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Aim In the UK, injectable medicines are often prepared and administered by nurses following the Injectable Medicines Guide (IMG). Our earlier study confirmed a higher frequency of correct administration with user-tested versus standard IMG guidelines. This current study aimed to model the cost-effectiveness of user-testing. Methods The costs and cost-effectiveness of user-testing were explored by modifying an existing probabilistic decision-analytic model. The adapted model considered administration of intravenous voriconazole to hospital inpatients by nurses. It included 11 error types, their probability of detection and level of harm. Model inputs (including costs) were derived from our previous study and other published data. Monte Carlo simulation using 20,000 samples (sufficient for convergence) was performed with a 5-year time horizon from the perspective of the 121 NHS trusts and health boards that use the IMG. Sensitivity analyses were undertaken for the risk of a medication error and other sources of uncertainty. Results The net monetary benefit at £20,000/quality-adjusted life year was £3,190,064 (95% credible interval (CrI): −346,709 to 8,480,665), favouring user-testing with a 96% chance of cost-effectiveness. Incremental cost-savings were £240,943 (95% CrI 43,527–491,576), also favouring user-tested guidelines with a 99% chance of cost-saving. The total user testing cost was £6317 (95% CrI 6012–6627). These findings were robust to assumptions about a range of input parameters, but greater uncertainty was seen with a lower medication error risk. Conclusions User-testing of injectable medicines guidelines is a low-cost intervention that is highly likely to be cost-effective, especially for high-risk medicines. more...

Our work considers the sociotechnical and organisational constraints of web archiving in order to understand how these factors and contingencies influence research engagement with national web collections. In this article, we compare and contrast our experiences of undertaking web archival research at two national web archives: the UK Web Archive located at the British Library and the Netarchive at the Royal Danish Library. Based on personal interactions with the collections, interviews with library staff and observations of web archiving activities, we invoke three conceptual devices (orientating, auditing and constructing) to describe common research practices and associated challenges in the context of each national web archive. Through this framework we centre the early stages of the research process that are often only given cursory attention in methodological descriptions of web archival research, to discuss the epistemological entanglements of researcher practices, instruments, tools and methods that create the conditions of possibility for new knowledge and scholarship in this space. In this analysis, we highlight the significant time and energy required on the part of researchers to begin using national web archives, as well as the value of engaging with the curatorial infrastructure that enables web archiving in practice. Focusing an analysis on these research infrastructures facilitates a discussion of how these web archival interfaces both enable and foreclose on particular forms of researcher engagement with the past Web and in turn contributes to critical ongoing debates surrounding the opportunities and constraints of digital sources, methodologies and claims within the Digital Humanities. more...

Encoding is the process of coding geographic information defined by application schemas (Chap. 1) into a system-independent data structure suitable for transport and storage. The inverse process is called decoding. The resulting data structure may be stored on digital media or transferred over a network using transfer protocols. While the data structure is intended to be read and interpreted by computers, it may be in a form that is human-readable. In order to allow for unambiguous encoding and decoding of data without loss of information, rules are needed to specify the types of data to be coded and the syntax, structure, and coding schemes used in the resulting data structure. Such encoding rules decouple the representation of data during transport or storage from the representation for other purposes such as spatial analysis, presentation to the user, etc. more...

The representation of geometrical properties of spatial objects as well as of their structural aspects (topology) is crucial for GIS operations, analyses and visualizations. This chapter introduces the most important geometrical and topological concepts, considering the two dimensional as well as the three dimensional case. Particularly, the concepts of the standard ISO 19107 Spatial schema are introduced. more...

While various open source and commercial image processing software exist, they often lack high level image processing skills, like the flexibility to assemble custom algorithm workflows, where the experience of the evaluator can be built into the system. We have developed the Giwer (acronym for GeoImage Workflow Editing Resources) open source software package for handling, processing and analysing drone images. Giwer can be used to process large quantities of drone images – both traditional RGB and multispectral recordings. Organized into a database, the images can be searched and collected based on their attribute data. Processing is aided by a rich library of functions such as digital filterbank, classification, clustering, mathematical statistics, raster calculator, image algebra, and more. From the functions available in Giwer, arbitrary processing processes, i.e. workflows can be created, so the user can compile the most suitable processing processes based on his own knowledge and experience, which he can run not only for one image, but also for many images at once. more...

Fractal image reconstruction through iterated function systems (IFS) is an interesting and challenging topic of research. Several methods have been described in the literature to tackle this issue. However, existing methods have focused exclusively on binary or gray level images. To the best of authors’ knowledge, no method has addressed the problem of colored fractal images so far. This paper fills this gap by introducing a new approach based on the combination of a swarm intelligence method called bat algorithm and a color-based image clustering through multi-level thresholding. To show the performance of the method, an illustrative example is discussed in detail. The numerical and graphical results show that the method is effective and very promising, as it is able to recover both the geometry and color of the input fractal image and yields good (albeit sub-optimal) solutions to this problem. more...

