Information Retrieval

ncreasingly, the explosion in scien- tific knowledge is overwhelming both researchers and practitioners in the health professions. The man- aged care pharmacist today needs a con - stant and current sense of the basic phar- maceutical sciences, clinical pharmacy, pharmacy management systems, health policy and ethics, new drug introductions, pharmacoeconomics, and pharmacoepi- demiology. Monitoring the ever-changing pharmaceutical industry is now

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or author's location remains a challenge thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state-of-the-art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.

7th International Conference on Artificial Intelligence and Applications (AI 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference
looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

Predicting the runtime complexity of a programming code is an arduous task. In fact, even for humans, it requires a subtle analysis and comprehensive knowledge of algorithms to predict time complexity with high fidelity, given any code. As per Turing’s Halting problem proof, estimating code complexity is mathematically impossible. Nevertheless, an approximate solution to such a task can help developers to get real-time feedback for the efficiency of their code. In this work, we model this problem as a machine learning task and check its feasibility with thorough analysis. Due to the lack of any open source dataset for this task, we propose our own annotated dataset, (The complete dataset is available for use at https://github.com/midas-research/corcod-dataset/blob/master/README.md) CoRCoD: Code Runtime Complexity Dataset, extracted from online coding platforms. We establish baselines using two different approaches: feature engineering and code embeddings, to achieve state of the art...

Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.

9
th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2021)
provides a forum for researchers who address this issue and to present their work in a peerreviewed forum. Authors are solicited to contribute to the conference by submitting articles that
illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to these topics only.
Authors are solicited to contribute to this conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the areas of Artificial Intelligence & applications

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

175 A Study of Information Seeking and Retrieving. I. Background and Methodology* Tefko Saracevic School of Communication, Information and Library Studies, Rutgers, The State University of New Jersey, 4 Huntington St., New Brunswick, NJ 08903 Paul Kantor Tantalus Inc. and ...

An important text mining problem is to find, in a large collection of texts, documents related to specific topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to find the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain predefined sets of keywords (that define the topics researchers are interested in) are restricted to specific intervals of topic assignments. We present a case study on a Russian LiveJournal dataset aimed at ethnicity discourse analysis.

This paper reports on a study of evaluating and generating requirements for the user interface of a digital library. The study involved observation of librarians using the digital library, working on information finding problems on behalf of clients of the library. The study showed that librarians, familiar with the particular digital library system and with information retrieval work in general, possess a repertoire of relatively simple, yet effective, strategies for carrying out searches, and that non-librarians tend not to deploy the same strategies. After ...

The retrieval sensitivity in regard to the recovery of aerosol size distributions using particle angular scattering properties is investigated. Previous retrieval methods have all assumed that the scattering characteristics of spheres is adequate to retrieve size distributions. The present results show that particle nonsphericity can have a significant effect upon the retrieval. In particular assumptions based upon Mie spheres may lead to interpretations of spurious multimodal distributions. Furthermore, it is also shown that shape effects cannot be separated from composition effects. The effect on retrieved size distributions is investigated due to (1) variations in the particle index of refraction, (2) truncation of angular scattering intensities due to measurement limitations, (3) the use of two-component shell particles, (4) nonsphericity using the semiempirical approach, and (5) the replacement of spherically shaped particles by cylindrically shaped fibeis.

This paper describes empirical results of information retrieval in 13 languages of the Cross Language Evaluation Forum (CLEF) collection augmented with results of Turkish using syllables as a means to manage morphological variation in the languages. This kind of approach has been used in speech retrieval (e.g. Larson and Eickeler 2003), but for some reason it has not been much tried out in text-based IR, although it has many clear advantages. Firstly, a quite well working version of it can be implemented with a very simple syllabification algorithm, consisting of only variants of one syllable structure rule, CV, consonant vowel. Secondly, although syllable-based word form variation management resembles n-gramming (McNamee and Mayfield 2004), it has the advantage, that the number of grams with syllables is more restricted which keeps the size of the text index smaller and retrieval faster. Thirdly, syllable-based approach makes possible to use different types of syllabification procedures, which can be either very fine grained, i.e. language specific or very coarse, i.e. more language independent. Fourthly, syllable based methods work for both speech and text retrieval. Our results show, that the two different CV syllabification procedures produced good results with four morphologically complex languages of the CLEF collection. For Turkish they produced also good results. For three of the languages that got good results with the CV syllabification (De, Fi and Tu), we tried also language specific, accurate syllabification procedures. Accurate syllabification was not able to produce as good IR results as CV procedures.

We present a bayesian framework for XML document retrieval. This framework allows us to consider content only. We perform the retrieval task using inference in our network. Our model can adapt to a specific corpus through parameter learning and uses a grammar to speed up the retrieval process in big or distributed databases. We also experimented list filtering to avoid element overlap in the retrieved element list.

