework. although techniques such as static analysis, random testing and evolutionary testing have been used to automate the testing process, it is not clear what is the best approach. previous research on evolutionary testing has mainly focused on procedural programming languages with code coverage as the main optimization parameter. in this work, we present an approach that combines a genetic algorithm with static analysis to automatically test eiffel classes using the number of faults found as the optimization parameter. a total of 115 experiments on 22 eiffel classes were executed to evaluate the effectiveness of evolutionary testing compared to the results obtained by running a random test case generator for 15, 30 and 60 minutes. the results show a genetic algorithm combined with static analysis can considerably increase the number of faults found compared to a random testing approach. in some cases, evolutionary testing found more faults in 15 minutes than a random testing strategy found in 60 minutes.
named and default arguments for polymorphic object-oriented languages: a discussion on the design implemented in the scala language. this article describes the design and implementation of named and default arguments in the scala programming language. while these features are available in many other languages there are significant differences in the actual implementations. we present a design that unifies the most reasonable properties for an object-oriented language and provides new possibilities by allowing default arguments on generic and implicit parameters. we also present a solution for the problem of writing a lightweight generic update function for algebraic datatypes.
extending a hybrid tag-based recommender system with personalization. tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. tags themselves can be therefore used for finding personalized recommendations of items. this paper proposes a semantic extension for a hybrid tag-based recommender system, which suggests similar web pages based on the similarity of their tags. the semantic extension aims at discovering tag relations which are not considered in basic syntax similarity. with the goal of generating more semantically grounded recommendations, the proposal extends a hybrid tag-based recommender system with a semantic factor, which looks for tag relations in different semantic sources. in order to evaluate the benefits acquired with the semantic extension, we have compared the new findings with results from a previous experiment involving 38 people from 12 countries using data from del.icio.us.
mining uncertain data for frequent itemsets that satisfy aggregate constraints. many existing algorithms mine transaction databases of precise data for frequent itemsets. however, there are situations in which the user is interested in only some tiny portions of all the frequent itemsets, and there are also situations in which data in the transaction databases are uncertain. this calls for both (i) constrained mining (for finding only those frequent itemsets that satisfy user constraints, which express the user interest) and (ii) mining uncertain data. in this paper, we propose a tree-based algorithm that effectively mines transaction databases of uncertain data for only those frequent itemsets satisfying the user-specified aggregate constraints. the algorithm avoids candidate generation and pushes the aggregate constraints inside the mining process, which reduces computation and avoids unnecessary constraint checking.
clearminer: a new algorithm for mining association patterns on heterogeneous time series from climate data. recently, improvements in sensor technology contributed to increasing in spatial data acquisition. the use of remote sensing in many countries and states, where agricultural business is a large part of their gross income, can provide a valuable source to improve their economy. the combination of climate and remote sensing data can reveal useful information, which can help researchers to monitor and estimate the production of agricultural crops. data mining techniques are the main tools to analyze and extract relationships and patterns. in this context, this paper presents a new algorithm for mining association patterns in geo-referenced databases of climate and satellite images. the clearminer (climate association patterns miner) algorithm identifies patterns in a time series and associates them with patterns in other series within a temporal sliding window. experiments were performed with synthetic and real data of climate and noaa-avhrr sensor for sugar cane fields. results show a correlation between agroclimate time series and vegetation index images. rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast having the burden of dealing with many data charts.
s-search: finding rfid tags using scalable and secure search protocol. massively deploying rfid systems that preserve data integrity and security is a major challenge of the coming years. since rfid tags are extremely constrained in time and space, enforcing high level of security with excessive cryptographic computation is not possible. secured mechanisms for tag authentication have been in the midst of researcher's interest for almost a decade. one extension of rfid authentication is rfid tag searching, which has not been given much attention so far. but we firmly believe that in near future tag searching will be a significant issue. and tag searching need to be scalable as rfid tags are deployed comprehensively within a system. in this paper we propose a scalable and lightweight rfid tag searching protocol. this protocol can search a particular tag efficiently as the approach is not based on exhaustive search. this approach does not employ extreme computing or cryptographic functions. our proposed scalable search protocol is secured against major security threats and it is suitable to be used in numerous real life situations.
