Query Elements Research Articles

On the Privacy of Counting Bloom Filters Under a Black-Box Attacker

Counting Bloom Filters (CBFs) are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">approximate</i> membership checking data structures, and it is normally believed that at most an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">approximate</i> reconstruction of the underlying set can be derived when interacting with a CBF. This paper decisively refutes this assumption. In a recent paper, we considered the privacy of CBFs when the attacker has access to the implementation details and thus, it sees the filter as a white-box. In that setting, we showed that the attacker may be able to extract the elements stored in the filter when the number of false positives over the entire universe is not significantly larger than the number of elements stored in the filter. In this work, we consider a black-box attacker that can only perform user interactions on the CBF to insert, remove and query elements with no knowledge of the filter implementation details. We show that even in this case, an attacker may be able to extract information from the filter at the cost of using more complex and time-consuming attack algorithms. The proposed algorithms have been implemented and compared with the white-box attack, showing that in most cases, almost the same information can be extracted from the filter.

Radiation Oncology: Future Vision for Quality Assurance and Data Management in Clinical Trials and Translational Science.

The future of radiation oncology is exceptionally strong as we are increasingly involved in nearly all oncology disease sites due to extraordinary advances in radiation oncology treatment management platforms and improvements in treatment execution. Due to our technology and consistent accuracy, compressed radiation oncology treatment strategies are becoming more commonplace secondary to our ability to successfully treat tumor targets with increased normal tissue avoidance. In many disease sites including the central nervous system, pulmonary parenchyma, liver, and other areas, our service is redefining the standards of care. Targeting of disease has improved due to advances in tumor imaging and application of integrated imaging datasets into sophisticated planning systems which can optimize volume driven plans created by talented personnel. Treatment times have significantly decreased due to volume driven arc therapy and positioning is secured by real time imaging and optical tracking. Normal tissue exclusion has permitted compressed treatment schedules making treatment more convenient for the patient. These changes require additional study to further optimize care. Because data exchange worldwide have evolved through digital platforms and prisms, images and radiation datasets worldwide can be shared/reviewed on a same day basis using established de-identification and anonymization methods. Data storage post-trial completion can co-exist with digital pathomic and radiomic information in a single database coupled with patient specific outcome information and serve to move our translational science forward with nimble query elements and artificial intelligence to ask better questions of the data we collect and collate. This will be important moving forward to validate our process improvements at an enterprise level and support our science. We have to be thorough and complete in our data acquisition processes, however if we remain disciplined in our data management plan, our field can grow further and become more successful generating new standards of care from validated datasets.

Sparse attention block: Aggregating contextual information for object detection

It is well recognized that the contextual information of surrounding objects is beneficial for object detection. Such contextual information can often be obtained from long-range dependencies. This paper proposes a sparse attention block to capture long-range dependencies in an efficient way. Unlike the conventional non-local block, which generates a dense attention map to characterize the dependency between any two positions of the input feature map, our sparse attention block samples the most representative positions for contextual information aggregation. After searching for local peaks in a heat map of the given input feature map, it adaptively selects a sparse set of positions to represent the relationship between query and key elements. With the obtained sparse positions, our sparse attention block can well model long-range dependencies, and greatly improve the object detection performance at the additional cost of <2% GPU memory and computation of the conventional non-local block. This sparse attention block can be easily plugged into various object detection frameworks, such as Faster R-CNN, RetinaNet and Mask R-CNN. Experiments on COCO benchmark confirm that our sparse attention block can boost the detection accuracy with significant gains ranging from 1.4% to 1.9% and negligible overhead of computation and memory usage.

A survey on single server private information retrieval in a coding theory perspective

In this paper, we present a new perspective of single server private information retrieval (PIR) schemes by using the notion of linear error-correcting codes. Many of the known single server schemes are based on taking linear combinations between database elements and the query elements. Using the theory of linear codes, we develop a generic framework that formalizes all such PIR schemes. This generic framework provides an appropriate setup to analyze the security of such PIR schemes. In fact, we describe some known PIR schemes with respect to this code-based framework, and present the weaknesses of the broken PIR schemes in a unified point of view.

