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Part 8: Pattern Recognition and Image Processing; International audience; Paper-based documents are important and still widely used in government agencies and private entities as some documents cannot be replaced by electronic documents. These include loan agreements, dispatch or contracts, household registrations and passports. They must be paper-based. Paper-based documents can be easily forged with a printer and a scanner, and imaging software can easily edit them. This paper presents a paper-based document authentication by applying a digital signature and HC2D barcode to verify the integrity of the text message and the sender of the document. This is useful both for a quick inspection of documents with large quantities and monitoring that may help prevent fraud and forgery which may have occurred.

Identifying the natural gestures for a tabletop application is one of the most challenging tasks developers must accomplish in order to achieve a good system user interface. This problem is even more difficult in a collaborative environment. Cooperative Gestures allow richer interaction and must be evaluated when designing a new multi-user tabletop interface. In this paper we present the use of paper prototyping to analyze user interaction on a tabletop collaborative game application. Our results show that it is possible to extract natural gestures for an application using this technique, regardless of some limitations.

Personal Digital Assistants (PDAs) are a viable technology for providing access to Electronic Medical Records at the point-of-care. However, acceptance of this technology by clinicians will depend on how easy they can make the transition from the system they normally use to a PDA-based system. Since many hospitals are still using paper-based patient record systems this study intends to provide some insight on the aspects that need to be considered in the transition from a paper-based system to a PDA-based system. The study compares the interaction of nurses with PDA-based and paper-based nursing documentation systems in terms of performance and subjective satisfaction. Twenty staff nurses from a metropolitan hospital performed twelve tasks on each system. The study supports the conclusion that a PDA-based nursing documentation system can be superior to a paper-based system in term of performance for tasks that don't required writing notes. Nurses were significantly more satisfied with the PDA-based system than with the paper-based system with every interaction and system aspect evaluated on the study. In general the results of the study provide hard evidence to predict an easy transition for nurses from a paper-based system to a PDA-based system nursing documentation system.

There has been considerable interest recently on developing a system to track items like pharmaceutical drugs or food products. Such a system can help prevent counterfeits, aid product recall, and improve general logistics. In this paper, we present such system based on radio frequency identity (RFID) technology. Our solution provides the means of storing the entire movement of the item from original manufacturer to final consumer on the RFID tag itself, and also makes it more difficult to introduce large numbers of counterfeits. The solution also allows the end user to easily verify the authenticity of the item.

The mission of theoretical cryptography is to define and construct provably secure cryptographic protocols and schemes. Without proofs of security, cryptographic constructs offer no guarantees whatsoever and no basis for evaluation and comparison. As most security proofs necessarily come in the form of a reduction between the security claim and an intractability assumption, such proofs are ultimately only as good as the assumptions they are based on. Thus, the complexity implications of every assumption we utilize should be of significant substance, and serve as the yard stick for the value of our proposals. Lately, the field of cryptography has seen a sharp increase in the number of new assumptions that are often complex to define and difficult to interpret. At times, these assumptions are hard to untangle from the constructions which utilize them. We believe that the lack of standards of what is accepted as a reasonable cryptographic assumption can be harmful to the credibility of our field. Therefore, there is a great need for measures according to which we classify and compare assumptions, as to which are safe and which are not. In this paper, we propose such a classification and review recently suggested assumptions in this light. This follows the footsteps of Naor Crypto 2003. Our governing principle is relying on hardness assumptions that are independent of the cryptographic constructions.

Process and workflow technology have traditionally not allowed for the specification of, nor run-time enforcement of, real-time requirements, despite the fact that time-to-market and other real-time constraints are more stringent than ever. Without specification of timing constraints, process designers cannot effectively reason about real-time constraints on process programs and the efficacy of their process programs in satisfying those constraints. Furthermore, without executable semantics for those timing specifications, such reasoning might not be applicable to the process as actually executed. We seek to support reasoning about the real-time requirements of software processes. In this paper, we describe work in which we have added real-time specifications to a process programming language, and in which we have added deadline timers and task scheduling to enforce the real-time requirements of processes.

