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The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and development due to overuse. There are many approaches to managing this problem; however, the impact of individual users in infrastructural maintenance is mostly ignored. In this position paper, we hypothesise that important changes torwards a more efficient use of the transport network start with its users and their behavioural changes. To this end, we introduce our vision on how to employ data driven, intelligent agent-based modelling, incorporating human factors aspects, as a toolset to understand travellers and to stimulate behavioural changes. The aim is to achieve better balanced and integrated mobility usage within the transport network. The idea is explored with a few guided questions, and a methodology is proposed. We employ 1) cognitive work analysis to investigate the reasons for travellers' mode choice; 2) computational intelligence to extract and represent knowledge from related datasets; 3) agent-based modelling to represent the real-world and to observe both individual and emergent behaviours. Future directions to adapt our methodology to alternative smart mobility projects are also discussed.

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.

In the era of emerging Smart Built Environments (SBEs), a smart house, unlike regular houses with static "components", consists of numerous interconnected and often actuated devices, capable of executing tasks independent of user supervision. Living in such a SBE, where for example, the furniture can rearrange itself, and the doors open and close of their own volition, may be difficult and unpredictable. Furthermore, cyber-security attacks and intrusion could allow attackers to assume control of the SBE, damage its components and to potentially harm its inhabitants. Such novel characteristics of SBEs present developers with several unique challenges with regards to implementing the needed safety and security measures and protocols that go along with them. With such environments, therefore, there is a need for a system that is capable of monitoring user activities in real-time, identifying the safety and security hazards to users in their immediate local context, warning users of these hazards, and perhaps even taking preventative and mitigative action against the hazards that it identified. In this paper, we survey some of these challenges and explore the design and implementation of a system designed around the safety and security of SBE inhabitants. We propose an approach to modeling SBE safety that combines the three laws of robotics and the swarm behavior model. We also present a preliminary prototype and discuss a case study.

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data. Even though IIV is easy to detect and fix, it still commonly happens in practice. In this paper, we study to what extent developers can detect IIV and investigate underlying reasons. This knowledge is essential to better understand how to support developers in creating secure software systems. We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience. Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence. Yet, a total of 60 participants could not detect the vulnerability even after the warning. Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV. Preprint: https://arxiv.org/abs/2102.06251. Data and materials: https://doi.org/10.5281/zenodo.3996696.

The Panama Papers is a collection of 11.5 million leaked records that contain information for more than 214,488 offshore entities. This collection is growing rapidly as more leaked records become available online. In this paper, we present a work in progress on a web browser plugin that detects company names from the Panama Papers and alerts the user by means of unobtrusive visual cues. We matched a random sample of company names from the Public Works and Government Services Canada registry against the Panama Papers using three different string matching techniques. Monge-Elkan is found to provide the best matching results but at increased computational cost. Levenshtein-based approach is found to provide the best tradeoff between matching and computational cost, while Jacquard index like approach is found to be less sensitive to slight textual change.

In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time., 12 pages, 3 figures

Gamification is one of the trends in education that is best suited to the current learning environment. Putra BINGO is a transformative assessment tool that uses gamification techniques. This research starts by elaborating the concept of Putra BINGO and its implementation using traditional method with pen and paper. The feedbacks gathered from the implementation are analyzed and applied to develop a multiplayer educational online game using Unity Game Engine. This paper describes details of the development for its two components; the database and the digital online game. Putra Bingo is available on PC and Android devices. It required additional plugins such as Photon Networking to handle game communication between multiple players. This is crucial to accommodate its test case application as an alternative assessment for teamwork skills using Rubrics and questionnaires. Further research will be conducted to enhance its functionality and user experience after the initial development of the digital online version is completed.

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation.

A general dynamic resource allocation scheme is introduced, which iteratively reconfigures an elastic optical network according to traffic dynamism and service diversity. As an application example, the scheme is employed for impairment-aware power-efficient resource allocation in a short-haul metro elastic optical network to demonstrate a typical 5% power plus service penalty for compensating bandwidth limitations compared with the corresponding limitless counterpart.