In the present multi-cloud climate, the administrations offered by different cloud specialist co-ops appear to be altogether indistinguishable as for functionalities even still they vary as far as value, the computational force, stockpiling strategy, and so on. Indeed, even the generally utilized web search tools like Google and MSN neglect to distinguish proper cloud benefits that would fulfill client necessities. A cloud service essentially incorporates set of resources that needs to be composed dynamically between service provider and service broker and must be virtualized with respect to the requirements on an on-demand basis. With the development in the amount of organizations, there is an augmentation pursued and execution of cloud organizations, and making multi-cloud organization ID and disclosure emerged as a troublesome task. It is a result of changed organization depictions, non-standardized naming shows, heterogeneity in sort, and features of cloud organizations. The semantic question processor is the crucial area in this proposed structure, which receives customer, shows commonsense requirements from a GUI for the duration of the disclosure degree, and attempts to make the RDF request to locate semantically associated data in cloud organizations, and later the semantic rating of all of the recuperated cloud groups is resolved with the assistance of semantic likeness work. Finally, tests were coordinated to evaluate the request for profitability in assurance of cloud organizations subject to precision appraisal gauges, for instance, accuracy, review, and F-measure. more...

Local planner makes a trajectory physically executable for an agent. Open Space Planner of Apollo framework based on nonlinear optimization methods smooths the trajectory received from a global planner. Such dependency on a global planner forces an agent to relaunch both planners when local changes occur (e.g., when an environment has dynamic obstacles), what can waste too much time. In this article, we consider a different approach which is based on reinforcement learning. This method allows agent generate a trajectory using information about environment (the current and goal state, lidar sensors, etc.). Experiments conducted on the simplified environment show that such algorithm can be implemented as the local planner in Apollo infrastructure. more...

Security Token Offering (STO) is the new and reshaped form of the famous Initial Coin Offering (ICO). The thing that distinguishes STO from ICO is the inclusion of several rules from regulators and auditors. The control by regulators and auditors will generate a trade-off between control and decentralization of the STO. We did a simulation for selecting the most suitable blockchain platform for the STO process using the Multi-Criteria Decision Model simulator to cover this issue. The simulation result shows the relation between the selected features, software quality, and the most suitable solution for STO. more...

Tsunami evacuation is a short process that must be carried out in minutes. Determining the evacuation routes and safe areas is equally important in tsunami evacuation planning. It can be established from least cost distance (LCD) anisotropic model. We use the coastal of Drini, Gunungkidul, Java, Indonesia, as the study area, which is located in a tropical coastal karst region, with a typical karst cone formation. The topographic condition of the karst cone could be an ideal location for tsunami vertical evacuation (TVE). High-resolution orthomosaic images and digital elevation model (DEM) generated from the structure from motion (SfM) process were used as the main data. The tsunami inundation model was calculated based on neighboring operations using raster calculator from the elevation value in the DEM. Land cover identification from high-scale orthomosaic images showed the variation in speed conservation values (SCVs). The slope value was also used as a surface cost that will affect the travel time to the TVE shelters. The results of the model show that TVE is the main alternative in tsunami evacuation planning beside horizontal evacuation. They provide an evaluation of the location of the assembly points and evacuation routes provided by the government, which have been considered ineffective because of the inappropriate model. The results of the study provide an overview of determining the evacuation routes and shelters. Optimum locations can minimize travel time, provide adequate capacity, and be safe from inundation. more...

Jump-Oriented Programming (JOP) is a seldom studied form of advanced code-reuse attacks, very different from return-oriented programming (ROP). JOP identifies snippets of code ending in an indirect jump or indirect call (gadgets), and these are chained together to construct exploits. All applications contain gadgets in executable memory. In this paper we present a mature tool, JOP ROCKET, to facilitate JOP gadget discovery and classification. Additionally, it automates generation a complete JOP chain to bypass Data Execution Prevention (DEP), using a limited virtual machine with emulation. The JOP chain generation utilizes a novel variation to the approach to JOP. Automating JOP chain generation can help provide for automatic detection of vulnerabilities in an application prior to being released, allowing for remediation. more...

An augmented reality (AR) system is developed that helps beginners practice the electronic drums. The player sitting on the drums is captured by a USB camera mounted on top of a large display device placed in front of the drums and the player. Their mirror image is shown on the display device in real time with superimposed graphical instructions that indicate when and which drum to bang along a musical note. Each bang on the drum set is sent from the drum controller as a MIDI signal to the PC where it is compared with the musical note. Throughout the practice, the player only has to look straight at the display screen because everything including the drum sticks, arms, legs of his/her own, and the instructions is integrated on the screen. more...

