waashk/dblp huggingface dataset:
hypervisor-based prevention of persistent rootkits. rootkits are prevalent in today's internet. in particular, persistent rootkits pose a serious security threat because they reside in storage and survive system reboots. using hypervisors is an attractive way to deal with rootkits, especially when the rootkits have kernel privileges, because hypervisors have higher privileges than os kernels. however, most of the previous studies do not focus on prevention of persistent rootkits. this paper presents a hypervisor-based file protection scheme for preventing persistent rootkits from residing in storage. based on security policies created in a secure environment, the hypervisor makes critical system files read-only and unmodifiable by rootkits even if they have kernel privileges. our scheme is designed to significantly reduce the size of hypervisors when combined with the architecture of bitvisor, a thin hypervisor for enforcing i/o device security, thereby contributing to the reliability of hypervisors. our hypervisor consists of only 37 kilo lines of code in total, and its overhead on windows xp with a fat32 file system is only 1.1% -- 14.0%.
time and alternation: an automata based framework to software model checking. in this paper, we present a class of powerful canonical timed alternating automata and a formalism for describing timed linear temporal logic to software model checking. time and alternation, these two "metaphors" have dominated automata theory research in recent years. for real-time systems, it is important to augment untimed and asynchronous models of computation with the notion of time. nevertheless, alternation is a powerful parallelism feature that has the potential to improve and reduce the state-space explosion problem in building large software model checking systems. we show that the dual connection between timed automata-theoretical and propositional-logic frameworks support and model software specifications. this can be established through languages over infinite timed words; and has a direct impact for expressing logical aspects and properties of model checking software systems.
extraction of component-environment interaction model using state space traversal. scalability of software engineering methods can be improved by application of the methods to individual components instead of complete systems. this is, however, possible only if a model of interaction between each component and its environment (rest of the system) is available. since constructing formal models of interaction by hand is hard and tedious, techniques and tools for automated inference of the models from code are needed. we present a technique for automated extraction of models of component-environment interaction from multi-threaded software systems implemented in java, which is based on state space traversal. models are captured in the formalism of behavior protocols, which allows to express parallel behavior explicitly. java pathfinder is used to perform the state space traversal. we have implemented the technique in the java2bp tool and applied the tool on two non-trivial software systems to show that our approach is feasible.
addressing the limited scope problem of focused crawling using a result merging approach. focused crawling refers to a process of fetching domain-specific pages from the web. it is an important method to build domain-specific document collections, but it suffers from low recall due to the local nature of crawling algorithms associated with web's community structure. in this study, we address the problem of limited crawling scope of focused crawling using a result merging approach. the results of crawling processes based on different start url sets and focused crawling methods were merged. we found that merging improves considerably the effectiveness of focused crawling. the results reported here are based on 10 test topics and 140 crawls in the domains of genomics and genetics.
visualizing time-series data in processlines: design and evaluation of a process enterprise application. in modern process industry, it is often difficult to analyze a manufacture process due to its numerous time-series data. analysts wish to not only interpret the evolution of data over time in a working procedure, but also examine the changes in the whole production process through time. to meet such analytic requirements, we have developed processlines, an interactive visualization tool for a large amount of time-series data in process industry. the data are displayed in a fisheye timeline. processlines provides good overviews for the whole production process and details for the focused working procedure. a user study using beer industry production data has shown that the tool is effective.
micho: a scalable constraint-based algorithm for learning bayesian networks. bayesian networks have a wide array of applications with its ability to model causal relationships in any given system. given the immense complexity and size of real problems, it is impossible to manually construct bayesian networks. the automatic learning of bayesian networks from data is hence an important task. however, in most industrial applications, the number of variables involved in any given system is large. existing algorithms need an impractical amount of time to learn such networks due to poor scalability with dimensionality. in this paper, a constraint-based algorithm, named micho, is introduced to overcome this barrier. micho synergistically integrates an information-theory-based approach and an independence-based approach to efficiently learn a bayesian network. using mutual information (mi), basic bayesian network and graph concepts to reduce the search space, a preliminary base graph can be quickly generated. refinements are then carried out using a minimal number of higher order tests involving minimum cardinality d-separating sets to obtain the final bayesian network structure. experiments involving real, large and high-dimensional datasets show that micho can perform up to 25 times faster than k2 while achieving similar accuracy.