Cyber Physical Systems (CPSs) refer to a new generation of embedded ICT systems (PCs, smartphones, sensors, actuators, etc.) that are interconnected and collaborating to provide users with a wide range of innovative applications and services. Many application domains, e.g., Emergency management, factories of the future, personalized healthcare, just to name a few, require the definition, design and development of systems able to carry out complex processes that coordinate the services offered by the CPS in the "physical" real world. The physical world, however, is not entirely predictable, and such processes must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this paper, we tackle the above issue and we propose an approach and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events.

In an ongoing discussion comparing the security properties of electronic and paper voting, decreased privacy is often presented as an argument against remote Internet voting. We contribute to this discussion by presenting a side-channel attack against the physical environment of traditional paper-based elections. More precisely, we build a device based on an Arduino development board and cheap electret microphones, capable of triangulating the locations of marks made on wooden tables with high precision. In the best configuration, we are able to determine the correct cell having dimensions 4×5 cm with more than 90% accuracy. This will allow breaching privacy of ballot sheet designs that rely on the voter marking her choice(s) between a potentially high number of candidates printed on one large sheet. We complement our attack with a study on various aspects of deployment of facial recognition. This gives rise to the setup where the attacker installs cameras in the polling stations, aiming at automated detection of people leaving the voting booths. Combining the two approaches, we will have a completely automated (and hence relatively well scalable) attack against the privacy of paper-based voting.

It is with great pleasure that we present the Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR) 2018 Conference Papers track. As the next evolution of the IEEE Symposium on 3D User Interfaces, the IEEE VR Conference Papers track is now a fully integrated part of IEEE VR. This has resulted in a broader scope of submissions than ever before, from many different disciplines, including virtual, augmented, and mixed reality, human-computer interaction, mobile computing, computer graphics, cognitive and perceptual psychology, games, education, and the social sciences. This breadth of submissions also reflects the recent explosion of interest in VR and 3DUI technologies; never before has VR/3DUI technology been so available to so many people. There are therefore many open research questions in VR, and more new technologies under examination.

Data storage in the Cloud became a very popular service. However, delegation of data management results in loss of control from user perspective, in particular regarding the real location where data are stored. Thus, data location verification in the Cloud is a challenging issue. Among the huge methods proposed to consider data location verification, this paper focuses on machine learning based methods, which use network Round Trip Times as main metric. In particular, it provides experimental results based on country-wide dataset collected through Grid'5000 platform. Results show the capacities of regression-based methods to support data location verification at specific accuracy depending on user requirements.

Source code analysis tools are designed to analyze code artifacts with different intents, which span from improving the quality and security of the software to easing refactoring and reverse engineering activities. However, most tools do not come with features to periodically schedule their analysis or to be executed on a battery of repositories, and lack support to combine their results with other analysis tools. Thus, researchers and practitioners are often forced to develop ad-hoc scripts to meet their needs. This comes at the risk of obtaining wrong results (because of the lack of testing) and of hindering replication by other research teams. In addition, the resulting scripts are often not meant to be customized nor designed for incrementality, scalability and extensibility. In this paper we present Graal, which empowers users with a customizable, scalable and incremental approach to conduct source code analysis and enables relating the obtained results with other software project data. Graal leverages on and extends the functionalities of GrimoireLab, a strong free software tool developed by Bitergia, a company devoted to offer commercial software development analytics, and part of the CHAOSS project of the Linux Foundation.

Code obfuscation is a popular approach to turn program comprehension and analysis harder, with the aim of mitigating threats related to malicious reverse engineering and code tampering. However, programming languages that compile to high level bytecode (e.g., Java) can be obfuscated only to a limited extent. In fact, high level bytecode still contains high level relevant information that an attacker might exploit. In order to enable more resilient obfuscations, part of these programs might be implemented with programming languages (e.g., C) that compile to low level machine-dependent code. In fact, machine code contains and leaks less high level information and it enables more resilient obfuscations. In this paper, we present an approach to automatically translate critical sections of high level Java bytecode to C code, so that more effective obfuscations can be resorted to. Moreover, a developer can still work with a single programming language, i.e., Java.