In recent years, Additive Manufacturing (AM) unfolds its huge capability to fabricate an object in remarkable ways which either created directly from Computer Aided model and STL (Standard Tessellation Language) file or using Coordinate points and G codes. AM used in various field such as engineering, aerospace, biomedical, etc. Fused Filament Fabrication (FFF) is the most frequently used process for AM. It uses the technique of depositing a viscous material on the build platform to fabricate an object to use it for creating a mould for an industrial tool or developing models for preoperative planning for medical surgery or even Anatomical structure for demonstration purposes. FDM generally used to manufacture homogeneous object but when it comes to heterogeneous object it finds difficulty in altering desired process parameter in its software. The commercial FFF software generate toolpath whose parameters are selected by users but the choices are constrained and only limited number of variation of parameters such as raster angle, infill density, layer thickness toolpath pattern, etc. are available. Although this limitation does not affect the properties of homogenous material but fails to provide heterogeneity on a single layer. In AM certain techniques have been used in producing parts of various infill density. The optimization of infill parameter and multi contouring on a single layer gives a new method to model heterogeneous structure using FFF. A new approach has been used to obtain the density variation of human bone at its cross-section using digital image processing technique. The Developed algorithm is capable to extract useful data from an image based on density variation of bone and its variability is dependent on the value of grayscale for each pixel. Each pixel value is associated with the attenuation coefficient from the X-ray of the object which give the relative value of density at each pixel. The accumulated data correlates the heterogeneity and efficiency of the designed model with the actual bone. This single material modelling technique is more feasible and adaptable for biomedical application. more...

Industry is becoming more and more automated and the deployment of robots for work improvement and production time reduction has been crucial in the evolution of the industry itself. At the storage level, the implementation of robots is practically mandatory. These robots have improved the speed, safety and organization of industrial warehouses. However, at the workshops, frequently, tools are still stored and organized manually on conventional shelves. In this work project it was studied the development of an automated warehouse based on a storage machine for automatic rental of VHS and/or DVD cassettes. It is intended to study the implementation of an automatic storage cabinet adapted for storing small tools and consumables, such as cutting inserts, screws, nuts, washers, etc., in a much faster and more organized way. The main functions of the storage machine were studied in order to solve the challenges placed by the new usage: the dimension limitations imposed by the cabinet shelfs and the differences in weight of the new material to be stored and transported. In its original application, the handling robot is expected to carry about 550 g while a box full with screws can easily exceed 2 kg. This paper presents the solution proposed to solve this problem, and presents the envisaged future works. more...

Programming in Maude is executable mathematical modeling. Your mathematical model is the code you execute. Both deterministic systems, specified equationally as so-called functional modules and concurrent ones, specified in rewriting logic as system modules, are mathematically modeled and programmed this way. But rewriting logic is also a logical framework in which many different logics can be naturally represented. And one would like not only to execute these models, but to reason about them at a high level. For this, symbolic methods that can automate much of the reasoning are crucial. Many of them are actually supported by Maude itself or by some of its tools. These methods are very general: they apply not just to Maude, but to many other logics, languages and tools. This paper presents some tapas about these Maude-based symbolic methods in an informal way to make it easy for many other people to learn about, and benefit from, them. more...

Mapping sequence data with the available structures from the Protein Data Bank (PDB), it is possible to generate hypothesis about the role of Spike mutations in ACE2 binding. Methodologically, there are several possible approaches. more...

Web archives are a historically valuable source of information. In some respects, web archives are the only record of the evolution of human society in the last two decades. They preserve a mix of personal and collective memories, the importance of which tends to grow as they age. However, the value of web archives depends on their users being able to search and access the information they require in efficient and effective ways. Without the possibility of exploring and exploiting the archived contents, web archives are useless. Web archive access functionalities range from basic browsing to advanced search and analytical services, accessed through user-friendly interfaces. Full-text and URL search have become the predominant and preferred forms of information discovery in web archives, fulfilling user needs and supporting search APIs that feed complex applications. Both full-text and URL search are based on the technology developed for modern web search engines, since the Web is the main resource targeted by both systems. However, while web search engines enable searching over the most recent web snapshot, web archives enable searching over multiple snapshots from the past. This means that web archives have to deal with a temporal dimension that is the cause of new challenges and opportunities, discussed throughout this chapter. more...

Traditional recommender systems (RSs) try to quantify the user preferences about items using a numerical value, called rating. However, since employing of RSs has been increased, user expectations have been differentiated. Users may judge items according to different criteria. This gives birth to multicriteria recommender systems where users provide the rating on multiple aspects of an item in new dimensions, thereby increasing the rating matrix’s size and opening up some challenges for researchers in the field, such as sparsity, scalability, and the aggregation of multicriteria rating problems. In this paper, we propose a multicriteria recommender system based on a deep autoencoder to learn the nonlinear relation between users on a multicriteria context in order to reconstruct the missing ratings, and on a Multi-Criteria Decision Making method, which proposes a Correlation Coefficient and Standard Deviation (CCSD) integrated approach to determine the weight of the criteria. We compare our results to some other single and multicriteria recommendation models. The results show that our proposed approach boosts the performance up and outperforms all other methods in terms of recommendation accuracy measures. more...