Literature Based Discovery (LBD) is a process that searches for hidden and important connections among information embedded in published literature. Employing techniques from Information Retrieval and Natural Language Processing, LBD has potential for widespread application yet is currently implemented primarily in the medical domain. This article examines several published LBD systems, comparing their descriptions of domain and input data, techniques

Abstract. Knowledge Sharing is a crucial issue in the Semantic Web: SW ser-vices expose and share knowledge content (expressed through ontologies and related knowledge bases) arising from distinct languages, locales, and personal perspectives; in this scenario, semantic ...

Negotiation, an important facet of business, is an iterative and complex process. Depending on the nature of business and the type of transaction, negotiation criteria, duration and complexity vary widely. An intelligent software agent can help the consumers and enterprises in business negotiation. It can negotiate with humans or with other software agents to arrive at mutually agreeable terms and

Extraction of text from image and video is an important step in building efficient indexing and retrieval systems for multimedia databases. We adopt a hybrid approach for such text extraction by exploiting a number of characteristics of text blocks in color images and video frames. Our system detects both caption text as well as scene text of different font, size, color and intensity. We have developed an application for on-line extraction and recognition of texts from videos. Such texts are used for retrieval of video clips based on any given keyword. The application is available on the web for the readers to repeat our experiments and also to try text extraction and retrieval from their own videos.

In this paper, a novel approach for building synopses is proposed by using a service and message-oriented architecture. The Sain-tEtiQ summarization system initially designed for very large stored databases, by its intrinsic features, is capable of dealing with the requirements inherent to the data stream environment. Its incremental maintenance of the output summaries and its scalability allows it to be a serious challenger to existing techniques. The resulting summaries present on the one hand the incoming data in a less precise form but is still on the other hand very informative on the actual content. We expose a novel way of exploiting this semantically rich information for query answering with an approach midway between blunt query answering and midway between data mining.

With increasingly higher numbers of non-English language web searchers the problems of efficient handling of non-English Web documents and user queries are becoming major issues for search engines. The main aim of this review paper is to make researchers aware of the existing problems in monolingual non-English Web retrieval by providing an overview of open issues. A significant number of

Image Retrieval is a technique of searching, browsing, and retrieving the images from an image database. There are two types of different image retrieval techniques namely text based image retrieval and content based image retrieval techniques. Text- Based image retrieval uses traditional database techniques to manage images. Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape, texture, and spatial layout to represent and index the image. This combination of features provides a robust feature set for image retrieval. Evaluate the performance of proposed methods at different precision value of the image retrieval on each category of image database. Recent research indicates that since these features are not exactly associated with image semantic meaning, query-by-one (QBO), which means to query with only one image, usually is insufficient to achieve good performance. Thus, query-by-multiple (QBM) methods are introduced and applied in many content-based image retrieval systems. However, how to maximize major features and minimize minor ones of these inputs while matching could influence retrieval results significantly. This technique is proposed novel multiple- query information retrieval algorithm that combines the Pareto front method with efficient Manifold ranking. Uniquely, to decrease the complexity of user input and reduce user-computer interaction, a topology preserve hashing algorithm will be introduced.

The Classical Models of Information Retrieval (IR) only consider the content-based information to retrieve relevant documents. The information is captured by comparing document keywords against the user query terms. Considering the Web, the structure of links among the documents (or pages) is a rich source of information that has not been used by the Classic Models. On the other hands, Bayesian Network models for IR has revealed that link-based combined with content-based information can improve the quality of the retrieved documents. In this work we discuss this combination in the context of the Classic Vector Space Model(VSM). We present three new contributions. We make the combination of link-based with contentbased information close to the IR community, through an extension of the VSM. We define a query language that allows users to state document preferences according to the structure of links. Finally, we propose a metric that allows the comparison among documents using link-based information.

Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals.

Large scale information retrieval systems need to refine the flow of documents which will receive further fine-grain analysis to those documents with a high potential for relevance to their respective users. This paper reports on research we have conducted into the usefulness of semantic codes from a machine readable dictionary for filtering large sets of incoming documents for their broad subject appropriateness to a topic of interest. The Subject Field Coder produces a summary-level semantic representation of a text's contents by tagging each word in the document with the appropriate, disambiguated Subject Field Code (SFC). The within-document SFCs are normalized to produce a vector of the SFCs representing that document's contents. Queries are likewise represented as SFC vectors and then compared to SFC vectors of incoming documents, which are then ranked according to similarity to the query SFC vector. Only those documents whose SFC vectors exhibit a predetermined degree of similarity to the query SFC vector are passed to later system components for more refined representation and matching. The assignment of SFCs is fully automatic, efficient and has been empirically tested as s reasonable approach for ranking documents from a very large incoming flow of documents. We report details of the implementation, as well as results of an empirical testing of the Subject Field Coder on fifty queries.