This paper analyzes what stylistic characteristics differentiate different styles of writing, and specifically types of different A-level computer science articles. To do so, we compared various full papers using stylistic feature sets and a supervised machine learning method. We report on the success of this approach in identifying papers from the last 6 years of the following three conferences: SIGIR, ACL, and AAMAS. This approach achieves high accuracy results of 95.86%, 97.04%, 93.22%, and 92.14% for the following four classification experiments: (1) SIGIR / ACL, (2) SIGIR / AAMAS, (3) ACL / AAMAS, and (4) SIGIR / ACL / AAMAS, respectively. The Part of Speech (PoS) and the Orthographic sets were superior to all others and have been found as key components in different types of writing.

RC4 is (still) a very widely-used stream cipher. Previous work by AlFardan et al. (USENIX Security 2013) and Paterson et al. (FSE 2014) exploited the presence of biases in the RC4 keystreams to mount plaintext recovery attacks against TLS-RC4 and WPA/TKIP. We improve on the latter work by performing large-scale computations to obtain accurate estimates of the single-byte and double-byte distributions in the early portions of RC4 keystreams for the WPA/TKIP context and by then using these distributions in a novel variant of the previous plaintext recovery attacks. The distribution computations were conducted using the Amazon EC2 cloud computing infrastructure and involved the coordination of 213 hyper-threaded cores running in parallel over a period of several days. We report on our experiences of computing at this scale using commercial cloud services. We also study Microsoft’s Point-to-Point Encryption protocol and its use of RC4, showing that it is also vulnerable to our attack techniques.

Memory transactions provide programmers with a convenient abstraction for concurrent programs: a keyword such as atomic designating a region of code that appears, from the perspective of concurrent threads, to execute atomically. Unfortunately, existing implementations in the form of software transactional memory STM are often ineffective due to their monolithic nature: every single read or write access is automatically tracked and recorded. In this statement, we advocate a transactional model of programming without a heavyweight software transactional memory, and describe some related, open research challenges. We suggest that a model based on persistent data structures could permit a variety of transactional algorithms to coexist in a library of composable transactional objects. Applications are constructed by snapping these objects together to form atomic transactions, in much the same way that today's Java programmers compose their applications from libraries such as java.util.concurrent. We report preliminary results developing this library in ScalaSTM, and discuss the challenges ahead.

With the increasing of the amount of the scientific papers, it is very important and difficult for paper-sharing platforms to recommend related papers accurately for users. This paper tackles the problem by proposing a method that models user historical behavior. Through collecting the operations on scientific papers of online users and carrying on the detailed analysis, we build preference model for each user. The personalized recommendation model is constructed based on content-based filtering model and statistical language model.. Experimental results show that users’ historical behavior plays an important role in user preference modeling and the proposed method improves the final predication performance in the field of technical papers recommendation.

In this paper, we present a storytelling system able to dramatize interactive narratives in augmented reality over a conventional sheet of paper. The system allows users to freely interact with virtual characters by sketching objects on the paper. Users interacting with the system can indirectly affect the characters' decisions, even to the point of radically subverting the storyline.

Paper documents are still an integral component of our everyday working lives, and are likely to remain so for the foreseeable future. Fortunately, advances in electronic technology in recent years have made it possible to develop digital systems that better integrate paper and digital document management. However, as far as we are aware, none of these integrated document management systems have been evaluated to demonstrate whether the users would indeed find them valuable for managing paper documents. In this paper we present a user study of one such system, called SOPHYA, that provides a means of linking the management of real world document artefacts (e.g., folders) with their electronic counterparts, so that document management activities such as locating, retrieving, and filing documents can be better supported, especially for large collections. The study is in the form of a laboratory-based evaluation, in which participants with experience in document management used the system to perform several tasks, such as locating and retrieving folders, and provided feedback based on their own experience. The results of this study show that users are able to apply software functionality they use for storage and retrieval of electronic documents to effectively manage paper documents as well. Our study has also identified a range of other potential benefits that the users envisage for such a technology in their diverse work environments.