profit and penalty aware (pp-aware) scheduling for tasks with variable task execution time. as computing devices and the internet technology advances, real-time on-line services are emerging. different from traditional real-time applications for which the scheduling objective is to meet task deadlines, the optimization goal for on-line service systems is to maximize profit obtained through providing timely services. for this class of applications, there are two distinctive characteristics: (1) tasks, i.e., client requests, are associated with a pair of unimodal time functions, representing system accrued profit when a task is completed before its deadline, or accrued penalty if otherwise; and (2) requests execution times vary in a wide range. the paper presents a new scheduling algorithm, i.e., the profit and penalty aware (pp-aware) scheduling algorithm, with an objective to maximize system's total accrued profit. our simulation results have empirically shown the advantages, in respect of system total accrued profit, of the proposed algorithm over other commonly used scheduling algorithms, such as earliest deadline first (edf) and utility accrual (ua) algorithms.
an approach for architectural layer recovery. in this paper we present an approach to identify software layers for the understanding and evolution of object oriented software systems. the approach first identifies relations between the classes and then uses the kleinberg algorithm to group them into layers. additionally to assess the approach and the underlying techniques, the paper also presents a prototype of a supporting tool to identify layers within java software systems. to assess the feasibility of both the approach and the system prototype, the results from a case study conducted on an open source java software system are presented and discussed.
online mining of temporal maximal utility itemsets from data streams. data stream mining has become an emerging research topic in the data mining field, and finding frequent itemsets is an important task in data stream mining with wide applications. recently, utility mining is receiving extensive attentions with two issues reconsidered: first, the utility (e.g., profit) of each item may be different in real applications; second, the frequent itemsets might not produce the highest utility. in this paper, we propose a novel algorithm named guide (generation of temporal maximal utility itemsets from data streams) which can find temporal maximal utility itemsets from data streams. a novel data structure, namely, tmui-tree (temporal maximal utility itemset tree), is also proposed for efficiently capturing the utility of each itemset with one-time scanning. the main contributions of this paper are as follows: 1) guide is the first one-pass utility-based algorithm for mining temporal maximal utility itemsets from data streams, and 2) tmui-tree is efficient and easy to maintain. the experimental results show that our approach outperforms other existing utility mining algorithms like two-phase algorithm under the data stream environments.
an electronic-signature based circular resolution database system. secure and efficient decision making processes are of particular importance especially for small and medium-sized enterprises. in this context, delocalization of responsible decision makers often leads to decision making processes relying on circular resolutions. although circular resolutions based on written consent are usually efficiently manageable for a limited number of decision makers, involving a potential large number of persons inevitably complicates these processes in practice. in this paper, a circular resolution database system that addresses this problem is introduced. our solution, which is based on the austrian citizen card concept, makes use of qualified electronic signatures that provide means for secure authentication of users as well as for electronic signing of digital documents. by enhancing decision making processes in terms of security, usability, and effectiveness while assuring auditing acceptability, the presented circular resolution database system especially contributes to the future competitiveness of small and medium-sized enterprises.
a well-founded approach to service modelling with casl4soa: part 1 (service in isolation). we propose in this paper the first part of casl4soa a notation and a technique to model a soa (service oriented architecture), i.e., here we consider only services in isolation. our casl4soa approach is to provide a well-founded modelling to soa, using the casl-ltl formal specification language as an underlying foundation. we explore then the various possible visual presentation of the casl4soa notation, so as to ensure as much as possible readability and communicability. given the casl-ltl concept of a simple system as a labelled transition system we propose a new way to model the services that is not object-oriented. while modelling the static, the behavioural, and the semantic aspects of a service, we pay a specific attention to the protocols between the provider and the consumer of the service for which we propose either a logical specification of their properties, or a constructive specification expressed by interaction machines. our ideas are illustrated by the example of a printing service offered by a printer to a user.
