Triple Format Research Articles

WiLife: Long-term Daily Status Monitoring and Habit Mining of the Elderly Leveraging Ubiquitous Wi-Fi Signals

The global aging demographic underscores the imperative for continuous in-home monitoring of the empty-nest elderly, ensuring their safety and well-being. The widespread deployment of Wi-Fi infrastructure has paved the way to monitor the elderly in a non-intrusive and privacy-preserving manner. Numerous studies have explored the potential of utilizing Wi-Fi signals to address urgent life safety concerns such as fall detection and vital sign monitoring. However, apart from these acute safety issues, the early detection of potential disease symptoms and managing the progression of chronic diseases are also crucial for elderly care, which calls for long-term and continuous monitoring of the elderly’s daily routines. Unfortunately, challenges like continuous activity segmentation and location/orientation dependencies have hindered the implementation of a long-term, around-the-clock activity monitoring system for the elderly. This work introduces ”WiLife”, a cutting-edge Wi-Fi based framework for continuous monitoring of the elderly’s spatio-temporal daily status information. Specifically, WiLife adopts a strategy of partitioning living spaces into functional areas and categorizing daily activities into atomic states . By encapsulating daily life status into a unique series of triple unit format: \(\left\langle{{Time, Area, State}}\right\rangle\) , WiLife is able to offer valuable insights into when, where, and how activities occur. Field implementations spanning 1,080 hours ( \(45days\times 24hours\) ) in real-world home environments highlight WiLife’s exceptional capability in understanding individual living habits and timely detection of irregularities.

Incorporating Triple Entry Accounting as an Audit Tool—Enhancing Modern Accounting Systems

Abstract: Despite the advancements in modern accounting systems, fraud remains a prevalent issue, necessitating extensive time and financial resources for audits. Integrating modern accounting ledgers with a public blockchain could facilitate easily verifiable accounting records, streamlining the audit evidence gathering and analytics process. Continuous audits could become feasible through readily available audit evidence from the public blockchain. The proposed audit solution aims to establish verifiable "Financial Fingerprints" (signed indexes) for each accounting entry, subsequently publishing them to specific addresses on the public blockchain. This initiative seeks to structure accounting ledgers in a Triple Entry Accounting format, where the third entry involves notarizing an index of each accounting entry on the public blockchain. The essence of Triple Entry Accounting lies in its atomicity, ensuring a single authoritative set of books. Any attempt to present an alternative set of books would be automatically detected. The approach to achieving this objective can be articulated through two value propositions. Firstly, Triple Entry Accounting offers a means to establish a provable official set of books, providing market access independent of network effects. Secondly, it aims to dismantle data silos by leveraging the blockchain as a shared ledger, thereby assisting auditors in efficiently gathering adequate audit evidence. With its standalone value proposition, Triple Entry Accounting offers benefits even before the network effect comes into play, leading to the development of an open standardized protocol for implementing it as an extension to modern accounting systems.

Ultrapotent Broadly Neutralizing Human-llama Bispecific Antibodies against HIV-1.

Broadly neutralizing antibodies are proposed as therapeutic and prophylactic agents against HIV-1, but their potency and breadth are less than optimal. This study describes the immunization of a llama with the prefusion-stabilized HIV-1 envelope (Env) trimer, BG505 DS-SOSIP, and the identification and improvement of potent neutralizing nanobodies recognizing the CD4-binding site (CD4bs) of vulnerability. Two of the vaccine-elicited CD4bs-targeting nanobodies, G36 and R27, when engineered into a triple tandem format with llama IgG2a-hinge region and human IgG1-constant region (G36×3-IgG2a and R27×3-IgG2a), neutralized 96% of a multiclade 208-strain panel at geometric mean IC80s of 0.314 and 0.033µg mL-1, respectively. Cryo-EM structures of these nanobodies in complex with Env trimer revealed the two nanobodies to neutralize HIV-1 by mimicking the recognition of the CD4 receptor. To enhance their neutralizing potency and breadth, nanobodies are linked to the light chain of the V2-apex-targeting broadly neutralizing antibody, CAP256V2LS. The resultant human-llama bispecific antibody CAP256L-R27×3LS exhibited ultrapotent neutralization and breadth exceeding other published HIV-1 broadly neutralizing antibodies, with pharmacokinetics determined in FcRn-Fc mice similar to the parent CAP256V2LS. Vaccine-elicited llama nanobodies, when combined with V2-apex broadly neutralizing antibodies, may therefore be able to fulfill anti-HIV-1 therapeutic and prophylactic clinical goals.

