Specific Query Research Articles

World wars and sociotechnical change in energy, food, and transport: A deep transitions perspective.

This paper explores the relationship between world wars and sociotechnical transitions in energy, food, and transport. We utilise and contribute to the Deep Transitions framework, which explores long-term, multi-systemic sociotechnical transitions and integrate a conceptual approach tailored to this particular topic. This approach bridges between historical literatures focused on world wars and sociotechnical perspectives. We explore in what ways the three sociotechnical systems of energy, food, and transport were influenced by the conditions of the First and Second World War and the extent to which these developments influenced lasting change. Our framework is based around three analytical steps: first, we analyse sociotechnical developments during wartime through the interpretive lens of mechanisms of total war. Second, we examine these developments in terms of how they can be interpreted as influencing the development of rules and meta-rules in sociotechnical systems. Third, we focus on the legacy of ‘total war’ utilising the concept of imprinting to examine the rules-based, infrastructural, and wider symbolic and cultural legacy of world wars in peacetime. This paper presents a novel interpretation of world war and sociotechnical transitions and contributes to a specific query of the Deep Transitions framework regarding the role of war in the emergence of the first deep transition. We consider the significance of this analysis in the context of sustainability transitions research.

Query bot for retrieving patients’ clinical history: A COVID-19 use-case

ObjectiveWith increasing patient complexity whose data are stored in fragmented health information systems, automated and time-efficient ways of gathering important information from the patients' medical history are needed for effective clinical decision making. Using COVID-19 as a case study, we developed a query-bot information retrieval system with user-feedback to allow clinicians to ask natural questions to retrieve data from patient notes. Materials and methodsWe applied clinicalBERT, a pre-trained contextual language model, to our dataset of patient notes to obtain sentence embeddings, using K-Means to reduce computation time for real-time interaction. Rocchio algorithm was then employed to incorporate user-feedback and improve retrieval performance. ResultsIn an iterative feedback loop experiment, MAP for final iteration was 0.93/0.94 as compared to initial MAP of 0.66/0.52 for generic and 1./1. compared to 0.79/0.83 for COVID-19 specific queries confirming that contextual model handles the ambiguity in natural language queries and feedback helps to improve retrieval performance. User-in-loop experiment also outperformed the automated pseudo relevance feedback method. Moreover, the null hypothesis which assumes identical precision between initial retrieval and relevance feedback was rejected with high statistical significance (p ≪ 0.05). Compared to Word2Vec, TF-IDF and bioBERT models, clinicalBERT works optimally considering the balance between response precision and user-feedback. DiscussionOur model works well for generic as well as COVID-19 specific queries. However, some generic queries are not answered as well as others because clustering reduces query performance and vague relations between queries and sentences are considered non-relevant. We also tested our model for queries with the same meaning but different expressions and demonstrated that these query variations yielded similar performance after incorporation of user-feedback. ConclusionIn conclusion, we develop an NLP-based query-bot that handles synonyms and natural language ambiguity in order to retrieve relevant information from the patient chart. User-feedback is critical to improve model performance.

A Formalisation of Digital Forensic String Search Specifications

Computer forensic tools are tools concerned with the identification, recovery, preservation and analysisof evidence in digital media involved in criminal investigations and illegal cases. String searchtools are a special class of such forensic tools, which are used to search for specific queries in theinvestigated medium. However, one main issue we find in literature mainly is that computer forensictools, including string search tools, are generally specified in an informal and oftentimes ambiguousand inconstant manner. We adopt in this short paper formal specification approaches, in particularEvent-B, in order to define clearly the behaviour and properties of string search tools, a class of computerforensic tools, and investigate whether there are any requirements that cannot be achieved. Ourresults show that some basic and optional requirements as stated in literature related to string searchtools could be not be consistent and some are unnecessarily restrictive.

Translating synthetic natural language to database queries with a polyglot deep learning framework

The number of databases as well as their size and complexity is increasing. This creates a barrier to use especially for non-experts, who have to come to grips with the nature of the data, the way it has been represented in the database, and the specific query languages or user interfaces by which data are accessed. These difficulties worsen in research settings, where it is common to work with many different databases. One approach to improving this situation is to allow users to pose their queries in natural language. In this work we describe a machine learning framework, Polyglotter, that in a general way supports the mapping of natural language searches to database queries. Importantly, it does not require the creation of manually annotated data for training and therefore can be applied easily to multiple domains. The framework is polyglot in the sense that it supports multiple different database engines that are accessed with a variety of query languages, including SQL and Cypher. Furthermore Polyglotter supports multi-class queries. Good performance is achieved on both toy and real databases, as well as a human-annotated WikiSQL query set. Thus Polyglotter may help database maintainers make their resources more accessible.

