Evaluating applications of the unmanned aerial system in construction project management

Using unmanned aerial vehicle systems (UAS) or drones in project management (PM) is a novel methodology aimed at enhancing the performance of the PM system. This technology is still in its infancy, and some serious progress is required to cover and advance in this field. UAS is used in various applications ranging from site mapping, surveying, traffic surveillance, bushfire monitoring and aerial photography. Despite the multiple functions offered by UAS, which are well covered in various sources, industry practitioners still have little confidence and knowledge on this technology. The value of the data collected using UAS technology is still poorly utilised and understood. This project aims to explore areas in PM that can be enhanced while using UAS and understand the added value of adopting this new technology. This research will utilise Unmanned Aerial Vehicle (UAV) with high- definition (HD) cameras to collect real time imageries of construction sites. The collected data, with the aid of a photogrammetric software Pix4D, is used to develop a detailed UAS system to determine the accuracy of performed work, the generation of the corresponding progress payment reports, and referencing and tracking information in real time for a residential project. This study also discusses combining the UAS and 5D Building Information Modelling (BIM) data to develop smart construction sites. The UAS–BIM combination enables the project stakeholders to be fully informed of the work’s progress and quality to prevent mistakes that could lead to additional costs and delays. The paper identified the primary obstacles to applying the UAS via interviews with the project managers and tradespersons involved in the selected project. Assuredly, digital culture is essential for an intelligent construction site to shift the project team from a passive data user to a more proactive analyser to improve performance and site safety. This research is aimed at building a holistic digital system which will be applied and utilised in Construction Project Management (CPM) fields to improve the performance of site management and the quality of work performed. Other obstacles include ethical reservations, legal requirements, liability risks, weather conditions and the continuation of using a UAS in non-open-air construction environments

bdbms -- A Database Management System for Biological Data

Biologists are increasingly using databases for storing and managing their data. Biological databases typically consist of a mixture of raw data, metadata, sequences, annotations, and related data obtained from various sources. Current database technology lacks several functionalities that are needed by biological databases. In this paper, we introduce bdbms, an extensible prototype database management system for supporting biological data. bdbms extends the functionalities of current DBMSs to include: (1) Annotation and provenance management including storage, indexing, manipulation, and querying of annotation and provenance as first class objects in bdbms, (2) Local dependency tracking to track the dependencies and derivations among data items, (3) Update authorization to support data curation via content-based authorization, in contrast to identity-based authorization, and (4) New access methods and their supporting operators that support pattern matching on various types of compressed biological data types. This paper presents the design of bdbms along with the techniques proposed to support these functionalities including an extension to SQL. We also outline some open issues in building bdbms.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

Expression analysis of liver-specific circulating micrornas in hcv-induced hepatocellular carcinoma in Egyptian patients

Introduction: The prevalence of hepatocellular carcinoma (HCC) in Africa is higher compared to the rest of the world due to the high incidence of chronic infection with hepatitis C virus (HCV). In Egypt, HCV infection is the leading cause for the high HCC incidence, which is usually diagnosed at late stages. Due to the absence of reliable and accurate biomarkers for early detection of liver cancer, circulating microRNAs have recently emerged as great candidates for early diagnosis of HCC. These small non-coding RNA molecules are responsible for regulating gene expression and RNA stability. Therefore, the aim of this study is to investigate the potential of liver-specific circulating microRNAs as an accurate non-invasive diagnostic tool for the early detection of HCV-induced HCC. Methods: Eight main miRNAs (miR-16, miR-34a, miR-122a, miR-125a, miR-139, miR-145, miR-199a, and miR-221) were selected due to their expression patterns in HCC as well as their contribution to the development of hepato-carcinogenesis. A total of 165 patients were enrolled in this study, from which serum samples were collected and categorized into four main patient groups: 42 chronic hepatitis C (CHC) without cirrhosis, 45 CHC with cirrhosis (LC), 38 HCC with HCV patients, and 40 healthy controls. The expression profile of the eight miRNAs was analyzed using TaqMan real-time reverse transcription-polymerase chain reaction. Additionally, the conventional markers for HCC α-fetoprotein (AFP) and des-γ-carboxyprothrombin (DCP) were measured using commercial kits. Results: Serum levels of miRNA-122a, miRNA-125a, miRNA-139, miRNA-145 and miRNA-199a were significantly lower (p\u3c0.01) in HCC than in both CHC and LC groups. On the other hand, no significant difference was shown in the expression of miR-16, miR-34a, and miR-221 between the CHC, LC, and HCC groups. MiR-16, miR-34a, and miR-221 were significantly elevated in the HCC group compared to the control group. MiR-122a showed the highest specificity and sensitivity, followed by miR-125a, which had the second highest specificity, indicating its significance in diagnosis. Conclusions: The results indicated that measurement of serum levels of miR-122a, miR-125a, miR-139, miR-145, and miR-199a can help to differentiate HCC from CHC and LC. Measurement of serum levels of miR-16, miR-34a, and miR-221 were shown to have a prognostic value. Highly significant correlation was established between different miRNAs within the same patient group or between two different groups, indicating a great diagnostic value for the early detection of HCC. MiR-122a had the highest specificity and sensitivity, indicating that serum miR-122a might serve as a novel and potential non-invasive biomarker for HCV-induced HCC