Abstract PO3-15-11: Soluble sFas, sFasL, sPD1, and sPD-L1 analyses in the blood peripheral of locally advanced breast cancer women before and after neoadjuvant chemotherapy

Abstract Assess soluble sFAS, sFASL, sPD1, and sPD-L1 levels in patients with HER2 or triple-negative (TN) breast cancer submitted to neoadjuvant chemotherapy. A prospective cohort study was performed with TN and HER2+ breast cancer patients between 2015 and 2018. Soluble levels of sFasL, sFas, sPD-L1, and sPD1 were collected before and after neoadjuvant chemotherapy (NAC). NAC regimens included a dense dose of Adriablastin and cyclophosphamide during four cycles, followed by Paclitaxel for 12 weeks. No clinical evidence of tumor in the breast and axillary lymph nodes was defined as a pathological complete response (pCR). Twenty-one women with TN (56,7%) and 16 (43,3%) HER2+ breast cancer were included. Twenty-one women (56.7%) presented complete pathological response (pCR) after neoadjuvant chemotherapy (NAC), defined as the absence of invasive disease in the breast and lymph nodes. High sPD1 and sFas levels in the TN and HER2+ patients compared to the control group (P< 0.05). In paired analysis, statistical differences of sFas and sFasL levels in TN and HER2+ patients before and after NAC (P< 0.05). Low levels of sPDL1 in HER2+ patients with pCR compared to non-pCR (P< 0.05). High sFasL levels in TN patients with pCR compared to the non-pCR group (P< 0.05). Conclusion: soluble sFas, sFas-L, sPD1, and sPD-L1 levels were apoptosis-related markers with potential predictive value of neoadjuvant chemotherapy response in TN and HER2+ breast cancer. Keywords: breast cancer; FAS receptor; Programmed death 1, HER2+, Triple negative Citation Format: Carolina Vasconcelos, Marcelo Salgado, Carlos Eduardo Anunciação, Denise Viana, Leuridan Torres, Nathália Valois Montarroyos de Moraes. Soluble sFas, sFasL, sPD1, and sPD-L1 analyses in the blood peripheral of locally advanced breast cancer women before and after neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-15-11.

Intelligent Technology Assessment of High-Speed Railway Based on Knowledge Graphs

This paper introduces an intelligent technology assessment framework for high-speed railways based on a knowledge graph approach. We employ rule-based knowledge extraction algorithms and a Bert-BiLSTM-CRF model for entity extraction from technical texts. Subsequently, we establish relationships among various entities, constructing a knowledge graph specific to high-peed railways. The knowledge graph is stored in a Neo4j graph database in triple format. Furthermore, we establish a comprehensive evaluation metric system, integrating knowledge graph insights to assess the utility of enabling technologies. We employ the CRITIC weighting method to calculate the value assessment results for target technologies. Simulation results indicate that the training results for word segmentation and sentence splitting are favorable, and the Bert-BiLSTM-CRF model achieves convergence in accuracy, recall, and F1 score after 30 iterations. The technical assessment results in this paper align closely with the actual technological value assessment.