In the application domain of online information services such as online census information, health records and real-time stock quotes, there are at least two fundamental challenges: the protection of users' privacy and the assurance of service availability. We present a fault-tolerant scheme for private information retrieval (FT-PIR) that protects users' privacy and ensures service provision in the presence of malicious server failures. An error detection algorithm is introduced into this scheme to detect the corrupted results from servers. The analytical and experimental results show that the FT-PIR scheme can tolerate malicious server failures effectively and prevent any information of users from being leaked to attackers. This new scheme does not rely on any unproven cryptographic premise and the availability of tamperproof hardware. An implementation of the FT-PIR scheme on a distributed database system suggests just a modest level of performance overhead.

Topic-based language model has attracted much attention as the propounding of semantic retrieval in recent years. Especially for the ASR text with errors, the topic representation is more reasonable than the exact term representation. Among these models, Latent Dirichlet Allocation(LDA) has been noted for its ability to discover the latent topic structure, and is broadly applied in many text-related tasks. But up to now its application in information retrieval(IR) is still limited to be a supplement to the standard document models, and furthermore, it has been pointed out that directly employing the basic LDA model will hurt retrieval performance. In this paper, we propose a lexicon-guided two-level LDA retrieval framework. It uses the HowNet to guide the first-level LDA model's parameter estimation, and further construct the second-level LDA models based on the first-level's inference results. We use TRECID 2005 ASR collection to evaluate it, and compare it with the vector space model(VSM) and latent semantic Indexing(LSI). Our experiments show the proposed method is very competitive.

Context: Traceability relations among software artifacts often tend to be missing, outdated, or lost. For this reason, various traceability recovery approaches-based on Information Retrieval (IR) techniques-have been proposed. The performances of such approaches is often influenced by "noise" contained in software artifacts (e.g., recurring words in document templates or other words that do not contribute to the retrieval itself). Aim: As a complement and alternative to stop word removal approaches, this paper proposes the use of a smoothing filter to remove "noise" from the textual corpus of artifacts to be traced. Method: We evaluate the effect of a smoothing filter in traceability recovery tasks involving different kinds of artifacts from five software projects, and applying three different IR methods, namely Vector Space Models, Latent Semantic Indexing, and Jensen-Shannon similarity model. Results: Our study indicates that, with the exception of some specific kinds of artifacts (i.e., tracing test cases to source code) the proposed approach is able to significantly improve the performances of traceability recovery, and to remove "noise" that simple stop word filters cannot remove. Conclusions: The obtained results not only help to develop traceability recovery approaches able to work in presence of noisy artifacts, but also suggest that smoothing filters can be used to improve performances of other software engineering approaches based on textual analysis.

Abstract. Soft-cardinality spectra (SC spectra) is a new method of approximation for text strings in linear time, which divides text strings into character q-grams of different sizes. The method allows simultaneous use of weighting at term and q-gram levels. SC spectra in combination with resemblance coefficients allows the construction of a family of text similarity functions that only use the surface information of the texts and weights obtained in the same text collection. These similarity measures can be used in various tasks of natural ...

Recommender Systems have emerged to guide users in the task of efficiently browsing/exploring a large product space, helping users to quickly identify interesting products. However, suggestions generated with traditional Recommender Systems usually do not produce diverse results, though it has been argued that diversity is a desirable feature. The study of diversity aware Recommender Systems has become an important research challenge in recent years, drawing inspiration from diversification solutions for Information Retrieval. However, we argue it is not enough to adapt Information Retrieval techniques towards Recommender Systems, as they do not place the necessary importance to factors such as serendipity, novelty and discovery which are imperative to Recommender Systems. In this report, we propose a diversification technique for Recommender Systems that generates a diversified list of results which not only balances the trade-off between quality (in terms of accuracy) and diversit...

We present a new and efficient algorithm to solve the 'threshold all vs. all' problem, which involves searching of two strings (with length N and M respectively) for finding all maximal approximate matches of length at least S and with up to K differences. The algorithm is based on a novel graph model, and it solves the problem in time O(NMK 2).

Detecting academic plagiarism is a pressing problem, e.g., for educational and research institutions, funding agencies, and academic publishers. Existing plagiarism detection systems reliably identify copied text, or near copies of text, but often fail to detect disguised forms of academic plagiarism, such as paraphrases, translations, and idea plagiarism. We present Semantic Concept Pattern Analysis - an approach that performs an integrated analysis of semantic text relatedness and structural text similarity. Using 25 officially retracted academic plagiarism cases, we demonstrate that our approach can detect plagiarism that established text matching approaches would not identify. We view our approach as a promising addition to improve the detection capabilities for strong paraphrases. We plan to further improve Semantic Concept Pattern Analysis and include the approach as part of an integrated detection process that analyzes heterogeneous similarity features to better identify the many possible forms of plagiarism in academic documents.