Towards the Correctness of Security Protocols

AbstractIn [19], the authors presented a type-theoretic approach to the verification of security protocols. In this approach, a universal type system is proposed to capture in a finite way all the possible computations (internal actions or protocol instrumentations) that could be performed by a smart malicious intruder. This reduces the verification of cryptographic protocols to a typing problem where types are attack scenarios. In this paper, we recall this type system and we prove its completeness i.e. if the intruder can learn a message from a given protocol instrumentation, then this message could be infered from the type system. A significant result of this paper is the presentation of a new transformation that allows us to abstract a non-terminating type inference system into a terminating deductive proof system. We demonstrate how these results could be used to establish the security of cryptographic protocols from the secrecy standpoint. Finally, the usefulness and the efficiency of the whole approach is illustrated by proving the correctness of a new version of the Needham-Shoreder protocol with respect to the secrecy property

A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

In this paper, we propose a hybrid approach for sentence paraphrase identification. The proposal addresses the problem of evaluating sentence-to-sentence semantic similarity when the sentences contain a set of named-entities. The essence of the proposal is to distinguish the computation of the semantic similarity of named-entity tokens from the rest of the sentence text. More specifically, this is based on the integration of word semantic similarity derived from WordNet taxonomic relations, and named-entity semantic relatedness inferred from Wikipedia entity co-occurrences and underpinned by Normalized Google Distance. In addition, the WordNet similarity measure is enriched with word part-of-speech (PoS) conversion aided with a Categorial Variation database (CatVar), which enhances the lexico-semantics of words. We validated our hybrid approach using two different datasets; Microsoft Research Paraphrase Corpus (MSRPC) and TREC-9 Question Variants. In our empirical evaluation, we showed that our system outperforms baselines and most of the related state-of-the-art systems for paraphrase detection. We also conducted a misidentification analysis to disclose the primary sources of our system errors

A comparative study of conversion aided methods for WordNet sentence textual similarity

In this paper, we present a comparison of three methods for taxonomic-based sentence semantic relatedness, aided with word parts of speech (PoS) conversion. We use WordNet ontology for determining word level semantic similarity while augmenting WordNet with two other lexicographical databases; namely Categorial Variation Database (CatVar) and Morphosemantic Database in assisting the word category conversion. Using a human annotated benchmark data set, all the three approaches achieved a high positive correlation reaching up to (r = 0.881647) with comparison to human ratings and two other baselines evaluated on the same benchmark data set

Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

An approach for named entity classification based on Wikipedia article infoboxes is described in this paper. It identifies the three fundamental named entity types, namely; Person, Location and Organization. An entity classification is accomplished by matching entity attributes extracted from the relevant entity article infobox against core entity attributes built from Wikipedia Infobox Templates. Experimental results showed that the classifier can achieve a high accuracy and F-measure scores of 97%. Based on this approach, a database of around 1.6 million 3-typed named entities is created from 20140203 Wikipedia dump. Experiments on CoNLL2003 shared task named entity recognition (NER) dataset disclosed the system’s outstanding performance in comparison to three different state-of-the-art systems