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This white paper argues that formal methods need to be better rooted in higher education curricula for computer science and software engineering programmes of study. To this end, it advocates

When it comes to leaked credentials and credit card information, we observe the development and use of anti-fingerprinting browsers by malicious actors. These tools are carefully designed to evade detection, often by mimicking the browsing environment of the victim whose credentials were stolen. Even though these tools are popular in the underground markets, they have not received enough attention by researchers. In this paper, we report on the first evaluation of four underground, commercial, and research anti-fingerprinting browsers and highlight their high success rate in bypassing browser fingerprinting. Despite their success against well-known fingerprinting methods and libraries, we show that even slightest variation in the simulated fingerprint compared to the real ones can give away the presence of anti-fingerprinting tools. As a result, we provide techniques and fingerprint-based signatures that can be used to detect the current generation of anti-fingerprinting browsers.

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services, and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors have evolved to design exploitation techniques that circumvent protection. With the ever-increasing complexity of technology infrastructure, security and privacy-preservation specialists have started to look for adaptable and flexible protection methods that can evolve (potentially autonomously) as the adversarial actor changes its techniques. For this, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) were put forward as saviours. In this paper, we look at the promises of AI, ML, and DL stated in academic and industrial literature and evaluate how realistic they are. We also put forward potential challenges a DL based security and privacy protection system has to overcome. Finally, we conclude the paper with a discussion on what steps the DL and the security and privacy-preservation community have to take to ensure that DL is not just going to be hype, but an opportunity to build a secure, reliable, and trusted technology infrastructure on which we can rely on for so much in our lives.

The enhanced capabilities of large scale parallel and distributed platforms produce a continuously increasing amount of data which have to be stored, exchanged and used by various tasks allocated on different nodes of the system. The management of such a huge communication demand is crucial for reaching the best possible performance of the system. Meanwhile, we have to deal with more interferences as the trend is to use a single all-purpose interconnection network whatever the interconnect (tree-based hierarchies or topology-based heterarchies). There are two different types of communications, namely, the flows induced by data exchanges during the computations, and the flows related to Input/Output operations. We propose in this paper a general model for interference-aware scheduling, where explicit communications are replaced by external topological constraints. Specifically, the interferences of both communication types are reduced by adding geometric constraints on the allocation of tasks into machines. The proposed constraints reduce implicitly the data movements by restricting the set of possible allocations for each task. This methodology has been proved to be efficient in a recent study for a restricted interconnection network (a line/ring of processors which is an intermediate between a tree and higher dimensions grids/torus). The obtained results illustrated well the difficulty of the problem even on simple topologies, but also provided a pragmatic greedy solution, which was assessed to be efficient by simulations. We are currently extending this solution for more complex topologies. This work is a position paper which describes the methodology, it does not focus on the solving part.

The paper industry is the industry with the third highest energy consumption in the European Union. Using recycled paper instead of fresh fibers for papermaking is less energy consuming and saves resources. However, adhesive contaminants in recycled paper are particularly problematic since they reduce the quality of the resulting paper-product. To remove as many contaminants and at the same time obtain as many valuable fibres as possible, fine screening systems, consisting of multiple interconnected pressure screens, are used. Choosing the best configuration is a non-trivial task: The screens can be interconnected in several ways, and suitable screen designs as well as operational parameters have to be selected. Additionally, one has to face conflicting objectives. In this paper, we present an approach for the multi-criteria optimization of pressure screen systems based on Mixed-Integer Nonlinear Programming. We specifically focus on a clear representation of the trade-off between different objectives.

A novel paper-based biosensors for the measurement of urinary phenylalanine (Phe) was developed and the experimental results here reported. The proposed biosensor is featured by a silicon part integrating temperature sensors and heaters and a polycarbonate ring to form a microchamber. The reagent-on-board format allows a fast and easy self-testing directly from patients. The biosensors is thermally driven by a customized instrument and software. The detection strategy employed is based on the specific reaction of Phenylalanine Ammonia Lyase enzyme to produce ammonia and trans-cinnamic acid from Phenylalanine. The increase of pH value is proportional to the Phe amount and can be monitored by the color changes of a dye solution. The proposed system is suitable to detect the phenylalanine levels in a linear dynamic range concentration from 20 to 3000 μM.