In many machine scheduling studies, individual algorithms for each problem have been developed to cope with the specifics of the problem. On the other hand, the same underlying fundamentals (e.g. Shortest Processing Time, Local Search) are often used in the algorithms and only slightly modified for the different problems. This paper deals with the synthesis of machine scheduling algorithms from components of a repository. Especially flow shop and job shop problems with makespan objective are considered to solve with Shortes/Longest Processing Time, NEH, Giffler & Thompson algorithms. For these components, the paper includes an exemplary implementation of an agile scheduling system that uses the Combinatory Logic Synthesizer to recombine components of scheduling algorithms to solve a given scheduling problem. Special attention is given to the composition heuristics and the process of recombination to executable programs. The advantages of this componentization are discussed and illustrated with examples. It will be shown that algorithms can be generalized to deal with scheduling problems of different machine environments and production constraints. more...

Social media is a platform where people communicate, interact, share ideas, interest in careers, photos, videos, etc. The study says that social media provides an opportunity to observe human behavioral traits, spatial and temporal relationships. Based on study Crime analysis using social media data such as Facebook, Newsfeed articles, Twitter, etc. is becoming one of the emerging areas of research across the world. Using spatial and temporal relationships of social media data, it is possible to extract useful data to analyse criminal activities. The research focuses on implementing textual data analytics by collecting the data from different news feeds and provides visualization. This research’s motivation was identified based on relevant work from different social media crime and Indian government crime statistics. This article focuses on 68 types of different crime keywords for identifying the type of crime. Naive Bayes classification algorithm is used to classify the crime into subcategories of classes with geographical factors, and temporal factors from RSS feeds. Mallet package is used for extracting the keywords from the newsfeeds. K-means algorithm is used to identify the hotspots in the crime locations. KDE algorithm is used to identify the density of crime, and also our approach has overcome the challenges in the existing KDE algorithm. The outcome of research validated the proposed crime prediction model with that of the ARIMA model and found equivalent prediction performance. more...

The increasing recognition of the need for integrating data and processes, both at conceptual and system levels, raises a new demand in standard-friendly, verifiable data-aware process modelling languages. So far, a few proposals in the area have been largely focusing on either uncharted approaches or conceptual proposals that would lack in tool support. In this work, we propose delta-BPMN – a verifiable operational framework for data-aware processes that employs (block-structured) BPMN to capture the process backbone, and a SQL-based language for representing and manipulating volatile and persistent data. We also propose a proof-of-concept implementation of delta-BPMN by realising the front-end part in Camunda and the back-end in a framework that translates language specifications into the executable code of a state-of-the-art SMT-based model checker. more...

Given the advanced threats, man-in-the-middle attacks, and other forms of sophisticated attacks that challenge the security and integrity of data transmission, CISOs rely on Code Signing to ensure that all source code, internally and externally sourced libraries and binaries, feature/functionality updates, and security updates are all signed digitally by appropriate digital certificates, wherever practical and feasible. They must also ensure that capability exists to implement dynamic whitelisting to eliminate cyber risk from third party risk. more...

In this demonstration, we present a GPU-enhanced product knowledge retrieve system called GPKRS, which stores product knowledge based on the sparse matrix compression, and introduces a query transformation module to transform the query operation into the corresponding matrix operations. By this way, we can take advantage of the powerful parallel computing power of GPU to accelerate the processing of SPARQL query. Further, GPKRS adopts an optimized pipeline query strategy to speed up the query execution. The experiments show that the GPKRS achieves state-of-the-art query performances on the LUMB dataset and a synthetic product knowledge dataset. more...

Labour’s dramatic advance in the 2017 election for Labour had confounded critics of radical left leader Jeremy Corbyn and left many of his allies believing they were on the brink of power. Yet Labour did not build effectively on this advance and over the following two years became internally divided and unpopular. Brexit was one cause of this, and here the party faced an almost impossible task in balancing the competing demands of different parts of its coalition. Controversies over foreign policy and particularly anti-semitism also damaged the party and tarnished the image of its leader in the eyes of voters. The once united team supporting Corbyn in the Leader of the Opposition office and the Shadow Cabinet became steadily more divided as the Parliament wore on, with Corbyn himself a persistent problem. The struggles to resolve internal feuds or address external crises were worsened because too often Corbyn could not or would not offer the leadership his party needed. more...

For more than 20 years, there has been collaboration in the Rotterdam region on good health care for patients with an ischemic stroke. Despite this collaborative effort, coordinated by the Rotterdam Stroke Service (RSS), care remained too fragmented. Four health-care providers from the RSS and a health insurance company joined forces to maximize the value of care for patients with an ischemic stroke in the Rotterdam region. more...