Relations between Russia, Turkey and Iran in the context of military-political ties with the Syrian Arab Republic

The subject of the study is the political relations that arose as a result of Russia’s initiation of the «triple format» on the Syrian issue. The purpose of the study is to comprehensively analyze the development of the Astana format in the context of military-political relations with the Syrian Arab Republic in the political aspect. The methodological basis of the study consists of descriptive, comparative, typological and systematic methods. Descriptive and systematic methods made it possible to provide a holistic picture of the political processes related to the combination of factors that provoked military-political conflicts in Syria. The typology made it possible to study the processes occurring in Syria during the chronological period: from the moment of the Syrian color revolution to the present. The use of a systematic approach made it possible to present the Syrian crisis as an integral system of interacting elements. The scientific novelty of the study is determined by the fact that new sources were involved in the work, which relate to the diverse aspects of military-political relations with the Syrian Arab Republic in the context of the Syrian crisis. The practical significance of the work lies in the fact that the materials presented in it can be used for the political and diplomatic resolution of crisis situations that help preserve the integrity and sovereignty of states, preventing armed intervention in their internal processes. Based on these circumstances, it seems that the plans of the Western community, including the United States, provided for the elimination of countries as distinctive states with their own sovereignty, aimed at pursuing a policy of a bipolar world order. It was in this aspect that these countries used such a mechanism of influence as provoking “color revolutions.”

Triple Hybrid (TriHy): What Happened When COVID Hit the Research Study

This study examines whether student achievement in a Master’s degree course (Teaching English to International Learners – TESOL) on assessment is comparable regardless of the chosen participation modality for this “Triple Hybrid” (or “TriHy”) course. The three delivery modes for this course were face-to-face (F2F), synchronous online (SO), and asynchronous online (ASO). The study began in January 2020, before the COVID-19 pandemic reached the university. For the first ten weeks, the course was taught as originally scheduled, but due to the interruption of face-to-face classes at the university, this group had to join the Synchronous Online (SO) group for the last five weeks of the semester. Although differences in student achievement in the course were not statistically significant, perceptions of factors that contributed to students’ personal success were. We can conclude that the quality of teaching is not compromised in a Triple Hybrid format with considerable institutional support, significant commitment from teachers, including their time, and thoughtful student choices.

Implementing Triple Entry Accounting as an Audit Tool—An Extension to Modern Accounting Systems

Despite the technological leaps modern accounting systems have brought about, fraud is still prevalent. This necessitates spending time and money on audits. Connecting modern accounting ledgers to a public blockchain would make accounting records easily verifiable, aiding the audit evidence gathering and analytics process. Continuous audits will be made viable by readily available audit evidence from the public blockchain. The proposed audit solution creates verifiable “Financial Fingerprints” (signed indexes) for every accounting entry. These Financial Fingerprints are then published to a certain range of addresses on the public blockchain. The objective is to help accounting ledgers be structured in a Triple Entry Accounting format. The third entry can be defined as publishing a notarized index of each accounting entry to the public blockchain. The crux of Triple Entry Accounting is atomicity, meaning there can only be one book. Trying to present an alternative set of books would still be possible, but it would be automatically detected. The method used to achieve the objective can be presented as two value propositions. The first value proposition of Triple Entry Accounting is to define a provable official true set of books. This is important because it is a door into the market without being dependent on network effects. The second value proposition of Triple Entry Accounting is to break down the data silos by using the blockchain as a shared ledger and thus help auditors immensely in their tedious work to gather sufficient and appropriate audit evidence. Because of value proposition number one, Triple Entry Accounting presents standalone value even before the network effect begins. The result of this is an open standardized protocol for implementing Triple Entry Accounting as an extension to modern accounting systems.

Ontological knowledge inferring approach: Introducing Directed Collocations (DC) and Joined Directed Collocations (JDC)

The growing need of utilizing unstructured knowledge embedded in open-domain natural language text into machine-processable forms requires the induction of hardly extracted structured knowledge into knowledge bases which makes the Semantic Web vision a reality. In this context, ontologies, and ontological knowledge (triples) plays a vital role. This research introduces two novel concepts named Directed Collocation (DC) and Joined Directed Collocation (JDC) along with a methodical application of them to infer new ontological knowledge. Introduced Quality-Threshold-Value (QTV) parameter improves the quality of the inferred ontological knowledge. Having set a moderate value (3) for QTV, this approach inferred 95,491 new ontological knowledge from 43,100 triples of open domain Sri Lankan English news corpus. Indeed, the outcome was approximately doubled in size as the source corpus. Some inferred ontological knowledge was identical with the original corpus content, which evidences the accuracy of this approach. The remaining were validated using inter-rater agreement method (high reliability) and out of which around 56% were estimated as effective. The inferred outcome which is in the triple format may use in any knowledge base. The proposed approach is domain independent. Thus, helps to construct/extend ontologies for any domain with the help of less or no human specialists.