It has long been recognised by researchers that the affordances of paper are likely to ensure that it will continue to be in widespread use in the work place, homes and public spaces. Consequently, numerous research projects have investigated ways of integrating paper with digital media and services. In recent years, a lot of this research has revolved around the digital pen and paper technology developed by the Swedish company Anoto, since it offers a robust solution for tracking the position of a pen on paper. While the commercial sector has tended to focus on applications related to the capture of handwriting, many of these research projects have investigated the use of the pen for real-time interaction and possibilities of turning paper into an interactive medium. Researchers were also quick to realise that digital pen and paper technology could be adapted to support other forms of pen-based interaction and have developed digital whiteboards and tabletops based on the technology. In addition, some systems have combined the technology with touch devices to support bimanual pen and touch interfaces. In the case of document manipulation, this means that touch could be used to perform actions such as a moving a document or turning pages, while the pen could be used to select elements within a document or to annotate it. Further, there are projects which have integrated the work on interactive paper and pen-based interaction on digital tabletops, investigating ways of allowing users to transfer document elements back and forth between paper and digital surfaces. Despite the success of these research projects in terms of demonstrating the capabilities of digital pen and paper technology and how it could be exploited to support a wide variety of everyday tasks, there are still some technical and non-technical issues that need to addressed if there are to be major breakthroughs in terms of widespread adoption. The first part of the talk will review research in the field, while the second part will examine these issues and the way ahead.

We investigate the automatic harvesting of research paper metadata from recent scholarly events. Our system, Kairos, combines a focused crawler and an information extraction engine, to convert a list of conference websites into a index filled with fields of metadata that correspond to individual papers. Using event date metadata extracted from the conference website, Kairos proactively harvests metadata about the individual papers soon after they are made public. We use a Maximum Entropy classifier to classify uniform resource locators (URLs) as scientific conference websites and use Conditional Random Fields (CRF) to extract individual paper metadata from such websites. Experiments show an acceptable measure of classification accuracy of over 95% for each of the two components.

The aim of this paper is to explore new metaphors for interaction design on tabletop system. Tabletop systems are shared horizontal surface for co-located collaboration, which leads to original problems when designing interactions. We propose two metaphors based on the paper: the peeling metaphor, and the slot metaphor, and then suggest a way of using them to design new interactions for solving some of the problems of tabletop systems: documents organization, documents transmission and documents duplication.

Many IT departments use remote administration products to configure, monitor, and maintain the systems they manage. These tools can be beneficial in the right hands, but they can also be devastating if attackers exploit them to seize control of machines. As a case study, we analyze the security of a remote administration product called Absolute Manage. We find that the system's communication protocol suffers from serious design flaws and fails to provide adequate integrity, confidentiality, or authentication. Attackers can exploit these vulnerabilities to issue unauthorized commands on client systems and execute arbitrary code with administrator privileges. These blatant vulnerabilities suggest that remote administration tools require increased scrutiny from the security community. We recommend that developers adopt defensive designs that limit the damage attackers can cause if they gain control.

Mobile devices with small screens and minimal facilities for interaction are increasingly being used in complex human activities for accessing and processing information, while the user is moving. This paper presents a case study of the design and evaluation of a mobile system, which involved transformation of complex text and tables to digital format on a PDA. The application domain was an emergency medical ward, and the user group was junior registrars. We designed a PDA-based system for accessing information, focusing on the ward instruction, implemented a prototype and evaluated it for usability and utility. The evaluation results indicate significant problems in the interaction with the system as well as the extent to which the system is useful for junior registrars in their daily work.