DBTagger

Translating Natural Language Queries (NLQs) to Structured Query Language (SQL) in interfaces deployed in relational databases is a challenging task, which has been widely studied in database community recently. Conventional rule based systems utilize series of solutions as a pipeline to deal with each step of this task, namely stop word filtering, tokenization, stemming/lemmatization, parsing, tagging, and translation. Recent works have mostly focused on the translation step overlooking the earlier steps by using adhoc solutions. In the pipeline, one of the most critical and challenging problems is keyword mapping; constructing a mapping between tokens in the query and relational database elements (tables, attributes, values, etc.). We define the keyword mapping problem as a sequence tagging problem, and propose a novel deep learning based supervised approach that utilizes POS tags of NLQs. Our proposed approach, called DBTagger (DataBase Tagger), is an end-to-end and schema independent solution, which makes it practical for various relational databases. We evaluate our approach on eight different datasets, and report new state-of-the-art accuracy results, 92.4% on the average. Our results also indicate that DBTagger is faster than its counterparts up to 10000 times and scalable for bigger databases.

Deciphering Gene Regulation Using Massively Parallel Reporter Assays

Massively parallel reporter assays (MPRAs) serve to measure the transcriptional activity of up to millions of DNA elements. These elements are cloned en masse in a reporter construct, with each element typically linked to a unique DNA barcode located in the transcription unit (A). The activity of the elements is inferred from the abundance of mRNA after in vivo expression, either as episomes [1] or integrated in the genome [2] (B). In some designs, the query elements are part of the transcription unit and serve as themselves [3]. Alternatively, the reporter does not carry barcodes but encodes a fluorescent protein; cells are then sorted by fluorescence levels and the activity of the query elements is inferred from their abundance in the sorted cell pools [4] (C). Input DNA sequences for MPRAs can be generated by synthesis [1], PCR [2] or DNA capture methods [5]. Alternatively, entire genomes can be queried as random fragments [3].

Scalable Geo Web Techniques using Spatial Data Mining for Air Pollution

This paper proposes to bolster Geo Web Techniques and spatial data mining techniques for air pollution in Delhi-NCR for real time analysis and identification of relationship among the pro &anti pollutant objects effecting in a specific zone of area. A Geo Web based solution is proposed by provisioning of feature and attribute data services over the web. The functional constituents are collection, transformation, Spatial Data Mining, OGC Web Services i.e. Web Map Service (WMS),Web Feature Service (WFS) ,Web Coverage Service (WCS) query and Geo Web Publishing elements. The collecting element provisioned to access the onsite monitored data through a interface with sources of actual data sets . It need to transform the data into the required spatial format and mining component give an interface to the end user. Spatial information mining systems can be connected to different fields' specifically human services, metrological information, activity examination, client insight, transport administration, urban arranging and utilities industry. Albeit spatial data mining does not yet belong to the most commonly used spatial information analysis, it was found effective to detect solid relationship among geological feature objects. Keywords : Spatial Data Mining, Geo Web, Web Map Service (WMS) , Ambient Air Quality

User Interface for Visually Impaired People

A user interface enables a visually impaired person to operate a multifunctional system. The user interface includes a plurality of tactile selection elements that enable selection of options, a tactile guiding structure that enables leading of an object to the tactile selection elements, and an audible assisting device that reads out a plurality of phrases, each one of the phrases identifying a selectable option during operation of the multifunctional system. Tactile query elements are provided along the tactile guiding structure, each one of the tactile query elements being arranged upstream from a corresponding group of tactile selection elements, the activation of each tactile query element causing the audible assisting device to read out a plurality of phrases each identifying a selectable option from the group. The portion of the tactile guiding structure located downstream from the tactile query element includes a plurality of paths, each one of the paths leading to a tactile selection element of the group.

Evaluation of SPARQL-compliant semantic search user interfaces

A regular user of a semantic search system frequently posses no knowledge about the SPARQL language nor about the ontology of a given knowledge base, especially when it provides domain-unspecific data obtained from heterogeneous sources. Nevertheless, he/she should be provided with tools enabling both intuitive and effective exploration of RDF-compliant knowledge bases. Natural language querying is one of the solutions that have been proposed so far as means for making knowledge bases more user-friendly. However, the results of natural language querying usually have lower precision and recall than analogical results of graph-based querying. In the paper, we introduce an evaluation methodology based on the 2011 QALD workshop queries that allows to measure the accuracy of a semantic search system as well as the complexity of the query formulation process. The obtained results confirm the intuition that graph-based querying, although assuring comparatively high accuracy of the results, is usually still too difficult for regular users. On the other hand, on the basis of results obtained for an experimental search system referred to as Semantic Focused Crawler, we claim that enhancing a SPARQL-compliant graph-based system by an entity-type recommendation feature may reduce the number of query elements necessary to formulate a query without compromising the quality of the results.

LinkedISA: semantic representation of ISA-Tab experimental metadata.