Creating Bengali Freebase Using Wikidata

Freebase is a large collaborative knowledge base and database of general, structured information for public use. Its structured data had been harvested from many sources, including individual, user-submitted wiki contributions. Its aim is to create a global resource so that people (and machines) can access common information more effectively which is mostly available in English. In this research work, we have tried to build the technique of creating the Freebase for Bengali language. Today the number of Bengali articles on the internet is growing day by day. So it has become a necessary to have a structured data store in Bengali. It consists of different types of concepts (topics) and relationships between those topics. These include different types of areas like popular culture (e.g. films, music, books, sports, television), location information (restaurants, geolocations, businesses), scholarly information (linguistics, biology, astronomy), birth place of (poets, politicians, actor, actress) and general knowledge (Wikipedia). It will be much more helpful for relation extraction or any kind of Natural Language Processing (NLP) works on Bengali language. In this work, we identified the technique of creating the Bengali Freebase and made a collection of Bengali data. We applied SPARQL query language to extract information from natural language (Bengali) documents such as Wikidata which is typically in RDF (Resource Description Format) triple format.

Open-world link prediction via type-constraint embedding and hybrid attention for knowledge reuse of AI chip design

AI chips play a vital role in real-time AI applications. AI chip design requires vast knowledge reuse in integrated circuits and neural networks, and this knowledge continues to grow at a rapid rate, leading to a heavy burden on designers. Automated methods are urgently needed to handle the massive and rapidly growing literature. In the realm of traditional EDA, deep learning can produce excellent designs by learning from a large number of design examples. However, in front-end design such as algorithm and architecture design, a considerable amount of knowledge exists in the fast-growing literature. The use of this knowledge is still in a primitive state of manual processing. Therefore, this paper leverages the knowledge graph and the link prediction to organize and reuse AI chip design knowledge (ACDK). First, a knowledge graph is built to organize ACDK in triple format. Second, an open-world link prediction model named TC-THA is proposed, which aims at the closed-world limitation, the lack of type constraint, and the deep inner-association learning weakness of the related methods. TC-THA converts the type constraint of ACDK triples into vectors for better training and inference. It also uses a hybrid attention mechanism for the sufficient semantic interaction among inner-triple information, which is used to capture the deep internal associations of knowledge for the superior ACDK reuse. Experiments show that the TC-THA outperforms other methods and achieves the best MRR and H@10 score of 0.34 and 0.51 in ACDK link prediction. A case study of ACDK reuse shows that, for the given problems, the TC-THA predicts highly feasible solutions, which can be confirmed in the latest AI chip studies.

Research progress of large-scale knowledge graph completion technology

With the continued growth of various knowledge graphs, such as Google Knowledge Map, DBpedia, Microsoft Concept Graph, and YAGO, the knowledge representation system, constructed based on RDF, has become more well-known. The RDF triple format has become the basic description of knowledge in the real world. Due to its simple structure and clear logic, it is easy to understand and implement. Nevertheless, when faced with extremely complicated knowledge and common sense, complete knowledge can become difficult to describe. The construction process of knowledge graphs is bound to lead to incomplete knowledge contained in the graphs. At this point, the knowledge-based completion technology is particularly important for managing such situations. Any existing knowledge graph must be improved continuously through completion technology and newly inferred knowledge. Beginning with the construction of a knowledge graph, this paper divides the problem of knowledge graph completion into two levels: concept completion and instance completion. (1) The concept completion level primarily focuses on the completion of entity types. It is described in terms of three development stages: a logical reasoning mechanism, based on description logic, a type inference mechanism, based on traditional machine learning, and a type inference mechanism, based on representation learning. (2) The instance completion level can be further divided into an RDF triple completion and new instance discovery. This paper focuses on RDF triples completion learning, which includes entity completion or relationship completion and is described in three development stages, such as statistical relational learning, probability learning based on random walks, and knowledge representation learning. Through the review and discussion of the research process, the development status, and the latest progress in the above-mentioned large-scale knowledge graph completion, we present the challenges that the technology will face and the development prospects of future work.