The Multi-Domain International Search on Speech 2020 ALBAYZIN Evaluation: Overview, Systems, Results, Discussion and Post-Evaluation Analyses

The large amount of information stored in audio and video repositories makes search on speech (SoS) a challenging area that is continuously receiving much interest. Within SoS, spoken term detection (STD) aims to retrieve speech data given a text-based representation of a search query (which can include one or more words). On the other hand, query-by-example spoken term detection (QbE STD) aims to retrieve speech data given an acoustic representation of a search query. This is the first paper that presents an internationally open multi-domain evaluation for SoS in Spanish that includes both STD and QbE STD tasks. The evaluation was carefully designed so that several post-evaluation analyses of the main results could be carried out. The evaluation tasks aim to retrieve the speech files that contain the queries, providing their start and end times and a score that reflects how likely the detection within the given time intervals and speech file is. Three different speech databases in Spanish that comprise different domains were employed in the evaluation: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the SPARL20 database, which contains Spanish parliament sessions. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the evaluation results and some detailed post-evaluation analyses based on specific query properties (in-vocabulary/out-of-vocabulary queries, single-word/multi-word queries and native/foreign queries). The most novel features of the submitted systems are a data augmentation technique for the STD task and an end-to-end system for the QbE STD task. The obtained results suggest that there is clearly room for improvement in the SoS task and that performance is highly sensitive to changes in the data domain.

A quantitative systems pharmacology modeling platform for evaluating triglyceride profiles in patients with high triglycerides receiving evinacumab.

A model to quantitatively characterize the effect of evinacumab, an investigational monoclonal antibody against angiopoietin‐like protein 3 (ANGPTL3) on lipid trafficking is needed. A quantitative systems pharmacology (QSP) approach was developed to predict the transient responses of different triglyceride (TG)‐rich lipoprotein particles in response to evinacumab administration. A previously published hepatic lipid model was modified to address specific queries relevant to the mechanism of evinacumab and its effect on lipid metabolism. Modifications included the addition of intermediate‐density lipoprotein and low‐density lipoprotein compartments to address the modulation of lipoprotein lipase (LPL) activity by evinacumab, ANGPTL3 biosynthesis and clearance, and a target‐mediated drug disposition model. A sensitivity analysis guided the creation of virtual patients (VPs). The drug‐free QSP model was found to agree well with clinical data published with the initial hepatic liver model over simulations ranging from 20 to 365 days in duration. The QSP model, including the interaction between LPL and ANGPTL3, was validated against clinical data for total evinacumab, total ANGPTL3, and TG concentrations as well as inhibition of apolipoprotein CIII. Free ANGPTL3 concentration and LPL activity were also modeled. In total, seven VPs were created; the lipid levels of the VPs were found to match the range of responses observed in evinacumab clinical trial data. The QSP model results agreed with clinical data for various subjects and was shown to characterize known TG physiology and drug effects in a range of patient populations with varying levels of TGs, enabling hypothesis testing of evinacumab effects on lipid metabolism.

Privacy-Preserving and Lightweight Selective Aggregation with Fault-Tolerance for Edge Computing-Enhanced IoT.

Edge computing has been introduced to the Internet of Things (IoT) to meet the requirements of IoT applications. At the same time, data aggregation is widely used in data processing to reduce the communication overhead and energy consumption in IoT. Most existing schemes aggregate the overall data without filtering. In addition, aggregation schemes also face huge challenges, such as the privacy of the individual IoT device’s data or the fault-tolerant and lightweight requirements of the schemes. In this paper, we present a privacy-preserving and lightweight selective aggregation scheme with fault tolerance (PLSA-FT) for edge computing-enhanced IoT. In PLSA-FT, selective aggregation can be achieved by constructing Boolean responses and numerical responses according to specific query conditions of the cloud center. Furthermore, we modified the basic Paillier homomorphic encryption to guarantee data privacy and support fault tolerance of IoT devices’ malfunctions. An online/offline signature mechanism is utilized to reduce computation costs. The system characteristic analyses prove that the PLSA-FT scheme achieves confidentiality, privacy preservation, source authentication, integrity verification, fault tolerance, and dynamic membership management. Moreover, performance evaluation results show that PLSA-FT is lightweight with low computation costs and communication overheads.

