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Editor’s note: This article describes a method for efficient hypervector operations using a grouping strategy for reduced computations. Quantization is used for reducing the number of multiplications, whereas caching of magnitude is used for eliminating redundant computations. — Shao-Wen Yang, Intel Corporation

The study aimed to explore EEG correlates of a person’s creative (non-trivial) and trivial decisions finding in conditions of associative search by means of event-related synchronization/event-related desynchronization (ERS/ERD) spectral power changes analysis. Sixty subjects had to fulfill RAT (Remote associative test) alike tasks while EEG registering. There were no differences in time responses while remote association and trivial decisions search (around 3 seconds in both case). An increase in ERS was observed for the central frequencies 4 Hz at F7 site (1500-1000 ms before note of decision), 3 Hz at the Fz site (500-1000 ms before note of decision), and 3.5 Hz at T3 site (in the last 500 ms before decision). Obtained findings stressing the role of low frequency bands (theta and delta) in wide associative field search leading for creative and original decisions.

The paper presents a multi-agent system design technique and algorithms for relay intercity freighting within a geographical cluster using swap body trucks. The efficiency of the technique proposed is understood as the stability of a compound dynamic system. An approach to system’s stability margin prediction based on associative search and wavelet analysis is offered.

International audience; The paper presents control methodology and algorithms for multi-agent systems based onpredictive identification and simulation models. The efficiency of the technique proposed is understoodas the stability of a compound dynamic system. An approach to system’s stability margin predictionbased on associative search and wavelet analysis is offered.

超高層大気に於ける地球規模の物理現象の機構を解明する為には，様々なデータを用いた分野横断的な研究推進が重要である．インターネット上の様々なデータを直ちに取得する為の基盤が，分野横断的な研究の１つの鍵となる．IUGONET プロジェクトは，複数の研究機関によって分散管理されている，様々なデータのURL 等の所在情報等を提供可能なメタデータ・データベースを構築することによって，この課題に対処した．しかしながら，このメタデータ・データベースは広範な研究領域を対象としている為，専門分野外のユーザーにとって単語検索時における適切な検索語句の選択が容易でないことが指摘された．これを補助するべく，検索語句の関連語の取得とそれらを用いた再検索クエリ文字列の自動生成を行う為に，そのメタデータ・データベースと連想検索エンジンGETAssoc を連携させた．そして，この連想検索用の辞書を作成する為に，SAO/NASA Astrophysics Data System からメタデータを抽出した．再クエリ文字列を生成する為に用意した辞書を用いることで，期待通りの連想検索結果を得ることが出来た．, In order to understand the mechanism of global-scale phenomena in the upper atmosphere, multidisciplinary researches using many kinds of data are important. An infrastructure to access to many kinds of data on the Internet is one of the keys to the multidisciplinary researches. The Inter-university Upper atmosphere Global Observation NETwork (IUGONET) project solved this problem by developing a metadata database to provide information such as URL of database or each data file, which are managed dispersively by several institutes. Because the metadata database covers a wide scientific field, it is likely that non-specialized users can not easily select a right search phrase. We connected an associative search engine which is called GETAssoc with the metadata database, in order to get the related words and to generate re-query phrases by using them automatically. We collected metadata from SAO/NASA Astrophysics Data System to create the dictionary for associative search. We show that the prepared dictionary gives the related terms as expected for generating re-query phrases., 著者人数: 12名, 形態: カラー図版あり, Number of authors: 12, Physical characteristics: Original contains color illustrations, 資料番号: AA0062302010, レポート番号: JAXA-RR-13-010

Research trend survey is an essential preliminary step for any academic researches, but many beginning researchers have difficulty because they are still foreign to appropriate keywords in his/her research field. We constructed a support system for research trend surveys not only to accelerate the preliminary step but also to let students have a better grips of trend progresses and keyword transitions. Our system assumes a fair amount of data accumulation, for which we employed KAKEN database excerpts, but does not assume manual keyword registration or any other heuristic preprocesses: with an associative search module, it dynamically searches relevant words that are frequently used in the targeted academic field and gives users effective visualizations to understand trend transitions. Preliminary evaluations suggest that the trend transitions that our system presents are effective for trend surveys.

Criminal Intelligence Analysis often requires a search different from the semantic and keyword based searching to reveal the associations among semantically and operationally connected objects within a crime knowledge base. In this paper we introduce associative search as a search along the networks of association between objects like people, places, other organizations, products, events, services, and so on. We also propose an associative search model based on the 5WH associated concepts of a crime, i.e. WHAT (what has happened), WHO (who was involved in the crime), WHEN (the temporal information of the crime), WHERE (the geo-spatial information of the crime) HOW (the modus-operandi used in committing a crime). We have employed Formal Concept Analysis theory to reveal the associations, highlighting Hot Spots, offender's profile and its associated offenders in a criminal activity.

This paper offers a concept of developing an intelligent multi-agent system that maintains stability of Russia's Smart Grid incorporating an active analytical network (AAN) and new algorithms to determine the degree of the system's stability by using Gramians.

