waashk/dblp huggingface dataset:
an intelligent procedure assistant built using regulus 2 and alterf. we will demonstrate the latest version of an ongoing project to create an intelligent procedure assistant for use by astronauts on the international space station (iss). the system functionality includes spoken dialogue control of navigation, coordinated display of the procedure text, display of related pictures, alarms, and recording and playback of voice notes. the demo also exemplifies several interesting component technologies. speech recognition and language understanding have been developed using the open source regulus 2 toolkit. this implements an approach to portable grammar-based language modelling in which all models are derived from a single linguistically motivated unification grammar. domain-specific cfg language models are produced by first specialising the grammar using an automatic corpus-based method, and then compiling the resulting specialised grammars into cfg form. translation between language centered and domain centered semantic representations is carried out by alterf, another open source toolkit, which combines rule-based and corpus-based processing in a transparent way.
towards interactive text understanding. this position paper argues for an interactive approach to text understanding. the proposed model extends an existing semantics-based text authoring system by using the input text as a source of information to assist the user in re-authoring its content. the approach permits a reliable deep semantic analysis by combining automatic information extraction with a minimal amount of human intervention.
a prototype text to british sign language (bsl) translation system. we demonstrate a text to sign language translation system for investigating sign language (sl) structure and assisting in production of sign narratives and informative presentations. the system is demonstrable on a conventional pc laptop computer.
kiwi: a multilingual usage consultation tool based on internet searching. we present a usage consultation tool based on internet searching. when a user enters a string of words that he wants to find the usage for, the system sends a query to the search engine to obtain a corpus about the string. the corpus is statistically analyzed and results are displayed. as the system uses neither language-dependent analysis nor initial data, queries can be made in any language, even languages for which there are no well-established analytical methods. also, since the corpus is dynamically obtained from the search engines, the usages provided to the user are always up to date. kiwi can fill in the missing parts of the collocations frequently used by native speakers.
ineats: interactive multi-document summarization. we describe ineats -- an interactive multi-document summarization system that integrates a state-of-the-art summarization engine with an advanced user interface. three main goals of the system are: (1) provide a user with control over the summarization process, (2) support exploration of the document set with the summary as the staring point, and (3) combine text summaries with alternative presentations such as a map-based visualization of documents.
a debug tool for practical grammar development. we have developed willex, a tool that helps grammar developers to work efficiently by using annotated corpora and recording parsing errors. willex has two major new functions. first, it decreases ambiguity of the parsing results by comparing them to an annotated corpus and removing wrong partial results both automatically and manually. second, willex accumulates parsing errors as data for the developers to clarify the defects of the grammar statistically. we applied willex to a large-scale hpsg-style grammar as an example.
approaches to zero adnominal recognition. this paper describes our preliminary attempt to automatically recognize zero adnominals, a subgroup of zero pronouns, in japanese discourse. based on the corpus study, we define and classify what we call "argument-taking nouns (atns)," i.e., nouns that can appear with zero adnominals. we propose an atn recognition algorithm that consists of lexicon-based heuristics, drawn from the observations of our analysis. we finally present the result of the algorithm evaluation and discuss future directions.
automatic detection of grammar elements that decrease readability. this paper proposes an automatic method of detecting grammar elements that decrease readability in a japanese sentence. the method consists of two components: (1) the check list of the grammar elements that should be detected; and (2) the detector, which is a search program of the grammar elements from a sentence. by defining a readability level for every grammar element, we can find which part of the sentence is difficult to read.
chinese unknown word identification using character-based tagging and chunking. lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved r, g, b samples. we investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. we also propose a low-complexity adaptive context-based golomb-rice coding technique to compress the coefficients of mallat wavelet packet transform. the lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.
improving the performance of the random walk model for answering complex questions. we consider the problem of answering complex questions that require inferencing and synthesizing information from multiple documents and can be seen as a kind of topic-oriented, informative multi-document summarization. the stochastic, graph-based method for computing the relative importance of textual units (i.e. sentences) is very successful in generic summarization. in this method, a sentence is encoded as a vector in which each component represents the occurrence frequency (tf*idf) of a word. however, the major limitation of the tf*idf approach is that it only retains the frequency of the words and does not take into account the sequence, syntactic and semantic information. in this paper, we study the impact of syntactic and shallow semantic information in the graph-based method for answering complex questions.