Efficient processing of top- k queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient top- k processing in domains such as the Web, multimedia search, and distributed systems has shown a great impact on performance. In this survey, we describe and classify top- k processing techniques in relational databases. We discuss different design dimensions in the current techniques including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions. We show the implications of each dimension on the design of the underlying techniques. We also discuss top- k queries in XML domain, and show their connections to relational approaches.

This paper proposes a scheme for scheduling disk requests that takes advantage of the ability of high-level functions to operate directly at individual disk drives. We show that such a scheme makes it possible to support a Data Mining workload on an OLTP system almost for free: there is only a small impact on the throughput and response time of the existing workload. Specifically, we show that an OLTP system has the disk resources to provide a consistent one third of its sequential bandwidth to a background Data Mining task with close to zero impact on OLTP throughput and response time at high transaction loads. At low transaction loads, we show much lower impact than observed in previous work. This means that a production OLTP system can be used for Data Mining tasks without the expense of a second dedicated system. Our scheme takes advantage of close interaction with the on-disk scheduler by reading blocks for the Data Mining workload as the disk head "passes over" them while satisfying demand blocks from the OLTP request stream. We show that this scheme provides a consistent level of throughput for the background workload even at very high foreground loads. Such a scheme is of most benefit in combination with an Active Disk environment that allows the background Data Mining application to also take advantage of the processing power and memory available directly on the disk drives.

Systems like SciScope and Data Access System for Hydrology (DASH) rely on data catalogs to facilitate data discovery. These catalogs describe several nation-wide data repositories that are important for scientists including US Geological Survey's National Water Information System (NWIS), Environmental Protection Agency's STOrage and RETrieval System (EPA STORET) and National Climatic Data Center (NCDC) data collections which contain a wealth

Intuitively, words forming phrases are a more precise description of content than words as a sequence of keywords. Yet, evidence that phrases would be more effective for information retrieval is inconclusive. This paper isolates a neglected class of phrases, that is abundant in eommuuication, has an established theoretical foundation, and shows promise for an effective expression of the user's information need: the noun-noun compound (NNC). In an experiment, a variety of meaningful NNCs were used to isolate relevant passages in a large and varied corpus. In a first pass, passages were retrieved based on textual proximity of the words or their semantic peers. A second pass retained only passages containing a syntactically coherent structure equivalent to the original NNC. This second pass showed a dramatic increase in precision. Preliminary resuits show the validity of our intuition about phrases in the special but very productive case of NNCs.

In this paper we present our work on personal information management by exploiting both context ontology and landmarks. The context model introduced is based on location, agent, time, and category of information corresponding to the 4Ws: where, who, when, and what. Each of them represents one dimension of an information item. Semantics of each dimension are explicitly described in the ontology for personal information items along with their interrelation. Therefore a necessary modification of the LATCH model has been investigated in-depth for its usage for personal information management and further retrieval. Navigation in the information space is supported by the use of landmarks.

Adaptive search systems apply user models to provide better separation of relevant and non-relevant documents in a list of results. This paper presents our attempt to leverage this ability of user models in the context of visual information exploration. We developed an adaptive visualization approach for presentation and exploration of search results. We simulated a visual intelligence search/analysis scenario with log data extracted from an adaptive information foraging study and were able to verify that our method could improve the ability of traditional relevance visualization to separate relevant and irrelevant information.

In this work, we present a recently developed evaluation framework for video OCR specifically for English Text but could well be generalized for other languages as well. Earlier works include the development of an evaluation strategy for text detection and tracking in video, this work is a natural extension. We sucessfully port and use the ASR metrics used in the speech community here in the video domain. Further, we also show results on a small pilot corpus which involves 25 clips. Results obtained are promising and we believe that this is a good baseline and will encourage future participation in such evaluations.

Every modern portal ships with some form of internal document library portlet tools that can be used to enable groups to share files. Unfortunately, certain limitations --that the portlet can only view data managed directly by the portal and that document and data files are the only resource that can be browsed --make these tools less valuable in many real-world collaborations. This paper describes a semantically-enhanced scientific resource library portlet that extends the traditional document library to enable interaction with multiple distributed repositories in the cloud and to broaden the set of resources that can be viewed beyond simple hierarchical document folder-files structure to include people, sensors, data streams and other complex digital entities and their relationships. Our technology is based on semantic content abstraction and context aggregation functionality supported by Tupelo, a semantic content middleware and is implemented as a portlet plugin to the Liferay-based CyberCollaboratory portal. We describe the architecture components and the browsing features currently implemented and present a water science use case in which users are able to share documents, raw sensor data streams, and derived virtual sensor data (rainfall) from distributed sources within the same semantic resource library.