making sense of unstructured flash-memory dumps. this paper presents an alternative to traditional file carving, targeted to flash-memory based devices. the proposed algorithm processes the flash memory dump thanks to a previous partial knowledge of the content of the regular files present in the device. the memory dump is decomposed into elementary parts, each part classified according to the file type it is supposed to belong to, and finally ordered in a sequence representing the recovered file. the sequence is then transformed into a real file. this paper presents the formal model behind the algorithm.
dispatching agvs with noisy estimation of crane operation time. the operation of both quay cranes and stacking cranes used in a container terminal is dynamically affected by a variety of factors. for this reason, errors could arise regarding the estimation of the time involved in processing containers. these errors make it difficult to dispatch automated guided vehicles (agvs) used for internal transportation of containers in the terminal. in this paper, we propose a simulation-based dispatching algorithm which reduces the impact of the noisy estimation of crane processing time. when dispatching an agv, the proposed algorithm collects stochastic samples of the evaluation value for each dispatching option by conducting multiple stochastic simulations of the agv operation. based on such samples, the proposed algorithm compares the dispatching options and selects the best one. for the stochastic simulation, we use a simple noise model of crane processing time. the effectiveness of the proposed algorithm is validated by simulation experiments.
multi-objective evolutionary approach for solving facility layout problem using local search. facility layout problem (flp) is an emerging problem in the manufacturing industries due to the fact that the computational complexity increases with the number of departments, which leads it to a combinatorial optimization problem. evolutionary algorithms have successfully been applied to flp by many researchers. unfortunately, most of these researches are predominantly on a single objective. previously, we proposed an evolutionary approach for multi-objective flp using pareto optimality [1]. simulation results indicate that it was capable of maintaining consistency and convergence of the trade-off, nondominated layout solutions. however, sometimes the solutions may be too diverse and the gap between the best and average solution is too large. this paper extends this idea by incorporating local search in the form of jumping gene operations introduced in jumping gene genetic algorithm (jgga). experimental results reveal that our proposed approach can search for the near-optimal and non-dominated solutions with better convergence and controlled-diversity by optimizing multiple criteria simultaneously.
towards the induction of terminological decision trees. a concept learning framework for terminological representations is introduced. it is grounded on a method for inducing logic decision trees as an adaptation of the classic tree induction methods to the description logics representations adopted in the semantic web context. differently from the original setting of logical trees based on clausal representations, tree-nodes contain terminological concept descriptions (corresponding to owl-dl classes) which makes it appealing for the semantic web applications. the method has been implemented in a prototypical system which has been experimentally evaluated on real ontologies.
mistral: open source biometric platform. mistral is an open source software for biometrics applications. this software, based on the well-known ubm/gmm approach includes also the latest speaker recognition developments such as latent factor analysis, unsupervised adaptation or svm supervectors. the software performance is highlighted in the framework of the nist evaluation campaigns.
a hybrid prefetch policy for the retrieval of link-associated information on vehicular networks. this paper proposes and measures the performance of a hybrid prefetch scheme for the gateway cache in the vehicular telematics network. aiming at improving response time and instant reply ratio, the proposed scheme combines the advantage of lru and far to best exploit temporal and spatial locality. for the advanced path recommendation service, links are categorized into two groups, namely, one referenced during the given interval, and the other not referenced. each group orders the links according to the distance from the gateway, finalizing the prefetch order. simulation results, based on the real-life trajectory data obtained from a real-time tracking system, show that the hybrid prefetch can improve instant reply ratio by up to 5.3% and response time by up to 12.5%, compared with far in spite of low link-level hit ratio.
a bayesian classifier for uncertain data. data uncertainty is widespread in a variety of applications. this paper proposes a new bayesian classification algorithm for classifying uncertain data. in the paper, we apply probability and statistics theory on uncertain data model, and provide solutions for model parameter estimation for both uncertain numerical data and uncertain categorical data. we also prove the correctness of the solutions. the experimental results demonstrate the proposed uncertain bayesian classifier can be efficiently constructed, and it significantly outperforms the traditional bayesian classifier in prediction accuracy when data is highly uncertain.