The popularity and wide adoption of JavaScript both at the client and server side makes its code analysis more important than ever before. Most of the algorithms for vulnerability analysis, coding issue detection, or type inference rely on the call graph representation of the underlying program. Despite some obvious advantages of dynamic analysis, static algorithms should also be considered for call graph construction as they do not require extensive test beds for programs and their costly execution and tracing. In this paper, we systematically compare five widely adopted static algorithms - implemented by the npm call graph, IBM WALA, Google Closure Compiler, Approximate Call Graph, and Type Analyzer for JavaScript tools - for building JavaScript call graphs on 26 WebKit SunSpider benchmark programs and 6 real-world Node.js modules. We provide a performance analysis as well as a quantitative and qualitative evaluation of the results. We found that there was a relatively large intersection of the found call edges among the algorithms, which proved to be 100% precise. However, most of the tools found edges that were missed by all others. ACG had the highest precision followed immediately by TAJS, but ACG found significantly more call edges. As for the combination of tools, ACG and TAJS together covered 99% of the found true edges by all algorithms, while maintaining a precision as high as 98%. Only two of the tools were able to analyze up-to-date multi-file Node.js modules due to incomplete language features support. They agreed on almost 60% of the call edges, but each of them found valid edges that the other missed.

Composite commits are a common mistake in the use of version control software. A composite commit groups many unrelated tasks, rendering the commit difficult for developers to understand, revert, or integrate and for empirical researchers to analyse. We propose an algorithmic foundation for tool support to identify such composite commits. Our algorithm computes both a program dependence graph and the changes to the abstract syntax tree for the files that have been changed in a commit. Our algorithm then groups these fine-grained changes according to the slices through the dependence graph they belong to. To evaluate our technique, we analyse and refine an established dataset of Java commits, the results of which we also make available. We find that our algorithm can determine whether or not a commit is composite. For the majority of commits, this analysis takes but a few seconds. The parts of a commit that our algorithm identifies do not map directly to the commit's tasks. The parts tend to be smaller, but stay within their respective tasks.

The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time.

Information connectivity and retrieval has a role in our daily lives. The most pervasive source of online information is databases. The amount of data is growing at rapid rate and database technology is improving and having a profound effect. Almost all online applications are storing and retrieving information from databases. One challenge in supplying the public with wider access to informational databases is the need for knowledge of database languages like Structured Query Language (SQL). Although the SQL language has been published in many forms, not everybody is able to write SQL queries. Another challenge is that it may not be practical to make the public aware of the structure of the database. There is a need for novice users to query relational databases using their natural language. To solve this problem, many natural language interfaces to structured databases have been developed. The goal is to provide more intuitive method for generating database queries and delivering responses. Social media makes it possible to interact with a wide section of the population. Through this medium, and with the help of Natural Language Processing (NLP) we can make the data of the Atmospheric Radiation Measurement Data Center (ADC) more accessible to the public. We propose an architecture for using Apache Lucene/Solr [1], OpenML [2,3], and Kafka [4] to generate an automated query/response system with inputs from Twitter5, our Cassandra DB, and our log database. Using the Twitter API and NLP we can give the public the ability to ask questions of our database and get automated responses.

Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TensorCast, a novel method that forecasts time-evolving networks more accurately than current state of the art methods by incorporating multiple data sources in coupled tensors. TensorCast is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple real-world networks, including DBLP and a Twitter temporal network with over 310 million non-zeros, where we predict the evolution of the activity of the use of political hashtags.

Within the Austrian Grid project phase 2, three different groups, each allocated to a different work package, join their efforts to implement a grid infrastructure for the european research project "Breath Gas Analysis for molecular oriented diseases''. This position paper provides background on the task and the resulting requirements, a presentation on solutions developed during related projects in the application domain, identifies problems that have not yet been solved, and finally presents the intended solution to be developed.