The Pret a Voter election scheme provides high assurance of accuracy and secrecy, due to the high degree of transparency and auditability. However, the assurance arguments are subtle and involve some understanding of the role of cryptography. As a result, establishing public understanding and trust in such systems remains a challenge. It is essential that a voting system be not only trustworthy but also widely trusted. In response to this concern, we propose to add a mechanism to Pret a Voter to generate a conventional (i.e. human readable) paper audit trail that can be invoked should the outcome of the cryptographic count be called into question. It is hoped that having such a familiar mechanism as a safety net will encourage public confidence. Care has to be taken to ensure that the mechanism does not undermine the carefully crafted integrity and privacy assurances of the original scheme. We show that, besides providing a confidence building measure, this mechanism brings with it a number of interesting technical features: it allows extra audits of mechanisms that capture and process the votes to be performed. In particular, the mechanism presented here allows direct auditing of ballots that are actually used to cast votes. This is in contrast to previous versions of Pret a Voter, and indeed other verifiable schemes, that employ a cut-and-choose mechanism. The mechanism proposed also has the benefit of providing a robust counter to the danger of voters undermining the receipt-freeness property by trying to retain the candidate list.

In this paper we describe the design of a hybrid search that combines simple metadata search with a traditional keyword search over unstructured context data. This paradigm provides the inquirer additional options to narrow the search with some semantic aspects through the XML metadata query. We demonstrate a paper search for a case study of the hybrid search, and describe a data integration hub to integrate those data dispersed on the Net.

The Lost Cosmonaut is an interactive narrative based on digitally enhanced paper. This technology uses an electronic pen to mediate between paper and computer. Thus any actions of the pen on the paper can be captured and manipulated by a computer as well as we can map digitally controlled events onto paper. The story in this narrative environment reveals itself partially through written text and images on the paper surface just as any other printed story. However, additional information in form of digitally controlled outputs such as sound, light and projections can be accessed through interaction with pen and paper. Furthermore the audience is not only supposed to read and otherwise perceive information, we also want them to actively produce content for this environment by writing onto the paper. By doing so they also add content to the database containing the digital output at the same time. Hence we produce a complex multimedia environment that works on three levels: On paper, in a digitally controlled visual and acoustic environment and in the combination of both worlds. Last but not least this environment is an open system, which grows as a collaborative effort over time as each user adds his own entries to paper and database. We argue that using paper as an integrated part of a digital environment is a best-of-both-world approach that opens up new possibilities for producing and perceiving narrative.

Office workers tend to produce paper piles of documents to read or to process sometime later. The information contained in these piles is often lost if it is not transferred to electronic format and connected to knowledge structures. Information that is not part of the knowledge worker’s electronic information space is frequently overlooked because it is not proactively provided during actual processes or tasks he is involved in. This paper presents a novel prototype for an intelligent office appliance, which results from an integration of three state-of-the-art office applications/appliances: a workflow system, a document classification system, and a multi-functional peripheral. The resulting system allows for leveraging an office worker’s papers to her personal knowledge space in order to realize a pro-active and context-sensitive information support within knowledge-intensive tasks and processes.

Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBA) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers.

With the increased use of Internet and information technol- ogy all over the world, there is an increased amount of criminal activities that involve computing and digital data. These digital crimes (e-crimes) impose new challenges on prevention, detection, investigation, and pros- ecution of the corresponding offences. Computer forensics (also known as cyberforensics) is an emerging research area that applies computer in- vestigation and analysis techniques to help detection of these crimes and gathering of digital evidence suitable for presentation in courts. This new area combines the knowledge of information technology, forensics science, and law and gives rise to a number of interesting and challeng- ing problems related to computer security and cryptography that are yet to be solved. In this paper, we present and discuss some of these prob- lems together with two successful cases of computer forensics technology developed in Hong Kong that enable the law enforcement departments to detect and investigate digital crimes more efficiently and effectively. We believe that computer forensics research is an important area in ap- plying security and computer knowledge to build a better society.