Trust has become a major concern in wireless sensor networks (WSN), since many WSNs are deployed in data sensitive applications, especially in healthcare and surveillance. Factors, such as the sensitivity to internal and external noises, constraints on sensing devices, varying deployment platforms and network topologies, generate uncertainty in data accuracy and consistency. With the advent of smart sensing solutions, the data accuracy is increased to a greater extent. However, explicit discrimination of uncertainty from the sensor data is still remain challenging, as it is difficult to quantify the individual impact. The environmental uncertainty is one of the difficult parameter to quantify and to predict; moreover, it greatly influences the received signal strength (RSS) in outdoor sensor networks, leveraging frequent data inconsistency ended up with sensor distrust. We propose a framework to maximize sensors’ trust by classifying the level of impact under the existence of a few environmental uncertainties, such as temperature, humidity and wind speed. We have applied multiclass support vector machine (SVM) classifier to analyze RSS of sensor under the individual and combined presence of defined environmental uncertainties and to classify the dataset into four groups; moreover, the penalty corresponding to the level of uncertainty is added to boost the sensor trust. We have selected Quadratic SVM to train the dataset, as the data varied non-linearly. The experiment shows 97% accuracy during training and 96.2% accuracy during testing with 3.8% misclassifications. With these predicted level of uncertainties and corresponding boosting in RSS, the framework is found to move 42% of sensors from uncertain to trusted category. more...

Brain tumor is an abnormal cell population that occurs in the human brain. Nowadays, medical imaging techniques play an essential role in tumor diagnosis. Magnetic resonance imaging (MRI) is a medical imaging technique that uses radio waves and a magnetic field as sound waves are created to produce detailed images of tissues and organs in the human body by computer. In this study, three different methods were reviewed and compared to the tumor’s extraction from a set of MRI brain images. These methods are seeded region growing, k-means, and global thresholding. The images used in this study are obtained from the Cancer Imaging Archive (TCIA) and Kaggle. All images are grayscale and in JPEG format. The images from TCIA dataset are 100 images that contain abnormal (with a tumor) brain MRI images while there are 35 images in the Kaggle dataset. The Kaggle dataset contains 20 normal images and 15 abnormal images. The results show that the k-means segmentation algorithm performed better than the others on TCIA dataset according to the Root Mean Square Error (RMSE), the Peak to Signal Noise Ration (PSNR), and Segmentation Accuracy while global thresholding is the best on Kaggle dataset. more...

Modern SAT solvers can emit independently checkable proof certificates to validate their results. The state-of-the-art proof system that allows for compact proof certificates ispropagation redundancy(PR). However, the only existing method to validate proofs in this system with a formally verified tool requires a transformation to a weaker proof system, which can result in a significant blowup in the size of the proof and increased proof validation time. This paper describes the first approach to formally verify PR proofs on a succinct representation; we present (i) a newLinear PR(LPR) proof format, (ii) a tool to efficiently convert PR proofs into LPR format, and (iii) , a verified LPR proof checker developed in CakeML. The LPR format is backwards compatible with the existing LRAT format, but extends the latter with support for the addition of PR clauses. Moreover, is verified using CakeML ’s binary code extraction toolchain, which yields correctness guarantees for its machine code (binary) implementation. This further distinguishes our clausal proof checker from existing ones because unverified extraction and compilation tools are removed from its trusted computing base. We experimentally show that LPR provides efficiency gains over existing proof formats and that the strong correctness guarantees are obtained without significant sacrifice in the performance of the verified executable. more...

The work presented in this paper is engaging with and contributes to the implementation and evaluation of Semantic Web applications in the cultural Linked Open Data (LOD) domain. The main goal is the semantic integration, enrichment and interlinking of data that are generated through the documentation process of artworks and cultural heritage objects. This is accomplished by using state-of-the-art technologies and current standards of the Semantic Web (RDF, OWL, SPARQL), as well as widely accepted models and vocabularies relevant to the cultural domain (Dublin Core, SKOS, Europeana Data Model). A set of specialized tools such as KARMA and OpenRefine/RDF-extension is being used and evaluated in order to achieve the semantic integration of museum data from heterogeneous sources. Interlinking is achieved using tools such as Silk and OpenRefine/RDF-extension, discovering links (at the back-end) between disparate datasets and other external data sources such as DBpedia and Wikidata that enrich the source data. Finally, a front-end Web application is developed in order to exploit the semantically integrated and enriched museum data, and further interlink (and enrich) them (at application run-time), with the data sources of DBpedia and Europeana. The paper discusses engineering choices made for the evaluation of the proposed framework/pipeline. more...

International audience; SPARQL property path queries provide a succinct way to write complex navigational queries over RDF knowledge graphs. However, the evaluation of these queries over online knowledge graphs such as DBPedia or Wikidata is often interrupted by quotas, returning no results or partial results. To ensure complete results, property path queries are evaluated client-side. Smart clients decompose property path queries into subqueries for which complete results are ensured. The granularity of the decomposition depends on the expressivity of the server. Whatever the decomposition, it could generate a high number of subqueries, a large data transfer, and finally delivers poor performance. In this paper, we extend a preemptable SPARQL server with a partial transitive closure operator (PTC) based on a depth limited search algorithm. We show that a smart client using the PTC operator is able to process SPARQL property path online and deliver complete results. Experimental results demonstrate that our approach outperforms existing smart client solutions in terms of HTTP calls, data transfer and query execution time. more...