Collaborations in Environmental Initiatives for an Effective “Adaptive Governance” of Social–Ecological Systems: What Existing Literature Suggests

Moving from the scientific literature on the evaluation of environmental projects and programs, this study identifies how and under which conditions collaborations in environmentally sustainable projects are considered effective for the adaptive governance of SES. The method adopted is a systematic literature review based on the quantitative and qualitative analysis of 56 articles selected through specific queries on the SCOPUS database and published from 2004 to 2020. Results of the quantitative analysis identify conditions able to evaluate collaborations, highlighting the need to adopt a transdisciplinary approach analysing both social and ecological challenges and assessing both social and ecological results. Moreover, they suggest preferring using primary data involving multi-sector and multi-scale actors and enlarging the geographical context to the most vulnerable countries. The results of the qualitative analysis provide specific recommendations for collaborations being effective when related to communication, equity, foresight, and respect, which need to be further strengthened by all actors. Multiplicity in visions and approaches should be seen as a resource able to stimulate creativity in social arrangements and environmental practices, making collaborations in environmental projects instrumental for the effectiveness of adaptive governance of SES.

TemporalRI: subgraph isomorphism in temporal networks with multiple contacts

Temporal networks are graphs where each edge is associated with a timestamp denoting when two nodes interact. Temporal Subgraph Isomorphism (TSI) aims at retrieving all the subgraphs of a temporal network (called target) matching a smaller temporal network (called query), such that matched target edges appear in the same chronological order of corresponding query edges. Few algorithms have been proposed to solve the TSI problem (or variants of it) and most of them are applicable only to small or specific queries. In this paper we present TemporalRI, a new subgraph isomorphism algorithm for temporal networks with multiple contacts between nodes, which is inspired by RI algorithm. TemporalRI introduces the notion of temporal flows and uses them to filter the search space of candidate nodes for the matching. Our algorithm can handle queries of any size and any topology. Experiments on real networks of different sizes show that TemporalRI is very efficient compared to the state-of-the-art, especially for large queries and targets.

Liability Framework for Cognitive Computing in Healthcare: Standing at the Crossroad

Abstract: Digitization across the healthcare industry has witnessed the advent of emerging Cognitive Computing (CC) healthcare technologies that improve diagnostic accuracy and efficiency, predict illnesses, automate routine healthcare tasks, and refine processes and care beyond human capabilities. Increased adoption of this technology can be attributed to its ability of processing enormous amounts of data promptly in addressing specific queries and producing customized intelligent recommendations. While CC’s transformative technologies offer profound benefits to the healthcare industry, it also carries an unpredictable burden of risk and mistakes with damaging consequences to patients. At this juncture, CC’s legal place in healthcare is largely undefined as the applicable liability framework is ambiguous. CC fits into the traditional liability rules in a piecemeal manner; however a single theory of recovery sufficiently addressing the potential liability questions arising from a computer system capable of practicing medicine and possessing the ability of parsing through enormous data for better patient outcomes is absent. The present research therefore sets out to chart the analysis of cases involving emerging medical technologies comparable to CC, in hope of examining ways in which the traditional theories of liability is projected to develop in adapting to this novel contrivance. A doctrinal and case study methods formed an integrated qualitative approach adopted by this research in opting the deployment of emerging medical technologies akin to CC and the bearing it has on the imposition of liability in the United States. CC’s potential contributions to healthcare are revolutionary, however its legal repercussions are just as alarming and therefore demands for more discussion in addressing the concerns. 
 
 Keywords: Cognitive computing and law, Cognitive computing and legal liability

VRGQ

While much of the research on graph analytics over large power-law graphs has focused on developing algorithms for evaluating a single global graph query, in practice we may be faced with a stream of queries. We observe that, due to their global nature, vertex specific graph queries present an opportunity for sharing work across queries. To take advantage of this opportunity, we have developed the VRGQ framework that accelerates the evaluation of a stream of queries via coarsegrained value reuse. In particular, the results of queries for a small set of source vertices are reused to speedup all future queries. We present a two step algorithm that in its first step initializes the query result based upon value reuse and then in the second step iteratively evaluates the query to convergence. The reused results for a small number of queries are held in a reuse table. Our experiments with best reuse configurations on four power law graphs and thousands of graph queries of five kinds yielded average speedups of 143×, 13.2×, 6.89×, 1.43×, and 1.18×.

Ontological Modelling and Execution of Phenotypic Queries in the Leipzig Health Atlas.