Abstract P4-06-14: Heterogeneity of multigene signatures in breast cancer among multiple specimens taken by core needle biopsy

Abstract Use of multigene predictive signatures pre-chemotherapy in core needle biopsy specimens in low-resource systems in not applied due to multiple reasons. We are seeking a method of cost-effective integration of gene expression analysis into initial breast cancer subtype assessment pre-chemotherapy.The aim of the analysis was to assess the heterogeneity of independent cores taken from primary tumor in the context of prediction of its clinical parameters by gene expression profiling.Material and methods. The study group consisted of 65 women with high-risk breast cancer, before preoperative chemotherapy, median age at the start of therapy 41,2 years (range 29-62 yr). Among the diagnosed breast cancers, 71% were grade 3 (G3), while G1 only in 3% of individuals. The most common breast cancer biological subtypes were triple negative breast cancer (38%), luminal B HER2-negative (defined as Ki67>20% or PR-negative, 24%) and HER2-positive tumors (23%) of tumors were of lobular histology. In core needle biopsies taken before preoperative breast cancer chemotherapy gene expression profiling was carried out by oligonucleotide microarrays (U133A/U133 Plus 2.0), three independent cores were analyzed (192 microarrays). Analysis was carried out by JMP Genomics (SAS Institute, Cary, USA) and R/Bioconductor genefu package.At first step, the overall tumor profile was analysed by Principal Component Analysis. Based on first two principal components, 44 patients (68%) showed highly concordant profile in all three cores, whereas in 21 tumors (32%) larger scatter was observed. We analysed research versions of commercially available multigene predictors, PAM50 are presented in abstract. Prediction was done independently for each core. In 40% of patients all three cores provided identical predictions for all analysed signatures (mainly triple-negative tumors). In 17% of patients we observed minor discordances (in one core 1 or 2 signatures discordant), in 10% discordances in 2 cores. Profound differences were found in 33% of patients (similar to overall profile scatter seen by PCA), mainly in luminal and HER2-positive tumors. Subtypes predicted by genomic testing were - as expected - slightly different from immunohistochemistry prediction. The most often occurring was Basal subtype (87% of TNBC patients, but occurring also in HER2-positive tumors, 20-25% and luminal B tumors, 26.7%). The second molecular subtype was LumB. These tumors constituted 60% of clinically diagnosed luminal B patients, molecular LumB subtype was also frequent among clinically luminal B HER2-positive individuals (30%). Genomic LumA subtype was found in all clinical subgroups, in 10-25% of these patients. Her2 subtype by PAM50 classification was occurring less frequently, in 50% of HER2-nonluminal and 40% luminal B HER2-positive tumors. The concordance of results improved, when classification included both immunohistochemistry results as well as the results of genomic measurements. ER and HER2 showed excellent concordance between three tumor cores and showed bimodal expression, while MKI67 and PGR although concordant showed continuous pattern of expression rather than any visible subgroups. Conclusions. Significant heterogeneity is seen in genomic profile of at least 1/3 of tested breast cancers and shall be taken into account. The error of sampling was lowest in triple negative subtype Citation Format: Michal Jarzab. Heterogeneity of multigene signatures in breast cancer among multiple specimens taken by core needle biopsy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-06-14.