Interest in cryptocurrencies has skyrocketed since their introduction a decade ago, with hundreds of billions of dollars now invested across a landscape of thousands of different cryptocurrencies. While there is significant diversity, there is also a significant number of scams as people seek to exploit the current popularity. In this paper, we seek to identify the extent of innovation in the cryptocurrency landscape using the open-source repositories associated with each one. Among other findings, we observe that while many cryptocurrencies are largely unchanged copies of Bitcoin, the use of Ethereum as a platform has enabled the deployment of cryptocurrencies with more diverse functionalities.

DDoS attack detection using entropy-based features in network traffic has become a popular approach among researchers in the last five years. The use of traffic distribution features constructed using entropy measures has been proposed as a better approach to detect Distributed Denial of Service (DDoS) attacks compared to conventional volumetric methods, but it still lacks in the generality of detecting various intensity DDoS attacks accurately. In this paper, we focus on identifying effective entropy-based features to detect both high- and low-intensity DDoS attacks by exploring the effectiveness of entropy-based features in distinguishing the attack from normal traffic patterns. We hypothesise that using different entropy measures, window sizes, and entropy-based features may affect the accuracy of detecting DDoS attacks. This means that certain entropy measures, window sizes, and entropy-based features may reveal attack traffic amongst normal traffic better than the others. Our experimental results show that using Shannon, Tsallis and Zhou entropy measures can achieve a clearer distinction between DDoS attack traffic and normal traffic than Renyi entropy. In addition, the window size setting used in entropy construction has minimal influence in differentiating between DDoS attack traffic and normal traffic. The result of the effectiveness ranking shows that the commonly used features are less effective than other features extracted from traffic headers.

Coreference resolution is essential for automatic text understanding to facilitate high-level information retrieval tasks such as text summarisation or question answering. Previous work indicates that the performance of state-of-the-art approaches (e.g. based on BERT) noticeably declines when applied to scientific papers. In this paper, we investigate the task of coreference resolution in research papers and subsequent knowledge graph population. We present the following contributions: (1) We annotate a corpus for coreference resolution that comprises 10 different scientific disciplines from Science, Technology, and Medicine (STM); (2) We propose transfer learning for automatic coreference resolution in research papers; (3) We analyse the impact of coreference resolution on knowledge graph (KG) population; (4) We release a research KG that is automatically populated from 55,485 papers in 10 STM domains. Comprehensive experiments show the usefulness of the proposed approach. Our transfer learning approach considerably outperforms state-of-the-art baselines on our corpus with an F1 score of 61.4 (+11.0), while the evaluation against a gold standard KG shows that coreference resolution improves the quality of the populated KG significantly with an F1 score of 63.5 (+21.8).

Blockchain technology enables a transaction to be handled in a decentralized fashion. In this position paper, we aim to introduce the background of blockchain technology, discuss one of its important component — smart contract, and present its recent applications in many fields such as cryptocurrency, financial services, risk management, and Internet of Things.

This paper presents a retrospective analysis of students’ use of self-regulated learning strategies while using an educational technology that connects physical and digital learning spaces. A classroom study was carried out in a Data Structures & Algorithms course offered by the School of Computer Science. Students’ reviewing behaviors were logged and the associated learning impacts were analyzed by monitoring their progress throughout the course. The study confirmed that students who had an improvement in their performance spent more time and effort reviewing formal assessments, particularly their mistakes. These students also demonstrated consistency in their reviewing behavior throughout the semester. In contrast, students who fell behind in class ineffectively reviewed their graded assessments by focusing mostly on what they already knew instead of their knowledge misconceptions.

Developing valuable materials from the by-products of paper industry can help to address some environmental and economic issues associated with traditional synthetic composites. Particularly, the management of paper mill sludge (PMS) waste remains an economic and environmental challenge for the pulp and paper industry. 11 million tons of PMS is generated annually in Europe from the wastewater treatment (WWT) process of paper mills. PMS is mostly used in low value applications. However, PMS contains fibers and minerals with physio-chemical properties that exhibit a high potential to substitute some conventional materials in other industries. The research presented in this paper aims to explore new directions for further investigation on PMS material applications by reviewing the literature on PMS materials and subsequently characterizing sludge from 6 different mills. The study shows the technical feasibility, opportunities and technological readiness of fiber and mineral based composites obtained from PMS, such as; cementitious products, polymer reinforcement and fiberboards.