BackgroundReporting and sharing experimental metadata- such as the experimental design, characteristics of the samples, and procedures applied, along with the analysis results, in a standardised manner ensures that datasets are comprehensible and, in principle, reproducible, comparable and reusable. Furthermore, sharing datasets in formats designed for consumption by humans and machines will also maximize their use. The Investigation/Study/Assay (ISA) open source metadata tracking framework facilitates standards-compliant collection, curation, visualization, storage and sharing of datasets, leveraging on other platforms to enable analysis and publication. The ISA software suite includes several components used in increasingly diverse set of life science and biomedical domains; it is underpinned by a general-purpose format, ISA-Tab, and conversions exist into formats required by public repositories. While ISA-Tab works well mainly as a human readable format, we have also implemented a linked data approach to semantically define the ISA-Tab syntax.ResultsWe present a semantic web representation of the ISA-Tab syntax that complements ISA-Tab's syntactic interoperability with semantic interoperability. We introduce the linkedISA conversion tool from ISA-Tab to the Resource Description Framework (RDF), supporting mappings from the ISA syntax to multiple community-defined, open ontologies and capitalising on user-provided ontology annotations in the experimental metadata. We describe insights of the implementation and how annotations can be expanded driven by the metadata. We applied the conversion tool as part of Bio-GraphIIn, a web-based application supporting integration of the semantically-rich experimental descriptions. Designed in a user-friendly manner, the Bio-GraphIIn interface hides most of the complexities to the users, exposing a familiar tabular view of the experimental description to allow seamless interaction with the RDF representation, and visualising descriptors to drive the query over the semantic representation of the experimental design. In addition, we defined queries over the linkedISA RDF representation and demonstrated its use over the linkedISA conversion of datasets from Nature' Scientific Data online publication.ConclusionsOur linked data approach has allowed us to: 1) make the ISA-Tab semantics explicit and machine-processable, 2) exploit the existing ontology-based annotations in the ISA-Tab experimental descriptions, 3) augment the ISA-Tab syntax with new descriptive elements, 4) visualise and query elements related to the experimental design. Reasoning over ISA-Tab metadata and associated data will facilitate data integration and knowledge discovery.

What on World is the Earth?: The Anthropocene and Fictions of the World

This paper develops a concept of the terrestrial as an abyssal boundary to the thinkable, looking to David Wood's deconstructive eco-phenomenology. The main section deploys this concept of the terrestrial to read various accounts and images of the sight of the whole earth from space. This then suggests a reading of Derrida's arguments on the ‘fiction of the world’. This material is framed at both ends of the paper by consideration of the “Anthropocene” as an ‘event’ which seems to exceed or query elements of Derrida's definition of the ‘event’.

Approximate Nearest Subspace Search

Subspaces offer convenient means of representing information in many pattern recognition, machine vision, and statistical learning applications. Contrary to the growing popularity of subspace representations, the problem of efficiently searching through large subspace databases has received little attention in the past. In this paper, we present a general solution to the problem of Approximate Nearest Subspace search. Our solution uniformly handles cases where the queries are points or subspaces, where query and database elements differ in dimensionality, and where the database contains subspaces of different dimensions. To this end, we present a simple mapping from subspaces to points, thus reducing the problem to the well-studied Approximate Nearest Neighbor problem on points. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its capabilities on synthetic and real data. Our experiments indicate that an approximate nearest subspace can be located significantly faster than the nearest subspace, with little loss of accuracy.

XML data partitioning schemes for parallel holistic twig joins

PurposeThe purpose of this paper is to propose Extensible Markup Language (XML) data partitioning schemes that can cope with static and dynamic allocation for parallel holistic twig joins: grid metadata model for XML (GMX) and streams‐based partitioning method for XML (SPX).Design/methodology/approachGMX exploits the relationships between XML documents and query patterns to perform workload‐aware partitioning of XML data. Specifically, the paper constructs a two‐dimensional model with a document dimension and a query dimension in which each object in a dimension is composed from XML metadata related to the dimension. GMX provides a set of XML data partitioning methods that include document clustering, query clustering, document‐based refinement, query‐based refinement, and query‐path refinement, thereby enabling XML data partitioning based on the static information of XML metadata. In contrast, SPX explores the structural relationships of query elements and a range‐containment property of XML streams to generate partitions and allocate them to cluster nodes on‐the‐fly.FindingsGMX provides several salient features: a set of partition granularities that balance workloads of query processing costs among cluster nodes statically; inter‐query parallelism as well as intra‐query parallelism at multiple extents; and better parallel query performance when all estimated queries are executed simultaneously to meet their probability of query occurrences in the system. SPX also offers the following features: minimal computation time to generate partitions; balancing skewed workloads dynamically on the system; producing higher intra‐query parallelism; and gaining better parallel query performance.Research limitations/implicationsThe current status of the proposed XML data partitioning schemes does not take into account XML data updates, e.g. new XML documents and query pattern changes submitted by users on the system.Practical implicationsNote that effectiveness of the XML data partitioning schemes mainly relies on the accuracy of the cost model to estimate query processing costs. The cost model must be adjusted to reflect characteristics of a system platform used in the implementation.Originality/valueThis paper proposes novel schemes of conducting XML data partitioning to achieve both static and dynamic workload balance.