A new bioinformatics tool to help assess the significance of BRCA1 variants

BackgroundGermline pathogenic variants in the breast cancer type 1 susceptibility gene BRCA1 are associated with a 60% lifetime risk for breast and ovarian cancer. This overall risk estimate is for all BRCA1 variants; obviously, not all variants confer the same risk of developing a disease. In cancer patients, loss of BRCA1 function in tumor tissue has been associated with an increased sensitivity to platinum agents and to poly-(ADP-ribose) polymerase (PARP) inhibitors. For clinical management of both at-risk individuals and cancer patients, it would be important that each identified genetic variant be associated with clinical significance. Unfortunately for the vast majority of variants, the clinical impact is unknown. The availability of results from studies assessing the impact of variants on protein function may provide insight of crucial importance.Results and conclusionWe have collected, curated, and structured the molecular and cellular phenotypic impact of 3654 distinct BRCA1 variants. The data was modeled in triple format, using the variant as a subject, the studied function as the object, and a predicate describing the relation between the two. Each annotation is supported by a fully traceable evidence. The data was captured using standard ontologies to ensure consistency, and enhance searchability and interoperability. We have assessed the extent to which functional defects at the molecular and cellular levels correlate with the clinical interpretation of variants by ClinVar submitters. Approximately 30% of the ClinVar BRCA1 missense variants have some molecular or cellular assay available in the literature. Pathogenic variants (as assigned by ClinVar) have at least some significant functional defect in 94% of testable cases. For benign variants, 77% of ClinVar benign variants, for which neXtProt Cancer variant portal has data, shows either no or mild experimental functional defects. While this does not provide evidence for clinical interpretation of variants, it may provide some guidance for variants of unknown significance, in the absence of more reliable data.The neXtProt Cancer variant portal (https://www.nextprot.org/portals/breast-cancer) contains over 6300 observations at the molecular and/or cellular level for BRCA1 variants.

A Knowledge Base Completion Model Based on Path Feature Learning.

Large-scale knowledge bases, as the foundations for promoting the development of artificial intelligence, have attracted increasing attention in recent years. These knowledge bases contain billions of facts in triple format; yet, they suffer from sparse relations between entities. Researchers proposed the path ranking algorithm (PRA) to solve this fatal problem. To improve the scalability of knowledge inference, PRA exploits random walks to find Horn clauses with chain structures to predict new relations given existing facts. This method can be regarded as a statistical classification issue for statistical relational learning (SRL). However, large-scale knowledge base completion demands superior accuracy and scalability. In this paper, we propose the path feature learning model (PFLM) to achieve this urgent task. More precisely, we define a two-stage model: the first stage aims to learn path features from the existing knowledge base and extra parsed corpus; the second stage uses these path features to predict new relations. The experimental results demonstrate that the PFLM can learn meaningful features and can achieve significant and consistent improvements compared with previous work.

Mining semantic association rules from RDF data

The Semantic Web opens up new opportunities for the data mining research. Semantic Web data is usually represented in the RDF triple format (subject, predicate, object). Large RDF-style Knowledge Bases contain hundreds of millions of RDF triples that represent knowledge in a machine-understandable format. Association rule mining is one of the most effective techniques for detecting frequent patterns. In the context of Semantic Web data mining, most existing methods rely on users intervention that is time-consuming and error-prone due to a large amount of data. Meanwhile, rule quality factors (e.g. support and confidence) usually consider knowledge at the instance-level. Namely, these factors disregard the knowledge embedded at the schema-level. In this paper, we demonstrate that ignoring knowledge encoded at the schema-level negatively impacts the interpretation of discovered rules. We introduce an approach called SWARM (Semantic Web Association Rule Mining) that automatically mines Semantic Association Rules from RDF data. The main achievement of SWARM is to reveal common behavioural patterns associated with knowledge at the instance-level and schema-level. We discuss how to utilize knowledge encoded at the schema-level to add more semantics to the rules. We compare the semantic of rules discovered by SWRAM with one of the latest approaches in this field to show the importance of considering schema-level knowledge. Initial experiments performed on RDF-style Knowledge Bases demonstrate the effectiveness of the proposed approach.