jaaf+t: a framework to implement self-adaptive agents that apply self-test. appropriate implementation of self-adaptive software systems able not only to check the needs for the adaptations and perform them but also to ensure their compliance with new environment requirements is still an open issue. therefore, this paper proposes an extension to the java self-adaptive agent framework (jaaf) in order to apply the self-test concept. this framework allows for the creation of self-adaptive agents based on a process composed of a set of four main activities (monitor, analyze, plan and execute). in this paper we extend the process and framework by including the test activity that will check the adapted behavior before its execution. the applicability of the proposed process is demonstrated by a case study where a system responsible for generating susceptibility maps, i.e., maps that show locations with landslides risks in a given area, searches to adapt its behavior and checks the adaptations before using them.
mining preorder relation between knowledge units from text. preorder relation between knowledge units (ku) is the precondition for navigation learning. although possible solutions, existing link mining methods lack the ability of mining preorder relation between knowledge units which are linearly arranged in text. through the analysis of sample data, we discovered and studied two characteristics of knowledge units: the locality of preorder relation and the distribution asymmetry of domain terms. based on these two characteristics, a method is presented for mining preorder relation between knowledge units from text documents, which proceeds in three stages. firstly, the associations between text documents are established according to the distribution asymmetry of domain terms. secondly, candidate ku-pairs are generated according to the locality of preorder relation. finally, the preorder relations between ku-pairs are identified by using classification methods. the experimental results show the method can efficiently extract the preorder relation, and reduce the computational complexity caused by the quadratic problem of link mining.
dynamic register-renaming scheme for reducing power-density and temperature. the manufacturing process of microprocessors becomes increasingly fine and the clock frequency is rapidly growing. since the corresponding power consumption, however, is not reduced, the power density is increased dramatically. the high temperature and heat generated by the power density causes many problems: malfunction, aging, leakage power and cooling costs. the register file produces the highest temperature in the microprocessor because of extremely high access frequency and its small area. we demonstrated that the traditional renaming unit causes high temperature since it allocates registers imbalanced. our idea is to redistribute evenly register allocations and accesses across the full range of the register file; consequently, the overall power density is lowered and then the temperature is reduced. the proposed method can be implemented by adding a small logic to the traditional renaming unit with around 1.5% overheads. the results are as follows. temperature drop was up to 11% on average 6%; leakage power saving was up to 24% on average 13%; performance improvement was up to 103% on average 84%.
efficient shortest path finding of k-nearest neighbor objects in road network databases. this paper addresses an efficient path finding scheme that complements classic k-nn (nearest neighbor) queries for the road network. aiming at finding both k objects and the shortest paths to them at the same time, this paper first selects candidate objects by the k-nn search scheme based on the underlying index structure and then finds the path to each of them by the modified a* algorithm. the path finding step stores the intermediary paths from the query point to all of the scanned nodes and then attempts to match the common segment with a path to a new node, instead of repeatedly running the a* algorithm for all k points. additionally, the cost to the each object calculated in this step makes it possible to finalize the k objects from the candidate set as well as to order them by the path cost. judging from the result, the proposed scheme can eliminate redundant node scans and provide one of the most fundamental building blocks for location-based services in the real-life road network.
scalable indexing for layout based document retrieval and ranking. in this paper we propose a schema for querying large documents collections by document layout. we develop a model of layout indexing of a collection adapted for the quick retrieval of top k relevant documents. fort the sake of scalability, we avoid a direct evaluation of the similarity between a query and each document in the collection; their similarity is instead approximated by the similarity between their projections on the set of representative blocks which are inferred from the collection on the indexed step. the technique also proposes new functions for the relevance ranking and the cluster pruning that ensure a scalable retrieval and ranking.
a robust link-translating proxy server mirroring the whole web. link-translating proxies are widely used for anonymous browsing, policy circumvention and webvpn functions. these are implemented by encoding the destination url in the path of the url submitted to the proxy and rewriting all links before returning the document to the client. commonly these are based on the cgiproxy implementation or a variant. while popular, broken links and very slow load times are common. this is so, since the use of scripting languages makes finding and translating links (the essential task of such a proxy) very difficult. some web-sites become entirely non-functional when loaded. this paper proposes a novel architecture for a link-translating proxy. using a sub-domain mapping technique we entirely eliminate the need to translate (or even find) relative links in content. then, using robust absolute link rewriting, cookie re-assignment, and wildcard certificates we achieve an extremely robust and performant proxy. our architecture preserves the same-origin policy separation of sites, and thus entirely eliminates cookie-stealing and other security concerns of cgiproxy. in measurements our proxy is far more robust, loads pages more quickly, and is more scalable than cgiproxy. we call our system aboproxy, since it involves the address bar only. it is invoked by appending. aboproxy. com to the hostname of any url.