We consider a game-theoretic setting of contention in communication networks. In a contention game each of \(n \ge 2\) identical players has a single information packet that she wants to transmit in a fast and selfish way through one of \(k \ge 1\) multiple-access channels by choosing a protocol. Here, we extend the model and results of the single-channel case studied in [2] by providing equilibria characterizations for more than one channels, and giving specific anonymous, equilibrium protocols with finite and infinite expected latency. For our equilibrium protocols with infinite expected latency, all players, with high probability transmit successfully in optimal time, i.e. \(\varTheta (n/k)\).

This paper presents an attack and defence modelling framework for conceptualising the security of the speech interface. The modelling framework is based on the Observe-Orient-Decide-Act (OODA) loop model, which has been used to analyse adversarial interactions in a number of other areas. We map the different types of attacks that may be executed via the speech interface to the modelling framework, and present a critical analysis of the currently available defences for countering such attacks, with reference to the modelling framework. The paper then presents proposals for the development of new defence mechanisms that are grounded in the critical analysis of current defences. These proposals envisage a defence capability that would enable voice-controlled systems to detect potential attacks as part of their dialogue management functionality. In accordance with this high-level defence concept, the paper presents two specific proposals for defence mechanisms to be implemented as part of dialogue management functionality to counter attacks that exploit unintended functionality in speech recognition functionality and natural language understanding functionality. These defence mechanisms are based on the novel application of two existing technologies for security purposes. The specific proposals include the results of two feasibility tests that investigate the effectiveness of the proposed mechanisms in defending against the relevant type of attack.

Recently, high-speed IPv4 scanners, such as ZMap, have enabled rapid and timely collection of TLS certificates and other security-sensitive parameters. Such large datasets led to the development of the Censys search interface, facilitating comprehensive analysis of TLS deployments in the wild. Several recent studies analyzed TLS certificates as deployed in web servers. Beyond public web servers, TLS is deployed in many other Internet-connected devices, at home and enterprise environments, and at network backbones. In this paper, we report the results of a preliminary analysis using Censys on TLS deployments in such devices (e.g., routers, modems, NAS, printers, SCADA, and IoT devices in general). We compare certificates and TLS connection parameters from a security perspective, as found in common devices with Alexa 1M sites. Our results highlight significant weaknesses, and may serve as a catalyst to improve TLS security for these devices.

With the purpose of delivering more robust systems, this paper revisits the problem of Inverse Uncertainty Quantification that is related to the discrepancy between the measured data at runtime (while the system executes) and the formal specification (i.e., a mathematical model) of the system under consideration, and the value calibration of unknown parameters in the model. We foster an approach to quantify and mitigate system uncertainty during the development cycle by combining Bayesian reasoning and online Model-based testing.

Understanding the current research trends, problems, and their innovative solutions remains a bottleneck due to the ever-increasing volume of scientific articles. In this paper, we propose NLPExplorer, a completely automatic portal for indexing, searching, and visualizing Natural Language Processing (NLP) research volume. NLPExplorer presents interesting insights from papers, authors, venues, and topics. In contrast to previous topic modelling based approaches, we manually curate five course-grained non-exclusive topical categories namely Linguistic Target (Syntax, Discourse, etc.), Tasks (Tagging, Summarization, etc.), Approaches (unsupervised, supervised, etc.), Languages (English, Chinese, etc.) and Dataset types (news, clinical notes, etc.). Some of the novel features include a list of young popular authors, popular URLs and datasets, list of topically diverse papers and recent popular papers. Also, it provides temporal statistics such as yearwise popularity of topics, datasets, and seminal papers. To facilitate future research and system development, we make all the processed dataset accessible through API calls. The current system is available at http://nlpexplorer.org.