comparison between cfg filtering techniques for ltag and hpsg. an empirical comparison of cfg filtering techniques for ltag and hpsg is presented. we demonstrate that an approximation of hpsg produces a more effective cfg filter than that of ltag. we also investigate the reason for that difference.
mapping between compositional semantic representations and lexical semantic resources: towards accurate deep semantic parsing. this paper introduces a machine learning method based on bayesian networks which is applied to the mapping between deep semantic representations and lexical semantic resources. a probabilistic model comprising minimal recursion semantics (mrs) structures and lexicalist oriented semantic features is acquired. lexical semantic roles enriching the mrs structures are inferred, which are useful to improve the accuracy of deep semantic parsing. verb classes inference was also investigated, which, together with lexical semantic information provided by verbnet and propbank resources, can be substantially beneficial to the parse disambiguation task.
artificial intelligence medicine, 5th conference on artificial intelligence in medicine in europe, aime'95, pavia, italy, june 25-28, 1995, proceedings although researchers have made great progresses on music genre classification in recent years, the need for more accurate system is still not satisfied. in this paper, we propose a method for further reducing the classification error rate based on multiple classifier fusion. first of all, mfccs and four features from mpeg-7 audio descriptor are extracted in every short time frame, and then a group of frames are gathered into a longer segment, in which mean and variance of these short time frames features are calculated. the segment is considered as the basic unit for training and testing module. then random forest (rf) and multilayer perceptron neural network (mlp) are executed on such segment independently. finally, a weighted voting fusion strategy is employed to fusion the result of the two classifiers on each segment, and the whole file decision is made by selecting the most frequently labeled genre over all the segments. experiments showed that the approach is effective. the fusion result gets 12.4% relative reduction in error rate compared to our baseline system.
artificial intelligence medicine, 6th conference on artificial intelligence in medicine in europe, aime'97, grenoble, france, march 23-26, 1997, proceedings feature selection (fs) is a technique for dimensionality reduction. its aims are to select a subset of the original features of a dataset which are rich in the most useful information. the benefits include improved data visualisation, transparency, a reduction in training and utilisation times and potentially, improved prediction performance. many approaches based on rough set theory have employed the dependency function which is based on the information contained in the lower approximation as an evaluation step in the fs process with much success. this paper presents a novel rough set fs technique which uses the information of both the lower approximation dependency value and a distance metric for the consideration of objects in the boundary region. the use of this measure in rough set feature selection can result in smaller subset sizes than those obtained using the dependency function alone.
artificial intelligence medicine, 8th conference on ai in medicine in europe, aime 2001, cascais, portugal, july 1-4, 2001, proceedings since multisensor image fusion is becoming a promising technique, many image fusion techniques based on the multiresolution wavelet decomposition have been developed. however, wavelets have been proved to have disappointing behaviors in higher dimensions. so, we proposed a novel multisensor image fusion scheme based on contourlet transform, which can capture the intrinsic geometrical structure that is key in visual information. we have estimated the validity of the fusion method by analyzing, visually and quantitatively, and the approaches proposed in this paper result in fused images with improved quality with respect to those obtained by wavelet-based fusion methods.
artificial intelligence in medicine, 9th conference on artificial intelligence in medicine in europe, aime 2003, protaras, cyprus, october 18-22, 2003, proceedings scene matching measures the similarity of scenes in photos and is of central importance in applications where we have to properly organize large amount of digital photos by scene categories. in this paper, we present a novel scene matching method using local features representatives. for a given image, its scene is compactly represented as a set of cluster centers, called local feature representatives, where the clusters are obtained using the affinity propagation (ap) algorithm to aggregate local features according to their spatial closeness and appearance similarity. the similarity of scenes in two images is then measured by a modified earth mover distance (emd) between their corresponding sets of local feature representatives. empirical experiments on real world photos shows that our method is comparable to the state-of-the-arts.