can complexity, coupling, and cohesion metrics be used as early indicators of vulnerabilities? it is difficult to detect vulnerabilities until they manifest themselves as security failures in the operational stage of software, because the security concerns are not addressed or known sufficiently early during software development. complexity, coupling, and cohesion (ccc) related software metrics can be measured during the earlier phases of software development. if empirical relationships can be discovered between ccc metrics and vulnerabilities, these metrics could aid software developers to take proactive actions against potential vulnerabilities in software. in this paper, we conduct an extensive case study on mozilla firefox to provide empirical evidence on how vulnerabilities are related to complexity, coupling, and cohesion. we find that ccc metrics are correlated to vulnerabilities at a statistically significant level. we further examine the correlations to determine which level (design or code) of ccc metrics are better indicators of vulnerabilities. we also observe that the correlation patterns are stable across multiple releases of the software. these observations show that ccc metrics can be dependably used as early indicators of vulnerabilities in software.
towards automatic extraction of epistemic items from scientific publications. the exponential growth of the world wide web in the last decade, brought with it an explosion in the information space. one, heavily affected area is the scientific literature, where finding relevant work in a particular field, and exploring links between relevant publications represents a cumbersome task. in this paper we make the initial steps in the direction of automatic extraction of epistemic items (i.e. claims, positions, arguments) from scientific publications. our approach will provide the foundation for a comprehensive solution that will partly alleviate the information overload problem. we detail the actual extraction process, the evaluation we have performed and relevant use-cases for our work.
an approach based upon owl-s for method fragments documentation and selection. it is currently admitted in both mainstream software engineering and agent-oriented software engineering that there is no one-size-fit-all methodology or process. one solution is proposed by the situational method engineering paradigm that provides means for constructing ad-hoc software engineering processes following an approach based on the reuse of portions of existing design processes, the so called method fragments, stored in a repository called method base. one problem raised by this type of approaches is to describe or document fragments and to choose among existing fragments in order to build a new process. the approach proposed in this paper uses owl-s and documents fragments as services in order to semantically annotate methodologies concepts and fragments. a scenario illustrating an example of fragment selection is reported.
a secure multiparty computation privacy preserving olap framework over distributed xml data. privacy preserving distributed olap is becoming a critical challenge for next-generation business intelligence (bi) scenarios, due to the "natural suitability" of olap in analyzing distributed massive bi repositories in a multidimensional and multigranularity manner. in particular, in these scenarios xml-formatted bi repositories play a dominant role, due to the wellknow amenities of xml in modeling and representing distributed business data. however, while privacy preserving distributed data mining has been widely investigated, very few efforts have focused on the problem of effectively and efficiently supporting privacy preserving olap over distributed collections of xml documents. in order to fulfill this gap, we propose a novel secure multiparty computation (smc)-based privacy preserving olap framework for distributed collections of xml documents. the framework has many novel features ranging from nice theoretical properties to an effective and efficient protocol. the efficiency of our approach has been validated by an experimental evaluation over distributed collections of synthetic xml documents.
image representation and classification based on data compression. with the development of the information technology, the number of different electronic information has been increased rapidly. in which, for enormous number of digital images the demand of automatic classification technology exceeded human processing capacity. as an automatic classification technique, representing and classifying text-transformed image based on data compression is proposed in this paper. in the step of transforming image into text, image is divided into segments which are replaced as characters. then, the similarity between compressibility vectors is used in the classification step. in which, we focus on the compressibility of the text string. finally, the effectivity of the proposed method is verified in our experiments.
traveling among clusters: a way to reconsider the benefits of the cluster hypothesis. relying on the cluster hypothesis which states that relevant documents tend to be more similar one to each other than to non-relevant documents, most of information retrieval systems organizing search results as a set of clusters seek to gather all relevant documents in the same cluster. we propose here to reconsider the benefits of the entailed concentration of the relevant information. contrary to what is commonly admitted, we believe that systems which aim to distribute the relevant documents in different clusters, since being more likely to highlight different aspects of the subject, may be at least as useful for the user as systems gathering all relevant documents in a single group. since existing evaluation measures tend to greatly favor the latter systems, we first investigate ways to more fairly assess the ability to reach the relevant information from the list of cluster descriptions. at last, we show that systems distributing the relevant information in different clusters may actually provide a better information access than classical systems.