collaborative context management framework for mobile ad hoc network environments. managing context in mobile ad hoc network (manet) environments is challenging due to environmental characteristics and limitations. different aspects of context management such as location management, context acquisition, context quality management, context discovery and other related tasks need to be dealt with to provide reliable context management support. in this paper, we propose a new approach to context management in the manet environments. we model contexts and situations based on context spaces (cs) theory and the dempster-shafer (ds) theory of evidences. a heuristic approach to computing the relevancy value of context information to the defined situation of context-aware applications is also proposed and developed. the relevancy value is then incorporated in the fusion mechanism based on the discount rule. we have developed a hybrid location model and integrated the approach to the model. the prototype of the framework is built based on the on-demand multicast routing protocol (odmrp) as the underlying messaging protocol among the collaborative hosts. finally, the implementation and experimentation issues of the prototype are discussed.
ncl-inspector: towards improving ncl code. ginga-ncl and the ncl language were selected as a recommendation as the interactive multimedia environment and language for iptv by the international telecommunication union (itu) [5]. in order to promote the use of these technologies, it is necessary to create tools to help develop applications using ncl. at present, the support provided by tools for ncl development is quite limited. in this paper, we propose ncl-inspector, a critique system of the ncl code, which aims at leveraging the developer's skills in detecting error-prone ncl applications. we also present a taxonomy of ncl code problems, which has proven useful to help develop ncl-inspector and may also improve the developers' knowledge of the domain.
canonical random correlation analysis. canonical correlation analysis (cca) is one of the most well-known methods to extract features from multi-view data and has attracted much attention in recent years. however, classical cca is unsupervised and does not take class label information into account. in this paper, we introduce the within-class cross correlation into cca and propose a new method called canonical random correlation analysis (rca). in rca, besides considering the correlation between two views from the same sample, the cross correlations between two views respectively from different within-class samples are also used to achieve good performance. two approaches for randomly generating cross correlation samples are developed.
transparent security for cloud. large distributed systems such as clouds are increasingly becoming targets of attacks. virtualization can be leveraged to increase the security of such systems by protecting the integrity of guest components. this paper discusses the integrity protection problem in the clouds and sketches a novel architecture, transparent cloud protection system (tcps) for increased security of cloud resources. tcps can be tailored to different cloud flavors to monitor the integrity of guests and infrastructure components while remaining transparent to virtual machines.
box consistency through adaptive shaving. the canonical algorithm to enforce box consistency over a constraint relies on a dichotomic process to isolate the leftmost and rightmost solutions. we identify some weaknesses of the standard implementations of this approach and review the existing body of work to tackle them; we then present an adaptive shaving process to achieve box consistency by tightening a domain from both bounds inward. experimental results show a significant improvement over existing approaches in terms of robustness for difficult problems.
a wavelet-based sampling algorithm for wireless sensor networks applications. this work proposes and evaluates a sampling algorithm based on wavelet transforms with coiflets basis to reduce the data sensed in wireless sensor networks applications. the coiflets basis is more computationally efficient when data are smooth, which means that, data are well approximated by a polynomial function. as expected, this algorithm reduces the data traffic in wireless sensor network and, consequently, decreases the energy consumption and the delay to delivery the sensed information. the main contribution of this algorithm is the capability to detect some event by adjusting the sampling dynamically. in order to evaluate the algorithm, we compare it with a static sampling strategy considering a real sensing data where an external event is simulated. the results reveal the efficiency of the proposed method by reducing the data without loosing its representativeness, including when some event occurs. this algorithm can be very useful to design energy-efficient and time-constrained sensor networks when it is necessary to detect some event.
an experience in using a tool for evaluating a large set of natural language requirements. requirements analysis is an important phase in a software project. it is often performed in an informal way by specialists who review documents looking for ambiguities, technical inconsistencies and incompleteness. automatic evaluation of natural language (nl) requirements documents has been proposed as a means to improve the quality of the system under development. we show how the tool quars express, introduced in a quality analysis process, is able to manage complex and structured requirement documents containing metadata, and to produce an analysis report rich of categorized information that points out linguistic defects and indications about the writing style of nl requirements. in this paper we report our experience using this tool in the automatic analysis of a large collection of natural language requirements, produced inside the modcontrol project.