Model-based similarity estimation of multidimensional temporal sequences

Content-based queries in multimedia sequence databases where information is sequential is a tough issue, especially when dealing with large-scale applications. One of the key points is similarity estimation between a query sequence and elements of the database. In this paper, we investigate two ways to compare multimedia sequences, one—that comes from the literature—being computed in the feature space while the other one is computed in a model space, leading to a representation less sensitive to noise. We compare these approaches by testing them on a real audio dataset, which points out the utility of working in the model space.

Retrieval of Spatial Join Pattern Instances from Sensor Networks

We study the continuous evaluation of spatial join queries and extensions thereof, defined by interesting combinations of sensor readings (events) that co-occur in a spatial neighborhood. An example of such a pattern is “a high temperature reading in the vicinity of at least four high-pressure readings”. We devise protocols for ‘in-network’ evaluation of this class of queries, aiming at the minimization of power consumption. In addition, we develop cost models that suggest the appropriateness of each protocol, based on various factors, including selectivity of query elements, energy requirements for sensing, and network topology. Finally, we experimentally compare the effectiveness of the proposed solutions on an experimental platform that emulates real sensor networks.

A Fusion Approach to XML Structured Document Retrieval

In this paper we evaluate the application of data fusion or meta-search methods, combining different algorithms and XML elements, to content-oriented retrieval of XML structured data. The primary approach is the combination of a probabilistic methods using Logistic regression and the Okapi BM-25 algorithm for estimation of document relevance or XML element relevance, in conjunction with Boolean approaches for some query elements. In the evaluation we use the INEX XML test collection to examine the relative performance of individual algorithms and elements and compare these to the performance of the data fusion approaches.

SPLICE: A program to assemble partial query solutions from three-dimensional database searches into novel ligands

SPLICE is a program that processes partial query solutions retrieved from 3D, structural databases to generate novel, aggregate ligands. It is designed to interface with the database searching program FOUNDATION, which retrieves fragments containing any combination of a user-specified minimum number of matching query elements. SPLICE eliminates aspects of structures that are physically incapable of binding within the active site. Then, a systematic rule-based procedure is performed upon the remaining fragments to ensure receptor complementarity. All modifications are automated and remain transparent to the user. Ligands are then assembled by linking components into composite structures through overlapping bonds. As a control experiment, FOUNDATION and SPLICE were used to reconstruct a know HIV-1 protease inhibitor after it had been fragmented, reoriented, and added to a sham database of fifty different small molecules. To illustrate the capabilities of this program, a 3D search query containing the pharmacophoric elements of an aspartic proteinase-inhibitor crystal complex was searched using FOUNDATION against a subset of the Cambridge Structural Database. One hundred thirty-one compounds were retrieved, each containing any combination of at least four query elements. Compounds were automatically screened and edited for receptor complementarity. Numerous combinations of fragments were discovered that could be linked to form novel structures, containing a greater number of pharmacophoric elements than any single retrieved fragment.

FOUNDATION: a program to retrieve all possible structures containing a user-defined minimum number of matching query elements from three-dimensional databases.

A program is described that searches three-dimensional, structural databases, given a user-defined query, in order to retrieve all structures that contain any combination of a user-specified minimum number of matching elements. Queries consist of three-dimensional coordinates of atoms and/or bonds. Numerous query constraints are described which allow the investigator to define the chemical nature of the desired structures as well as the environment within which these structures must reside. They include: (1) Bonded vs. isolated atom distinction; (2) Atom type designation; (3) Definition of subsets with occupancy specification (>, =, < X atoms); (4) RMS-fit; (5) Active site volume accessibility of atoms linking query elements; (6) Number, atom type, and cyclic structure constraints for atoms linking pharmacophoric elements; (7) Automatic error boundary adjustment--ad infinitum constraint. To illustrate the capabilities of this program, queries based on the crystal structure of a thermolysin-inhibitor complex were tested against a subset of the Cambridge Crystallographic Database. Several compounds were returned which satisfied various aspects of the query, including fitting within the active site. Combination of segments of compounds which satisfy partial queries should provide a method for generating unique compounds with affinity for sites of known three-dimensional structure.