artificial intelligence in medicine, 10th conference on artificial intelligence in medicine, aime 2005, aberdeen, uk, july 23-27, 2005, proceedings minutiae pattern remains a widely used representation of a fingerprint. the research on minutiae matching never stops due to its complexity and intractability. in this paper, an efficient fingerprint minutiae matching algorithm is proposed. to obtain reliable reference minutiae pairs, the bank of coordinate systems is introduced. the coordinate systems bank is derived from the original minutiae features and applied to get more useful information about the minutiae. to improve the accuracy of minutiae matching, a global optimum alignment approach is developed, which is targeted on the alignment of the set of reference minutiae pairs. experimental results show that this algorithm achieves excellent performance with high matching speed and high matching reliability
artificial intelligence in medicine, 11th conference on artificial intelligence in medicine, aime 2007, amsterdam, the netherlands, july 7-11, 2007, proceedings as networks grow in size and complexity, both the probability and the impact of failures increase. the pre-allocated backup bandwidth, which has been widely investigated in the literature, may not be able to provide full protection guarantee when multiple failures occur in a network. in this study, we consider multiple concurrent failures where concurrent means that a new failure occurs before a previous failure is repaired. to combat the effect of multiple concurrent failures, new backups can be reprovisioned after one failure such that the next potential failure can be handled effectively and efficiently. we consider dynamic traffic where a pair of link-disjoint primary and backup paths is provisioned when a new connection request arrives. after a failure occurs, the affected connections switch traffic from their primary paths to backup paths. to protect against next potential failure, we reprovision new backups for connections that become unprotected or vulnerable because of losing their primary or their backup due to the previous failure or due to backup resource sharing. this approach is called minimal backup reprovisioning (mbr). an alternative approach is to globally rearrange backups for all connections after one failure occurs, which is called global backup reprovisioning (gbr). backup reprovisioning can be performed whenever the network's state changes, e.g., (1) when a new request arrives, (2) when an existing connection terminates, (3) when a network failure occurs, (4) when a failed link/node is repaired, etc., to utilize the available resources more efficiently or to recover quickly from the next failure. in this study, we perform mbr or gbr after one network failure occurs to protect against the next potential failure in a wavelength-convertible wdm mesh network. the link-vector network model which can maximally explore the backup-sharing potential is assumed in this study. we then analyze the complexity of mbr and gbr under such a network model. a r- - eprovisioning algorithm is proposed for mbr which can significantly reduce the connection vulnerability without the knowledge of the location of the next failure. in gbr, both integer linear program (ilp) and heuristic-based approaches are proposed. we compare capacity requirement and computational complexity of mbr to that of gbr through numerical examples. mbr demonstrates a good tradeoff between complexity and capacity efficiency to handle multiple concurrent failures
artificial intelligence in medicine, 12th conference on artificial intelligence in medicine, aime 2009, verona, italy, july 18-22, 2009. proceedings that ensuring the electronic documents security is the key to the protection of internal information. with the extent of file sharing enlarged, it is hard to keep balance between the security and sharing. to achieve this goal, file tracing and audit, dynamic adjustment of right must be solved better. in this paper, a electronic document security system based on lineage mechanism is proposed,it support dynamic adjust right in mutable environment. it implement the file trace through recording the access logs and path of transmission. in fact, it implement one of the ucon models: pre-authorization with post update.
sherlog: error diagnosis by connecting clues from run-time logs. computer systems often fail due to many factors such as software bugs or administrator errors. diagnosing such production run failures is an important but challenging task since it is difficult to reproduce them in house due to various reasons: (1) unavailability of users' inputs and file content due to privacy concerns; (2) difficulty in building the exact same execution environment; and (3) non-determinism of concurrent executions on multi-processors. therefore, programmers often have to diagnose a production run failure based on logs collected back from customers and the corresponding source code. such diagnosis requires expert knowledge and is also too time-consuming, tedious to narrow down root causes. to address this problem, we propose a tool, called sherlog, that analyzes source code by leveraging information provided by run-time logs to infer what must or may have happened during the failed production run. it requires neither re-execution of the program nor knowledge on the log's semantics. it infers both control and data value information regarding to the failed execution. we evaluate sherlog with 8 representative real world software failures (6 software bugs and 2 configuration errors) from 7 applications including 3 servers. information inferred by sherlog are very useful for programmers to diagnose these evaluated failures. our results also show that sherlog can analyze large server applications such as apache with thousands of logging messages within only 40 minutes.