In Nigeria, poor waste management practices are evident in many urban centers. This study considered a University community as the model to explore the use of digital solutions in proper waste management. This study developed a web-based waste management system for the collection of plastics and paper in a University community. The framework developed utilized the use case and system block diagram. In this study, HTML, CSS, MySQL and Java programming language was used in the design of the web-based system. The result of this study was presented using screenshots. The web-based system had an interface to show profiles of the users, waste pickup information, incentive platform, and the admin dashboard to monitor and approve pickups and incentives. Past studies have indicated the success of using incentives to build sustainable behavioural change towards efficient waste disposal. Therefore, this study was able to introduce the incentive platform which converts weight of paper and plastics waste to points then to rewards for the users. The web-based waste management system for the collection of plastics and paper can be deployed for University communities to help reduce the number of paper and plastics that end up in landfills. Also, by picking the paper and plastics waste from source helps to prevent bin contamination.

Several network growth models have been proposed in the literature that attempt to incorporate properties of citation networks. Generally, these models aim at retaining the degree distribution observed in real-world networks. In this work, we explore whether existing network growth models can realize the diversity in citation growth exhibited by individual papers – a new node-centric property observed recently in citation networks across multiple domains of research. We theoretically and empirically show that the network growth models which are solely based on degree and/or intrinsic fitness cannot realize certain temporal growth behaviors that are observed in real-world citation networks. To this end, we propose two new growth models that localize the influence of papers through an appropriate attachment mechanism. Experimental results on the real-world citation networks of Computer Science and Physics domains show that our proposed models can better explain the temporal behavior of citation networks than existing models.

Although the Domain Name System (DNS) was designed as a naming system, its features have made it appealing to repurpose it for the deployment of novel systems. One important class of such systems are security enhancements, and this work sheds light on their deployment. We show the characteristics of these solutions and measure reliability of DNS in these applications. We investigate the compatibility of these solutions with the Tor network, signal necessary changes, and report on surprising drawbacks in Tor’s DNS resolution.

Although considerable effort is made to secure organisational infrastructures and to protect organizational assets, it is widely acknowledged that it is equally important to ensure that organisations need to define appropriate ways to harden their overall resilience including recovery from security incidents. In this exploratory paper we outline research challenges and we present the motivation and the foundations of a novel framework that is based on security resilience and capability modelling theory.

In this paper, we propose a framework that combines aspects of user role modeling and user-interface features with retrieval and recommender systems components. The framework is based on emergent themes identified from participants feedback in a user evaluation study conducted with a prototype assistive system. 119 researchers participated in the study for evaluating the prototype system that provides recommendations for two literature review and one manuscript writing tasks. NRF (Natl Research Foundation, S’pore) Accepted version

Recommendation of scientific papers is a task aimed to support researchers in accessing relevant articles from a large pool of unseen articles. When writing a paper, a researcher focuses on the topics related to her/his scientific domain, by using a technical language. The core idea of this paper is to exploit the topics related to the researchers scientific production (authored articles) to formally define her/his profile; in particular we propose to employ topic modeling to formally represent the user profile, and language modeling to formally represent each unseen paper. The recommendation technique we propose relies on the assessment of the closeness of the language used in the researchers papers and the one employed in the unseen papers. The proposed approach exploits a reliable knowledge source for building the user profile, and it alleviates the cold-start problem, typical of collaborative filtering techniques. We also present a preliminary evaluation of our approach on the DBLP.

We present an approach for performing the tallying work in the coercion-resistant JCJ voting protocol, introduced by Juels, Catalano, and Jakobsson, in linear time using fully homomorphic encryption (FHE). The suggested enhancement also paves the path towards making JCJ quantum-resistant, while leaving the underlying structure of JCJ intact. The pairwise comparison-based approach of JCJ using plaintext equivalence tests leads to a quadratic blow-up in the number of votes, which makes the tallying process rather impractical in realistic settings with a large number of voters. We show how the removal of invalid votes can be done in linear time via a solution based on recent advances in various FHE primitives such as hashing, zero-knowledge proofs of correct decryption, verifiable shuffles and threshold FHE. We conclude by discussing some of the advantages and challenges resulting from our proposal, followed by an outline of future work and possible lines of attack.