a heterogeneous approach to service-oriented systems specification. service-oriented architecture (soa) is a relatively new approach to software system development. it divides system functionality to independent, loosely coupled, interoperable services. in this paper we propose a new heterogeneous specification approach for soa systems where a heterogeneous structured specification consists of a number of specifications of individual services written in a "local" logic and where the specification of their interactions is separately described in a "global" logic. a main feature of our global logic is the possibility of describing the dynamic change of service communications over time. our approach is based on the theory of institutions: we show that both logics form institutions and that these institutions are connected by an institution comorphism. we illustrate our approach by a simple scenario of an e-university management system and show the power of the heterogeneous specification approach by a compositional refinement of the scenario.
an empirical investigation on the relation between analysis models and source code comprehension. this paper presents a controlled experiment to investigate whether the comprehension of source code increases in case novice software engineers use abstract software models produced in the early phase of the software development, i.e., requirements analysis. the study has revealed that there is not significant difference in the comprehension of source code achieved by using or not abstract software models.
paving the road for formally defined architecture description in software development. informal and formal approaches to documenting software architecture design offer disjoint advantages and disadvantages. informal approaches are often used in practice since they are easily accessible and support creativity and flexibility during design. but they are hard to maintain and validate. this is the strength of formally defined approaches, which can be automatically processed, maintained and validated, but are expensive to use. combining the advantages of both approaches promises to increase the reach of formal approaches and to make the aforementioned advantages more accessible. we present an approach that offers a seamless transition from relaxed and informal architecture descriptions to a detailed and formally defined architecture definition.
an approach to intelligently crop and scale video for broadcast applications. within the scope of the eu-funded project portivity (portable interactivity), an application has been developed, that automatically modifies sdtv (standard definition television) sports productions for viewing on mobile tv displays by means of intelligent cropping and scaling. it crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. this approach allows a context-based composition for cropped images. it provides a differentiation between the original sd-version of the production and the processed one adapted to the requirements for mobile tv. envisaged is the integration of the tool in post-production and live workflows.
dynamic context-aware business process: a rule-based approach supported by pattern identification. making a business process more dynamic is an open issue, and we think it is feasible if we decompose the business process structure in a set of rules, like eca (event condition action) rules, each of them representing a transition of the business process, i.e. an edge of the business process graph structure. as a consequence the business process engine can be realized by reusing and integrating an existing rule engine. we are proposing a way for representing dynamic business process in terms of rules based on patterns identification. with this approach it is easy to apply on a business process instance both user-based personalization rules and automatic rules inferred by an underlying context-aware system.
mining temporal relationships among categories. temporal text mining deals with discovering temporal patterns in text over a period of time. a theme evolution graph (teg) is used to visualize when new themes are created and how they evolve with respect to time. teg, however, does not represent relationships among themes (or categories) that share same timestamp. we focus on identifying such relationships and represent them in relationship evolution graph (reg). we favorably compare passage misclassification and association rule mining with three existing approaches, namely kl divergence (kld), consistent bipartite spectral co-partitioning graph (cbscg) and document misclassification. our evaluations indicate that association rule mining approach statistically significantly (99% confidence) outperforms the other existing approaches, while passage misclassification approach is the second most effective approach.
mitigating denial of capability attacks using sink tree based quota allocation. network capabilities have been proposed to prevent distributed denial of service (ddos) attacks proactively. a capability is a ticket-like token, checkable by routers, that a server can issue for legitimate traffic. still, malicious hosts may swamp a server with requests for capability establishment, essentially causing possible denial-of-capability (doc). in this paper, we propose an algorithm to mitigate doc attacks. the algorithm divides the server's capacity for handling capability requests into quotas. quotas are allocated based on a sink tree architecture. randomization and bloom filters are used as tools against threats (attacking scenarios). we both analytically and experimentally show that legitimate hosts can get service with guaranteed probability. we also address issues on fault-tolerance and the deployment of the approach proposed.