There are several standardized protocols based on SpaceWire which provide data exchange between several nodes. SpaceWire is also suitable for interprocess communication (IPC), by the help of higher level protocols. However, currently there is no standardized protocol which is targeting IPC on SpaceWire networks. This paper proposes a protocol, which uses the capabilities of SpaceWire to build up networks for distributed computing on a spacecraft. The core of this protocol is the IPC mechanism for communication between the nodes and methods to support a reconfiguration of the network. A key feature of this protocol is an interface for a reconfiguration mechanism, which can be implemented on application level. This enables the utilization of unreliable commercial off the shelf (COTS) nodes, allowing system recovery from erroneous state. Additionally, the reconfiguration can be used to adapt the distributed computer to different mission phases. The protocol has the potential to build the foundation of a distributed on-board computer consisting of COTS components. Such distributed computer could be capable of fulfilling high performance demands as well as high reliability needs. Though, the protocol itself is not restricted to be used solely in fully-featured reconfigurable distributed systems. The IPC methods can be applied stand-alone as well, to establish a lightweight communication between nodes on a SpaceWire network by excluding the reconfiguration parts of the protocol.

This paper documents the successful development and evaluation of a novel membrane switch. The process results in lower environmental burdens and manufacturing costs than current screen-printed polyester solutions. Detailed is the evaluation of offset lithographically printed conductive tracks on paper substrates for application in computer keyboard assemblies. Offset lithographic printing of conductive tracks is an emerging fabrication technique finding application in low-cost, high volume, flexible circuitry applications. Several fully featured demonstration keyboards and switch membranes have been constructed and tested. These are currently in use in office environments and are presented.

We address the problem of the access to the results of scientific publications in the agri-food domain. We focus on the description of main contributions of the papers treating them as accepted or rejected beliefs of their authors expressed in the form of scientific laws. We define the structure of different kinds of scientific laws present in the domain in the form of an ontology. The main concern of the paper is a process in which we proceed from the abstracts of papers to the ontological representation of laws. Moreover, we present examples of SPARQL queries which show how the resulting knowledge base can be used. Among the uses we point out discovering new scientific hypotheses and incoherencies among scientific laws.

This paper argues the need for considering mitigating circumstances in cybercrime. Mitigating circumstances are conditions which moderate the culpability of an offender of a committed offence. Our argument is based on several observations. The cyberspace introduces a new family of communication and interaction styles and designs which could facilitate, make available, deceive, and in some cases persuade, a user to commit an offence. User’s lack of awareness could be a valid mitigation when using software features introduced without a proper management of change and enough precautionary mechanisms, e.g. warning messages. The cyber behaviour of users may not be necessarily a reflection of their real character and intention. Their irrational and unconscious actions may result from their immersed and prolonged presence in a particular cyber context. Hence, the consideration of the cyberspace design, the “cyber psychological” status of an offender and their inter-relation could form a new family of mitigating circumstances inherent and unique to cybercrime. This paper elaborates on this initial argument from different perspectives including software engineering, cyber psychology, digital forensics, social responsibility and law.

Internet of Things (IoT) with Cloud Computing (CC) is a new paradigm incorporating a pervasive presence of a wide range of things/objects which can interact with each other and cooperate, creating new services and reaching common goals. This will lead to more intelligent smart environments in a wide range of applications. In this context, protecting the Internet of Things with Cloud Computing (IoTwCC) against interference, including service attacks and viruses, becomes paramount. In this paper, we introduce a transition system representation to capture the information flow in IoTwCCs, and then investigate the opacity of the information flow model. In addition, we introduce a threat model to describe the actions of the system, and propose entropy as a security metrics to quantify the amount of information related to a service that might be exposed to other users or adversaries. It turns out that the opacity of the system is affected by the availability of the services. As a result, the trade-off between opacity and service availability can be analyzed.