We overview a recent line of work [Rozhoň and Ghaffari at STOC 2020; Ghaffari, Harris, and Kuhn at FOCS 2018; and Ghaffari, Kuhn, and Maus at STOC 2017], which proved that any (locally-checkable) graph problem that admits an efficient randomized distributed algorithm also admits an efficient deterministic distributed algorithm, thereby resolving several central and decades-old open problems in distributed graph algorithms. We present a short and self-contained version of the proofs, and conclude by discussing several related questions that remain open. This article accompanies a keynote talk of the author at the International Colloquium on Structural Information and Communication Complexity (SIROCCO) 2020. The writing is based on [24, 28, 45] and primarily targets non-experts. ISSN:0302-9743 ISSN:1611-3349

Many datasets change over time. As a consequence, long-running applications that cache and repeatedly use query results obtained from a SPARQL endpoint may resubmit the queries regularly to ensure up-to-dateness of the results. While this approach may be feasible if the number of such regular refresh queries is manageable, with an increasing number of applications adopting this approach, the SPARQL endpoint may become overloaded with such refresh queries. A more scalable approach would be to use a middle-ware component at which the applications register their queries and get notified with updated query results once the results have changed. Then, this middle-ware can schedule the repeated execution of the refresh queries without overloading the endpoint. In this paper, we study the problem of scheduling refresh queries for a large number of registered queries by assuming an overload-avoiding upper bound on the length of a regular time slot available for testing refresh queries. We investigate a variety of scheduling strategies and compare them experimentally in terms of time slots needed before they recognize changes and number of changes that they miss.

Alongside the intensive growth of Online Social Networks (OSNs), privacy has become an important concept and requirement when sharing content online, leading users to enforce privacy often using encryption when sharing content with multiple recipients. Although cryptographic systems achieve common privacy goals such as confidentiality, key privacy, and recipient privacy, they have not been designed aiming at dynamic types of networks. In fact, the interactive nature of OSNs provides adversaries new attack vectors against privacy, and in particular against recipient privacy. We present the notion of frientropy, and argue that privacy of recipients is maintained in OSNs provided that the social graph has a high frientropy, besides the conventional recipient privacy notion. We compute the frientropy for various theoretical settings, and discuss its implications on some practical settings.

Software Defined Networking (SDN) was introduced with a philosophy of decoupling the control plane from the data plane which facilitates network management while ensuring programmability in order to improve performance and monitoring. OpenFlow which enabled SDN was first introduced to match twelve header fields whilst at current it matches forty one which is expected to grow exponentially. Therefore future networks must have the ability to flexibly parse packets through a common interface. Programming Protocol independent Packet Processing (P4) was introduced to achieve the aforementioned by programming the underlying switch, providing instructions and utilizing APIs to populate the forwarding tables. A P4 programmed switch will forward packets through a parser into multiple stages of match+action tables to find the destination node which is considered the most efficient mechanism for routing. This paper takes into the account the latest platform developed for service providers, Open Networking Operating System (ONOS) to deploy two environments configured in the aforementioned technologies in order to test their performance. Four case studies were drawn which were simulated in Mininet which incorporated SDN + P4 switches. A significant increase of performances were recorded when compared with the performance of cases using SDN only.

In this paper, we examine whether a user’s tweets can help to a generate more serendipitous recommendations. In addition, we investigate whether the use of diversification applied on a list of recommended items further improves serendipity. To this end, we conduct an experiment with \(n=22\) subjects. The result of our experiment shows that the subject’s tweets did not improve serendipity, but diversification results in more serendipitous recommendations.

The continuing rise in the popularity of smartphones has led to an accompanying rise in the exposure of users to privacy threats as in the case of unintended leakage of personal information from apps. To improve transparency and the ability of users to control data leakage, the design of privacy-enhancing tools aimed at reducing the burden of informed privacy-decisions should be grounded upon users’ tacit needs and preferences. To this end, the present study explores users’ personal perception and concerns toward privacy and their expectations. Initial recommendations include: (1) consideration of the preferences of users for preserving functionalities of their apps, informing users about both (2) the real benefits and actual possibility of using privacy management tools and (3) suspected applications’ data collection behaviours in a way that matches their real concerns and values.