In the design of multimedia computing systems, one of the most important issues is how to search and analyze media data (images, music, movies and documents), according to user’s impressions and contexts. This paper presents “Kansei-Multimedia Computing System” for realizing international and cross-cultural research environments, as a new platform of multimedia computing system. We introduce a “Kansei” and semantic associative search method based on the “Mathematical Model of Meaning (MMM)”. The concept of “Kansei” includes several meanings on sensitive recognition, such as “emotion”, “impression”, “human senses”, “feelings”, “sensitivity”, “psychological reaction” and “physiological reaction”. MMM realizes “Kansei” processing and semantic associative search for media data, according to user’s impressions and contexts. This model is applied to compute semantic correlations between keywords, images, music, movies and documents dynamically in a context-dependent way. This system based on MMM realizes (1) “Kansei” image and music search and analysis for cooperative creation and manipulation of multimedia objects and (2) Cross-cultural communications with music and images databases.

A system identification method using predictive models based on the imitation of analyst's associative thinking is developed. Identification algorithms using associative search for description of the knowledge about control plant are presented. Wavelet analysis is applied for time-varying objects identification.

The paper discusses smart soft sensor design methods for industrial processes. The approach proposed is based on virtual models and associative search techniques. Fuzzy model is applied in combination with production knowledgebase to compensate for the lack of lab data. Fuzzy specification of certain process variables is practiced.

The electronic library of the State and University Library Bremen (E-LIB) is based on search engine technology and offers an integrated catalogue of electronic and print media. All of the library’s available media and services can be accessed via a single search interface (based on the idea of a ‘one- stop-shop portal’) instead of the separate catalogues for different media types still to be found in many libraries. The idea behind this system is to combine all kinds of local holdings and all the external resources available for our users in one single retrieval system and to offer new methods for supporting associative search processes.

A technology for estimating the dynamics of power grid's generating facilities participation in overall primary frequency regulation in contingencies is developed based on frequency and generating capacity time series. The paper discusses process identification algorithms based on virtual models design using process data archives and knowledge bases. Associative search methods are used for identification algorithms development.

The authors set the functional requirement of “reduce your own risk” and ran sessions of associative search to extract useful knowledge from databases. Ninety engineers found the most analogous knowledge using the associative search engine “GETA/IMAGINE” on scenario-based databases “Failure Knowledge Database” and “100 Scenarios of Failure.” Above 60% of their risk concern cases successfully reached the most analogous accident cases or failure scenarios from either database in about 10 min. Associative search can aid the designer in selecting design solutions for the functional domain.

The paper presents process identification algorithms based on virtual models design using process data archives and knowledge bases. Associative search methods are used for identification algorithms development.

Received Signal Strength Indicator (RSSI) is commonly considered and is very popular for target localization applications, since it does not require extra-circuitry and is always available on current devices. Unfortunately, target localizations based on RSSI are affected with many issues, above all in indoor environments. In this paper, we focus on the pervasive localization of target objects in an unknown environment. In order to accomplish the localization task, we implement an Associative Search Network (ASN) on the robots and we deploy a real test-bed to evaluate the effectiveness of the ASN for target localization. The ASN is based on the computation of weights, to ”dictate” the correct direction of movement, closer to the target. Results show that RSSI through an ASN is effective to localize a target, since there is an implicit mechanism of correction, deriving from the learning ASN approach.

We present Selene, a source code recommendation tool based on an associative search engine. It spontaneously searches and displays example programs while the developer is editing a program text. By using an associative search engine, it can search a repository of two million example programs within a few seconds. This paper discusses issues that are revealed by our ongoing implementation of Selene, in particular those of performance, similarity measures and user interface.

We applied associative OR search on a Failure Knowledge Database with 1,242 failure accidents and 41 failure scenarios in the book "100 Scenarios of Failure" to find cases most analogous to risks that engineers were concerned with. Ninety engineers provided 203 input cases of risk concerns and the search for accidents most analogous to each input returned the most analogous accidents for 64% of the input cases of risk concerns within several minutes. Analogous scenario searches returned the most analogous scenarios for 63% of the input. Regular keyword AND searches take tens of minutes to narrow down the candidates to a few analogous cases, and thus associative search is a more effective tool for risk management.

The technology of record, storage and processing of the texts, based on creation of integer index cycles is discussed. Algorithms of exact-match search and search similar on the basis of inquiry in a natural language are considered. The software realizing offered approaches is described, and examples of the electronic archives possessing properties of intellectual search are resulted.

The Translation Look-aside Buffer (TLB), a content addressable memory, consumes significant power due to the associative search mechanism it uses in the virtual to physical address translation. Based on our analysis of the TLB accesses, we make two observations. First, the entropy or information content of the stack virtual page numbers is low due to high spatial locality of stack memory references. Second, the entropy of the higher order bits of global memory references is low since the size of the global data is determined and fixed during compilation of a program. Based on these two characteristics, we propose two techniques: an entropy-based speculative stack address TLB and a deterministic global address TLB to achieve energy reducing. Our results show an average of 47% energy savings in the data TLB with less than 1%overall performance impact.