Under the traditional accounting process, the information provided by the accountant still cannot meet the needs of corporate management. This research mainly discusses the application of computer information processing technology in financial accounting information system. Based on the business data and analysis data, the pre-defined data warehouse model can use data conversion service tools such as SQL Server to extract, organize and load data from the business database to the data warehouse. The system will generate accounting vouchers according to the established rules. Accounting personnel only need to maintain the corresponding classification and summary mechanism, which realizes the accounting information systematization of all tasks from data collection to data output, greatly liberating the basic workload of accounting personnel. Authorization approval is carried out by means of digital signatures, realizing a networked and digital financial process. After the implementation of network finance, business processing speed has been accelerated by 27.5%, and the accuracy of production and procurement plans has been increased from 30% to more than 90%. This research will help advance the process of enterprises to realize computerized accounting. more...

Ten years ago, the Mathematics Subject Classification MSC 2010 was released, and a corresponding machine-readable Linked Open Data collection was published using the Simple Knowledge Organization System (SKOS). Now, the new MSC 2020 is out. This paper recaps the last ten years of working on machine-readable MSC data and presents the new machine-readable MSC 2020. We describe the processing required to convert the version of record, as agreed by the editors of zbMATH and Mathematical Reviews, into the Linked Open Data form we call MSC2020-SKOS. The new form includes explicit marking of the changes from 2010 to 2020, some translations of English code descriptions into Chinese, Italian, and Russian, and extra material relating MSC to other mathematics classification efforts. We also outline future potential uses for MSC2020-SKOS in semantic indexing and sketch its embedding in a larger vision of scientific research data., Comment: Extended version of the CICM article more...

Lossy image compression can reduce the space and bandwidth required for image storage and transmission, which is increasinly in demand by the iris recognition systems developers. Deep learning techniques (i.e. CNN, and GAN networks) are quickly becoming a tool of choice for general image compression tasks. But some key quality criteria, such as high perceptual quality and the spatial precision of the images, need to be satisfied when applying such modules for iris images compression tasks. We investigate and evaluate the expediency of a deep learning based compression model for iris data compression. In particular, we relate rate-distortion performance as measured in PSNR, and Multi-scale Structural Similarity Index (MS-SSIM) to the recognition scores as obtained by a concrete recognition system. We further compare the model performance against a state-of-the-art deep learning base image compression technique as well as some lossy compression algorithms currently used for iris compression (namely: the current ISO standard JPEG2000, JPEG, H.265 derivate BPG, and WEBP), to figure out the most suited compression algorithm which can be used for this purpose. The experimental results show superior compression, and promising recognition performance of the model over all other techniques on different iris data. more...

The JPEG standard is one of the most widely used image formats. If a JPEG image is tampered and saved in JPEG format again, it would lead to double JPEG compression (DJPEG). Therefore, DJPEG detection is a research hotspot in image forensics. On the other side, tamperer would try to perform anti-forensics operation on DJPEG image to avoid detection, but there are few related reports so far. In this paper, we propose a DJPEG anti-forensics method based on a generative adversarial network called AFDJ-GAN (Anti-Forensics for Double JPEG Compression with Generative Adversarial Network). On behalf of the anti-forensics side, the generator takes the double JPEG compressed image as input, and obtains the reconstructed image removed from compression artifacts, and uses simulated JPEG compression to convert it into generated simulated Single JPEG compressed (SJPEG) image. On behalf of the forensics side, the discriminator tries to distinguish between real SJPEG image and generated simulated SJPEG image by the feature extracted by the simulated histogram layer. Via alternant updating these two networks, the generator can learn to erase the DJPEG traces. The experimental results show that the proposed method has achieved state-of-the-art anti-forensics performance against different DJPEG detectors, and outperformed existing methods with regards to image quality. more...

The emergence of Semantic Web offered an opportunity for developing new approaches regarding the integration, deployment and operation of healthcare applications. By combining network connectivity, contemporary software architectural styles and semantic-based technologies efficient representation and communication of data and knowledge is feasible between different system components. In this context, standards such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are underpinning the definition of ontologies in the healthcare domain. The aim of this work is to provide insights on the design and implementation of ontology-based healthcare applications. The focus is on the domain of mental healthcare and in particular the development of a game platform for mild cognitive impairment rehabilitation is examined closely as a case study. The positive impact of the synergy between healthcare applications and ontologies is discussed and some of the major challenges and obstacles in this scope are identified. more...

International audience; SPARQL property path queries allow to write sophisticated navigational queries on knowledge graphs (KGs). However, the evaluation of these queries on online KGs are often interrupted by fair use policies, returning only partial results. SaGe-Path addresses this issue by relying on the concept of Partial Transitive Closure (PTC). Under PTC, the graph exploration for SPARQL property path queries is limited to a predefined depth. When the depth limit is reached, frontier nodes are returned to the client. A PTC-client is then able to reuse frontier nodes to continue the exploration of the graph. In this way, SaGe-Path follows a pay-as-you-go approach to evaluate SPARQL property path queries. This demonstration shows how queries that do not complete on the public Wikidata SPARQL endpoint can complete using SaGe-Path. An extended user-interface provides real-time visualization of all SaGe-Path internals, allowing to understand the approach overheads and the effects of different parameters on performance. SaGe-Path demonstrates how complex SPARQL property path queries can be efficiently evaluated online with guaranteed complete results. more...