It is possible to relay signals between a contactless EMV card and a shop’s EMV reader and so make a fraudulent payment without the card-owner’s knowledge. Existing countermeasures rely on proximity checking: the reader will measure round trip times in message-exchanges, and will reject replies that take longer than expected (which suggests they have been relayed). However, it is the reader that would receive the illicit payment from any relayed transaction, so a rogue reader has little incentive to enforce the required checks. Furthermore, cases of malware targeting point-of-sales systems are common. We propose three novel proximity-checking protocols that use a trusted platform module (TPM) to ensure that the reader performs the time-measurements correctly. After running one of our proposed protocols, the bank can be sure that the card and reader were in close proximity, even if the reader tries to subvert the protocol. Our first protocol makes changes to the cards and readers, our second modifies the readers and the banking backend, and our third allows the detection of relay attacks, after they have happened, with only changes to the readers.

Automated techniques for analysis and optimization of finite-precision computations have recently garnered significant interest. Most of these were, however, developed independently. As a consequence, reuse and combination of the techniques is challenging and much of the underlying building blocks have been re-implemented several times, including in our own tools. This paper presents a new framework, called Daisy, which provides in a single tool the main building blocks for accuracy analysis of floating-point and fixed-point computations which have emerged from recent related work. Together with its modular structure and optimization methods, Daisy allows developers to easily recombine, explore and develop new techniques. Daisy’s input language, a subset of Scala, and its limited dependencies make it furthermore user-friendly and portable.

We present a scenario where browsers’ network and computation capabilities are used by an attacker without the user’s knowledge. For this kind of abuse, an attacker needs to trigger JavaScript code on the browser, e.g. through an advertisement. However, unlike other Web attacks, e.g. cross-site scripting, the attack can be executed isolated from the Origin of the site visited by the user.

This paper puts forward a vision of a universal ontology (UO) aiming at solving, or at least greatly alleviating, the semantic integration problem in the field of conceptual modeling and the understandability problem in the field of the semantic web. So far it has been assumed that the UO is not feasible in practice, but we think that it is time to revisit that assumption in the light of the current state-of-the-art. This paper aims to be a step in this direction. We try to make an initial proposal of a feasible UO. We present the scope of the UO, the kinds of its concepts, and the elements that could comprise the specification of each concept. We propose a modular structure for the UO consisting of four levels. We argue that the UO needs a complete set of concept composition operators, and we sketch three of them. We also tackle a few issues related to the feasibility of the UO, which we think that they could be surmountable. Finally, we discuss the desirability of the UO, and we explain why we conjecture that there are already organizations that have the knowledge and resources needed to develop it, and that might have an interest in its development in the near future.

This work presents a new, scalable solution to the problem of extracting citation contexts: the textual fragments surrounding citation references. These citation contexts can be used to navigate digital libraries of research papers to help users in deciding what to read. We have developed a prototype system which can retrieve, on-demand, citation contexts from the full text of over 15 million research articles in the Mendeley catalog for a given reference research paper. The evaluation results show that our citation extraction system provides additional functionality over existing tools, has two orders of magnitude faster runtime performance, while providing a 9% improvement in F-measure over the current state-of-the-art.

International audience; When developing and maintaining distributed systems, auditing privacy properties gains more and more relevance. Nevertheless, this task is lacking support of automated tools and, hence, is mostly carried out manually. We present a formal approach which enables auditors to model the flow of critical data in order to shed new light on a system and to automatically verify given privacy constraints. The formalization is incorporated into a larger policy analysis and verification framework and overall soundness is proven with Isabelle/HOL. Using this solution, it becomes possible to automatically compute architectures which follow specified privacy conditions or to input an existing architecture for verification. Our tool is evaluated in two real-world case studies, where we uncover and fix previously unknown violations of privacy.

We present a unique approach for integrating research in High Performance Computing (HPC) as well as photovoltaic (PV) solar farming and battery technologies into a container-based compute center designed for a maximum of energy efficiency, performance and extensibility/scalability. We use NVIDIA Jetson TK1 boards to build a considerably dimensioned cluster of 60 low-power GPUs, attach a 7.5 kWp solar farm and a 8 kWh Lithium-Ion battery power supply and integrate everything into a single-container, standalone housing. We demonstrate the success of our system by evaluating the performance and energy efficiency for common versatile dense and sparse linear algebra kernels as well as a full CFD code. By this work we can show, that with current technology, energy consumption-induced follow-up cost of HPC can be reduced to zero.