This paper presents a novel method for automated manipulation of individual paper fiber bonds using a microrobotic platform, a computer vision algorithm and a robotic software framework. This is a challenging task due to the three-dimensional, heterogeneous and complex morphology of the fiber bonds. The goal is to automatically grasp the fiber bond, and break it by pulling apart the fibers it consists of. We present the components of the microrobotic platform, and the different rules utilized in detecting suitable grasp points from a 3D reconstruction of the bond generated from an image pair. We demonstrate the functionality of the approach with bond breaking experiments of seven fiber bonds. The time required for grasping and breaking of a bond is 10 – 15 seconds making the approach much faster than the current state-of-the-art testing, which is based on manual manipulation. The success rate of the tests is as high as 80 %. acceptedVersion

This paper proposes a method for exploring technical phrase frames by extracting word n-grams that match our information needs and interests from research paper titles. Technical phrase frames, the outcome of our method, are phrases with wildcards that may be substituted for any technical term. Our method, first of all, extracts word trigrams from research paper titles and constructs a co-occurrence graph of the trigrams. Even by simply applying Page Rank algorithm to the co-occurrence graph, we obtain the trigrams that can be regarded as technical key phrases at the higher ranks in terms of Page Rank score. In contrast, our method assigns weights to the edges of the co-occurrence graph based on Jaccard similarity between trigrams and then apply weighted Page Rank algorithm. Consequently, we obtain widely different but more interesting results. While the top-ranked trigrams obtained by unweighted Page Rank have just a self-contained meaning, those obtained by our method are technical phrase frames, i.e., A word sequence that forms a complete technical phrase only after putting a technical word (or words) before or/and after it. We claim that our method is a useful tool for discovering important phrase logical patterns, which can expand query keywords for improving information retrieval performance and can also work as candidate phrasings in technical writing to make our research papers attractive., 29th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2015; Gwangju; South Korea; 25 March 2015 through 27 March 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 7096236; 2015

Traditional activity-centric process modeling languages treat data as simple black boxes acting as input or output for activities. Many alternate and emerging process modeling paradigms, such as case handling and artifact-centric process modeling, give data a more central role. This is achieved by introducing lifecycles and states for data objects, which is beneficial when modeling data-or knowledge-intensive processes. We assume that traditional activity-centric process modeling languages lack the capabilities to adequately capture the complexity of such processes. To verify this assumption we conducted an online interview among BPM experts. The results not only allow us to identify various profiles of persons modeling business processes, but also the problems that exist in contemporary modeling languages w.r.t. The modeling of business data. Overall, this preliminary empirical study confirms the necessity of data-awareness in process modeling notations in general.

Internet of Things (IoT) is a socio-technical phenomena with the power to disrupt our society such as the Internet before. IoT promises the (inter-) connection of myriad of things proving services to humans and machines. It is expected that by 2020 tens of billions of things will be deployed worldwide. It became evident that the traditional centralized computing and analytic approach does not provide a sustainable model this new type of data. A new kind of architecture is needed as a scalable and trusted platform underpinning the expansion of IoT. The data gathered by the things will be often noisy, unstructured and real-time requiring a decentralized structure storing and analysing the vast amount of data. In this paper, we provide an overview of the current IoT challenges, will give a summary of funded IoT projects in Europe, USA, and China. Additionally, it will provide detailed insights into three IoT architectures stemming from such projects.

Along with the invention of computers and interconnected networks, physical societal notions like security, trust, and privacy entered the digital environment. The concept of digital environments begins with the trust (established in the real world) in the organisation/individual that manages the digital resources. This concept evolved to deal with the rapid growth of the Internet, where it became impractical for entities to have prior offline (real world) trust. The evolution of digital trust took diverse approaches and now trust is defined and understood differently across heterogeneous domains. This paper looks at digital trust from the point of view of security and examines how valid trust approaches from other domains are now making their way into secure computing. The paper also revisits and analyses the Trusted Platform Module (TPM) along with associated technologies and their relevance in the changing landscape. We especially focus on the domains of cloud computing, mobile computing and cyber-physical systems. In addition, the paper also explores our proposals that are competing with and extending the traditional functionality of TPM specifications.

In this paper, we are presenting a framework for “User's Personalized Workspace” by augmenting the physical paper and digital document. The paper based interactions are seamlessly integrated with digital document based interactions for reading as a activity. For instance when user is involved in reading activity, writing becomes complimentary. In a academic system, paper based presentation mode has facilitated such exercises. Despite rendering the annotation on digital document and store it onto the database, the content of the paper encircled or underlined is used to hyperlink the document. Synchronizing a physical paper and those of digital version in seamless fashion from a user's perspective is the main objective of this work. We have also compared the existing systems which focus on one activity or the other in our proposed system.