Sharing data is of great importance for research in medical sciences. It is the basis for reproducibility and reuse of already generated outcomes in new projects and in new contexts. FAIR data principles are the basics for sharing data. The Leipzig Health Atlas (LHA) platform follows these principles and provides data, describing metadata, and models that have been implemented in novel software tools and are available as demonstrators. LHA reuses and extends three different major components that have been previously developed by other projects. The SEEK management platform is the foundation providing a repository for archiving, presenting and secure sharing a wide range of publication results, such as published reports, (bio)medical data as well as interactive models and tools. The LHA Data Portal manages study metadata and data allowing to search for data of interest. Finally, PhenoMan is an ontological framework for phenotype modelling. This paper describes the interrelation of these three components. In particular, we use the PhenoMan to, firstly, model and represent phenotypes within the LHA platform. Then, secondly, the ontological phenotype representation can be used to generate search queries that are executed by the LHA Data Portal. The PhenoMan generates the queries in a novel domain specific query language (SDQL), which is specific for data management systems based on CDISC ODM standard, such as the LHA Data Portal. Our approach was successfully applied to represent phenotypes in the Leipzig Health Atlas with the possibility to execute corresponding queries within the LHA Data Portal.

Designing a Chat-bot for College Information using Information Retrieval and Automatic Text Summarization Techniques

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available on the internet quickly. Even for a specific domain like a college or university website, it may be difficult for a user to browse through all the links to quickly get the relevant answers. Objective:: In this scenario, the design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper, a novel conversational interface chat-bot application with information retrieval and text summarization skill is designed and implemented. Firstly, this chat-bot has a simple dialog skill; when it can understand the user query intent, it responds from the stored collection of answers. Secondly, for unknown queries, this chat-bot can search the internet, and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning techniques of Latent Semantic Analysis (LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and TextRank is reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot improves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for a variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers, patents, etc. more efficiently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.

Information System for Registering the Result of Scientific Institution Employees’ Intellectual Activity

The article describes a typical object-oriented WEB-system designed for storing and providing various reference and statistical data on the scientific works of employees of an institution (group of institutions), developed by specialists of the JSCC RAS. The system contains information about publications of employees and reports made by them at scientific conferences, symposiums, and seminars. The system is focused on working with objects belonged to classes connected between each other, such as "author", "organization", "publication", "report", "event". The metadata profile of objects of each class includes attributes that are necessary to get detailed information about both an individual object of this class and a group of objects associated with the specified attribute values of objects of other classes. For example, you have to get a list of articles by employees of a given organization published articles in a given journal for a given period of time. A distinctive feature of the system is the introduced concept of "equivalent" objects. Such objects are "persons" corresponding to the same author with different spellings of the last name in the bibliographic descriptions of publications; organizations with different versions of names; articles which are published without changes in different languages. This article describes in detail the features of the system, its user interface, and provides examples of performing specific queries.

Two Decades of Research in Artificial Pancreas: Insights from a Bibliometric Analysis.

Artificial pancreas is a well-known research topic devoted to achieving better glycemic outcomes that has been attracting increasing attention over the years. However, there is a lack of systematic, chronological, and synthesizing studies that show the background of the knowledge generation in this field. This study implements a bibliometric analysis to recognize the main documents, type of publications, research categories, countries, keywords, organizations, and authors related to this topic. Web of Science core collection database was accessed from 2000 to 2020 in order to select high-quality scientific documents based on a specific search query. Bibexcel, MS Excel, Power BI, R-Studio, VOSviewer, and CorText software were used for a descriptive and network analysis based on the local database obtained. Bibliometric parameters as the h-index, frequencies, co-authorship and co-ocurrences were computed. A total of 756 documents were included that show a growing scientific production on this topic with an increasing contribution from engineering. Outstanding authors, organizations, and countries were identified. An analysis of trends in research was conducted according to the scientific categories of the Web of Science database to identify the main research interests of the last 2 decades and the emerging areas with greater prominence in the coming years. A keyword network analysis allowed to identify the main stages in the development of the AP research over time. Results reveal a comprehensive background of the knowledge generation for the AP topic during the last 2 decades, which has been strengthened with international collaborations and a remarkable interdisciplinarity between endocrinology and engineering, giving rise to a growing number of research areas over time, where computer science and medical informatics stand out as the main emerging research areas.

Trends and future perspectives on the integration of phase change materials in heat exchangers

Traditional review papers provide mainly qualitative information, missing to show the volume, trends, and major contributions in a topic. In response to that, this paper asseses both quantitative and qualitative information regarding the implementation of thermal energy storage using phase change materials in heat exchangers. The data were retrieved from the Scopus database using specific queries relevant to the topic. To investigate the state-of-the-art and research gaps, a keywords analysis was carried out highlighting relevant connections among the major keywords. Moreover, a bibliometric analysis was performed to obtain the trends on the number of publications per year, type of document, authors, affiliation, geographical distribution, journals of publication, and subject areas of research. Based on the keywords and bibliometric analyses, this paper highlights the major contributions and research gaps on the topic.