In this paper, we study how to fold a specified origami crease pattern in order to minimize the impact of paper thickness. Specifically, origami designs are often expressed by a mountain-valley pattern (plane graph of creases with relative fold orientations), but in general this specification is consistent with exponentially many possible folded states. We analyze the complexity of finding the best consistent folded state according to two metrics: minimizing the total number of layers in the folded state (so that a “flat folding” is indeed close to flat), and minimizing the total amount of paper required to execute the folding (where “thicker” creases consume more paper). We prove both problems strongly NP-complete even for 1D folding. On the other hand, we prove the first problem fixed-parameter tractable in 1D with respect to the number of layers.

SNAKES (SNAKES is the Net Algebra Kit for Editors and Simulators) is a general purpose Petri nets library, primarily for the Python programming language but portable to other ones. It defines a very general variant of Python-coloured Petri nets that can be created and manipulated through the library, as well as executed to explore state spaces. Thanks to a variety of plugins, SNAKES can handle extensions of Petri nets, in particular algebras of Petri nets [4, 26]. SNAKES ships with a compiler for the ABCD language that is precisely such an algebra. Finally, one can use the companion tool Neco [14] that compiles a Petri net into an optimised library allowing to compute efficiently its state space or perform LTL model-checking thanks to library SPOT [8, 13]. This paper describes SNAKES’ structure and features.

Reliability, availability, maintenance and safety (RAMS) analysis is essential in the evaluation of safety critical systems like nuclear power plants and the railway infrastructure. A widely used methodology within RAMS analysis are fault trees, representing failure propagations throughout a system. We present DFTCalc, a tool-set to conduct quantitative analysis on dynamic fault trees including the effect of a maintenance strategy on the system dependability.

Protection of stone heritage requires detailed records of the state-of-preservation to ensure accurate decision-making for conservation interventions. This short paper explores the topic of using digital tools to better visualize and map in three-dimensional (3D) representations the deterioration state of stone statues. Technical photography, geomatics techniques, and 3D visualization approaches are combined to propose reproducible and adaptable solutions that can support the investigation of historical materials’ degradation. The short paper reports on the application of these multi-technique approaches regarding a bust sculpture from the Accademia Carrara in Bergamo (Italy).

Funder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266; Grant(s): EPSRC Grant Number: EP/L015900/1, The vitrification of radioactive waste within glass and subsequent disposal within a geological disposal facility (GDF) requires a comprehensive understanding of the effect of glass dissolution on GDF performance. This paper aims to analyse the effect of both high-level and intermediate-level waste (HLW and ILW) glass dissolution source terms on radionuclide release into the geosphere just above the disposal vault (the ‘crown’). Radionuclide migration was simulated in GoldSim for HLW in either granite or clay host rocks with a bentonite buffer using carbon steel or copper canisters, whereas ILW simulations considered either granite or clay host rocks, in either bentonite buffer or cement backfill, using concrete or cast-iron canisters. Glass dissolution source terms were varied by coupling GoldSim and MATLAB to modify the initial, residual, and resumption dissolution rates of the glass or by applying the analytical GRAAL model to glass dissolution. HLW glass results indicate no preference of granite over clay host rocks for a given canister type but that a copper canister is preferable to steel. ILW results suggest that a granite–bentonite–cast-iron environment yields lowest crown activities with cast-iron preferable to concrete as the canister, bentonite preferable to cement as the buffer/backfill, and granite preferable to clay as the host rock. Varying glass dissolution source terms (initial, residual, and resumption dissolution rates) had an understood effect on radionuclide migration, although changes were arguably insignificant considering peak crown activity for both HLW and ILW.