Peer review has become the most common practice for judging papers submitted to a conference for decades. An extremely important task involved in peer review is to assign submitted papers to reviewers with appropriate expertise which is referred to as paper-reviewer assignment. In this paper, we study the paper-reviewer assignment problem from both the goodness aspect and the fairness aspect. For the goodness aspect, we propose to maximize the topic coverage of the paper-reviewer assignment. This objective is new and the problem based on this objective is shown to be NP-hard. To solve this problem efficiently, we design an approximate algorithm which gives a 1/3-approximation. For the fairness aspect, we perform a detailed study on conflict-of-interest (COI) types and discuss several issues related to using COI, which, we hope, can raise some open discussions among researchers on the COI study. Finally, we conducted experiments on real datasets which verified the effectiveness of our algorithm and also revealed some interesting results of COI.

One of the most crucial components of modern Information Technology (IT) systems is data. It can be argued that the majority of IT systems are built to collect, store, modify, communicate and use data, enabling different data stakeholders to access and use it to achieve different business objectives. The confidentiality, integrity, availability, audit ability, privacy, and quality of the data is of paramount concern for end-users ranging from ordinary consumers to multi-national companies. Over the course of time, different frameworks have been proposed and deployed to provide data security. Many of these previous paradigms were specific to particular domains such as military or media content providers, while in other cases they were generic to different verticals within an industry. There is a much needed push for a holistic approach to data security instead of the current bespoke approaches. The age of the Internet has witnessed an increased ease of sharing data with or without authorisation. These scenarios have created new challenges for traditional data security. In this paper, we study the evolution of data security from the perspective of past proposed frameworks, and present a novel Unified Model for Data Security (UMDS). The discussed UMDS reduces the friction from several cross-domain challenges, and has the functionality to possibly provide comprehensive data security to data owners and privileged users.

Duplicated code or code clones are a kind of code smell that have both positive and negative impacts on the development and maintenance of software systems. Software clone research in the past mostly focused on the detection and analysis of code clones, while research in recent years extends to the whole spectrum of clone management. In the last decade, three surveys appeared in the literature, which cover the detection, analysis, and evolutionary characteristics of code clones. This paper presents a comprehensive survey on the state of the art in clone management, with in-depth investigation of clone management activities (e.g., tracing, refactoring, cost-benefit analysis) beyond the detection and analysis. This is the first survey on clone management, where we point to the achievements so far, and reveal avenues for further research necessary towards an integrated clone management system. We believe that we have done a good job in surveying the area of clone management and that this work may serve as a kind of roadmap for future research in the area

Service System refers to the group of entities that work together to implement a service. An important challenge for the service designer is to organize her conceptualization of the service in a way that helps her identify the functional components required to engineer the service. At a basic level of conceptualization, the functional relevance of an observed category can be historically inspired, empirically informed, rationally thought, or pragmatically focused. From a service-design perspective, the interest in a given phenomenon is limited to its functional organization, which requires taking an exclusively pragmatic view of the world. In this paper, we propose a role-based approach to modeling categories, which requires service-designers assign functional and non-functional roles to categories by making explicit their interpretation of the conceptual relevance of these categories. Staying aware of the design choices will help the service-designer develop an informed model of observed reality, leading to better alignment between the scope and the purpose of the design activity.

Some ballots such as those for local elections in Germany contain more than 500 candidates, allow for more than 70 votes and the system allows voters to perform cumulative voting, vote splitting and crossing out of candidates which results in huge ballot papers containing only one race. As none of the existing electronic voting systems seem to be feasible for this kind of election we propose a new approach in this paper. The main idea is to have a vote casting device that does not store votes but only prints a summary of the selection voters made at device. This printout is put into the ballot box. In addition, to the human readable summary, the so called Voter Verifiable Summary of the Vote Paper Audit Trail (VV-SV-PAT) contains a 2D barcode which makes it easier to properly enter votes into the counting software. In addition, we propose an improvement for the counting software. With this approach we aim to support the actual procedure of vote casting and tallying by an accurate use of technology while at the same time preserving, or even improving, verifiability and usability compared to the traditional system.