probabilistic job symbiosis modeling for smt processor scheduling. symbiotic job scheduling boosts simultaneous multithreading (smt) processor performance by co-scheduling jobs that have `compatible' demands on the processor's shared resources. existing approaches however require a sampling phase, evaluate a limited number of possible co-schedules, use heuristics to gauge symbiosis, are rigid in their optimization target, and do not preserve system-level priorities/shares. this paper proposes probabilistic job symbiosis modeling, which predicts whether jobs will create positive or negative symbiosis when co-scheduled without requiring the co-schedule to be evaluated. the model, which uses per-thread cycle stacks computed through a previously proposed cycle accounting architecture, is simple enough to be used in system software. probabilistic job symbiosis modeling provides six key innovations over prior work in symbiotic job scheduling: (i) it does not require a sampling phase, (ii) it readjusts the job co-schedule continuously, (iii) it evaluates a large number of possible co-schedules at very low overhead, (iv) it is not driven by heuristics, (v) it can optimize a performance target of interest (e.g., system throughput or job turnaround time), and (vi) it preserves system-level priorities/shares. these innovations make symbiotic job scheduling both practical and effective. our experimental evaluation, which assumes a realistic scenario in which jobs come and go, reports an average 16% (and up to 35%) reduction in job turnaround time compared to the previously proposed sos (sample, optimize, symbios) approach for a two-thread smt processor, and an average 19% (and up to 45%) reduction in job turnaround time for a four-thread smt processor.
a real system evaluation of hardware atomicity for software speculation. in this paper we evaluate the atomic region compiler abstraction by incorporating it into a commercial system. we find that atomic regions are simple and intuitive to integrate into an x86 binary-translation system. furthermore, doing so trivially enables additional optimization opportunities beyond that achievable by a high-performance dynamic optimizer, which already implements superblocks. we show that atomic regions can suffer from severe performance penalties if misspeculations are left uncontrolled, but that a simple software control mechanism is sufficient to reign in all detrimental side-effects. we evaluate using full reference runs of the spec cpu2000 integer benchmarks and find that atomic regions enable up to a 9% (3% on average) improvement beyond the performance of a tuned product. these performance improvements are achieved without any negative side effects. performance side effects such as code bloat are absent with atomic regions; in fact, static code size is reduced. the hardware necessary is synergistic with other needs and was already available on the commercial product used in our evaluation. finally, the software complexity is minimal as a single developer was able to incorporate atomic regions into a sophisticated 300,000 line code base in three months, despite never having seen the translator source code beforehand.
power routing: dynamic power provisioning in the data center. data center power infrastructure incurs massive capital costs, which typically exceed energy costs over the life of the facility. to squeeze maximum value from the infrastructure, researchers have proposed over-subscribing power circuits, relying on the observation that peak loads are rare. to ensure availability, these proposals employ power capping, which throttles server performance during utilization spikes to enforce safe power budgets. however, because budgets must be enforced locally -- at each power distribution unit (pdu) -- local utilization spikes may force throttling even when power delivery capacity is available elsewhere. moreover, the need to maintain reserve capacity for fault tolerance on power delivery paths magnifies the impact of utilization spikes. in this paper, we develop mechanisms to better utilize installed power infrastructure, reducing reserve capacity margins and avoiding performance throttling. unlike conventional high-availability data centers, where collocated servers share identical primary and secondary power feeds, we reorganize power feeds to create shuffled power distribution topologies. shuffled topologies spread secondary power feeds over numerous pdus, reducing reserve capacity requirements to tolerate a single pdu failure. second, we propose power routing, which schedules it load dynamically across redundant power feeds to: (1) shift slack to servers with growing power demands, and (2) balance power draw across ac phases to reduce heating and improve electrical stability. we describe efficient heuristics for scheduling servers to pdus (an np-complete problem). using data collected from nearly 1000 servers in three production facilities, we demonstrate that these mechanisms can reduce the required power infrastructure capacity relative to conventional high-availability data centers by 32% without performance degradation.
technology for developing regions: moore's law is not enough. the historic focus of development has rightfully been on macroeconomics and good governance, but technology has an increasingly large role to play. in this talk, i review several novel technologies that we have deployed in india and africa, and discuss the challenges and opportunities of this new subfield of eecs research. working with the aravind eye hospital, we are currently supporting doctor / patient videoconferencing in 30+ rural villages; more than 25,000 people have had their blindness cured due to these exams. although moore's law has led to great cost reductions and thus enabled new technologies, we have reached essentially the low point for cost: the computing is essentially free compared to the rest of the system. the premium is thus on a combination of 1) deeper integration (fewer compo-nents), 2) shared usage models (even phones are shared), and 3) lower operating costs in terms of power and connectivity.