In this paper, we present a new creation method of a semantic retrieval space for a specialized field of study, especially for the field of International Relations (IR), and new evaluation criteria for verifying practical effectiveness of the created space. This space creation method needs only a general dictionary and a lexicon of the technical terms for a specific field. Using the newly created space, we realized semantic associative search for documents which include various technical terms with general words, and also documents which include general words with technical terms. Furthermore, to verify practical effectiveness of the space creation method, that is, how much knowledge we can acquire from the created space newly, we present some evaluation criteria besides precision and recall rate.

We present an implementation of a semantic associative search space for medical document databases. We have implemented a semantic associative search space for the medical field by using encyclopedias which describe not only word definitions but also word explanations. This implementation realizes a medical semantic associative search space by using a medical encyclopedia which includes explicit word explanations. We have created an actual medical semantic associative search space which consists of 149 dimensions computed from the 217 /spl times/ 219 matrix consisting of medical data. By using this actual medical space, we show several experimental results of semantic associative search creation and medical document retrieval for clarifying feasibility of our implementation method.

This paper presents a hierarchical DNA memory based on nested PCR. Each DNA strand in memory consists of address blocks and a data block. In order to access specific data, we specify the order of the address primers, and nested PCR are performed by using these primers. Our laboratory experiments are also presented to demonstrate the feasibility of the proposed memory.

In this contribution an approach for document retrieval is presented which groups (pre-processed) documents using a similarity measure. The methods weredeveloped based on self-organising maps to realise interactive associative search and visual exploration of document databases. This helps a user to navigate through similar documents. The navigation, especially the search for the first appropriate document, is supported by conventional keyword search methods.

In this paper, Wang and Chen's (1991) intelligent control using an adaptive neuron for hydraulic turbine generators is studied. With the abilities of self-learning and associative search,the neuron intelligent controller runs without modeling. Such a controller is very simple and almost has no parameters to determine. The real-time dynamic simulation results show its good performance. It is very convenient to use in practice. >

In the current World Wide Web (WWW) environment, it is important to realize intelligent and effective information acquisition mechanisms. We propose a semantic associative search method based on our mathematical model of meaning to realize intelligent and effective information acquisition. Our method provides a dynamic context recognition mechanism for information acquisition according to user's queries given as contexts. We integrate two application systems of our semantic associative search method to realize an intelligent and effective information acquisition environment for WWW information resources. We apply our method to information retrieval on WWW information resources, which are identified by Uniform Resource Locators (URLs). This application system makes it possible to dynamically obtain semantically related information resources on WWW, according to user's queries given as contexts. Furthermore, we have applied our method to queries and information resources described in multiple languages. We integrate those applications for supporting WWW information acquisition.

In a paper presented last year at WMPP'01 [Walker01], we described the initial prototype of an associative processor implemented using field-programmable logic devices (FPLDs). That paper presented an overview of the design, and concentrated on the processor's instruction set and its implementation using FPLDs. This paper describes the implementation of the processor's associative operation -- associative searching and responder resolution -- in more detail.

We describe on-going experiments for executing associative search queries within synthesized DNA databases. Queries are executed by hybridization of a target database strand with a complementary query strand probe. In our initial annealing experiments for processing associative search queries, we employed fluorescently labeled query strands and performed separation of fluorescent versus non-fluorescent beads using Fluorescence Activated Cell Sorting (FACS or flow cytometry). We also tested polymerase chain reaction (PCR) as an output method, and developed a PCR technique for search in the pair-wise constructed library that exploits the particular properties of words in that library. We have also implemented computer software that provides a simulation (viewable on the internet) of the experimental search procedures, as well as a simulation of input/output from conventional 2D images.

This report is a comparison and documentation of techniques of associative retrieval. Techniques presented include conventional database, artificial neural networks, feature-based digital image associative search, holographic associative memory, and content-addressable processors.

EPAM is a theory of the processes of human perception and memory, first programmed for a computer by E. A. Feigenbaum in 1959, that has shown an excellent fit to experimental data from a wide variety of psychological tasks. Over the years, it has been progressively extended to new domains without essential change in its central mechanisms. This article examines EPAM IV, a version extended to account for expert memory, especially the work in recent years by Chase and Ericsson (1981, 1982) and Staszewski (1988a, 1988b, 1990). EPAM IV has also been adapted to deal with numerous other short-term and long-term memory tasks, which will be reported elsewhere. The main modifications of EPAM that are relevant to the serial recall task examined in this article are a schema in long-term memory (called a retrieval structure) created by the expert's learning and the addition of an associative search process in long-term memory. These new components operate in close interaction with the other EPAM structures to match the observed behavior. EPAM IV reproduces all of the phenomena explained previously by EPAM III and in addition gives an accurate detailed account of the performance (studied by Staszewski) of an expert recalling long sequences of digits. The theory substantially revises, improves, and extends Chase and Simon's earlier "chunking" explanation of expert memory.