Static Binary Instrumentation is a technique that allows compile-time program manipulation. In particular, by relying on ad-hoc tools, the end user is able to alter the program's execution flow without affecting its overall semantic. This technique has been effectively used, e.g., to support code profiling, performance analysis, error detection, attack detection, or behavior monitoring. Nevertheless, efficiently relying on static instrumentation for producing executables which can be deployed without affecting the overall performance of the application still presents technical and methodological issues. In this paper, we present Hijacker, an open-source customizable static binary instrumentation tool which is able to alter a program's execution flow according to some user-specified rules, limiting the execution overhead due to the code snippets inserted in the original program, thus enabling for the exploitation in high performance computing. The tool is highly modular and works on an internal representation of the program which allows to perform complex instrumentation tasks efficiently, and can be additionally extended to support different instruction sets and executable formats without any need to modify the instrumentation engine. We additionally present an experimental assessment of the overhead induced by the injected code in real HPC applications.

This paper is the result of reviewing all papers published in the proceedings of the former International Workshop on Mining Software Repositories (MSR) (2004–2006) and now Working Conference on MSR (2007–2009). We have analyzed the papers that contained any experimental analysis of software projects for their potentiality of being replicated. In this regard, three main issues have been addressed: i) the public availability of the data used as case study, ii) the public availability of the processed dataset used by researchers and iii) the public availability of the tools and scripts. A total number of 171 papers have been analyzed from the six workshops/working conferences up to date. Results show that MSR authors use in general publicly available data sources, mainly from free software repositories, but that the amount of publicly available processed datasets is very low. Regarding tools and scripts, for a majority of papers we have not been able to find any tool, even for papers where the authors explicitly state that they have built one. Lessons learned from the experience of reviewing the whole MSR literature and some potential solutions to lower the barriers of replicability are finally presented and discussed.

This paper proposes a multi-tenant workflow framework that allows users to create data analytic workflows whose tasks are efficiently scheduled and distributed in cloud computing environment. We provide a demo of an event room assignment (ERA) as a test application of the framework. The ERA dynamically and automatically assigns registered events (e.g., meetings, classes, conferences, etc.) to available rooms meeting the user requirements such as the event size, purpose, reservation period, etc. The assignment will lead to the energy efficiency with respect to the power usage (e.g., lighting, ventilation, devices, etc.), and the energy savings can be achieved without affecting people's comfort. We run the ERA with power consumption data (whose size is approximately 50GB) collected from each of over 200 rooms in a building at Dept. of Engineering, Tokyo University. Through the demonstration, we will show that the proposed framework accelerates the speed of data analysis by providing user-friendly workflow composition and parallel processing features utilizing cloud computing technologies.

Cross-site scripting defenses often focus on HTML documents, neglecting attacks involving the browser's content-sniffing algorithm, which can treat non-HTML content as HTML. Web applications, such as the one that manages this conference, must defend themselves against these attacks or risk authors uploading malicious papers that automatically submit stellar self-reviews. In this paper, we formulate content-sniffing XSS attacks and defenses. We study content-sniffing XSS attacks systematically by constructing high-fidelity models of the content-sniffing algorithms used by four major browsers. We compare these models with Web site content filtering policies to construct attacks. To defend against these attacks, we propose and implement a principled content-sniffing algorithm that provides security while maintaining compatibility. Our principles have been adopted, in part, by Internet Explorer 8 and, in full, by Google Chrome and the HTML 5 working group.

We develop a novel technique for authenticating physical documents by using random, naturally occurring imperfections in paper texture. To this end, we devised a new method for measuring the three-dimensional surface of a paper without modifying the document in any way, using only a commodity scanner. From this physical feature, we generate a concise fingerprint that uniquely identifies the document. Our method is secure against counterfeiting, robust to harsh handling, and applicable even before any content is printed on a page. It has a wide range of applications, including detecting forged currency and tickets, authenticating passports, and halting counterfeit goods. On a more sinister note, document identification could be used to de-anonymize printed surveys and to compromise the secrecy of paper ballots.