A secret and secure ballot is the core of every democracy. We all feel proud of being able to decide the future of our countries by making appropriate use of our right to vote in an election. However, how can we improve the efficiency of the voting process? Democratic governments should have mechanisms which ensure the integrity, security and privacy of its citizens at the polls during an election process. This paper describes a new electronic secure voting system, based on automatic paper ballot reading, which can be utilized to offer efficient help to officials and party representatives during elections. It presents how the system is organized, it also describes our OCR system and how it is implemented to read paper ballots, and it ends showing some experimental results.

ECampus is a project spanning all departments at the University of Fulda. It has been started to create a uniform learning environment at the university. The objective is to research and develop a user-friendly easy-to-use editor to generate SCORM 2004 conform E-Learning modules. This editor is based on Open Source software and new technologies such as XSL transformations and the Google web toolkit. The whole system is provided as a web application and embedded within the E-Learning environment of the university. Some E-Learning modules can be developed with the system immediately. These modules are now being used during the lessons with great success.

Composition of components or services is a crucial issue when building new applications by reusing existing pieces of software. This task turns out to be tedious when behavioural descriptions, acknowledged as one of the essential parts of component interfaces, are taken into account. Furthermore, mismatches may exist between component interfaces, and adaptation [2] is necessary to help the designer to solve them.

The paper documents recognition is fundamental for office automation becoming every day a more powerful tool in those fields where information is still on paper. Document recognition follows from data acquisition, from both journals, and entire books in order to transform them in digital objects. We present a new system DAN (Document Analysis on Network) for Document recognition that follows the Open Source methodologies, XML description for documents segmentation and classification, which turns to be beneficial in terms of classification precision, and general-purpose availability.

Over the last 30 years, a phenomenon of human-to-human interactive storytelling has developed, known as fantasy role-playing games (RPG) , beginning with the publishing of the game Dungeons & Dragons in 1974. Since then, a role-playing culture has emerged, incorporating concepts from theatre, board games, history reenactment and storytelling, remixed in an almost post-modern way.

Today's mobile data applications aspire to deliver services to a user anywhere – anytime while fulfilling his Quality of Service (QoS) requirements However, the success of the service delivery heavily relies on the QoS offered by the underlying networks As the services operate in a heterogeneous networking environment, we argue that the generic information about the networks' offered-QoS may enable an anyhow mobile service delivery based on an intelligent (proactive) selection of ‘any' network available in the user's context (location and time). Towards this direction, we develop a QoS-predictions service provider, which includes functionality for the acquisition of generic offered-QoS information and which, via a multidimensional processing and history-based reasoning, will provide predictions of the expected offered-QoS in a reliable and timely manner. We acquire the generic QoS-information from distributed mobile services' components quantitatively (actively and passively) measuring the applicationlevel QoS, while the reasoning is based on statistical data mining and pattern recognition techniques.

Radio Frequency Identification (RFID) systems promise large scale, automated tracking solutions but also pose a threat to customer privacy. The tree-based hash protocol proposed by Molnar and Wagner presents a scalable, privacy-preserving solution. Previous analyses of this protocol concluded that an attacker who can extract secrets from a large number of tags can compromise privacy of other tags. We propose a new metric for information leakage in RFID protocols along with a threat model that more realistically captures the goals and capabilities of potential attackers. Using this metric, we measure the information leakage in the tree-based hash protocol and estimate an attacker's probability of success in tracking targeted individuals, considering scenarios in which multiple information sources can be combined to track an individual. We conclude that an attacker has a reasonable chance of tracking tags when the tree-based hash protocol is used.

Since XML has the advantage of embedding logical structure information into documents, it is widely used as the universal format for structured documents on the Web. This makes it attractive to convert paper-based documents with logical hierarchy into XML representations automatically. Document image analysis and understanding [1] consists of two phases: geometric and logical structure analysis. Because the two phases take different kinds of data as input, it may not be desirable to apply the same method to them. Targeting technical journal document with multiple pages, we present a hybridization of knowledge-based and syntactic methods for geometric and logical structure analysis of document images.