The availability of the Global Positioning System (GPS) trajectory data is increasing along with the availability of different GPS receivers and with the increasing use of various mobility services. GPS trajectory is an important data source which is used in traffic density detection, transport mode detection, mapping data inferences with the use of different methods such as image processing and machine learning methods. While the data size increases, efficient representation of this type of data is becoming difficult to be used in these methods. A common approach is the representation of GPS trajectory information such as average speed, bearing, etc. in raster image form and applying analysis methods. In this study, we evaluate GPS trajectory data rasterization using the spatial join functions of QGIS, PostGIS+QGIS, and our iterative spatial structured grid aggregation implementation coded in the Python programming language. Our implementation is also parallelizable, and this parallelization is also included as the fourth method. According to the results of experiment carried out with an example GPS trajectory dataset, QGIS method and PostGIS+QGIS method showed relatively low performance with respect to our method using the metric of total processing time. PostGIS+QGIS method achieved the best results for spatial join though its total performance decreased quickly while test area size increases. On the other hand, both of our methods’ performances decrease directly proportional to GPS point. And our methods’ performance can be increased proportional to the increase with the number of processor cores and/or with multiple computing clusters. more...

21st century is a digital information age, different high technologies emerge in succession. Internet technology develops rapidly and is widely used in various industries and fields now. The popularity of 5G mobile communication technology, interactive multimedia, and remote sensing technology results in massive growth of image data. In recent years, image acquisition equipment has been widely used in production and life and the acquisition resolution has got continuous improvement as well. But how to store and transmit large numbers of image data has become big issues to production and life. How to efficiently compress, store and transmit images without destroying original information to be transmitted by the images is currently a major focus in the field of computer vision. The thesis mainly studies the methods of compressing images in time series. Firstly several basic image compression methods are introduced in details. Secondly traditional JEPG static image compression algorithm is fully analyzed. Thirdly time series are added on static image compression algorithm and groups of highly similar pictures are compressed in time series with video formats to eliminate redundant time for better efficiency optimization. Verifying through experiments and comparing with traditional compression algorithm, we get a conclusion that compressing images in time series can improve compression efficiency a lot. more...

Recommender Systems provide users with recommendations for potential items of interest in applications like e-commerce and social media. User information such as past item ratings and personal data can be considered as inputs of these systems. In this study, we aim to utilize a trust-graph-based Neural Network in the recommendation process. The proposed method tries to increase the performance of graph-based RSs by considering the inferred level of trust and its evolution. These recommendations will not only be based on the user information itself but will be fueled by information about associates in the network. To improve the system performance, we develop an attention mechanism to infer a level of trust for each connection in the network. As users are likely to be influenced more by those whom they trust the most, our method might lead to more personalized recommendations, which is likely to increase the user experience and satisfaction. more...

To Identify the Particular image from a huge dataset is a key problem in Image Processing. For image retrieval, a block truncation coding based method is used. The image features derived from colour quantizer as well as a Bitmap image. Using colour quantizer Color Histogram Features (CHF) is obtained, and using Bitmap image Bit Pattern Feature (BPF) is obtained. The various distance measures are employed to match the similarity between images. Simulated result shows better in term of Average Recall Rate (ARR) and Average Precision rate (APR). more...

Recommender systems (RSs) have been used worldwide in several fields to facilitate the tourists’ planning activities. Tourism is one of the fields that uses RSs to reduce the overload of information to end users. Accordingly, tourists get recommendations that are most suitable to their profiles. This paper presents a detailed overview of the tourism recommender systems that were developed since 2008. It focuses mainly on the content based systems and their applications in the tourism field. more...

In recent years, the Resource Description Framework data model has seen an increasing adoption in Web applications and IT in general. This has contributed to the establishment of standards such as the SPARQL query language and the emergence of production-ready database management systems based on this data model. In this paper, we however argue that by concentrating on transaction related functionalities rather than analytical operations, most of these systems address the wrong data market. We motivate this claim by presenting several concrete arguments. more...

The working principle of DFC compiler is introduced in this article. DFC is a grammatical extension of standard C language, with special DF function which describe the dependence of computing DAG. DFC compiler, dfcc, is used to convert the DFC source codes to standard C codes with the assistance of multi-threaded library. The lexical rules and grammatical rules are used to setup the AST of DFC codes, and then it is converted to the AST of standard C without DF function nodes. The derived AST is printed into a text file, a normal C file, and finally is processed by GCC to obtain the executable file. The experiment gives some demonstration of that converting details, and the memory footprint is studied to show an ideal scalability. more...