As cloud usage continues to increase, new issues with respect to managing and securing resources in the cloud are becoming more apparent. While some people may believe that security and privacy in the cloud can be addressed without the consumer considering the physical location and internal structure of the cloud, we show that this is clearly not the case. Furthermore, we describe a mechanism by which cloud consumers can inform cloud providers of their requirements in a manner that still allows the cloud to remain dynamic and flexible. Specifically, this paper explores the analogy between human migration in the real world and virtual machine migration in an IaaS cloud environment. It addresses issues such as jurisdictional control, zone evolution, migration, and instantiation based on an examination of these analogous real-world scenarios and their applicability to the cloud.

The domain of Digital Libraries presents specific challenges for unsupervised information extraction to support both the automatic classification of documents and the enhancement of userspsila navigation in the digital content. In this paper, we propose a combined use of machine learning techniques (i.e. Support Vector Machines) and Natural Language Processing techniques (i.e. Stanford NLP parser) to tackle the problem of unsupervised key-phrases extraction from scientific papers. The proposed method strongly depends on the robust structural properties of a scientific paper as well as on the lexical knowledge that we are able to mine from its text. For the experimental assessment we have use a subset of ACM papers in the Computer Science domain containing 400 documents. Preliminary evaluation of the approach shows promising result that improves - on the same data-set - on state-of-the-art Bayesian learning system KEA from a minimum 27% to a maximum 77% depending on KEA parameters tuning and specific evaluation set. Our assessment is performed by comparison with key-phrases assigned by human experts in the specific domain and freely available through ACM portal.

Structuring constitutes an important means for enhancing individual and collaborative learning processes. This paper introduces two new concepts for augmented semantic structuring of paper-based learning processes. Digital paper bookmarks can be used for individually structuring, indexing and tagging learning documents. The learner cube provides a new structuring method for collaborative face-to-face learning meetings. Both concepts rely on the Anoto technology, permitting to track and digitalize the paper-based learning process. A first pilot study indicates that digital paper bookmarks and tangible interaction are promising tools for structuring learning processes.

The Internet pervades virtually every aspect of our daily lives, and it seems there is no area that is immune from computing solutions. Computers can do things faster, with greater precision, more reliably, etc., etc., etc. Ironically, one area that most needs the mechanical rigor offered by computing solutions seems destined to abandon electronic solutions and return to paper as the operating medium of choice. As electronic voting falls from favor across America, we are concerned to hear talk of paper receipts provided to voters1. Though the department store receipt model is appealing in its simplicity, we posit that when this model is applied to voting systems, it introduces a complex combination of dangerously conflicting properties. We describe these properties and offer an alternate framework to address paper receipt concerns. We then extend this notion into a discussion of paper records and their contribution to forensics for election systems.

Resources are presented for fostering paper-based election technology. They comprise a diverse collection of real and simulated ballot and survey images, and software tools for ballot synthesis, registration, segmentation, and ground truthing. The grids underlying the designated location of voter marks are extracted from 13,315 degraded ballot images. The actual skew angles of sample ballots, recorded as part of complete ballot descriptions compiled with the interactive ground-truthing tool, are compared with their automatically extracted parameters. The average error is 0.1 degrees. These results provide a baseline for the application of digital image analysis to the scrutiny of electoral ballots.

This paper describes the use of mobile access points (home node base stations, femtocells) for providing TV streaming to mobile devices inside the home. The research is focused on finding commonalities between architectures of the home node base station for different technologies. The result is a general architecture, which can be easily extended to provide live media streaming to mobile devices.

Researchers have largely focused on analyzing citation links from one scholarly work to another. Such citing sentences are an important part of the narrative in a research article. If we can automatically identify such sentences, we can devise an editor that helps suggest when a particular piece of text needs to be backed up with a citation or not. In this paper, we propose a method for identifying citing sentences by constructing a classifier using supervised learning. Our experiments show that simple language features such as proper nouns and the labels of previous and next sentences are effective features to identifying citing sentences.