Email is among the cornerstones of our online lives. It has evolved from carrying text-only messages to delivering well-designed HTML contents. The uptake of web protocols into email, however, has facilitated the migration of web tracking techniques into email ecosystem. While recent privacy regulations have impacted the web tracking technologies, they have not directly influenced the email tracking techniques. In this short paper, we analyze a corpus of 5216 emails, give an overview of the identified tracking techniques, and argue that the existing email tracking methods do not comply with privacy regulations.

Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise our work presented on Learn2Reg challenge 2020. The main contributions of our work rely on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar and (ii) integration of variety of publicly available datasets used both for pretraining and for augmenting segmentation labels. Our method reports a mean dice of 0.64 for task 3 and 0.85 for task 4 on the test sets, taking third place on the challenge. Our code and models are publicly available at https://github.com/TheoEst/abdominal_registration and https://github.com/TheoEst/hippocampus_registration.

Online Social Networks (OSNs) are very popular and widely adopted by the vast majority of Internet users across the globe. Recent scandals on the abuse of users’ personal information via these platforms have raised serious concerns about the trustworthiness of OSN service providers. The unprecedented collection of personal data by OSN service providers poses one of the greatest threats to users’ privacy and their right to be left alone. The recent approval of the GDPR (General Data Protection Regulation) presents OSN service providers with great compliance challenges. A set of new data protection requirements are imposed on data controllers (OSN service providers) by GDPR that offer greater control to data subjects (OSN users) over their personal data. This position paper investigates the link between GDPR provisions and the use of blockchain technology for solving the consent management problem in online social networks. We also describe challenges and opportunities in designing a GDPR-compliant consent management mechanism for online social networks. Key characteristics of blockchain technology that facilitate regulatory compliance were identified. The legal and technological state of play of the blockchain-GDPR relationship is reviewed and possible ways to reconcile blockchain technology with the GDPR requirements are demonstrated. This paper opens up new research directions on the use of the disruptive innovation of blockchain to achieve regulatory compliance in the application domain of online social networks.

Dataflow test coverage criteria, such as all-defs and all-uses, belong to the most advanced coverage criteria. These criteria are defined by complex artifacts combining variable definitions, uses and program paths. Detection of polluting (i.e. inapplicable, infeasible and equivalent) test objectives for such criteria is a particularly challenging task. This short paper evaluates three detection approaches involving dataflow analysis, value analysis and weakest precondition calculus. We implement and compare these approaches, analyze their detection capacities and propose a methodology for their efficient combination. Initial experiments illustrate the benefits of the proposed approach.

In this position paper we introduce Reowolf: an on-going project that aims to replace the decades-old application programming interface, BSD sockets, for communication on the Internet. A novel programming interface is being implemented at the systems level that is inter-operable with existing Internet applications.

In this position paper, our goal is to argue the need for specific software development practices to early-stage startups. In order to reach this goal, we discuss the consequences of innovative and market-driven contexts, which are two of the key elements when describing software startups. We also argue that these practices could be applied to innovative initiatives within established companies since they share similar characteristics and challenges as those from startups.

With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings. Due to the scarcity of check-in data, the availability of geographical information offers an opportunity to improve the accuracy of POI recommendation. Moreover, matrix factorization methods provide effective models which can be used in POI recommendation. However, there are two main challenges which should be addressed to improve the performance of POI recommendation methods. First, leveraging geographical information to capture both the user’s personal, geographic profile and a location’s geographic popularity. Second, incorporating the geographical model into the matrix factorization approaches. To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users’ and locations’ points of view. To this end, an effective geographical model is proposed by considering the user’s main region of activity and the relevance of each location within that region. Then, the proposed local geographical model is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation. Experimental results on two well-known datasets demonstrate that the proposed approach outperforms other state-of-the-art POI recommendation methods.

Blockchain, a distributed secure digital ledger technology, is a relatively recent development with potentially transformational implications for economy and society. Its specific characteristics enable new decentralized models of distributed and trusted transactions. This position paper explores the implications of blockchain for collaborative networked organizations. In particular we aim at understanding the implications for companies in various economic sectors, and how new forms of networked organizations and new business models will be enabled. We also will focus on enablers of blockchain innovations, in particular with respect to governance of blockchain-based platforms and business networks. The paper results in a discussion of research challenges in the field of blockchain-enabled collaborative networked organizations.