Security and trust are two properties of modern computing systems that are the focus of much recent interest. They play an increasingly significant role in the requirements for modern computing systems. Security has been studied thoroughly for many years, particularly the sub-domain of cryptography. The use of computing science formal methods has facilitated cryptanalysis of security protocols. At the moment, trust is intensively studied, but not well understood. Here we present our approach based on formal methods for modelling and validating the notion of trust in computing science.

For the Web to reach its full potential, it must evolve into a Semantic Web, providing a universally accessible platform that allows data to be shared and processed by automated tools as well as by people. The ‘Semantic Web’ is a recent initiative of the World Wide Web Consortium (W3C), with the goal of extending the current Web to facilitate Web automation, universally accessible content, and the ‘Web of Trust’. However, if the semantic web is going to be adopted and assimilated a clear migration path from present technologies to new ones is required. The SWAD-Europe project aims to support the W3C’s Semantic Web initiative in Europe, providing targeted research, demonstrations and outreach to ensure Semantic Web technologies move into the mainstream of networked computing. The project aims to support the development and deployment of W3C Semantic Web specifications through implementation, research and testing activities.

Improvement in cross-language information retrieval results can come from a variety of sources - failure analysis, resource enrichment in terms of stemming and parallel and comparable corpora, use of pivot languages, as well as phonetic transliteration and Romanization. Application of these methodologies should contribute to a gradual increase in the ability of search software to cross the language barrier.

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.

Peer-to-peer (P2P) networks support a wide variety of network services including elastic services such as file-sharing and downloading and inelastic services such as real-time multiparty conferencing. Each peer who acquires a service will receive a certain level of satisfaction if the service is provided with a certain amount of resource. The utility function is used to describe the satisfaction of a peer when acquiring a service. In this paper we consider optimal resource allocation for elastic and inelastic services and formulate a utility maximization model which is an intractable and difficult non-convex optimization problem. In order to resolve it, we apply the successive approximation method and approximate the non-convex problem to a serial of equivalent convex optimization problems. Then we develop a gradient-based resource allocation scheme to achieve the optimal solutions of the approximations. After a serial of approximations, the proposed scheme can finally converge to an optimal solution of the primal utility maximization model for resource allocation which satisfies the Karush–Kuhn–Tucker conditions.

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.

BackgroundOptimal detection and surveillance of bladder cancer (BCa) rely primarily on the cystoscopic visualization of bladder lesions. AI-assisted cystoscopy may improve image recognition and accelerate data acquisition.ObjectiveTo provide a comprehensive review of machine learning (ML), deep learning (DL) and convolutional neural network (CNN) applications in cystoscopic image recognition.Evidence acquisitionA detailed search of original articles was performed using the PubMed-MEDLINE database to identify recent English literature relevant to ML, DL and CNN applications in cystoscopic image recognition.Evidence synthesisIn total, two articles and one conference abstract were identified addressing the application of AI methods in cystoscopic image recognition. These investigations showed accuracies exceeding 90% for tumor detection; however, future work is necessary to incorporate these methods into AI-aided cystoscopy and compared to other tumor visualization tools. Furthermore, we present results from the RaVeNNA-4pi consortium initiative which has extracted 4200 frames from 62 videos, analyzed them with the U-Net network and achieved an average dice score of 0.67. Improvements in its precision can be achieved by augmenting the video/frame database.ConclusionAI-aided cystoscopy has the potential to outperform urologists at recognizing and classifying bladder lesions. To ensure their real-life implementation, however, these algorithms require external validation to generalize their results across other data sets.