Participatory epidemiology is a new field that is focused on the use of participatory methods for gathering of data, veterinary information, and conventional oral history. Epidemiology involves group findings. Epidemiology is being used to bridge the gap between definition and practice through participatory research. Participatory GeoComputation approach is a kind of community-based spatial study in archaeology for spatial planning, spatial information, and communication management. The present chapter illustrates various of types of participatory GeoComputational techniques for emerging infectious disease and epidemiological control strategy. This chapter described about current participatory GeoComputational scenario, like crowdsourcing, crowdcasting, Web 2.0, Geotagging, Geo-Wiki, GeoBlog, GeoRSS feeds, and GeoCitizen, in disease control programme. This chapter also described about the social network, social distancing, social inequality, social relationships, and social capital and integration with the geospatial data for epidemiological application. more...

With recent growth in IT sector, the field of Machine Learning and Data Science is booming right now. The role of data science has become crucial in any field right from industries to health care. Especially the role of data science or data analytics has become crucial in educational sector. This is the domain where huge amounts of data is produced and it is impossible to process that much heavy amount of data in normal IDE like Jupyter Notebook or Spyder or Atom. This is the reason where Hadoop comes into play. Hadoop can process large amounts of data. Many machine learning algorithms are used to extract the information from the student databases to understand the behavior of students and find out the reasons for lagging in academics. This extracted data is useful for the educational institutions to modify and update their way of teachings and increase the effectiveness in learning process. In this paper the student dataset is used and data analysis is performed to find out the factors affecting the student’s performance. These features are also called Student’s behavioral features. Supervised and unsupervised approaches are used to analyze the data in Spark and the results provide a clear insight of which factors are responsible for improvement of student’s performance. more...

This chapter introduces to the reader a few commonly used freeware software tools—OpenGL, POV-Ray, VRML, and X3D—which will let the readers apply theoretical principles into practice without requesting expensive hardware and software solutions. Also, the readers will learn how immersive visual mathematics can be implemented using the function-based extensions of VRML and X3D, which allow for defining geometric shapes and their appearances with mathematical functions. Finally, the Shape Explorer tool will be presented to the reader as a multi-platform companion viewer for all the examples used in the book. more...

Nuclear reactor computing software need to process and maintain complex and massive data sets. In order to meet the requirement of mass data storage and processing in software, the data storage model and I/O method and programming interface of HDF5 (Hierarchical Data Format v5) are deeply studied. According to the data storage and processing requirement of typical nuclear reactor computing data such as assembly or core neutronics computing data and core thermal-hydraulic computing data, RCDF-H5 (Reactor Compute Data Framework based on HDF5) is proposed. KYMRES (KYlin-2 Main RESults databank) and COMRES (COre Main RESults databank) are designed and implemented based on RCDF-H5. The performance tests show that RCDF-H5 has a higher I/O efficiency than conventional storage solutions. A new data storage and management solution for nuclear reactor computing software is provided. more...

One of the important aims of climatic and bioclimatic studies is to define spatial differentiation of particular climate elements and/or bioclimatic indicators. Several approaches are used for this purpose. Isoline maps aim to create spatial variability of any climate variable based on data points from a network of meteorological stations. The second commonly used visualisation method is based on typological maps, which assume that values observed in particular locations represent wider areas with similar properties of natural and socio-economic environment. The combination of isoline and typological approach are raster maps. In these maps, the space is divided into several regular geometrical fields (raster). For each raster, meteorological variables and bioclimatic indices are defined. The aim of the chapter is to present methods of cartographical representation of the Universal Thermal Climate Index in different spatial scales (global, regional, local). more...

Import table is a very important data structure in PE file, and it is the basis of dynamic link and code reuse. The import address table (IAT) is a collection of all the JMP instruction operands in the PE file. The user program can call a specific import function by jumping to virtual address (VA) of an import function through unconditional transfer instruction. This paper proposes an information hiding algorithm based on IAT table. The algorithm makes use of the feature that the IAT table of PE file will be rewritten by the actual function entry address after loading into memory to hide information. Obviously, it will not increase the length of the file. At the same time, because all the hidden information will be rewritten after loading into memory, the PE image file loaded into memory is analyzed and dynamically tracked. Theoretical analysis and experimental results show that the algorithm can overcome the shortcomings of traditional PE file information hiding algorithm, such as too centralized hidden information, and improve the concealment and anti-attack ability. more...

Triple Pattern Fragments (TPFs) allow for querying large RDF graphs with high availability by offering triple pattern-based access to the graphs. The limited expressivity of TPFs leads to higher client-side querying and communication costs with potentially many intermediate results that need to be transferred. Thus, the challenge of devising efficient query plans when evaluating SPARQL queries lies in minimizing these costs. Different heuristics and cost-based query planning approaches have been proposed to obtain such efficient query plans. However, we also require means to visualize, manually modify, and execute alternative query plans, to better understand the differences between existing planning approaches and their potential limitations. To this end, we propose Slurp (https://people.aifb.kit.edu/zg2916/slurp/), an interactive SPARQL query planner that assists RDF data consumers to visualize, modify, and compare the performance of different query execution plans over TPFs. more...