Users with Disabilities, Especially Invisible Disabilities, Provide Insight into How Libraries Can Frame Accessibility Webpages

A Review of: Brunskill, A. (2020). “Without that detail, I’m not coming”: The perspectives of students with disabilities on accessibility information provided on academic library websites. College & Research Libraries, 81(5), 768–788. https://doi.org/10.5860/crl.81.5.768 Abstract Objective – To understand the needs and preferences of users with disabilities for libraries’ accessibility webpages (webpages dedicated to information on disability and accessibility). Design – Semi-structured interviews. Setting – A large public university in the United States of America. Subjects – 12 students who self-identify as having a disability. Methods – Participants were asked about their expectations (if any) and experiences using library accessibility webpages, how they felt they should be organized, and where and how they would expect to find such webpages. Two lists were printed out and provided to the participants. The first, compiled from a previous study, listed common website headings (categories) under which accessibility webpages had been found, and this aided participants in selecting where they would go to find such a webpage. The second listed common types of information found on accessibility webpages. Participants were asked to use the second list to come up with their five highest priority items for accessibility webpages to cover. Interviews were recorded, transcribed, and analyzed for responses to specific queries, but inductive coding was also used. Main Results – In most of the five response clusters of interest to the author (experience/expectation of such a page existing, navigation and language preferences, overall tone and feel for the website, organization for the page, and content for the page), answers were mixed. No consensus emerged with respect to participants’ expectation of an accessibility webpage existing, how they would find the page (including the best website heading), and what content the page should contain. Participants noted that language should be welcoming and inclusive and vetted for sensitivity. The physical layout of the library and information about ambiance and furniture was frequently noted as being an important and overlooked detail to include. Some services, such as shelf pulling and online chat, were highlighted as appealing to those with “invisible” disabilities. Conclusion – The needs and preferences of users with disabilities are varied and sometimes mutually conflicting. Based on the findings, fourteen recommendations are suggested, including providing detailed information about sensory aspects of the library, listing contact information (preferably to a named individual or group), providing useful headings within the page, and evaluating whether language on the website is welcoming.

NewsMeSH: A new classifier designed to annotate health news with MeSH headings

MotivationIn the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. MethodsWe present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. ResultsA classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. ConclusionsThe proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.

Experimenting with big data computing for scaling data quality-aware query processing

Combining query processing techniques with data quality management approaches enables enforcement of quality constraints, such as timeliness, accuracy and completeness, as part of ad-hoc query specification and execution, improving the quality of query results. Despite the emergence of novel data quality processing tools, there is a dearth of studies assessing performance and scalability in the execution of data quality assessment tasks during query processing. This paper reports on an empirical study aiming to investigate the extent to which a big data computing framework (Spark) can offer significant gains in performance and scalability when executing data quality querying tasks over a range of computational platforms including a single commodity multi-core machine and a cluster-based platform for a wide range of workloads. Our results show that substantial performance and scalability gains can be obtained by using optimized data science libraries combined with the parallel and distributed capabilities of big data computing. We also provide guidelines on choosing the appropriate computational infrastructure for executing DQ-aware queries.

Achieving Efficient and Privacy-Preserving Set Containment Search Over Encrypted Data

Set containment search, which aims to retrieve all set records containing a specific query set, has received considerable attention. Meanwhile, due to the dramatic growth of data, data owners tend to outsource their data to the cloud and deploy the cloud server to offer the set containment search services. However, as the cloud server is not fully trustable and the data may be sensitive, a straightforward strategy for the data owners is to encrypt the data before outsourcing them. Although the encryption technique can preserve data privacy, it inevitably hinders the functionality of set containment search. Many existing studies on the set containment search over outsourced data still suffer from the search efficiency and security issues. In this article, aiming at the above issues, we propose an efficient and privacy-preserving set containment search scheme. Specifically, we first deploy an asymmetric scalar-product-preserving encryption technique to design a set containment/intersection encryption (SCIE-Enc) scheme. Then, we build a radix tree to represent the set records. Based on the radix tree and SCIE-Enc construction, we present our scheme that can achieve efficient set containment search while preserving the privacy of set records, query sets, and query results, as indicated in our security analysis and performance evaluation.