Root cause analysis (RCA) is one of the most prominent tools used to comprehensively evaluate a biopharmaceutical production process. Despite of its widespread use in industry, the Food and Drug Administration has observed a lot of unsuitable approaches for RCAs within the last years. The reasons for those unsuitable approaches are the use of incorrect variables during the analysis and the lack in process understanding, which impede correct model interpretation. Two major approaches to perform RCAs are currently dominating the chemical and pharmaceutical industry: raw data analysis and feature-based approach. Both techniques are shown to be able to identify the significant variables causing the variance of the response. Although they are different in data unfolding, the same tools as principal component analysis and partial least square regression are used in both concepts. Within this article we demonstrate the strength and weaknesses of both approaches. We proved that a fusion of both results in a comprehensive and effective workflow, which not only increases better process understanding. We demonstrate this workflow along with an example. Hence, the presented workflow allows to save analysis time and to reduce the effort of data mining by easy detection of the most important variables within the given dataset. Subsequently, the final obtained process knowledge can be translated into new hypotheses, which can be tested experimentally and thereby lead to effectively improving process robustness.

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

In this paper an automatic classification system for pathological findings is presented. The starting point in our undertaking was a pathologic tissue collection with about 1.4 million tissue samples described by free text records over 23 years. Exploring knowledge out of this “big data” pool is a challenging task, especially when dealing with unstructured data spanning over many years. The classification is based on an ontology-based term extraction and decision tree build with a manually curated classification system. The information extracting system is based on regular expressions and a text substitution system. We describe the generation of the decision trees by medical experts using a visual editor. Also the evaluation of the classification process with a reference data set is described. We achieved an F-Score of 89,7% for ICD-10 and an F-Score of 94,7% for ICD-O classification. For the information extraction of the tumor staging and receptors we achieved am F-Score ranging from 81,8 to 96,8%.

Feature models are frequently used to capture the knowledge about configurable software systems and product lines. However, feature modeling of large-scale systems is challenging as models are needed for diverse purposes. For instance, feature models can be used to reflect the perspectives of product management, technical solution architecture, or product configuration. Furthermore, models are required at different levels of granularity. Although numerous approaches and tools are available, it remains hard to define the purpose, scope, and granularity of feature models. This paper first reports results and experiences of an exploratory case study on developing feature models for two large-scale industrial automation software systems. We report results on the characteristics and modularity of the feature models, including metrics about model dependencies. Based on the findings from the study, we developed FORCE, a modeling language, and tool environment that extends an existing feature modeling approach to support models for different purposes and at multiple levels, including mappings to the code base. We demonstrate the expressiveness and extensibility of our approach by applying it to the well-known Pick and Place Unit example and an injection molding subsystem of an industrial product line. We further show how our approach supports consistency between different feature models. Our results and experiences show that considering the purpose and level of features is useful for modeling large-scale systems and that modeling dependencies between feature models is essential for developing a system-wide perspective.

There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.

In the era of information, administration of personal data protection mingles with expectations of access to information as well as the overall sense of cyber (in)security. A failure to appropriately consider the system of data processing relationships easily reduces personal data protection to assurances in letter. The complexity of contemporary data transactions demands a systemic and structured normative approach to personal data protection. Any evaluation of relevant norms should not be isolated from factors that determine or condition their implementation. As privacy is an intrinsically subjective claim, enforcing data privacy is premised on data subject’s personal participation in the protection of her data.

The food industry has many points of vulnerability in its supply chain. It currently lacks integrated crisis management and response programs to understand the importance of decision-making during and in the aftermath of a bioterrorist attack on the food supply. Computer simulations have been used successfully in other industries as training and analysis tools. This paper describes an agent-based simulation for food defense training and analysis. Production information, consumption patterns, morbidity/mortality rates, recall costs and additional information were collected and provided to a data-driven simulation to anticipate the impact of decision-making on economic and public health during a terrorist attack. A case study is given with a representative exercise involving forty industry representatives who participated in a food defense simulation. Their decisions (recall and microbiological and toxicological testing) were derived from testing results, press releases, epidemiological data, and discussions with other industry and regulatory teams. Decisions made during the simulation resulted in over 76,000 illnesses, 45 deaths, and $132 million in recall costs. The no intervention, baseline scenario estimated to result in 91,000 illnesses and 54 deaths, indicating the improved public health outcomes resulting from players' decisions. Participants identified three key learning points: 1) communication between all groups is pertinent and challenging, 2) approaches to solve inherent food safety problems cannot be used to address food defense situations, and 3) human resource procedures regarding new hires and disgruntled employees should involve additional security measures. This computer simulation could be a valuable resource in food defense awareness and help educate companies and regulators about food defense risks and decision-making consequences.

Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.

Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accelerometer-equipped juvenile lemon sharks (Negaprion brevirostris) at Bimini, Bahamas (25°44′N, 79°16′W). The sharks were observed to exhibit chafing, burst swimming, headshaking, resting and swimming in a semi-captive environment and these observations were used to ground-truth data for ML training and testing. ML methods included logistic regression, an artificial neural network, two random forest models, a gradient boosting model and a voting ensemble (VE) model, which combined the predictions of all other (base) models to improve classifier performance. The macro-averaged F-measure, an indicator of classifier performance, showed that the VE model improved overall classification (F-measure 0.88) above the strongest base learner model, gradient boosting (0.86). To test whether the VE model provided biologically meaningful results when applied to accelerometer data obtained from wild sharks, we investigated headshaking behaviour, as a proxy for prey capture, in relation to the variables: time of day, tidal phase and season. All variables were significant in predicting prey capture, with predations most likely to occur during early evening and less frequently during the dry season and high tides. These findings support previous hypotheses from sporadic visual observations. Electronic supplementary material The online version of this article (10.1007/s00227-018-3318-y) contains supplementary material, which is available to authorized users.

A variety of anthropogenic compounds has been found to be capable of disrupting the endocrine systems of organisms, in laboratory studies as well as in wildlife. The most widely described endpoint is estrogenicity, but other hormonal disturbances, e.g., thyroid hormone disruption, are gaining more and more attention. Here, we present a review and chemical characterization, using principal component analysis, of organic compounds that have been tested for their capacity to bind competitively to the thyroid hormone transport protein transthyretin (TTR). The database contains 250 individual compounds and technical mixtures, of which 144 compounds are defined as TTR binders. Almost one third of these compounds (n = 52) were even more potent than the natural hormone thyroxine (T4). The database was used as a tool to assist in the identification of thyroid hormone-disrupting compounds (THDCs) in an effect-directed analysis (EDA) study of a sediment sample. Two compounds could be confirmed to contribute to the detected TTR-binding potency in the sediment sample, i.e., triclosan and nonylphenol technical mixture. They constituted less than 1 % of the TTR-binding potency of the unfractionated extract. The low rate of explained activity may be attributed to the challenges related to identification of unknown contaminants in combination with the limited knowledge about THDCs in general. This study demonstrates the need for databases containing compound-specific toxicological properties. In the framework of EDA, such a database could be used to assist in the identification and confirmation of causative compounds focusing on thyroid hormone disruption. Electronic supplementary material The online version of this article (doi:10.1007/s00216-015-8736-9) contains supplementary material, which is available to authorized users.

Formal verification aims to improve the quality of software by detecting errors before they do harm. At the basis of formal verification is the logical notion of correctness, which purports to capture whether or not a program behaves as desired. We suggest that the boolean partition of software into correct and incorrect programs falls short of the practical need to assess the behavior of software in a more nuanced fashion against multiple criteria. We therefore propose to introduce quantitative fitness measures for programs, specifically for measuring the function, performance, and robustness of reactive programs such as concurrent processes. This article describes the goals of the ERC Advanced Investigator Project QUAREM. The project aims to build and evaluate a theory of quantitative fitness measures for reactive models. Such a theory must strive to obtain quantitative generalizations of the paradigms that have been success stories in qualitative reactive modeling, such as compositionality, property-preserving abstraction and abstraction refinement, model checking, and synthesis. The theory will be evaluated not only in the context of software and hardware engineering, but also in the context of systems biology. In particular, we will use the quantitative reactive models and fitness measures developed in this project for testing hypotheses about the mechanisms behind data from biological experiments.