Semantic Triple Ranking based on Levenshtien Reverse Engineering Approach

In sematic Web data are represented in Resource Description Framework (RDF) in triple format (Subject, relation, Object) and retrieved using structured query such as SPARQL. These structured queries require complex syntax to formulate. In view of this therefore, several approaches have been researched to enables semantic formulation of natural language to structure query. The process involves the representation of natural language query to structured triple format. However, dues complex nature of natural language, one natural language query may have more than one possible triple format; therefore an effective semantic triple ranking framework is needed for semantic triple ranking. In this study, semantic triple ranking mechanism is proposed. The approach is based on using levenshtien string matching algorithm a reverse engineering approach. The result of the proposed triple ranking has increased precision to 0.04 and recall 0.06.

Silicon Valley at risk? Sustainability of a global innovation icon: An introduction to the Special Issue

The increasing dependence of Silicon Valley on external sources of human capital and technological innovation is a potential Achilles’ heel if competitive regions achieve ‘stickiness’ and retain these assets. Silicon Valley developed in a successive triple helix format, each helix building upon and reinforcing the other. A single helix university development model morphed into a dual helix university–industry symbiotic relationship that became a triple helix university–industry–government format through the provision of government funding, sporadically in the pre-war and consistently in the post-war eras, expanding the innovation dynamic and fostering growth firms. In the early 2000s, a patient advocate-driven social movement provided an alternative engine for stem-cell development in the face of federal government opposition, creating a new model for S&T financing and innovation. Bond issues that treat science as infrastructure, if diffused and replicated, have the potential to revivify the original pro bono venture model. A counter-cyclical innovation dynamic, originated during the great depression of the 1930s, may yet fulfill the promise of the triple helix model as a driver of economic and social development by providing an antidote for its successor economic crisis.

Semantic Data Extraction from Infobox Wikipedia Template

Wikis are established means for collaborative authoring, versioning and publishing of textual articles. The Wikipedia for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Wikis are created by wiki software and are often used to create collaborative works. One of the key challenges of computer science is answering rich queries. Several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. Semantic web allows of creation of such web. Also, Semantic web contents help us to answer rich queries. One of the new applications in semantic web is DBpedia. DBpedia project focus on creating semantically enriched structured information of Wikipedia. In this article, we describe and clarify the DBpedia project. We test the project to get structured data as triples from some Wikipedia resources. We clarify examples of car resource and Berlin resource. The output data is in RDF (Resource Description Framework) triple format which is the basic technology used for building the semantic web. We can answer rich queries by making use of semantic web structure. General Terms Information retrieval; semantic web.

A Method of Object-based De-duplication

Today, the world is increasingly awash in more and more unstructured data, not only because of the Internet, but also because data that used to be collected on paper or media such as film, DVDs and compact discs has moved online [1]. Most of this data is unstructured and in diverse formats such as e-mail, documents, graphics, images, and videos. In managing unstructured data complexity and scalability, object storage has a clear advantage. Object-based data de-duplication is the current most advanced method and is the effective solution for detecting duplicate data. It can detect common embedded data for the first backup across completely unrelated files and even when physical block layout changes. However, almost all of the current researches on data de-duplication do not consider the content of different file types, and they do not have any knowledge of the backup data format. It has been proven that such method cannot achieve optimal performance for compound files. In our proposed system, we will first extract objects from files, Object_IDs are then obtained by applying hash function to the objects. The resulted Object_IDs are used to build as indexing keys in B+ tree like index structure, thus, we avoid the need for a full object index, the searching time for the duplicate objects reduces to O(log n).We introduce a new concept of a duplicate object resolver. The object resolver mediates access to all the objects and is a central point for managing all the metadata and indexes for all the objects. All objects are addressable by their IDs which is unique in the universe. The resolver stores metadata with triple format. This improved metadata management strategy allows us to set, add and resolve object properties with high flexibility, and allows the repeated use of the same metadata among duplicate object.